CoverStackis in the Insurance IT Industry

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This is Part 8 in a series of articles about building — a 16-bit Windows 3 . If you’re not sure what this is or why I’m doing it you might like to Win3mu emulator read from the start. This post covers module loading — the process of loading .exe and .dll files. You’ve no doubt heard the term “Dynamic Link Library” — that’s what this post is mostly about — . dynamic linking This post is a little longer and a little more tedious than previous articles because the topic is fairly involved. Bear with me — by the end it’ll all make sense and we’ll have covered everything required to get a simple program running under Win3mu. The Module Manager In Win3mu the ModuleManager class is responsible to loading and unloading modules. It supports two kinds of modules both of which derive from a common base class called ModuleBase. Module16 — encapsulates a module loaded from 16-bit .dll or .exe file whose code will run on the emulated CPU. Module32 — a module written in C# that emulates a 16-bit module (often by calling the real Windows API). Modules based on Module32s are created and registered with the module manager during start up: Note: I use the term “32” throughout this project to refer to the host operating system platform. When running on Windows x64 “32” actually refers to 64-bit Windows. This is similar to how Windows API is often referred to Win32 — even though it covers both the x86 and x64 editions. ModuleBase Class The ModuleBase class provides an abstract interface to a module and many of these methods are used in the process of loading and linking modules: Most of these methods will be described in the following sections. Locating and Opening the File The first step in loading a module is to check if it’s already loaded. This is done by module name (rather than filename) and if found its reference count is incremented and nothing else needs to happen. If it’s not already loaded the next step is to locate the file. For the main .exe file this is already a fully qualified filename supplied on Win3mu’s command line. Sometimes the running program will use LoadLibrary to load a module with a fully qualified filename, but more typically a plain module name needs to be mapped to a file name. eg: “Win87em” There’s a search strategy for this but really it’s no more than appending “.dll” and looking in a couple of well defined locations for the file — mainly the same folder as the .exe. Once the file is located a Module16 is created, passed the filename and Module16 opens the file using the NeFileReader class (as described in a previous post). Resolve Referenced Modules Most modules reference other modules so the next thing to do is make sure they’re available and loaded. The ModuleBase class has a method to return a list of referenced modules: Note that this will often include modules like Kernel, User etc… these names are automatically mapped to previously registered Module32 implementations. Load Code and Data Segments Now that the referenced modules are loaded the module’s code and data segments can be loaded into the global heap. The ModuleManager calls the Module16.Load() method which does the heavy lifting: For each segment in the NE File: Allocate memory from the global heap Configure the memory’s selector according to the segment’s flags Read the segment from the NE file For the automatic data segment the amount of memory allocated is increased to make room for the local heap and for the stack. Unlike the real Windows, Win3mu immediately loads all segments. There’s no segment discarding because it assumes we’ve got plenty of memory available. Apply Code Relocations Once all the segments and all referenced modules are loaded the newly loaded modules need to be linked. We’re now getting to the heart of dynamic linking. The link process starts with the ModuleManager calling the ModuleBase.Link method. Module16 then goes through each segment and applies the relocations which describe both internal code fixups and references to external modules. There are several different kinds of relocations but the main two are Internal References — which link to another segment in the same module Imported Ordinals — which link an exported function in another module: There are many different combinations of relocation type and relocation address type. This is much simplified example. Each relocation entry points to the head of a chain of addresses that need to be relocated and the chain is ended with 0xFFFF. Also, relocations can be “additive” in which case the resolved value is added to the value already in the segment (rather than replacing it). Additive relocations aren’t in a chain. There’s one other class of relocation — “OSFixUp”. These are related to floating point math operations which I’ll cover it in another post. Patching Exported Functions Under Windows 3, any functions exported from a module must have a special prolog/epilog that serves two purposes. Ensuring that the correct data segment selector is loaded into the DS register Tweaking the BP register so that Windows can reliably walk the stack to apply address fix ups when code segments are moved around. I’m not going to cover stack walking because it’s not relevant however setting up the DS register is crucial. The DS register points to the module’s data segment and since each module has its own data segment when execution moves between modules the DS register needs to be updated to reference that module’s data segment. On disk (ie: as generated by the compiler) a function’s prolog looks like this: ; Load AX from DSPUSH   DSPOP    AXNOP ; Setup for stack walkingINC    BPPUSH   BPMOV    BP,SP ; Save DSPUSH   DS ; Load DS from AXMOV    DS,AX The default behaviour is to load AX from DS and then load DS from AX — ie: DS is left unaffected. For non-exported functions the prolog is left like this so that calls within the module maintain the same value for DS. When a function is exported from a .exe, the caller must set the AX register to the correct data segment selector before calling. In this case the prolog needs is patched to disable the first few instructions: ; AX already points to DS (zap the old instructions to NOPs)NOPNOPNOP ; the rest of the prolog as before ending with MOV DS, AX And when a function is exported from a DLL the prolog is patched to explicitly set the AX register: ; Patched to load correct AXMOV AX,0x1234 ; the reset of the prolog as before ending with MOV DS, AX (You can now see why that extra no-operation (NOP) instructions was required in the original prolog — to reserve room for the longer MOV AX,xxxx instruction.) These patches are applied in the Module16.Link() method after code relocations are applied: Call DllMain The final step in loading a DLL is to call its LibMain. After the module manager has loaded and linked the module it calls ModuleBase.Init() and Module16’s implementation runs LibMain: Finally the module’s all loaded and ready to go! What About GetProcAddress? One important thing I skimmed over is the implementation of GetProcAddress — the function that finds the address of an exported function during linking. Module16’s implementation uses NeFileReader to work out the address of the function: Module32’s implementation returns the address of a thunk (as described in the previous article) and I’ll show a more concrete example below. Implementing One Windows API Method Let’s now have a look at what Module32 does by creating a fake module that 16-bit code can call. FakeUserDll implements one Windows API method — MessageBox which is exported from the module user.dll as ordinal ID #1. Things to note: Its Init() method calls machine.CreateSystemThunk to create a thunk that when called from 16-bit code will invoke the FakeUserDll.MessageBox() function GetProcAddress returns the address of the thunk when asked for ordinal #1 The MessageBox method reads parameters from the VM stack/memory, calls the real Windows MessageBox function and sets AX to the return value. The first parameter is at SS:SP+4 because the return address of the caller will have been pushed after the parameters. First Win3mu Run! We’ve now covered just about everything required for Win3mu’s first run. In fact its first run was exactly what I’ve described — a simple .exe that calls MessageBox: I built it in Windows 98 with Visual C++ 1.5 Copied it to my main dev machine Ran it under Win3mu Debugged it, debugged it, debugged it some more Got a message box! So much work… for such a simple result! Phew! It’s been a long process to get here but that’s really the bare minimum to getting a 16-bit Windows program running under emulation. The main things that I haven’t covered are just some miscellaneous startup trivia — setting the correct registers for calling the .exe and one other function “InitTask” which doesn’t do anything too interesting. Next Steps The Windows API consists of about 1,100 API methods. If I have to write code like the MessageBox example above for every one I’m going to go insane! The next step is to reduce the amount of code required for each method to an absolute minimum — and in many cases no code at all. For the I’m going to cover off some miscellaneous topics like path mapping, config files and how I tracked down some subtle bugs in the processor that slipped through unit testing. next article Progress I mentioned OSFixup relocations above and how they’re related to floating point math. When I originally wrote the module loader I ignored them but once I encountered the first program using floating point math — Microsoft Tetris — I had to address them and they’re now working: Hi, I’m Brad Robinson — an independent software developer living in Sydney Australia. I write software for musicians and as an indie developer I rely on word of mouth. If you enjoyed this article please consider sharing it by hitting the “recommend heart” below or by sharing on Facebook/Twitter. It’s a small gesture but makes a real difference. Also, if your feed is lacking in hex dumps, disassembly listings and screen shots of old Windows 3 games you might like to follow me on . Twitter Continue reading… . Part 9 — Path Mapping

Win3mu Part 8 — The Module Loader

This is a hands-on course on how to deploy a fully Serverless web app using the . You will learn how to: AWS CDK Structure **CDK Stacks **to deploy an application from end-to-end Deploy a integrated with for dynamic requests processing Store data in a fast and cost-effective way with REST API AWS Lambda DynamoDB Use as a source for in an event-driven architecture DynamoDB streams Lambda Ingest and manipulate loads of data streams with Kinesis Firehose Deploy and query a with , and your entire application health in a single place using Data Lake Athena S3 GlueMonitor Dashbird You can use the resources declared in this demo application as a starting point to mix and adapt to your own architectures later, which should save you quite some time. The App and Architecture The demo app is a public blog where anyone can read, publish and like posts. It’s available . Go ahead and publish something in the top-left corner (yellow button) and also “like” articles already published. Check out the codebase on . on this link this repo Frontend Backend Data Lake and Analytical Querying What is the AWS CDK? CDK stands for Cloud Development Kit. Think of it as CloudFormation (CF) in your preferred language (Python, Typescript, C#, etc). Roughly speaking, it works like this: 1. You declare cloud resources using classes provided by the CDK libraries. Example: aws_cdk aws_s3
my_bucket = aws_s3.Bucket(self, ) from import 'MyBucket' 2. Run CDK deploy 3. CDK translates this to a CloudFormation template and deploy it on AWS for you. In case you would like to dig deeper, that will get your basics started. I also strongly recommend reading the . AWS also has a workshop official CDK documentation Advantages using the AWS CDK Use languages that are more expressive than YAML or JSON, for instance. Less - much less! - verbose than CloudFormation templates. Easier to apply reusability and inheritance principles to infrastructure code. Better integration with IDEs for code completion, IntelliSense, etc. Possible to test your infra code, just as any other software Portable: since it’s just a wrapper around CF, we can easily port it to JSON or YAML Disadvantages of using the AWS CDK Although released as a stable project by the AWS team, many parts (a lot of the good ones) are still experimental and APIs may change in backwards incompatible ways. It’s under constant development. During the preparation of this course, I had to upgrade my libraries three times. Documentation is still lacking at some parts and you will need to look at the CDK code, occasionally, to understand how to declare certain things. Deploy it yourself Although we have provided an online demo, you can also deploy this app in your own AWS account: Clone the repo: git clone git@github.com:byrro/serverless-website-demo.git sls-demo; cd sls-demo Setup your virtual environment: virtualenv -p /urs/bin/python3.8 .env; source .env/bin/activate; pip install -r requirements Specify an AWS account ID: ** export AWS_ACCOUNT_ID=1234567890 Deploy all three stacks: cdk deploy sls-blog; cdk deploy sls-blog-api; cdk deploy sls-blog-analytical ** You can also hard code your Account ID in the CDK project, as I’ll show in a minute; When starting a new project from scratch, you would run . This is not necessary for this demo, since the project is already created. cdk init --language [python|typescript|...] Monitoring Deploying this architecture in the cloud and blindly believing it will work flawlessly is not reasonable. We want to be the first one to know when something is not right to act upon it as quickly as possible. In this project, I used for its ease of use and seamless integration. Instead of having to deploy an agent inside my code, Dashbird plugs into my Stacks that I can deploy with the effort of one click. It not only monitors Lambda function errors, but also other resources that we’re using, such as DynamoDB tables. They even suggest cross-referenced against industry best practices. Dashbird through a CloudFormation template insights for architectural improvements Finally, Dashbird offers a free-forever plan. It’s a no-brainer to try it out by . registering for free How a CDK project is structured A CDK project creates an “Application”. This app may have one or more “Stacks”. A Stack is a group of cloud resources (Lambda functions, S3 buckets, etc) that are instantiated using CDK classes. It’s also possible to have multiple applications in a single CDK project. App Object Creating a CDK app is as simple as: app = core.App() When you run , an initial application with basic boilerplate code is created for you in the project root, under . cdk init --language [language] app.py The next thing we need is an environment, which is composed of an AWS Account ID and Region: env = env = core.Environment(
    account= ,
    region= ,
) 1234567890 'us-east-1' Declaring an environment is not required (CDK can infer from your AWS credentials), but is a good practice. Most of us work with multiple AWS accounts. It’s easy to mess around with several projects, accounts, credentials. When we explicitly set the environment in the CDK app, it’s locked and prevents mistaken deployments. Now we declare our stacks: my_project.my_project_stack MyStack

my_stack = MyStack(
    app, ,
    env=env,
) from import 'my-stack' This is how we our stacks for deployment. In the next section we’ll see how to those stacks. instantiate declare Stack Object The Stack object is where we our AWS resources. It inherits from the core.Stack CDK class and accepts a scope - which is our app object - a string identifier and an environment. declare ( . ): ( , : . , : , : . ,
            ** ,
            ) -> : (). ( , , ** )

        # class MyStack core Stack def __init__ self scope core Construct id str env core Environment kwargs None super __init__ scope id kwargs Declare AWS resources here Declaring AWS resources To declare AWS resources, we need a specific library for each service. Here’s a list of all and their counterparts. Other flavors are and . Python libraries Typescript Java .NET Let’s see how a basic REST API would be declared (typing expressions were removed for readability purposes): aws_cdk aws_apigateway, aws_lambda = aws_lambda.Function(
            self, ,
            runtime=aws_lambda.Runtime.PYTHON_3_8,
            code=aws_lambda.Code.asset( my_lambda.handler sls-blog-rest-api-gateway from import ( . ): ( , , , ): (). ( , , ** ) class MyStack core Stack def __init__ self scope id env super __init__ scope id kwargs my_lambda 'MyLambda' 'my_lambda_folder),
            handler=' ',
        )

        aws_apigateway.LambdaRestApi(
            self,
            ' ',
            handler=my_lambda,
        ) We first declare a Lambda function . We point its code to the . Inside this folder, there should be a file, containing a function called . This handler function should accept Lambda invocations normally (an and objects). my_lambda my_lambda_folder my_lambda.py handler event context Next, a is declared, using as the handler (not to confuse with the Lambda’s handler function). This will create a new API Gateway REST API integrated with using an . All HTTP requests will be routed to the Lambda function. LambdaRestApi my_lambda my_lambda AWS_PROXY integration type Our Project App & Stacks comprises one application with three Stacks. They’re all declared in the and files. This project app.py sls_website_stack.py Below we’ll walk through all Stacks in a high level. I encourage you to inspect the to learn how these resources are declared and also integrated. For example: a Kinesis Firehose is created in one Stack and referenced in another to include its name as an environment variable for the Lambda function that will interact with it. stacks file Except for the frontend static Stack - which is small - you will notice that resources are initialized with a (null) value in the beginning. The reason is that, even though the CDK is generally more succinct than CloudFormation, it can still be lengthy enough to clutter the view of the entire Stack. Having each resource declared first in one line, I can provide a short summary of everything that’s in the Stack and then instantiate the CDK classes in other methods. None = static_stack

        # SQS Queues
        self.queue_ddb_streams_dlq = None  # Dead-letter-queue DDB streams

        # DynamoDB Tables
        self.ddb_table_blog = None  # Single-table all blog content

        # DynamoDB Event Sources
        self.ddb_source_blog = None  # Blog table streams source

        # DynamoDB Indexes
        self.ddb_gsi_latest = None  # GSI ordering articles by timestamp

        # Lambda Functions
        self.lambda_blog = None  # Serves requests to the blog public API
        self.lambda_stream_reader = None  # Processes DynamoDB streams

        # Continues other resources... ( . ): ( , , , , ): (). ( , , ** ) . class SlsBlogApiStack core Stack def __init__ self scope id env static_stack super __init__ scope id kwargs self static_stack for for with Notice it takes another Stack object ( ) as an argument to its initialization. In the file, you can see that the is initialized passing the as an argument. static_stack app.py SlsBlogApiStack SlsBlogStack We use it to reference the CloudFront distribution domain ( ) in the Lambda environment variables. This variable can be used to customize the HTTP response header to comply with . This illustrates one way to easily integrate and reference information from one Stack into another within a CDK project. d1qmte5oc6ndq5.cloudfront.net Access-Control-Allow-Origin CORS standards At the end of the initialization, another method is called to instantiate the CDK classes for each resource and configure their parameters. self.create_cdk_resources() Next we’ll walk through each of our project’s Stacks. Stack 1: static website Our focus is on the Serverless backend, so the frontend here is terribly rough and simple. It’s stored in an S3 Bucket and distributed through a CloudFront CDN. CDK has a helpful class called BucketDeployment. It takes the contents of a directory and sync to an S3 bucket. In this case, we stored the frontend code in the folder. website_static aws_s3_deployment.BucketDeployment(
    self, ,
    sources=[aws_s3_deployment.Source.asset( )],
    destination_bucket=static_bucket,
    distribution=cdn,
) 'SlsBlogStaticS3Deployment' 'website_static' Stack 2: API/backend Our backend consists of an API Gateway (REST) connected to a . Microservices receives a lot of press, but you probably shouldn’t always break your applications in several functions. A Monolith is just fine - -, really. monolithic Lambda function and sometimes recommended This API & Lambda support a single endpoint (with GET and POST methods) with a queryString “action”, which takes three parameters: : populates the latest blog posts get-latest-articles : triggered when someone likes an article like-article : post a new blog article publish-article Here’s the power of the CDK model. We can create a REST API with 10 lines of code: rest_api_blog = aws_apigateway.LambdaRestApi(
    self, ,
    handler=lambda_blog,  # Previously declared Lambda 'sls-blog-rest-api-gateway' = . ( ),
    ),
) function deploy_options aws_apigateway StageOptions stage_name= ,
        throttling_rate_limit=lambda_param_max_concurrency,
        logging_level=aws_apigateway.MethodLoggingLevel( 'api' 'INFO' One nice little thing is that Lambda memory is used as a cache for the latest articles. We load the cache container outside the Lambda handler function. It remains in memory even after an invocation ends and is available for subsequent requests. Learn more here about . how to use Lambda as a cache mechanism MAX_CACHE_AGE: int = # In seconds
CACHE_LATEST_ARTICLES: Dict[str, Union[int, list]] = { : time.time(), : [],
} 120 'last_update' 'articles' All the data is stored in DynamoDB (DDB) using a , in . The site only shows the latest blog articles and items get after a few days by setting a attribute. single-table design on-demand mode auto-deleted by DDB time-to-live ddb_attr_time_to_live = ddb_param_max_parallel_streams = ddb_table_blog = aws_dynamodb.Table(
    self, ,
    partition_key=aws_dynamodb.Attribute(
        name= ,
        type=aws_dynamodb.AttributeType.STRING,
    ),
    billing_mode=aws_dynamodb.BillingMode.PAY_PER_REQUEST,
    point_in_time_recovery=True,
    removal_policy=core.RemovalPolicy.DESTROY,
    time_to_live_attribute=self.ddb_attr_time_to_live,
    stream=aws_dynamodb.StreamViewType.NEW_AND_OLD_IMAGES,
) 'time-to-live' 5 'sls-blog-dynamo-table' 'id' The DDB table also has a that makes it easier to retrieve articles ordered by date for the site: GSI (Global Secondary Index) self.ddb_table_blog.add_global_secondary_index(
    index_name= ,
    partition_key=aws_dynamodb.Attribute(
        name= ,
        type=aws_dynamodb.AttributeType.STRING,
    ),
    sort_key=aws_dynamodb.Attribute(
        name= ,
        type=aws_dynamodb.AttributeType.NUMBER,
    ),
    projection_type=aws_dynamodb.ProjectionType.ALL,
) 'latest-blogs' 'item-type' 'publish-timestamp' Modifications to DDB items generate streams that are processed by a . These streams are then repackaged and sent to a Kinesis Firehose stream processor. second Lambda function DDB doesn’t provide the flexibility that SQL databases offer and many choose , for example. Although Aurora is a great service, personally I prefer DDB for its simplicity and reliable, consistent performance. But sometimes we do need to run analytical queries, those with aggregations and on-the-fly filters. For that, we’ll be using Athena (more in the next Stack). Aurora Serverless Stack 3: analytical querying A Kinesis Firehose Stream is responsible for batching data inserted/modified in DDB, converting them to Apache Parquet format and storing in dedicated S3 buckets. From S3, we create a Data Lake with AWS Glue (used to declare our data schemas) and Athena (used to query the data). Athena is extremely powerful. We can statements ( ) to query terabytes of data and pay on-demand ($0.005 per GB of data scanned). Using Parquet not only improves query speed, but also reduces cost by minimizing the amount of data Athena needs to scan for each query. use SQL SELECT with some limitations Queries that are impossible or expensive/slow in DynamoDB, such as aggregations and JOINs, are fast and cheap in Athena. The two services combine each other in a perfect way, so that your application has optimized transactional storage and flexible analytical querying capabilities. We can use Athena to query all articles ever published and cross-reference with likes and HTTP metadata (source IP address, country, device type, etc). Even articles that were already expired by DynamoDB TTL (time-to-live) would continue to be available in the Data Lake. For example, which countries are liking the most articles? In the AWS Console, we get something like this: Queries can also be executed programmatically with or (e.g. ) to integrate anywhere we need this data. Athena API AWS SDKs Python’s boto3 Athena also supports JOINs. Here’s an example joining articles and HTTP metadata to analyze the most popular authors among readers of a particular country: Deployment CDK can deploy one Stack at a time. Since we have three, it’s necessary to specify which one when running the command. We do that by passing in the Stack ID as a CLI argument. For example, the following command will deploy the SlsBlogApiStack (id: ): CDK deploy sls-blog-api cdk deploy sls-blog-api Since all Stacks involve some type of permission granting, CDK asks for confirmation before deploying those resources. You can review the permissions requested and hit y when it’s good to go. Wrapping up We’ve covered how to structure CDK apps and add a bunch of AWS Resources to deploy with a simple command. CDK deploy If you’re new to the CDK - and as suggested early in the article -, it’s strongly recommended to follow AWS and . CDK workshop documentation Keep an eye on future publications as well, as is releasing other examples and tutorials to reap the most out of AWS serverless services with the power of infra automation with CDK or else. Dashbird Previously published at https://dashbird.io/blog/crash-course-aws-cdk-serverless-rest-api-data-lake-analytical-querying/

Quick Guide to AWS CDK and Serverless with REST API and Data Lake Analytical Querying

Top 33 Jira Alternatives For Agile Project Management Teams

Omnichannel eCommerce is a retail strategy that can provide invaluable optimization techniques and insights for businesses to improve the experience offered to customers. Crucially, it can also unify different channels to deliver unprecedented levels of control over inventory management. The continued emergence of the cloud and Internet of Things (IoT) technology means that stores can now access unprecedented volumes of data points to inform and even automate key business decisions. What could this mean for optimizing inventory management? Let’s take a deeper look at how omnichannel eCommerce is proving to be a revolutionary force in optimizing stock and improving the customer experience (CX) for leads. Defining Omnichannel eCommerce Before we get to grips with the next generation of inventory management, let’s first define what omnichannel eCommerce is. Omnichannel eCommerce is a retail strategy that leverages a frictionless shopping experience for customers with the help of many different online channels working in tandem. By uniting different channels like online stores, social media, mobile apps, websites, and email data with brick-and-mortar insights through POS purchase data and returns information, brands can gain a comprehensive understanding of many facets of their operations. These data points work in a similar way to omnichannel retail but with a clear focus on optimizing inventory management and CX for online shoppers. The Battle for Visibility One of the biggest innovations of omnichannel eCommerce will be felt in inventory management. The difficulty that many eCommerce stores face when it comes to managing inventory stems from a lack of visibility in real-time. If you don’t know what’s happening to your stock, you have no way of effectively managing it. However, omnichannel insights can help to deliver more comprehensive inventory management. Finding Solutions in Generative AI Generative AI is forecasted to become a $1.3 trillion market by 2032, according to Bloomberg Intelligence, and the rapid growth of the industry is certain to have significant implications for digital transformation in inventory management. The technology will become a valuable component in omnichannel eCommerce, and capable of interpreting and providing automated solutions based on the masses of unstructured big data that consumers create when interacting with brands. This can help to automatically adjust stock levels and action orders based on factors that would be impossible for human decision-makers to act upon. For instance, generative AI platforms could carry out social listening to monitor sentiment toward products and increase inventories if a certain store item begins trending online to prevent possible understocking. If the AI deems that demand for a product is trending lower, for instance, it can make adjustments on the fly to avoid understocking. In-Store Data Informing eCommerce Omnichannel data can also come from in-store devices like cloud-based point of sale (POS) systems. Thanks to innovations in the wealth of insights available through customer purchasing trends and preferences, the best POS systems on the market can use purchase insights to better understand how, why, and when customers are purchasing certain products. This can help to shape seasonal forecasting for eCommerce stores on an international scale and help to balance inventories accordingly to ensure orders can be fulfilled with ease and without the danger of overstocking by misinterpreting trends. Optimizing Returns and Storage Omnichannel orders can make inventory management more complex if the technology isn’t used to streamline processes. This inventory balancing act becomes increasingly difficult throughout warehouses, distribution centers, and online stores when it comes to handling returns. However, the implementation of blockchain technology can help to break down these barriers and optimize the returns process by mapping out the entire lifecycle of products from their inception. This means that faulty batches can be actively monitored on-chain to isolate issues, and when a customer returns an item, the product can be easily reincorporated into its storage facility without becoming lost throughout siloed departments. This process can help steer next-generation storage management without the hassle of incurring additional holding costs for items or depreciation expenses. The holistic overview offered by blockchain can help strike a balance between storage costs and ensuring that returned products return to market on an efficient basis. The Future of Inventory Management The eCommerce landscape is evolving at a rapid pace thanks to immersive technologies like generative AI and blockchain. These transformative tools will pave the way for seamless control over inventory for brands that can improve the customer experience across different platforms, websites, and brick-and-mortar stores. Omnichannel eCommerce will be crucial to these fresh data-driven insights for stock and can help to ensure that overstock and understock scenarios are a thing of the past. With intelligent insights steering the way for order fulfillment, eCommerce stores can operate on a more sustainable and cost-effective basis long into the future. Omnichannel eCommerce is a retail strategy that can provide invaluable optimization techniques and insights for businesses to improve the experience offered to customers. Crucially, it can also unify different channels to deliver unprecedented levels of control over inventory management. The continued emergence of the cloud and Internet of Things (IoT) technology means that stores can now access unprecedented volumes of data points to inform and even automate key business decisions. What could this mean for optimizing inventory management? Let’s take a deeper look at how omnichannel eCommerce is proving to be a revolutionary force in optimizing stock and improving the customer experience (CX) for leads. Defining Omnichannel eCommerce Before we get to grips with the next generation of inventory management, let’s first define what omnichannel eCommerce is. Omnichannel eCommerce is a retail strategy that leverages a frictionless shopping experience for customers with the help of many different online channels working in tandem. Omnichannel eCommerce By uniting different channels like online stores, social media, mobile apps, websites, and email data with brick-and-mortar insights through POS purchase data and returns information, brands can gain a comprehensive understanding of many facets of their operations. These data points work in a similar way to omnichannel retail but with a clear focus on optimizing inventory management and CX for online shoppers. The Battle for Visibility One of the biggest innovations of omnichannel eCommerce will be felt in inventory management. The difficulty that many eCommerce stores face when it comes to managing inventory stems from a lack of visibility in real-time. If you don’t know what’s happening to your stock, you have no way of effectively managing it. However, omnichannel insights can help to deliver more comprehensive inventory management. Finding Solutions in Generative AI Generative AI is forecasted to become a $1.3 trillion market by 2032, according to Bloomberg Intelligence, and the rapid growth of the industry is certain to have significant implications for digital transformation in inventory management. become a $1.3 trillion market become a $1.3 trillion market The technology will become a valuable component in omnichannel eCommerce, and capable of interpreting and providing automated solutions based on the masses of unstructured big data that consumers create when interacting with brands. This can help to automatically adjust stock levels and action orders based on factors that would be impossible for human decision-makers to act upon. For instance, generative AI platforms could carry out social listening to monitor sentiment toward products and increase inventories if a certain store item begins trending online to prevent possible understocking . prevent possible understocking prevent possible understocking If the AI deems that demand for a product is trending lower, for instance, it can make adjustments on the fly to avoid understocking. In-Store Data Informing eCommerce Omnichannel data can also come from in-store devices like cloud-based point of sale (POS) systems. Omnichannel data Thanks to innovations in the wealth of insights available through customer purchasing trends and preferences, the best POS systems on the market can use purchase insights to better understand how, why, and when customers are purchasing certain products. This can help to shape seasonal forecasting for eCommerce stores on an international scale and help to balance inventories accordingly to ensure orders can be fulfilled with ease and without the danger of overstocking by misinterpreting trends. Optimizing Returns and Storage Omnichannel orders can make inventory management more complex if the technology isn’t used to streamline processes. This inventory balancing act becomes increasingly difficult throughout warehouses, distribution centers, and online stores when it comes to handling returns. inventory balancing act inventory balancing act However, the implementation of blockchain technology can help to break down these barriers and optimize the returns process by mapping out the entire lifecycle of products from their inception. This means that faulty batches can be actively monitored on-chain to isolate issues, and when a customer returns an item, the product can be easily reincorporated into its storage facility without becoming lost throughout siloed departments. This process can help steer next-generation storage management without the hassle of incurring additional holding costs for items or depreciation expenses. The holistic overview offered by blockchain can help strike a balance between storage costs and ensuring that returned products return to market on an efficient basis. The Future of Inventory Management The eCommerce landscape is evolving at a rapid pace thanks to immersive technologies like generative AI and blockchain. These transformative tools will pave the way for seamless control over inventory for brands that can improve the customer experience across different platforms, websites, and brick-and-mortar stores. Omnichannel eCommerce will be crucial to these fresh data-driven insights for stock and can help to ensure that overstock and understock scenarios are a thing of the past. With intelligent insights steering the way for order fulfillment, eCommerce stores can operate on a more sustainable and cost-effective basis long into the future.

From Shelves to Screens: Is Omnichannel eCommerce Transforming Inventory Management?

Traditional logistics services are unable to cater to the increase in demand for efficiency and accuracy. Hence, AI solutions must be explored for cost optimization and resource usage. They must also at the same time ensure speed and precision. The success of AI largely depends on the accuracy and quality of data advancements. In essence, data annotation acts as the bridge enabling machine learning models to comprehend and address real-world issues. Over the years, data annotation has played a tremendous role in boosting growth across industries. Companies employ trained professionals to annotate data for their clients. Modern technologies utilize artificial intelligence (AI) and data annotation to cut costs and acquire customers. Data annotation is also facilitating rapid growth in the transportation industry by assisting machines develop an awareness of the world as it exists. The popularity of online shopping has also led to a rise in demand for logistics services by consumers. Both AI and data annotation have the capability to redefine each element of the through automated warehouse management, intelligent delivery route planning, predictive maintenance, and demand forecasting. So, let’s take a look at each of these elements. logistics industry Automated Warehouse Management: AI inventory management systems assist in better inventory decisions through the automation of several manual processes. Since manual processes like data entry and manual recording of items are very time-consuming and prone to human error, they may not offer real-time insights into inventory levels and demand patterns. AI is also useful in analyzing vast volumes of data and offering insights into inventory levels, demand, and supply in real time. It includes data analysis from point-of-sale items, historical sales data, and supplier data. It is also used for predicting demand for products and assisting businesses in adjusting their inventory levels to prevent overstocking or understocking. This assists retailers in bringing down costs linked with inventory stocking and improvement of order fulfillment times. Intelligent Delivery Route Planning or route optimization:  This is a major component of logistics as it establishes the best route for goods to be transported from one point to another. Data from different sources like GPS tracking, weather forecasts, and traffic sensors are used for determining the best route using AI algorithms. It can be used for optimizing routes as per weather conditions. The use of AI in businesses helps in time and cost saving enhancing the overall delivery experience. Predictive Maintenance: This is an important area in AI as it transforms the logistics industry by preventing downtime which is expensive leading to delays and loss of revenue. It utilizes data analysis and machine learning algorithms for predicting when maintenance is needed and assists logistics companies in identifying issues prior to their occurrence. AI is used to analyze data and identify patterns for predicting maintenance work including equipment repairs or replacement. AI assists businesses in identifying issues prior to them becoming serious and taking precautionary measures to limit business disruption. Demand Forecasting: This is an important part of supply chain management in e-commerce businesses as it assists them in optimizing inventory, improving supply chain planning, and limiting the risk of stockouts and waste. Moreover, it’s a complicated and challenging task for businesses dealing with a wide range of products and market segments. AI-backed demand forecasting assists eCommerce businesses in predicting sales and optimizing inventory levels resulting in enhanced supply chain efficiency and customer satisfaction. All in all, AI helps in transforming the manner in which businesses operate through the use of AI for data analysis and predicting future demand. Conclusion AI brings great opportunities for automating routine tasks, enhancing efficiency, and limiting costs. The implementation of AI in logistics assists businesses in reaping rewards and staying ahead of the competition. The continuing developments in AI have opened up the doors for endless possibilities. These include data integration and compatibility, scalability, accurate forecasting, bridging gaps in skills and knowledge, improving efficiency, etc.

The Vital Role Data Annotation Plays in the Logistics Industry

As businesses grapple with the complexities of global logistics, deploying AI in the supply chain has become a linchpin for those who want to stay ahead of the competition. In this article, we'll underscore the significance of AI in the supply chain and offer insights into a success story that vividly illustrates the tangible benefits of AI in optimizing supply chain management. A supply chain is the entire network of individuals and organizations producing and delivering a product or service to the final consumer. It includes every step, from sourcing raw materials, manufacturing and assembling the product to transportation, warehousing, and finally, reaching the customer's hands. 49% respondents from the supply chain sector expressed that disruptions led to planning difficulties. 44% indicated challenges due to supply chain footprint, which required them to make changes during the year. McKinsey Today, this multifaced network faces many disruptions, such as geopolitical tensions, economic fluctuations, climate change, and evolving regulations. Simultaneously, common goals transcend industries, including customer satisfaction, cost optimization resilience, and adaptation. Both challenges and goals demand innovative solutions, with supply chain optimization emerging as a crucial factor in ensuring efficiency. Brief Overview of Supply Chain Optimization Supply chain optimization (SCO) is a complex field dedicated to managing the flow of goods and services from raw materials to the end customer in the most efficient and cost-effective way possible. SCO is the adjustment of a supply chain's operations that ensures it is at the peak of possible efficiency. Top five challenges of supply chain optimization: Predicting customer demand accurately is crucial for optimizing inventory levels, transportation possibilities, and production schedules. Demand forecasting: Balancing the cost of holding inventory with the risk of stockouts. Inventory management: Selecting the most efficient and cost-effective routes for transporting goods, especially with factors like traffic congestion and fuel prices. Transportation optimization: Finding reliable suppliers who offer competitive prices and quality. Supplier selection and management: Efficient cooperation between all players in the supply chain. Collaboration and communication: To tackle these challenges, SCO employs data analytics, mathematical modeling, optimization algorithms, artificial intelligence (AI), and machine learning (ML). These tools collectively empower businesses to navigate the dynamic landscape of global supply chains with precision. AI and ML, in particular, emerge as powerful allies in the pursuit of supply chain efficiency. Leveraging and machine learning services helps automate tasks, enhance forecasting accuracy, and enable real-time decision-making, ensuring businesses can stay agile and responsive in the face of evolving circumstances. AI development Optimizing Supply Chain Management Through AI Integration AI plays a significant role in improving supply chain efficiency. It can be used in different areas of supply chain management. Enhancing visibility and transparency: Real-time data collection and analysis - Providing real-time insights into inventory levels, shipment locations, and potential disruptions. Predictive analytics - AI can analyze historical data and external factors like weather forecasts or economic trends, potential disruptions, and maintenance needs. Optimizing operations and decision-making: Inventory management - AI can optimize inventory levels by forecasting demand, considering lead times and seasonality patterns, and minimizing costs associated with overstocking or understocking. Route optimization - AI algorithms can determine the most efficient and cost-effective routes for transportation, considering factors like traffic conditions, fuel consumption, and schedules in a way of finding a balance between transportation and inventory costs. Warehouse automation - AI-powered robots can automate tasks like picking, packing, and sorting, improving speed, accuracy, and efficiency in warehouses. Resilience and risk management: Risk identification - AI can analyze data to identify potential disruptions or supply chain vulnerabilities, allowing companies to take pre-emptive measures to minimize their impact. Scenario planning - AI can simulate various scenarios like natural disasters or economic downturns, helping companies develop contingency plans to ensure business continuity. Fraud detection and prevention - AI can detect anomalies in order data and financial transactions, identifying potential fraud attempts, and protecting companies from financial losses. Sustainability and customer experience: Enhanced sustainability - AI can optimize processes to reduce waste, minimize energy consumption, and optimize fuel usage, contributing to a more sustainable supply chain. Personalization and customization - AI can personalize the customer experience by tailoring product recommendations and offerings based on individual preferences and purchase history. Let's explore how AI, in particular, plays a transformative role in reshaping the supply chain in the retail industry, illustrated by a real-world use case. Developing an Intelligent Supply Optimization System Our client, a manufacturer of plumbing tools and equipment with distribution points all around the United States, needed help optimizing their supply process. The main struggle stemmed from the elevated experiences associated with transporting goods from factories in China to distribution centers in the US. The customer maintained numerous warehouses and sales points across almost every state. Thus, determining the optimal routing posed a non-trivial task. In addition, it was crucial to consider the preferred transportation chain. Each product begins its journey in a shipping container from a Chinese seaport and travels the Pacific Ocean to reach a port in the United States, from where it continues its journey in a truck to the designated warehouse. Afterward, the product awaits last-mile delivery to the targeted distribution point. The customer wanted to have the following functionality: specify available warehouses; identify distribution points, with some automatically derived from historical data and others manually selected by the user; create a list of items along with their respective quantities; have a list of seaports. Finally, the client expressed a wish to be able to compare various methods of packing goods within shipping containers, either with or without pallets. The outcome of the optimization problem should yield the optimal transportation schedule and associated expense statistics. But before we dive into use case details, we will quickly outline a fundamental mathematical concept that is used in the intelligent solution. Integer Programming for Supply Chain Efficiency Integer (Linear) Programming (IP) is a mathematical optimization technique used to find the best solution to a problem with linear relationships while considering that either some or all the variables must take integer (whole number) values. The general form of an IP problem is to maximize (or minimize) a linear objective function subject to a set of linear constraints, where some or all the decision variables must be integers. The objective function is a linear combination of decision variables, and the constraints are linear inequalities or equalities that the variables must satisfy. The mathematical formulation of an IP problem can be expressed as follows: Values x₁, x₂, ..., xₙ are the decision variables, c₁, c₂, ..., cₙ are coefficients in the objective function, aᵢⱼ are coefficients in the constraint equations, and bᵢ are constants. Solving an IP problem involves finding the values of the decision variables that optimize the objective function while satisfying the linear constraints and the additional requirement that the variables must be integers. IP problems are commonly used in various fields, including operations research, logistics, manufacturing, finance, and telecommunications, where decision variables often represent quantities that must be whole numbers. Integer Programming is a valuable tool in solving supply chain optimization problems due to its ability to model complex decision-making processes involving discrete (integer) variables. The ways IP can be applied in the context of supply chain optimization: IP can assist in determining optimal inventory levels for different products at various stages of the supply chain, by considering constraints like storage capacity, demand variability, and order quantities, IP models can find the best balance between holding costs and stockouts. Inventory management: IP can optimize production schedules by considering factors such as production capacities, raw material availability, and demand forecasts. The model can help in determining the optimal quantities to produce for each product to meet demand while minimizing costs. Production planning: IP models can be applied to optimize the overall structure of the supply chain network. Routing and scheduling: IP models can be applied to optimize the overall structure of the supply chain network. Supply chain network optimization: IP models can incorporate demand forecasting data to optimize production and distribution decisions while aligning supply chain operations to anticipated customer demand, reducing the risk of overstock or stockouts. Demand planning: Enhancing Supply Chain Logistics – ELEKS’ Solution Overview To meet our client's requirements, we created a system that leverages the benefits of the Integer Programming technique, which optimizes supply schedules, routes, and inventory levels for five kinds of items: ceramic sinks, toilets, bidets, gas valves, and bathtub mixers (corresponding HS codes are 6910.10.0030, 6910.10.0010, 6910.10.0025, 8414.90.1080, 7323.93.0080). The system comprises three main parts (or pages): configuration, simulation, and analytics. Each of them will be explained in the following paragraphs. To ensure the confidentiality of data and application details, we anonymized names and real locations, creating a prototype for a straightforward demo application that illustrates the efficacy of AI in addressing optimization challenges. Configuration Page The user can select available warehouses, distribution points, and desirable seaports to take part in final optimized routing. Also, the client can specify desirable goods and their respective amount to be transported to their customers. The last thing is the date picker, in case delivery is planned. The system will calculate remains inventory at the delivery date based on forecasted demand and allocate planned shipments aligned with warehouse residuals. Clicking the 'optimize' button does all the magic! It sets optimization tasks and launches its solution. After a few seconds, results can be observed on the remaining two pages. Simulation (Map) Page This page offers the client the opportunity to view an accurate simulation of the proposed model's solution. In prior discussions, we deliberated on the suitable INCOTERMS for our client and reached a consensus on CFR. The customer can specify the number of hours for each step and press the corresponding button to see the state of the supply chain in this amount of time. First of all, there are five types of points: two for transport vehicles (ship and truck) and three for seaports (orange), warehouses (purple), and distribution points (green). The map is interactive, meaning the user can hover over any point or connection and see the information related to that object. For example, blue lines connecting warehouses and distribution points show the last-mile delivery plan and contain information about how much each good is delivered daily. If the user hovers over the ship, the information about its capacity, container types, loaded amount of each good, and routing are displayed. Analytics Page This page features all the data and statistics about the model's proposed solution. The user can observe the delivery plan, where each ship and truck are, its estimated arrival time, route, and filling. There are also a bunch of charts, starting from the pie chart with expenses altogether and ending with heat maps of delivery costs between different seaports, warehouses, and distribution points. Additionally, the client can observe a price graph per good for different distribution points and, based on that data, make corresponding business decisions. Performance Metrics: ELEKS’ Solution vs Client’s Legacy System The next challenge was to test our solution in real-world scenarios and compare it to the client's approach. For this purpose, the client considered a period of 1 month. The main conditions of the task (such as available seaports, location, and number of warehouses and clusters) are observed in the illustrations in the previous paragraphs. As a result, our model's solution was more effective regarding operating expenses, average time of last-mile delivery, and reduced idle time of warehouses. 5.76% average monthly cost savings achieved by the client and 50% reduction in delivery time for some clusters. ELEKS Data Science team The data indicates that the utilization of our solution proved to be more advantageous for the client, resulting in a reduction in the price per item. This outcome can be attributed to various factors, with the primary ones being as follows: More intelligent goods distribution More optimal delivery network More efficient routing and warehousing For example, our solution featured some clusters for which the model suggests last-mile delivery for different goods from distinct warehouses (an explanation lies deeply in the delivery network, and our model spotted this optimization possibility). The distribution of goods between warehouses differed (mainly between warehouses in Washington and New York). At first sight, the differences are not so significant, but the result speaks for itself. Conclusions The integration of artificial intelligence (AI) in supply chain management emerges as a critical component for businesses aiming to navigate the complexities of global logistics effectively. The multifaceted nature of modern supply chains, coupled with challenges such as disruptions, geopolitical tensions, and economic fluctuations, necessitates innovative solutions for achieving efficiency and resilience. The presented solution highlights AI's transformative role in streamlining supply chain managers' responsibilities and demonstrates the tangible benefits of AI and IP in supply chain logistics, showcasing a 5.76% average monthly cost savings, a 50% reduction in delivery time for some clusters. Its adaptability allows seamless adjustments, offering users access to a simulation studio, where they can execute various scenarios involving diverse items and quantities, facilitating a comparative analysis to determine optimal strategies for different situations. To schedule a demo of this transformative solution firsthand and explore its capabilities further, . reach out to us

A Real-World Case Study on Unleashing AI's Potential: AI in Supply Chain

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Running a business is no longer dependent on manual intervention. Imagine the busy schedules of entrepreneurs in the past. Attending inventory management, scouting for clients, hopping from one client meeting to the next, taking care of piles of paperwork, planning, adhering to compliances, promoting their brand, filing tax returns, and marketing! The very thought is enough to give me nightmares. *Phew! It’s relaxing to know that it’s not the case any longer.* As business operations have evolved over the years, we have tools to improve our efficiency, productivity, and profitability while also giving us time to take a step back and relax. Thanks to artificial intelligence and virtual assistants, entrepreneurs can now focus on the most important aspect of their business and avoid being stuck with repetitive tasks. If it’s routine, hand it over to AI or a virtual assistant. In fact, of global companies use AI, and when it comes to virtual assistants, of business owners hired at least one VA in 2021. 35% 67% Now that we’ve spoken about AI and virtual assistants being both lifesavers for business people let’s address the elephant in the room: Which one do you select for your business? Is it going to be AI or a VA that will make or break your venture? I will take you through both of them and help you decide. Mind you, it’s no small decision to choose between the two, as having a peaceful life along with a thriving business is what you can achieve through this decision. How Can AI Make Your Business Operations a Breeze? Artificial intelligence has really created a buzz! It’s percolated into our lives like oxygen. You need an example? Well, you needn’t look too far - don’t forget our very own Alexa and Siri. We are so dependent on AI that we do not even want to move our fingers to switch on the lights. We just go, “Hey Siri…”. So, if AI is so deep into our daily lives, imagine what its role is in business! I’ll break it down for you: Using AI to forecast trends to streamline inventory management and marketing efforts is not far-fetched. For example, AI can help you to predict the hottest-selling items for the upcoming season and adjust stock orders accordingly. Now, what it does is improve inventory efficiency, reduce overstock, limit the chances of stockouts, and increase sales. Predicting the next big trend: AI chatbots are all the rage in customer service. of companies prefer to use chatbots for personalized customer service. They improve the customer experience immensely by instantly handling customer queries 24/7. They also free up staff to take care of more complex responsibilities. Keeping your customers happy: 64% Now, this one is huge and highly significant in the current scenario. As cyberattacks increase, there is a need for businesses to protect themselves against cybercrime. AI enhances cybersecurity by monitoring transactions and detecting unusual patterns that indicate fraud. This goes a long way toward reducing financial losses and improving compliance with financial regulations. Looking out for fraud: Have you ever considered using AI to analyze customer behavior and data to create targeted marketing campaigns? of organizations use AI for marketing and sales. It’s more likely to increase engagement and conversion rates. No more one-size-fits-all marketing: 14% Businesses receive hundreds of job applications, and it is not easy to sift through them and identify the right candidates efficiently and timely. AI is a helping hand in screening the resumes and assessing the potential candidates in the initial stages. It goes without saying that this streamlines the recruitment process, improving the quality of candidates and reducing the time-to-hire. Selecting top talent for your business: If you find yourself wishing to make better financial and investment decisions, AI is the way to go. AI algorithms analyze financial data and help businesses make more informed financial decisions and manage risks in a better way. This leads to potentially higher returns and reduces the risk of financial losses. Hitting the bullseye with decisions: Gone are the days of manual intervention in each aspect of your business. Implementing AI to automate routine business workflows improves efficiency. Not only that, but it also frees up ample time for project managers to focus on more strategic tasks. The outcome is more efficient project management and a reduction of unnecessary manual workload. Passing on those routine tasks: If you’re keen on saving time and reducing errors, AI tools are here to help. AI tools can automatically generate business reports. Just imagine the workload it would take off an already burdened project manager if they used AI tools to generate accurate weekly performance reports automatically. Another benefit would be that businesses would get more timely access to important metrics. Making reporting hassle-free: The above are just a few ways AI makes handling business operations more efficient and streamlined. There are several other benefits that make AI very important for business. VA for Managing Your Business Better While AI is making waves when it comes to managing businesses and its usage is increasing with every passing day, it lacks something quite significant: the human element. While analytics and data, accuracy, and promptness are extremely important, AI cannot handle situations that need emotions or something different from routine tasks. It is virtual assistants that fill this gap. Let’s have a look at how VAs are making business operations more efficient and productive: You do not need to dip your toes into routine tasks like scheduling appointments, managing emails, and organizing documents. VAs are there to take care of it, and that too efficiently. This would free up ample time for you and your employees to focus on core tasks and increase productivity. If it’s routine work, hand it over to your VA. Handling administrative work like a pro: Data entry and management of records and trackers seem like a small task, but one miss can lead you to hours of scrutinizing all your records from scratch. It’s essential to maintain and regularly update data accurately for the integrity of your business, reducing errors and making sound decisions. VAs are adept at this task and ensure your data is up-to-date and correct. Handing your data on a platter: Don’t you secretly wish to be a social media influencer with a horde of people following you and being inspired by you? But then you get pulled back by other commitments requiring urgent intervention, and your social media account remains inactive. But this is a problem that VAs can quickly solve. They can manage your social media account, schedule your posts, engage with your followers, interact with others, analyze social media metrics, etc. This improves your online presence, makes your brand recognizable, and attracts more customers. Improving your online presence: Bookkeeping, invoicing, payroll processing, preparing financial reports—enough to cause sleepless nights. But VAs are pros at these critical aspects of financial management, allowing you to have a clearer understanding of your financial position and, in a way, aiding you in better financial planning and budget management. Financial management on track: It’s important to connect with potential clients; creating content that resonates with your target clients is a great way of doing so. VAs prove to be useful as they skillfully tackle all the requirements of content creation, such as writing, editing, graphic designing, video production, and much more. Getting your VA to create content for websites, newsletters, blogs, and social media posts is a hassle-free and effective way of doing content marketing. Creating content that hits the mark: One proven way to keep your customers coming back is great (not good) customer service. You can delegate this significant task to VAs as they handle customer inquiries promptly and provide the required support. You should note that of consumers prefer to speak to a real agent rather than get an instant response from an AI. Now, this has a two-fold benefit. First, your customers are satisfied with the timely support, making them loyal to your brand. Secondly, VAs are a great way of getting customer feedback, managing surveys, and compiling data for analysis. Smooth customer support: 60% If you’re considering expanding your business internationally and do not know the native language, it can negatively impact your venture. While learning a new language is an outstanding achievement, you might not have the time to invest in it. In such a situation, hiring a VA who is well-versed in the local language and culture is best. The VA can translate your communications, and the cultural insights would help grow your business. Breaking the language divide: As an entrepreneur, you’re dealing with multiple clients, multiple projects, and a lot of things that demand your attention. This might make meeting deadlines really tedious, and you might end up missing them sometimes. However, delegating projects to VAs can make managing them easy. VAs plan, execute, and monitor your projects. They coordinate with team members, follow up, and ensure that deadlines are met. Managing your projects: Burnout is a pretty common challenge entrepreneurs face. You’re all over the place managing your business, leaving no room for your personal commitments. Many times, your work-life balance goes for a toss. Delegating your routine tasks to VAs can improve your overall well-being, as they handle both your professional and personal tasks. Whether booking appointments or personal tickets and reservations, a VA can take on the task and free up some time for you. Nailing the work-life balance: The AI vs. VA Decision Choosing between AI and VA is a crucial decision for your business. I’ve included a few scenarios below to help you make the right decision. AI is the right choice for you if: AI can process and analyze vast amounts of data more efficiently than a VA. You can use these insights to drive your business strategies effectively. Your business relies heavily on data-driven decision-making and forecasts. One example of this is the use of AI-driven NLP to enhance customer support. This helps automate customer interactions, from handling inquiries and complaints to suggesting personalized recommendations. AI can read, interpret, and summarize complex documents for sectors like law or finance. AI is also helpful in quality control, image recognition, and complex decision-making. You’re looking to automate complex processes. AI is ideal for a dynamic environment that relies on adaptability. If we talk about tech startups, for instance, they need to evolve their algorithms based on feedback and current data constantly. AI systems make this possible as they adapt and improve over time. You’re looking to scale and continuous upgrades. AI can detect fraudulent activities and identify anomalies. This helps improve your business’s cybersecurity and reduce the risk of potential data breaches and cyberattacks. You prioritize privacy and security. VA is your go-to choice if: VAs handle routine tasks like managing your emails, scheduling appointments, etc. This frees up time to focus on core activities. You’re tired of handling routine tasks. If your business depends on high-end and personalized customer support or if you belong to an industry that needs emotional intelligence, cultural understanding, and personalized communication, like hospitality, then VA proves critical. That human touch matters. If you have a business that relies on project-based work, then VAs prove to be a boon. You can hire a VA with specific skills, like graphic design or digital marketing, for specific projects without the long-term commitment of a full-time employee. You need specialized skills on demand. If you have a small business or a startup, you might not be able to invest significantly in advanced AI systems or full-time employees. Virtual assistants cost less than full-time employees. VAs step in as essential, cost-effective support to run your business smoothly. You’re looking for cost-effective solutions. 78% Making the Right Choice As discussed above, I suggest basing this critical decision between AI and VA on carefully considering your business needs, budget, and long-term goals. In some cases, the best way to go would be to go for a combination of AI and VA. While AI can handle heavy tasks like automation, VAs can handle flexibility, human interactions, and flexibility. Ultimately, your choice should go hand-in-hand with your business objectives and improve your overall efficiency. Just remember that your business deserves the best support to thrive.

AI Vs VA: Who is Your Business’s Best Buddy?

I'm always on the lookout for new and exciting items to tinker with. Finding the right materials can be a challenge, especially on a budget. Luckily, with a little bit of creativity and research, there are plenty of places to find affordable items. These are just a few of my tips on where and how to find cheap gear! University Salvage University salvage & surplus stores are great places to find discarded items from labs and research facilities. These items often include electronics, lab equipment, books, and supplies making them perfect for projects. Check your local university or college to see if they have a storefront! Check out the iconic – one of the more historic examples. MIT SwapFest Other examples include... Oregon State, Purdue University, & University of Washington https://youtu.be/8thW6anxdTQ?embedable=true Thrift Stores & Flea Markets Thrift stores are outstanding places to find used items for projects too. These stores typically offer a wide range of items, from antique electronics to modern hardware, at pennies on the dollar. Look for items that are still in good condition but may not be of use anymore. For instance, an old DVR can be taken apart, and its HDDs salvaged. Make sure to check national chains as well as local storefronts. LGR Thrifts focuses on thrifting for retro computer hardware and software – wood paneling galore. https://youtu.be/FlCieLsUqXI?embedable=true Flea markets are equally excellent places to find items, as they often offer a variety of unexpected items at a fair price. Forgotten objects are perfect for creating something new. From radios to car parts flea markets can offer a trove. These markets also provide an opportunity to meet other like-minded individuals. Check for local and national flea markets... , , and are a few examples. Sell It Here Trader Bucks Swap-O-Rama Auctions, Surplus, & Liquidation Online auction sites like along with can be another resource for finding cheap gear in large quantities. These sites have a wide variety but be sure to check the items’ condition and factor in shipping before making a bid! These sites are best used when you know that you need a large quantity of one specific type of item. An example is a handful of GSM/LTE hotspots for use in a simulated forensics exercise or a bunch of padlocks for a village. Tip: Check the following sites for ideas when browsing devices... . eBay, ShopGoodwill, GovDeals, TSA confiscation & state surplus, wholesale liquidations WikiDevi, TechInfoDepot, FCC ID Search, & FCC.io Garage & Yard Sales Garage and yard sales are another great place to find bargains. Often with a wide variety of items for sale. You never know what you might find at a garage sale, so it's worth taking the time to look around and see what catches your eye. Unfortunately, there's no great way to see what sales are in your area other than driving around and scoping them out. Maybe use it as an excuse to go wardriving! Friends/Family, the "Table of Free Stuff", & the Junk Drawer Don't forget about your friends and family! They may have items that they no longer need, so don't be afraid to ask around, and see if anyone has anything they'd be willing to part with. Many dorms, apartment complexes, and workplaces may have a "Table of Free Stuff" where people leave items they no longer need. Much like the "Table of Free Stuff" we all have a junk drawer or two in our homes. Take a look through yours and see if there's anything that can be repurposed. FB Marketplace & Craigslist and are great resources filled with people selling all kinds of items. Be sure to read the descriptions carefully and ask the seller any questions you might have before making a purchase. Keep in mind that the best deals tend to go fast, so be sure to check these sites frequently and act quickly when you find something that you're interested in! Facebook Marketplace seems to have rapidly replaced Craigslist in both the usefulness and diversity of items. Craigslist Facebook Marketplace ! Stay safe out there meeting up with anyone FSA/HSA Stores Many healthcare providers offer flexible spending accounts (FSAs) and health savings accounts (HSAs) that can be used to purchase medical equipment. Look for items like blood pressure & glucose monitors and other medical devices that can be used in your next project. Recent updates to the Food, Drug, & Cosmetic act mandating that medical devices meet certain cybersecurity requirements has made it more difficult to find vulnerable devices - great for patients; bad for hackers. Keep in mind that most devices no longer process data locally but instead offload it elsewhere. Checkout this awesome BSidesDayton 2022 talk from on HSA Eligible Hardware Hacking! Jeremy Hong https://youtu.be/IPta5FJQnD4?embedable=true Reddit Markets & Woot Reddit attracts a large number of tech-knowledgeable users who may be looking to upgrade their equipment. This can create a market of individuals willing to sell their unwanted gear at a lower price than it would cost to buy new. Some may even be willing to work out a trade! Additionally, these subreddits can offer a of anonymity. It is always important to approach any online marketplace with caution and to verify the legitimacy of any seller before making a purchase. Check out this list of markets - Apple, Hardware, AV, MechKeyboards, etc. Recommend using PayPal G&S; If it seems like a scam it's probably a scam. level Many of these Markets have since moved to Discord after Reddit API changes! Recommend checking out for cheap daily deals and flash sales on electronics, computers, and mobile devices. They offer a wide range of products, from electronics to household items, at discounted prices. What overstock Amazon couldn't sell Woot will and at a discounted price. A great resource for picking up phones. Woot WiGLE DevKits (Amazon Sidewalk) Many companies offer development kits that include hardware and software! These kits can be a great resource for finding new and exciting items. The Dev Kit can create devices that can communicate on Amazon's low-bandwidth, long-range network of smart home devices. The kit includes a module that can be added to devices to enable them to communicate Amazon Sidewalk Scrap Yard, Recycling Center, Pick-a-Part If you're looking for materials can be a great resource. These places are a treasure trove of used items, including a variety of metals, electronics, and other materials that can be hacked and/or repurposed. Some recycling centers even have areas where you can take apart old electronics to salvage the parts. Be sure to check out these places for a goldmine of affordable items. scrap yards and recycling centers Pawn Shops Pawn shops are another great place to find cheap stuff to hack because they often have a wide variety of items. Many of these items are sold for a fraction of their original cost, making them perfect. Additionally, pawn shops are a good source for items that may be harder to find elsewhere. With a little bit of social engineering negotiation, you can often get an even better deal on the items. So if you're on a budget, don't overlook your local pawn shop. Never know you may find a Garrett Magnetometer Hand Wand....checkout 's physical security talk from GrrCON! dnsprincess AliExpress & International Shops AliExpress, AliBaba, DHgate, and other international marketplaces offer a wide range of products, including high-quality components, tools, and equipment. From circuit boards and sensors to power supplies and programmable chips, you can find everything you need. Check out some of the following creators making awesome content around treasures they've found on some of these sites! Shipping times may vary and delivery isn't always guaranteed. There are many places to find affordable items that can be hacked, modified, or repurposed and with a little bit of creativity and ingenuity, you can turn these items into something new and useful, all while saving a lot of money. So get out there and start hunting for your next hacking project! Also published here.

Where to Find Cheap Stuff to Tinker With

are everywhere. They have become indispensable in our everyday lives. They are also ubiquitous in various industries, notably e-commerce logistics. Mobile applications Nowadays, apps are redefining how e-commerce is conducted. One of the main reasons for their effectiveness is that these apps help immensely in attracting personnel. The traditional barriers tied to experience and competence levels are significantly diminished. This is, in a large part, because a mobile app introduces a rigid workflow, making it difficult for users to stray from prescribed tasks. Furthermore, these applications enable a high level of oversight, so all activities carried out by the user are thoroughly monitored. Apart from that, they assist in all areas: from product browsing to delivery tracking. They act like the bonding agent that links customers, retailers, and delivery services, allowing instant information exchange and effortless cooperation. For businesses, they enhance operational efficiency, improve customer relations, and provide a competitive advantage. For consumers, they offer a swift, engaging, and user-friendly interface for satisfying their shopping desires. How mobile apps influence e-commerce make different parts of the business, like finding products and delivering them, a lot easier thanks to their simple and clear interfaces. Mobile apps On the frontend, they deliver an engaging and personalized user experience. Advanced algorithms analyze user behavior to recommend products, tailor promotions, and facilitate personalized interactions. Mostly, apps are designed for easy navigation, quick checkouts, and secure payment processing. Many also offer features like wish lists, product comparisons, and customer reviews. On the backend, mobile applications are instrumental in managing inventory, processing orders, handling customer inquiries, and tracking deliveries. They help in making various office tasks more automatic and precise, reducing mistakes and making sure the operations run smoothly. In addition, these apps keep a bunch of helpful information that companies can use to tweak their operations, guess what their customers might want next, and make wise decisions about what's to come. Also, a good many of mobile apps have built-in tools for accessibility. These include voice-enabled search functions, speech synthesis, and the option for high-contrast displays. Such features guarantee that e-commerce platforms cater to the needs of all customers, including those with visual, auditory, or motor difficulties. What's their role? From what we can tell, mobile apps offer a bunch of tools and features that simplify and speed up a lot of operation parts, like taking care of inventory, tracking orders, and sorting out delivery systems. Inventory Management Applications like TradeGecko and Vend offer robust solutions here. These apps synchronize data across multiple sales channels, making it easier to manage stock levels, predict demand, and prevent both overstocking and stockouts. Features such as real-time inventory tracking, automated reordering, and reporting help businesses maintain optimal inventory levels and streamline their supply chain operations. Order Tracking For order tracking, mobile apps have significantly improved transparency and communication between businesses and customers. Applications such as AfterShip and Route offer real-time tracking and automatic updates on shipment progress, giving customers clear visibility of their orders' status. These platforms also allow businesses to customize their tracking pages, reinforcing brand identity and improving customer experience. Delivery Systems Mobile applications have revolutionized the way businesses coordinate their deliveries. Apps like Onfleet and Bringg provide sophisticated dispatching, route optimization, and driver-tracking features. They facilitate efficient delivery operations, reducing costs, improving delivery speed, and, as a result, enhancing customer satisfaction. Mobile apps in scaling One of the most critical challenges in e-commerce is scaling logistics to accommodate business growth. This requires managing larger volumes of orders, delivery routes, inventory, and customer communication, all of which can quickly become complex. Mobile applications help the scaling process. In a sense, they can grow along with a business. As the volume of transactions increases, mobile applications can handle the increased data and complexity without the need for significant investments in new hardware or infrastructure. They enable businesses to manage growth without disrupting existing operations, and their modular nature allows for additional features to be integrated as needed. For example, ShipBob, a logistics platform designed for e-commerce businesses, faces the challenge of managing increased order volume, as business operations grow. ShipBob offers a unified platform where companies can manage orders from various sales channels. The app provides inventory management across multiple warehouses, order management, and even advanced analytics to improve decision-making. With ShipBob, businesses can scale their logistics seamlessly without the headache of dealing with increased complexity. Similarly, applications like EasyPost allow businesses to manage an extensive network of carriers. As businesses expand geographically, they need to work with more delivery services. EasyPost offers API solutions that integrate with numerous carriers, making it easier for businesses to ship worldwide without getting bogged down by the complexity of managing multiple relationships. Sellbrite and Zoho Inventory provide further support for scaling logistics. Sellbrite facilitates multi-channel selling, allowing businesses to list and sell products across numerous online marketplaces from a single interface. This centralized control simplifies the management of increased sales channels as businesses grow. Zoho Inventory, on the other hand, is a robust inventory management solution that can manage the growing complexity of stock control. It handles tasks like inventory tracking, order fulfillment, and also integrates with multiple online sales channels, keeping all systems synchronized as a business scales. Who uses them, and how? ASOS This British online fashion retailer also made great progress in scaling its logistics with a mobile application. They integrated their operations with Metapack, a delivery management software that offers a range of features, including track-and-trace functionality, returns management, and delivery personalization. With Metapack's capabilities, ASOS was able to enhance its delivery process and manage a greater volume of orders. Stitch Fix Stitch Fix, an online personal styling service, used mobile applications to scale its operations and deliver personalized style boxes to customers across the United States. They used an app called Style Shuffle, which allows users to rate clothing and accessory items, thus helping the company gather data about customer preferences. This helped the company streamline its inventory management and improved the efficiency of its operations. Instacart Instacart, a North American leader in online groceries, turned to mobile apps to grow the scale of its operations. They developed their in-house app that connects customers, personal shoppers, and grocery stores for efficient delivery or pickup of groceries. This allowed them to manage a complex logistical challenge of coordinating between multiple stakeholders. As a result, Instacart became able to handle a growing volume of orders and expand their operations. The Home Depot The Home Depot, a leading home improvement retailer, provides another example of successful logistics scaling through the use of mobile applications. Facing increasing demand online, they realize the need to optimize their e-commerce logistics to handle the surge in orders. The company turned to mobile tech. The app they used was Roadie. It’s an Uber-like system that connects businesses with local drivers who can deliver goods on routes they were already taking. The Home Depot turned to Roadie to help make its delivery process smoother and deal with more orders. The company also created their own app that helped customers keep tabs on their orders, find things in stores, and even use augmented reality to see what products would look like in their homes. The payoff was a big boost to The Home Depot's e-commerce operations. Conclusion So, for e-commerce, mobile apps enhance clarity and speed, which results in an improved shopping journey for consumers, as well as businesses becoming able to work more efficiently. It's easy to expect that, with time, apps will gain an even bigger role in e-commerce logistics. We’ll probably be seeing a constant stream of more and more specialized apps, designed to offer even more help. Likely, they will introduce new methods to boost operational efficiency, cut costs, and amplify customer satisfaction. As these changes come, businesses that can use mobile apps well will lead the pack, ready for the opportunities and challenges the future brings. Try to be one of them.

Mobile Applications and their Impact on E-commerce Logistics Scaling

Data Management and Consolidation in the Integration of Corporate Information Systems

North and South by Elizabeth Cleghorn Gaskell, is part of the HackerNoon Books Series. You can jump to any chapter in this book . CHAPTER XVIII. here CHAPTER XVIII. LIKES AND DISLIKES. “My heart revolts within me, and two voices Make themselves audible within my bosom.” Wallenstein. On Margaret’s return home she found two letters on the table: one was a note for her mother,—the other, which had come by the post, was evidently from her aunt Shaw—covered with foreign post-marks—thin, silvery, and rustling. She took up the other, and was examining it, when her father came in suddenly: “So your mother is tired, and gone to bed early! I’m afraid, such a thundery day was not the best in the world for the doctor to see her. What did he say? Dixon tells me he spoke to you about her.” Margaret hesitated. Her father’s looks became more grave and anxious: “He does not think her seriously ill?” “Not at present; she needs care, he says; he was very kind, and he said he would call again, and see how his medicines worked.” “Only care?—he did not recommend change of air?—he did not say this smoky town was doing her any harm, did he, Margaret?” “No! not a word,” she replied, gravely. “He was anxious, I think.” “Doctors have that anxious manner; its professional,” said he. Margaret saw, in her father’s nervous ways, that the first impression of possible danger was made upon his mind, in spite of all his making light of what she told him. He could not forget the subject,—could not pass from it to other things; he kept recurring to it through the evening, with an unwillingness to receive even the slightest unfavourable idea, which made Margaret inexpressibly sad. “This letter is from Aunt Shaw, papa. She has got to Naples, and finds it too hot, so she has taken apartments at Sorrento. But I don’t think she likes Italy.” “He did not say anything about diet, did he?” “It was to be nourishing, and digestible. Mamma’s appetite is pretty good, I think.” “Yes! and that makes it all the more strange he should have thought of speaking about diet.” “I asked him, papa.” Another pause. Then Margaret went on: “Aunt Shaw says she has sent me some coral ornaments, papa: but,” added Margaret, half smiling, “she’s afraid the Milton dissenters won’t appreciate them. She has got all her ideas of Dissenters from the Quakers, has she not?” “If ever you hear or notice that your mother wishes for anything, be sure you let me know. I am so afraid she does not tell me always what she would like. Pray, see after that girl Mrs. Thornton named. If we had a good, efficient house-servant, Dixon could be constantly with her, and I’d answer for it we’d soon set her up amongst us, if care will do it. She’s been very much tired of late, with the hot weather, and the difficulty of getting a servant. A little rest will put her quite to rights—eh, Margaret?” “I hope so,” said Margaret,—but so sadly, that her father took notice of it. He pinched her cheek. “Come; if you look so pale as this, I must rouge you up a little. Take care of yourself, child, or you’ll be wanting the doctor next.” But he could not settle to anything that evening. He was continually going backwards and forwards, on laborious tiptoe, to see if his wife was still asleep. Margaret’s heart ached at his restlessness—his trying to stifle and strangle the hideous fear that was looming out of the dark places of his heart. He came back at last, somewhat comforted. “She’s awake now, Margaret. She quite smiled as she saw me standing by her. Just her old smile. And she says she feels refreshed, and ready for tea. Where’s the note for her? She want’s to see it. I’ll read it to her while you make tea.” The note proved to be a formal invitation from Mrs. Thornton, to Mr., Mrs., and Miss Hale to dinner, on the twenty-first instant. Margaret was surprised to find an acceptance contemplated, after all she had learnt of sad probabilities during the day. But so it was. The idea of her husband’s and daughter’s going to this dinner had quite captivated Mrs. Hale’s fancy, even before Margaret had heard the contents of the note. It was an event to diversify the monotony of the invalid’s life; and she clung to the idea of their going, with even fretful pertinacity, when Margaret objected. “Nay, Margaret: if she wishes it, I’m sure we’ll both go willingly. She never would wish it unless she felt herself really stronger—really better than we thought she was, eh, Margaret?” said Mr. Hale, anxiously, as she prepared to write the note of acceptance, the next day. “Eh! Margaret?” questioned he, with a nervous motion of his hands. It seemed cruel to refuse him the comfort he craved for. And besides, his passionate refusal to admit the existence of fear, almost inspired Margaret herself with hope. “I do think she is better since last night,” said she. “Her eyes look brighter, and her complexion clearer.” “God bless you,” said her father earnestly. “But is it true? Yesterday was so sultry every one felt ill. It was a most unlucky day for Mr. Donaldson to see her on.” So he went away to his day’s duties, now increased by the preparation of some lectures he had promised to deliver to the working people at a neighbouring Lyceum. He had chosen Ecclesiastical Architecture as his subject, rather more in accordance with his own taste and knowledge than as falling in with the character of the place or the desire for particular kinds of information among those to whom he was to lecture. And the institution itself, being in debt, was only too glad to get a gratis course from an educated and accomplished man like Mr. Hale, let the subject be what it might. “Well, mother,” asked Mr. Thornton that night, “who have accepted your invitations for the twenty-first?” “Fanny, where are the notes? The Slicksons accept, Collingbrooks accept, Stephenses accept, Browns decline. Hales—father and daughter come—mother too great an invalid—Macphersons come, and Mr. Horsfall, and Mr. Young. I was thinking of asking the Porters, as the Browns can’t come.” “Very good. Do you know, I’m really afraid Mrs. Hale is very far from well, from what Dr. Donaldson says.” “It’s strange of them to accept a dinner-invitation if she’s very ill,” said Fanny. “I didn’t say very ill,” said her brother, rather sharply. “I only said far from well. They may not know it either.” And then he suddenly remembered that, from what Dr. Donaldson had told him, Margaret, at any rate, must be aware of the exact state of the case. “Very probably they are quite aware of what you said yesterday, John—of the great advantage it would be to them—to Mr. Hale, I mean, to be introduced to such people as the Stephenses and the Collingbrooks.” “I’m sure that motive would not influence them. No! I think I understand how it is.” “John!” said Fanny, laughing in her little, weak, nervous way. “How you profess to understand these Hales, and how you never will allow that we can know anything about them. Are they really so very different to most people one meets with?” She did not mean to vex him; but if she had intended it, she could not have done it more thoroughly. He chafed in silence, however, not deigning to reply to her question. “They do not seem to me out of the common way,” said Mrs. Thornton. “He appears a worthy kind of man enough; rather too simple for trade—so it’s perhaps as well he should have been a clergyman first, and now a teacher. She’s a bit of a fine lady, with her invalidism; and as for the girl—she’s the only one who puzzles me when I think about her,—which I don’t often do. She seems to have a great notion of giving herself airs; and I can’t make out why. I could almost fancy she thinks herself too good for her company at times. And yet they’re not rich; from all I can hear they never have been.” “And she’s not accomplished, mamma. She can’t play.” “Go on, Fanny. What else; does she want to bring her up to your standard?” “Nay! John,” said his mother, “that speech of Fanny’s did no harm. I myself heard Miss Hale say she could not play. If you would let us alone, we could perhaps like her, and see her merits.” “I’m sure I never could!” murmured Fanny, protected by her mother. Mr. Thornton heard, but did not care to reply. He was walking up and down the dining-room, wishing that his mother would order candles, and allow him to set to work at either reading or writing, and so put a stop to the conversation. But he never thought of interfering in any of the small domestic regulations that Mrs. Thornton observed, in habitual remembrance of her old economies. “Mother,” said he, stopping, and bravely speaking out the truth, “I wish you would like Miss Hale.” “Why,” asked she, startled by his earnest, yet tender manner. “You’re never thinking of marrying her?—a girl without a penny.” “She would never have me,” said he, with a short laugh. “No, I don’t think she would,” answered his mother. “She laughed in my face, when I praised her for speaking out something Mr. Bell had said in your favour. I liked the girl for doing so frankly, for it made me sure she had no thought of you; and the next minute she vexed me so by seeming to think—— Well, never mind! Only you’re right in saying she’s too good an opinion of herself to think of you! The saucy jade! I should like to know where she’d find a better!” “Well, as I’m just as much convinced of the truth of what you have been saying as you can be; and as I have no thought or expectation of ever asking her to be my wife, you’ll believe me for the future that I’m quite disinterested in speaking about her. I foresee trouble for that girl—perhaps, want of motherly care—and I only wish you to be ready to be a friend to her, in case she needs one. Now, Fanny,” said he, “I trust you have delicacy enough to understand, that it is as great an injury to Miss Hale as to me—in fact, she would think it a greater—to suppose that I have any reason, more than I now give, for begging you and my mother to show her every kindly attention.” “I cannot forgive her her pride,” said his mother; “I will befriend her, if there is need, for your asking, John. I would befriend Jezebel herself if you asked me. But this girl, who turns up her nose at us all—who turns up her nose at you——” “Nay, mother; I have never yet put myself, and I mean never to put myself, within reach of her contempt.” “Contempt, indeed!”—(One of Mrs. Thornton’s expressive snorts.)—“Don’t go on speaking of Miss Hale, John, if I’ve to be kind to her. When I’m with her, I don’t know if I like or dislike her most; but when I think of her, and hear you talk of her, I hate her. I can see she’s given herself airs to you as well as if you’d told me out.” “And if she has,” said he—and then he paused for a moment—then went on: “I’m not a lad, to be cowed by a proud look from a woman, or to care for her misunderstanding me and my position. I can laugh at it!” “To be sure! and at her too, with her fine notions, and haughty tosses!” “I only wonder why you talk so much about her, then,” said Fanny. “I’m sure, I’m tired enough of the subject.” “Well!” said her brother, with a shade of bitterness. “Suppose we find some more agreeable subject. What do you say to a strike, by way of something pleasant to talk about?” “Have the hands actually turned out?” asked Mrs. Thornton, with vivid interest. “Hamper’s men are actually out. Mine are working out their week, through fear of being prosecuted for breach of contract. I’d have had every one of them up and punished for it, that left his work before his time was out.” “The law expenses would have been more than the hands themselves were worth—a set of ungrateful naughts!” said his mother. “To be sure. But I’d have shown them how to keep my word, and how I mean them to keep their’s. They know me by this time. Slickson’s men are off—pretty certain he won’t spend money in getting them punished. We’re in for a turn-out, mother.” “I hope there are not many orders in hand?” “Of course there are. They know that well enough. But they don’t quite understand all, though they think they do.” “What do you mean, John?” Candles had been brought, and Fanny had taken up her interminable piece of worsted-work, over which she was yawning; throwing herself back in her chair, from time to time to gaze at vacancy, and think of nothing at her ease. “Why,” said he, “the Americans are getting their yarns so into the general market, that our only chance is producing them at a lower rate. If we can’t, we may shut up shop at once, and hands and masters alike go on tramp. Yet these fools go back to the prices paid three years ago—nay, some of their leaders quote Dickinson’s price now—though they know as well as we do that, what with fines pressed out of their wages, as no honourable man would extort them, the real rate of wage paid at Dickinson’s is less than at ours. Upon my word, mother, I wish the old combination laws were in force. It is too bad to find out that fools—ignorant, wayward men like these—just by uniting their silly heads, are to rule over the fortunes of those who bring all the wisdom that knowledge and experience, and often painful thought and anxiety, can give. The next thing will be—indeed, we’ve all but come to it now—that we shall have to go and ask—stand hat in hand—and humbly ask the secretary of the Spinner’s Union to be so kind as to furnish us with labour at their own price. That’s what they want—they, who haven’t the sense to see that, if we don’t get a fair share of the profits to compensate us for our wear and tear here in England, we can move off to some other country; and that, what with home and foreign competition, we are none of us likely to make above a fair share, and may be thankful enough if we can get that, in an average number of years.” “Can’t you get hands from Ireland? I wouldn’t keep these fellows a day. I’d teach them that I was master, and could employ what servants I liked.” “Yes! to be sure, I can; and will, too, if they go on long. It will be trouble and expense, and I fear there will be some danger; but I will do it, rather than give in.” “If there is to be all this extra expense, I’m sorry we’re giving a dinner just now.” “So am I,—not because of the expense, but because I shall have much to think about, and many unexpected calls on my time. But we must have had Mr. Horsfall, and he does not stay in Milton long. And as for the others, we owe them dinners, and it’s all one trouble.” He kept on with his restless walk—not speaking any more, but drawing a deep breath from time to time, as if endeavouring to throw off some annoying thought. Fanny asked her mother numerous small questions, all having nothing to do with the subject, which a wiser person would have perceived was occupying her attention. Consequently, she received many short answers. She was not sorry when, at ten o’clock, the servants filed in to prayers. These her mother always read,—first reading a chapter. They were now working steadily through the Old Testament. When prayers were ended, and his mother had wished him good-night, with that long steady look of hers which conveyed no expression of the tenderness that was in her heart, but yet had the intensity of a blessing, Mr. Thornton continued his walk. All his business plans had received a check, a sudden pull-up, from this approaching turn-out. The forethought of many anxious hours was thrown away, utterly wasted by their insane folly, which would injure themselves even more than him, though no one could set any limit to the mischief they were doing. And these were the men who thought themselves fitted to direct the masters in the disposal of their capital; Hamper had said, only this very day, that if he were ruined by the strike, he would start life again, comforted by the conviction that those who had brought it on were in a worse predicament than he himself,—for he had head as well as hands, while they had only hands; and if they drove away their market, they could not follow it, nor turn to anything else. But this thought was no consolation to Mr. Thornton. It might be that revenge gave him no pleasure; it might be that he valued the position he had earned with the sweat of his brow, so much that he keenly felt its being endangered by the ignorance or folly of others,—so keenly that he had no thoughts to spare for what would be the consequences of their conduct to themselves. He paced up and down, setting his teeth now and then. At last it struck two. The candles were flickering in their sockets. He lighted his own, muttering to himself: “Once for all they shall know whom they have got to deal with. I can give them a fortnight,—no more. If they don’t see their madness before the end of that time, I must have hands from Ireland. I believe it’s Slickson’s doing,—confound him and his dodges! He thought he was overstocked; so he seemed to yield at first, when the deputation came to him,—and of course, he only confirmed them in their folly, as he meant to do. That’s where it spread from.” About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Elizabeth Cleghorn Gaskell (2003). North and South. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/4276/pg4276-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

LIKES AND DISLIKES.

In the ever-changing modern business landscape, business growth strategies, once grounded in industry experience, competitive benchmarking, and a fair share of professional intuition, are being reshaped by the influence of . The sheer volume, variety, and velocity of data available to businesses today offer invaluable insights promising rapid growth and unprecedented efficiency. Big Data At the forefront of this revolution marches machine learning. Not just a passing trend, machine learning provides a rigorous mathematical framework to interpret patterns and make strategic decisions based on accurate predictions. But how exactly does machine learning modify and transform business growth strategies? And in what ways can its advanced algorithms help a company aiming for market leadership? In this article, I will try to answer these and other questions, exploring the connection between machine learning and efficient business development. Content Overview How Machine Learning Affects Modern Business What Are the Basic Algorithms of Machine Learning How Machine Learning is Applied to Business Growth in Practice A Step-by-step Guideline to Implementing Machine Learning for Business Growth Technology Limitations and Practical Considerations Conclusion How Machine Learning Affects Modern Business In today's business environment, data plays a central role. Every customer interaction and transaction generates data that offers insights into market dynamics. The core advantage of machine learning in business is its ability to transform this extensive data into actionable decisions. is a computational method that allows its practitioners to identify patterns and make data-driven decisions without explicit programming. In business contexts, it enhances decision-making by eliminating human biases. Instead of relying on lengthy manual analysis or basic market surveys, machine learning provides an in-depth, objective view by examining numerous variables simultaneously. Machine learning Take product pricing as an example. Instead of using conventional methods like cost-plus pricing, machine learning offers dynamic pricing models. These models factor in real-time demand, inventory, competitor prices, and even external events to recommend optional pricing. Similarly, in customer relations, machine learning refines the broad understanding of target audiences. By evaluating browsing patterns, purchase histories, and customer feedback, it assists in building personalized marketing strategies, boosting conversion rates and customer loyalty. This series of examples can be continued . ad infinitum The benefits machine learning brings to present-day business are innumerable. Today, in the third decade of the 21st century, the question is not whether companies should adopt machine learning, but rather how swiftly they can integrate it into their internal processes to avoid falling behind the competition. But first, let us ask ourselves a question, what do people mean by machine learning? What Are the Basic Algorithms of Machine Learning Machine learning leverages an intricate combination of mathematics and computation, yet it is much more than just a set of convenient tools for processing large volumes of data. Machine learning is a discipline rooted in established mathematical principles, which ensures the accuracy and reliability of the results it delivers. To give you a peek into the nature of this technology, I will provide a brief description of some basic machine learning algorithms. While this section would tell nothing new to experienced professionals utilizing the power of machine learning in their jobs, like data analysts, I hope that a reader new to the topic will find it informative. a way to predict one value based on another by discerning patterns from available data. Essentially, it determines the relationships between different variables, influencing one another. Linear regression, for instance, assumes a straight-line relationship between two variables, while polynomial regression describes curved relationships; yet another example is logistic regression which predicts the probability of a binary outcome (true or false). By understanding these relationships, businesses can predict the effect of a change in one variable on another. For example, Airbnb uses regression analysis to suggest optimal pricing for hosts based on factors such as location, property type, time of year, and other listings’ prices. Similarly, Zillow's "Zestimate" applies linear regression to predict house prices based on features like size, location, and number of rooms, leveraging vast amounts of past sales data. Regression Analysis provides play a pivotal role when dealing with massive volumes of unlabeled data. They segment data into distinct groups, with items put in groups based on inherent similarities – and differences from other groups. K-means, for example, partitions data into 'k' number of centroids, and all data points are clustered around these centroids based on similarity. The New York Times, as an example, utilizes clustering to group news articles for readers, ensuring that similar stories, whether thematic or topical, can be navigated with ease. Clustering algorithms involve analyzing available data and learning patterns to apply to new, unknown data. Often used in conjunction with clustering, classification is about assigning predefined labels to data points. One well-known application is in the finance sector. Banks like JPMorgan Chase & Co. leverage classification to discern between 'fraudulent' and 'legitimate' client transactions, drawing upon patterns in historical data. In the healthcare domain, IBM's Watson Health utilizes classification algorithms to assess medical imaging, helping doctors determine whether a tumor is benign or malignant based on specific attributes. Classification methods l: extracting meaningful, actionable patterns from data. By laying out a mathematical framework, machine learning ensures these patterns are not coincidences but have statistically significant foundations, providing reliable insights that businesses can use. This leads us to the next part of this article – a deep dive into the real-life applications of machine learning in business development. All these algorithms share a common goa How Machine Learning is Applied to Business Growth in Practice Machine learning’s theoretical foundation is captivating, but where its transformative power shines is in its ability to spark business growth through practical applications. To better understand its impact, let us dive into how it has reshaped various sectors in recent years. Nowadays, machine learning plays a key role in shaping . By analyzing vast data sets ranging from local consumer behavior, cultural preferences, and competitive landscapes to regulatory requirements, machine learning models predict the success rate of a product or service in a particular region or demographic. Such data-driven insights can guide businesses on where to open new stores, how to adjust product features for local tastes, or even when to launch for optimal impact. This predictive capability empowers businesses to make informed, strategic decisions, optimizing investments and amplifying their chances for successful market expansion. Market Expansion Strategies In the realm of , gone are the days of relying solely on traditional demographic-based methods. Clustering algorithms have revolutionized this space, enabling businesses to segment their clientele based on buying behavior, browsing patterns, and even sentiments on social media. This radical shift has paved the way for marketing campaigns that more precisely resonate with individual consumers, ultimately fostering stronger customer acquisition and retention rates. Customer Segmentation The complexity of – predicting future sales trends – has been an age-old challenge. But with machine learning, tools like regression and time series forecasting have introduced a new layer of precision. By intricately analyzing both historical data and external variables such as economic indicators or seasonal patterns, businesses now possess sophisticated tools to project revenue, enhancing their strategic decision-making process. Sales Forecasting In the dynamic world of commerce, setting the right price can make or break a product's success. Machine learning aids businesses in devising smart strategies by analyzing a combination of internal data (like production costs and desired margins) and external factors (like market demand, competitor pricing, and seasonal influences). These algorithms can dynamically adjust prices in real-time, promptly responding to changes in the market environment. By ensuring that products are priced competitively and profitably, businesses can boost sales volumes, maximize revenues, and gain a strategic edge in crowded markets. Pricing Optimization One more vital area where machine learning has showcased its value is in . By analyzing historical workflow data, machine learning can identify bottlenecks and inefficiencies holding back the business growth, or repetitive tasks ripe for automation. For instance, in manufacturing, predictive maintenance powered by machine learning can forecast when a piece of machinery is likely to fail, allowing for proactive maintenance, reducing downtime, and enhancing overall production efficiency. Operational Efficiency And last but not least, has also witnessed a profound transformation. Instead of grappling with the perennial challenges of overstocking and stockouts, businesses now harness machine learning's predictive analytics to predict future demand. This means more balanced stock levels, optimized supply chain operations, and the mitigation of lost sales opportunities. Inventory Management While these examples highlight just a few of the many ways machine learning has optimized business operations and business development, it is paramount to remember that the effectiveness of these models relies on data quality and the right algorithmic choices. A careful and informed approach to integration is necessary when deploying machine learning techniques and algorithms to develop new and facilitate existing business growth strategies. With my extensive experience in this area – harnessing the power of machine learning to benefit businesses, I can provide some practical insights on the matter. A Step-by-step Guideline to Implementing Machine Learning for Business Growth Integrating machine learning into business processes extends beyond merely deploying algorithms. It demands structured planning, teamwork, and continuous adjustment. Here are the key steps businesses should consider when embracing the power machine learning provides in their business development strategies: Before implementing data analysis or choosing a model, a company has to specify the intended objectives. Whether the aim is to expand to new markets, increase customer lifetime value, or optimize ongoing operations for better profitability, setting precise, quantifiable growth targets ensures machine learning projects stay on track with the set business goals. Establish clear business growth objectives Incorporating machine learning for growth is more than a data scientist's task. Effective integration often calls for insights from market strategists, sales experts, customer relationship managers, and tech specialists. By fostering a collaborative environment, businesses can ensure the resulting models are both technically robust and tailored to drive business expansion. Form a cross-functional team with a focus on growth Machine learning offers a variety of algorithms and tools, applicable to different growth challenges. For example, clustering algorithms can identify new market segments, while time series forecasting might predict sales trends. The choice of tools is dictated by the specific growth strategies. Select appropriate tools and algorithms After a model is created, the next step is to assess its performance with real-world data. Initial outcomes might not always hit the mark, but machine learning thrives on adjustment. Feedback and results guide the necessary model revisions for enhanced precision, and it is crucial to measure outcomes based on business growth indicators in focus – whether it is market share expansion, customer acquisition rates, or an increase in revenue. Adhere to iterative testing and refinement keeping business metrics in mind With a refined model ready, you can move to the deployment stage, integrating the model into strategic execution points. This can entail embedding insights into sales dashboards, incorporating predictions into marketing campaigns, or using forecasts in supply chain adjustments. It is critical to ensure smooth integration with current business infrastructure, enabling actionable insights that contribute to growth. Deploy and integrate As market conditions, business strategies, and customer preferences keep evolving, machine learning models must adapt. Regular monitoring is essential to track performance, with updates accommodating new data or changing market dynamics. Maintain regular oversight and adjustments By following a structured approach and implementing machine learning with clear growth-centric goals, businesses can unlock untapped growth and innovation opportunities. Still, even machine learning has its limitations, and the incorporation of its techniques and methods into business operations presents certain challenges to take into account before you start. Technology Limitations and Practical Considerations As businesses rush to harness the capabilities of machine learning, it is crucial to approach its adoption with a balanced perspective. Although the concerns I address here are common when implementing machine learning algorithms in general, if anything, they hold even more true when incorporating machine learning with the explicit goal of driving business growth. Understanding these limitations is of momentous importance to realizing the full potential of machine learning in bolstering growth strategies. One of the main things to consider while employing machine learning in your business is . Machine learning models thrive on data, and if the input data is flawed or incomplete, the output will mirror these imperfections. Businesses have to invest time and resources in data cleansing, preprocessing, and validation to ensure the reliability of the insights derived. data integrity Another critical thing one should watch out for in data sets is . Machine learning bias means that an algorithm will produce flawed results due to erroneous (biased) assumptions in the data the algorithm is trained on. If historical data carries inherent biases, the algorithm will likely reflect them. For businesses, this can lead to skewed customer insights or even discriminatory practices, and so active measures must be taken to identify and rectify such biases in the data beforehand. potential biases In the pursuit of high accuracy, there is a temptation to build complex models that perform exceptionally well on training data but falter when exposed to new, unseen data. This phenomenon, known as , can lead businesses astray with overly optimistic predictions or recommendations. Regular validation and testing against independent datasets are vital to ensure the model's generalization capabilities. overfitting Some advanced machine learning models, especially deep neural networks, are often termed "black boxes" due to their complex architectures. While they might offer superior accuracy, their workings can pose severe . For businesses, this poses a challenge in understanding and explaining the rationale behind specific predictions or decisions, especially in regulated sectors where transparency is imperative. interpretability issues In essence, while machine learning is a formidable tool in the business arsenal, its implementation demands a judicious blend of technical expertise and domain knowledge. The promise of machine learning is not in its infallibility but in its capacity to elevate decision-making when applied with due consideration of its limitations. Conclusion As the dynamics of the business world continue to evolve, the importance of staying adaptive and forward-thinking is paramount. It is evident that every day, data plays an increasingly important role in shaping modern commerce. As I hope I was able to demonstrate in this article, decision-making in business, and especially in building balanced and effective business growth strategies, has been deeply and irreversibly transformed by the integration of machine learning, adding depth and precision to once purely intuition-driven processes. Today, embracing machine learning is more than just a strategic move – it is a necessity for businesses aiming for consistent growth in a digital age. Yet, naturally, integrating this advanced technology is not without its complexities. Constructing systems that not only gather but deeply analyze data to provide actionable insights is a task that requires both innovative thinking and unyielding persistence. And while machine learning offers businesses innumerable advantages, it is the combination of this technology with human expertise that creates a robust foundation for productive business strategies. This blend ensures that decisions are both data-informed and grounded in human judgment – something that no technology has been able to replace.  At least – for now.

Leveraging ML Algorithms for Business Growth Optimization

Retailers focus on sales. The more sales you make, the more you profit. But not every sale is successful. When a customer isn’t pleased or changes their mind, they return the merchandise, sometimes slightly used, according to the retailer’s return policy. In total, returns accounted for in lost sales for U.S. retailers in 2022. There is potential revenue hidden in those numbers, but many retailers struggle to maximize the opportunities inherent in customer returns. $816 billion Amazon, the in the United States with more than 2 billion combined web visits every year, has a return strategy that can serve as a guide to other businesses wondering how to harness some of the lost revenue that comes with customer returns. knows how to handle customer returns smoothly and with reasonable profitability. most visited e-commerce platform Amazon customer service Amazon Return Policy Of course, not every customer is satisfied with the Amazon review policy. Some consumers have shared online reviews regarding challenges with getting their returns, as seen with : this Amazon shopper “...if you return anything it goes into an amazon account. It is not refunded back to the card you used to pay with! … they refused to refund my money to me.” Another customer took to X, formerly Twitter, to claims he “returned the wrong product” despite it being checked by the delivery employee, and now he faces problems: complain that Amazon “What kind of fraud is going on in Amazon,..., they are saying, I returned the wrong the product so return couldn't be initiated, Who's responsible ?” While feedback on review websites and social media tells us that not every return is handled perfectly by the online retailer, the company handles a tremendous amount of returns every day. Presumably, the Amazon return options are effective for most customers. Otherwise, there would be even more disgruntled customers leaving negative online reviews. The states that, “Items shipped from Amazon.com, including Amazon Warehouse, can be returned within 30 days of delivery, with some exceptions.” The Amazon return policy then goes on to note a few exceptions to their policy, including unique return policies by some sellers on the platform, the possibility of a different policy for Amazon Global Store, and an extended return time for certain items. official Amazon return policy While the company has found several customer-friendly options for returning Amazon items, including scanning a barcode and dropping unwanted items off to be bulk packaged at UPS stores and retailers like Kohl's, what happens next makes the Amazon return strategy particularly distinctive. How Does an Amazon Return Work? Once the customer has returned an item, the Amazon return heads back to the company to be sorted. There are several options for what happens next to these  packages: Some items purchased through individual Amazon sellers are returned directly to the seller, often traveling for long distances, but will eventually be sorted and repackaged at Amazon warehouses. Amazon packages that contain items that can be used or sold again are often sold by the company to warehouses or liquidation retailers. If an Amazon return is damaged or there are legal or hygienic concerns, the item is either recycled or used for “energy recovery.” Energy recovery turns these non-recyclable items into usable energy sources through combustion or gas recovery. Some returns also wind up being donated. Reselling Amazon Returns In some cases, items returned through Amazon customer service make their way back to customers through the official website. Goods are sent back and then sorted by the company into categories like ‘Used-like new’ or ‘Used-acceptable.’ Once graded, they are resold through certain Amazon site segments as refurbished items, warehouse deals, or overstock. Amazon Returns and Liquidation Centers Amazon has found it most profitable to not bother putting items back on the shelf. When a customer decides to return on Amazon, the item is sent back to the company. If it is new or in like-new condition, the item may be sent to an Amazon warehouse, where it could be sold at a slight discount. However, most goods that are sent back accumulate in storerooms and then are bundled into a pallet of return items. These pallets are sold to a liquidation company for a bulk price. From there, the firm will unpack the pallet, sort the things present, and list them on their official website. In this way, the liquidation company will be effectively buying bulk items – sight unseen – at a discount, then doing the work of sorting the inventory, listing the items, and selling them to customers at a profit. The Effectiveness of Amazon Return Strategy With the bulk pallet strategy used for most of the company’s returns, Amazon winds up ahead by not paying for the time, storage space, and human resources needed to sort millions of returned items individually, restock them separately on shelves, and then adjust and maintain the correct inventory numbers. Amazon accounts for physical stock when the company sells items. After that, the inventory for most items is done digitally through the return codes and software. The company has considerable savings by pushing the human resources, additional physical count, and expenses to liquidation companies. To balance the transactions and make the trade-off of labor and costs appealing to the liquidators, Amazon sells the bulk pallets at a substantially discounted price, often through an auction. A quick glance at a reviews website will tell you that Amazon doesn’t allow returns for everything customers buy. Certain items like groceries, open software, gift cards, downloads, some jewelry, and health items can’t be returned once purchased. Amazon has a well-designed strategy for satisfying customers and handling returns, but the store does have limits. As you’re designing your own return policy, it’s essential to keep that balance in mind.

The Workable Approach Behind Amazon Returns

Artificial Intelligence (AI) has emerged as a transformative force, revolutionizing industries across the globe. From healthcare and finance to transportation and manufacturing, is reshaping the way businesses operate, unlocking new opportunities, and driving unprecedented growth. Understanding the profound impact of AI on industries is crucial in navigating the ever-evolving business landscape and harnessing its potential to thrive in the digital age. AI AI’s influence extends far beyond automation and efficiency gains. It empowers businesses to leverage vast amounts of data, make data-driven decisions, and uncover valuable insights that were previously untapped. By harnessing AI technologies, industries can enhance customer experiences, streamline operations, and gain a competitive edge in a rapidly evolving market. However, to truly benefit from AI’s potential, it is essential to comprehend its influence and explore the opportunities it presents. By understanding the , businesses can identify areas where AI can revolutionize their operations, improve productivity, and drive innovation. transformative power of AI In this article, we delve into the profound impact of AI on industries, examining its influence on healthcare, finance, transportation, manufacturing, and more. We explore how AI is transforming processes, driving efficiency gains, and revolutionizing customer experiences. By comprehending AI’s impact and seizing the opportunities it brings, businesses can position themselves at the forefront of the AI revolution and unlock a world of unprecedented growth and success. 1. Healthcare: The Transformative Impact of AI in the Medical Realm The healthcare industry stands to benefit immensely from the impact of Artificial Intelligence (AI). AI technologies are revolutionizing the way diagnoses are made, diseases are detected, and patients are treated, leading to improved healthcare outcomes and more efficient patient care. AI’s Role in Improving Diagnostics and Disease Detection AI plays a crucial role in improving diagnostics by analyzing medical images and data with exceptional accuracy and efficiency. can detect patterns and anomalies in medical images such as X-rays, MRIs, and CT scans, aiding in the early detection of diseases like cancer, cardiovascular conditions, and neurological disorders. By assisting healthcare professionals in diagnosing diseases at an earlier stage, AI enables timely intervention and potentially life-saving treatments. AI-powered algorithms Personalized Medicine and Treatment Plans Enabled by AI The era of personalized medicine has been ushered in by AI. With access to vast amounts of patient data and medical research, AI algorithms can identify individualized treatment options based on a patient’s genetic profile, medical history, and lifestyle factors. This enables healthcare providers to deliver tailored treatment plans, optimize drug dosages, and predict treatment outcomes with greater accuracy. By incorporating AI into the process, healthcare professionals can enhance the effectiveness and precision of medical interventions. Efficient Patient Care through AI-Driven Systems AI-driven systems are transforming patient care by streamlining administrative tasks, improving communication, and enhancing decision-making processes. Chatbots and are being used to provide personalized patient support, answer common healthcare queries, and schedule appointments, thereby reducing administrative burden and enhancing the overall patient experience. Moreover, AI-enabled predictive analytics can aid in resource allocation, patient flow management, and hospital capacity planning, optimizing operational efficiency and improving patient outcomes. virtual assistants powered by AI The healthcare industry is poised to witness tremendous advancements through AI, enabling accurate diagnostics, personalized medicine, and efficient patient care. By harnessing the power of AI, healthcare professionals can improve healthcare outcomes, enhance patient experiences, and ultimately save lives. 2. Finance: Unleashing the Power of Artificial Intelligence for Unprecedented Growth The finance industry has undergone a significant transformation with the integration of Artificial Intelligence (AI) technologies. From automating data analysis to enhancing customer experiences, AI is revolutionizing how financial institutions operate and interact with their clients. Automation of Data Analysis and Fraud Detection Using AI AI has revolutionized data analysis in the finance industry. With the ability to process vast amounts of financial data in real time, AI algorithms can quickly identify patterns, trends, and anomalies that may go unnoticed by human analysts. This enables financial institutions to make more accurate predictions, assess risks, and make informed decisions. Additionally, AI-powered fraud detection systems are employed to identify suspicious transactions and prevent fraudulent activities, protecting both financial institutions and their customers. AI-Powered Risk Assessment and Algorithmic Trading AI has proven instrumental in risk assessment and algorithmic trading. By analyzing market trends, historical data, and financial indicators, AI algorithms can generate predictive models that assess investment risks and recommend optimal trading strategies. These AI-powered systems can swiftly analyze vast amounts of data, adapt to changing market conditions, and execute trades with speed and precision, increasing the efficiency and profitability of financial operations. Enhanced Customer Experiences with AI-Driven Chatbots and Personalized Financial Services AI-driven chatbots and virtual assistants have transformed customer experiences in the finance industry. These intelligent systems can provide personalized recommendations, answer customer queries, and assist with financial planning, all in a timely and efficient manner. By leveraging AI, financial institutions can offer seamless and interactive customer support, improving satisfaction levels and fostering long-term relationships. Additionally, to suit individual needs, such as personalized investment portfolios and tailored loan options, providing customers with enhanced value and convenience. AI enables the customization of financial services The finance industry is leveraging AI to automate data analysis, detect fraud, optimize trading strategies, and enhance customer experiences. With AI’s capabilities, financial institutions are becoming more efficient, accurate, and responsive, empowering them to navigate the complexities of the financial landscape and deliver exceptional services to their clients. 3. Transportation: The Unstoppable Impact of AI on the Way We Move The transportation industry is undergoing a revolutionary transformation through the integration of Artificial Intelligence (AI) technologies. From advancements in autonomous vehicles to AI-powered navigation systems, the impact of AI is reshaping the way we travel and optimize transportation logistics. Advancements in Autonomous Vehicles and AI-Powered Navigation Systems AI is at the forefront of the development of autonomous vehicles, bringing us closer to a future where self-driving cars become commonplace. to perceive their surroundings, interpret traffic conditions, and make informed decisions in real time, ensuring safe and efficient navigation. These advancements have the potential to reduce human error, enhance road safety, and increase accessibility, leading to a more connected and convenient transportation experience. AI algorithms enable vehicles Impact of AI on Optimizing Transportation Logistics and Reducing Traffic Congestion AI is revolutionizing transportation logistics by optimizing route planning, fleet management, and supply chain operations. With AI algorithms analyzing real-time data on traffic patterns, weather conditions, and demand forecasting, logistics can be streamlined to minimize delivery time, reduce fuel consumption, and enhance overall operational efficiency. Additionally, AI-driven traffic management systems can dynamically adjust traffic flow, reducing congestion and improving the overall flow of vehicles, leading to reduced travel times and enhanced mobility. Improved Safety Measures through AI-Driven Monitoring and Collision Prevention AI is playing a pivotal role in enhancing safety measures within the transportation industry. AI-powered monitoring systems leverage real-time data and sensor inputs to detect potential hazards, monitor driver behavior, and identify risks proactively. By analyzing patterns and identifying anomalies, these AI systems can alert drivers and take preventive actions to mitigate potential accidents. Furthermore, AI-driven collision prevention systems utilize advanced sensors, computer vision, and predictive analytics to detect and respond to imminent collisions, significantly reducing the risk of accidents and enhancing overall road safety. The transportation industry is experiencing a profound impact from AI, with advancements in autonomous vehicles, optimized logistics, and improved safety measures. These developments are poised to transform the way we travel, making transportation more efficient, safer, and sustainable. As AI continues to evolve, it holds the potential to revolutionize mobility, reshape urban landscapes, and create a future of connected and intelligent transportation systems. 4. Manufacturing: Breaking Free of the 20th Century Habits The manufacturing industry is experiencing a significant impact from the integration of Artificial Intelligence (AI) technologies. AI is revolutionizing production processes, supply chain optimization, and quality control, leading to improved efficiency, reduced downtime, and enhanced product quality. AI’s Impact on Streamlining Production Processes and Supply Chain Optimization AI plays a crucial role in streamlining production processes by automating tasks, optimizing resource allocation, and improving operational efficiency. AI-powered systems can analyze real-time data from sensors, machines, and production lines to identify bottlenecks, optimize workflows, and minimize downtime. Additionally, AI algorithms can optimize supply chain management by forecasting demand, improving inventory management, and optimizing logistics, ensuring timely delivery and reducing costs. Predictive Maintenance for Improved Efficiency and Reduced Downtime AI-driven predictive maintenance systems are transforming the manufacturing industry by enabling proactive equipment monitoring and maintenance. By leveraging sensor data, historical performance, and AI algorithms, these systems can predict equipment failures before they occur. This allows manufacturers to schedule maintenance activities, prevent unexpected breakdowns, and minimize costly downtime. With AI-powered predictive maintenance, manufacturers can optimize equipment utilization, reduce maintenance costs, and improve overall operational efficiency. Quality Control and Defect Detection Using AI-Powered Systems AI is enhancing by detecting defects and ensuring product consistency. AI-powered vision systems can analyze images, detect anomalies, and identify product defects with high accuracy and speed. These systems can detect minute flaws, ensure adherence to quality standards, and minimize the risk of defective products reaching the market. By automating quality control processes with AI, manufacturers can improve product quality, enhance customer satisfaction, and reduce waste and rework. quality control in the manufacturing industry The manufacturing industry is reaping the benefits of AI with streamlined production processes, optimized supply chains, and improved quality control. AI’s impact in this industry translates into increased operational efficiency, reduced downtime, and enhanced product quality. By embracing AI technologies, manufacturers can stay competitive, meet customer demands more effectively, and drive innovation in the ever-evolving manufacturing landscape. 5. Retail: Unveiling the ‘Smart’ Revolution in Shopping The retail industry is undergoing a significant transformation with the integration of Artificial Intelligence (AI) technologies. AI is revolutionizing customer experiences, optimizing pricing strategies, and transforming inventory management in the retail sector. Personalized Marketing and Recommendation Systems Powered by AI AI-powered personalized marketing and recommendation systems are reshaping the retail industry. By analyzing vast amounts of customer data, AI algorithms can understand individual preferences, purchase history, and browsing behavior. This enables retailers to deliver targeted and personalized marketing campaigns, recommend products tailored to each customer’s interests, and enhance customer engagement. With AI, retailers can create personalized shopping experiences that cater to individual needs, increasing customer satisfaction and driving sales. Enhanced Customer Experiences through AI-Driven Chatbots and Virtual Assistants AI-driven chatbots and virtual assistants have become integral to providing seamless customer experiences in the retail industry. These intelligent systems can engage with customers in real-time, answer queries, provide product recommendations, and even assist with purchasing decisions. By leveraging natural language processing and machine learning, AI-powered chatbots and virtual assistants deliver personalized and efficient customer support, ensuring round-the-clock assistance. This enhances customer satisfaction, improves response times, and reduces the burden on customer service teams. AI’s Role in Optimizing Pricing Strategies and Inventory Management AI is revolutionizing pricing strategies and inventory management in the retail industry. AI algorithms can analyze market trends, competitor pricing, and customer demand to optimize pricing strategies dynamically. Retailers can use AI-powered pricing solutions to set optimal prices, identify sales opportunities, and maximize profit margins. Furthermore, AI-driven inventory management systems can analyze historical sales data, demand forecasts, and supply chain factors to optimize inventory levels, reducing stockouts and overstocking. This improves inventory turnover, reduces costs, and ensures product availability, leading to improved profitability. The retail industry is experiencing a profound impact from AI, with personalized marketing, enhanced customer experiences, and optimized pricing and inventory management. By leveraging AI technologies, retailers can meet evolving customer demands, improve operational efficiency, and gain a competitive edge in the dynamic retail landscape. With AI’s potential for innovation and transformation, the future of retail holds exciting possibilities for delivering personalized, seamless, and efficient shopping experiences. 6. Education: Unleashing the Cognitive Revolution of AI The with the integration of Artificial Intelligence (AI) technologies. AI is revolutionizing the way students learn, teachers teach, and educational institutions deliver knowledge. From adaptive learning platforms to improved teaching methods, AI has the potential to shape the future of education. education industry is witnessing a transformative impact AI-Driven Adaptive Learning Platforms and Personalized Assessments AI-driven adaptive learning platforms are reshaping the educational landscape by tailoring content and learning experiences to individual students’ needs. By analyzing student performance data, AI algorithms can identify knowledge gaps, provide personalized recommendations, and adapt the curriculum in real time. Additionally, AI-powered assessments can evaluate students’ progress more accurately and offer personalized feedback, enabling educators to monitor individual performance and provide targeted support. AI’s Potential to Improve Teaching Methods and Student Engagement AI holds great potential to enhance teaching methods and student engagement. AI-powered tools can automate administrative tasks, allowing teachers to focus more on personalized instruction and individualized learning plans. Furthermore, AI can facilitate interactive and immersive learning experiences through virtual reality (VR) and augmented reality (AR) technologies. AI-powered chatbots and virtual assistants can provide instant support, answer student queries, and foster continuous learning beyond the traditional classroom. Equal Access to Education through AI-Powered Educational Tools AI has the power to bridge the educational divide by providing equal access to education. AI-powered educational tools can break language barriers, enable personalized language learning, and offer translation services to non-native speakers. Additionally, AI-driven content delivery platforms can provide educational resources to remote or underserved areas with limited access to traditional educational institutions. By leveraging AI, educational institutions can promote inclusivity, reach a wider audience, and ensure that education becomes more accessible and equitable. The education industry is at the forefront of AI innovation, with adaptive learning platforms, improved teaching methods, and equal access to education. AI has the potential to revolutionize the way we learn, making education more personalized, engaging, and accessible to all. By embracing AI technologies, educational institutions can create a future of enhanced learning experiences, increased student success, and greater educational equity. Conclusion The has been nothing short of transformative. Throughout this article, we have explored how AI is revolutionizing various sectors, from healthcare and finance to transportation and manufacturing. The profound influence of AI is evident in improved diagnostics, personalized experiences, optimized processes, and enhanced decision-making across industries. impact of Artificial Intelligence (AI) on industries It is essential for businesses and individuals alike to embrace AI and adapt to its advancements to stay competitive in the ever-evolving landscape. By leveraging AI technologies, businesses can enhance efficiency, productivity, and customer satisfaction, gaining a significant edge in their respective industries. Looking ahead, the future potential of AI is boundless. Ongoing innovations continue to push the boundaries of what AI can achieve, and new applications are being discovered every day. As AI evolves, it will continue to impact industries in unprecedented ways, fueling innovation and driving economic growth. To fully capitalize on the , it is crucial to foster a culture of innovation, invest in AI research and development, and prioritize ethical considerations. Striking a balance between the power of AI and addressing societal concerns will be vital in ensuring a future where AI becomes a force for positive change. benefits of AI In conclusion, AI’s impact on industries is profound and far-reaching. By embracing AI and harnessing its potential, businesses can unlock new opportunities, drive innovation, and create a future where AI enhances our lives and transforms industries for the better. With continued advancements and ongoing innovations, AI will continue to shape industries, economies, and society as a whole. It is an exciting time to be part of the AI revolution, and the possibilities ahead are limitless. Also published . here

The Transformative Influence of AI: 6 Revolutionary Breakthroughs

The world of business is constantly evolving, driven by advancements in technology and changing market dynamics. Amidst this landscape, emerges as a powerful platform offering innovative solutions and opportunities for businesses. With its decentralized architecture, smart contract capabilities, and transparent ledger system, Obyte provides a fertile ground for diverse business use cases. Obyte From revolutionizing supply chain management to enabling secure transactions through smart contracts, unlocks new avenues for growth, efficiency, and profitability. In this ever-evolving business ecosystem, embracing Obyte has the power to reshape industries, foster trust, and open doors to exciting opportunities. Let’s explore some potential business use cases here. Obyte Supply Chain Management Obyte's distributed ledger technology can be leveraged to create a transparent and immutable supply chain management system. Businesses can track and verify the movement of goods, reduce counterfeit products, and ensure the authenticity and integrity of their supply chain data. Each stage of the supply chain process, These transactions are immutable and can be accessed by authorized parties, ensuring transparency and integrity throughout the supply chain. from raw material procurement to the final product delivery, can be recorded as transactions on the . Obyte platform Let's consider an example of a coffee company that sources its beans from multiple farms globally. With Obyte, each farm can record the details of their coffee beans, including origin, quality, and certifications, on a decentralized DAG (Directed Acyclic Graph). As the beans move through different stages, such as processing, packaging, and distribution, relevant information is added in the form of transactions. This includes timestamps, location data, and quality control measures. Using Obyte, the coffee company can easily track and verify the journey of their beans, ensuring that they meet the required standards and certifications. They can also provide end consumers with transparent information about the product's origin, ethical sourcing, and environmental impact. This increased transparency can build trust among consumers and differentiate any company from its competitors. Besides, by reducing counterfeit products and ensuring the authenticity of goods, The enhanced supply chain visibility provided by Obyte can also lead to more efficient inventory management, reducing costs associated with overstocking or stockouts. Additionally, improved transparency and traceability can attract consumers who value ethical sourcing and sustainable practices, potentially increasing market share and customer loyalty. the business can protect its brand reputation and avoid revenue losses due to fraudulent activities. Automated contracts and decentralized escrow Obyte offers several types of that would allow any business to automate and enforce contractual agreements without the need for intermediaries. They can be used for various purposes, such as escrow services, ensuring secure and transparent transactions, and reducing the risk of fraud. smart contracts If they’re not (a type of simple contract also available in Obyte), they ensure that transactions occur only when predefined conditions are met, providing trust and security for business interactions. It’s also possible for businesses to select an expert arbiter from the ArbStore to solve any potential dispute between parties. These contracts are self-executing and enforceable, with the terms and conditions directly written into code. prosaic An international example Let's say Company A, based in the United States, wants to purchase a batch of goods from a supplier in China (usually cheaper than other places). To ensure a smooth transaction and mitigate risks, they decide to use on the Obyte platform. a contract with arbitration Company A and the supplier agree on the terms of the purchase, including the quantity, quality standards, and delivery schedule. They create the contract with all these details on the Obyte wallet, including payment terms, conditions, and a mutually selected arbiter from the ArbStore. Once the smart contract is in place, Company A initiates the payment by depositing the agreed-upon amount into the contract in the wallet. Upon the arrival of the goods in the United States and a successful inspection by the buyer, the latter releases the funds to the supplier. The funds are then locked in the escrow until predefined conditions are met. Just in case Company A decides to misbehave and keep the funds locked, the supplier can contact the arbiter and present evidence of the situation Similarly, Company A can call the arbiter if the supplier doesn’t ship the agreed-upon goods. Then, the arbiter will decide who keeps the funds, since they have the power to release them to any of the parties. This way, both sides gain confidence in the international transaction, streamline the purchasing process, and reduce the potential for fraud. And the only costs involved are the DAG transaction fees, which are usually lower than $0.0001 in any available token. Only in the case of a dispute, an additional fee would be added to pay for the arbiter's services. Identity Verification Obyte can provide a secure and decentralized solution for identity verification and Know Your Customer (KYC) processes. , ensuring data privacy and reducing the risk of identity theft. It enables businesses to authenticate user identities without relying on centralized authorities or high costs At the same time, users could enjoy more privacy. After verifying themselves on Obyte, if it’s sufficient for the company or service they’re using, the only thing they’d have to share is , and not their personal data. the verification itself ID in a marketplace We can imagine here an example of an online marketplace that connects buyers and sellers. To ensure a safe and trustworthy environment, the marketplace implements identity verification using Obyte. When a user registers on the platform, they would need to have their own Real Name Attestation (unique ID), previously acquired through the Obyte wallet. A chatbot is available in the wallet to guide the process, and, in just a few minutes, any user can obtain their own self-sovereign ID. The user's information is cryptographically hashed and stored on the Obyte DAG, ensuring data privacy and security. providing confidence in the authenticity of each user's identity. This digital identity can be accessed by other users or businesses participating in the marketplace, For instance, a seller on the marketplace can view the verified status of a potential buyer before engaging in a transaction. This helps establish trust and reduces the risk of fraud or scams. Buyers, on the other hand, can make informed decisions based on the verified identities of sellers, ensuring a safer and more reliable shopping experience. As a key observation, according to a survey by the data firm , technology is quite needed to avoid identity fraud and protect the revenue of businesses. PMYNTS “Fraud limited international growth for 36% of companies using reactive, manual detection methods, compared to only 5% of companies using proactive, automated solutions. Companies using manual methods were also more likely to be difficult for customers to work with and to have trouble onboarding new ones.” Tokenization of Assets Obyte's tokenization capabilities enable businesses to represent physical or digital assets, such as real estate, intellectual property, or loyalty points, as tokens on the DAG. This allows for increased liquidity, fractional ownership, and efficient transfer of assets. By using the Obyte Asset Registry (no coding needed) or the numerous tools available for developers, any company can . create its own customized tokens We can use here an example of an already-existing company using Obyte this way: . They offer gold investments represented by tokens created on Obyte. As you may know, gold isn’t exactly portable, digital, or immediately liquid. To solve these issues, Aufort is representing their gold reserves with customized tokens internally. Their platform uses Obyte as a back-end system to store user balances and transactions –always visible on . Aufort the DAG Another potential use case would be a real estate company that owns multiple properties. They could decide to tokenize a commercial property using Obyte. The property is divided into digital tokens, and each token represents a fractional ownership stake in the asset. Even if it’s just $50 or less, any user would be able to buy and sell its ownership fraction on any property, regardless of the total price. The ownership and transfer of tokens are recorded on the Obyte DAG, providing transparency and immutability for businesses and individual users alike. More benefits The benefits of tokenizing the asset are twofold. Firstly, it allows the real estate company to unlock liquidity by offering investors the opportunity to buy and sell tokens representing fractional ownership. This improves access to investment opportunities and can attract a broader range of investors. Secondly, tokenization simplifies the process of transferring ownership. Instead of dealing with cumbersome paperwork and intermediaries, the transfer of tokens can be executed digitally and instantaneously, reducing administrative burdens and costs. As an extra benefit, the company can attract more investors, potentially driving up demand for tokens and increasing their value. Also, tokenization can enable the company to leverage its assets more efficiently, such as by offering rental income through tokenized properties or utilizing tokens as collateral for financing opportunities. According to by the Global Financial Markets Association (GFMA), (such as properties) by 2030. a report distributed ledger technology like Obyte could unlock access to around $16 trillion in tokenized illiquid assets Decentralized Applications The world of DApps is wide and full of possibilities, and Obyte can offer the most decentralized version of this. Developing (DApps) on the Obyte platform allows businesses to create all kinds of innovative and secure solutions. This platform provides transparency, immutability, and better user control. Decentralized Applications Let's examine an example of a financial services company that wants to develop a peer-to-peer lending platform as a DApp on Obyte. The company aims to connect borrowers and lenders directly, eliminating the need for traditional intermediaries like banks. Using Obyte's platform, the company develops a DApp that allows borrowers to create loan requests and lenders to fund those requests. The functionality of Obyte ensures that loan terms and repayment conditions are coded into the system, automating the lending process and reducing the risk of default. t Autonomous Agen Through the DApp, borrowers and lenders can interact directly, negotiate terms, and complete transactions securely. The decentralized nature of the DApp ensures transparency, as all loan transactions and repayment history are recorded on the Obyte DAG. The potential improvement in income for a company using DApps on Obyte can be substantial. By removing intermediaries and reducing operational costs, . This can attract a larger user base, increasing transaction volumes and potential revenue streams. businesses can offer more competitive interest rates to borrowers and attractive returns to lenders By taking advantage of all these , any business worldwide can improve its own systems and income sources. Obyte serves as a versatile platform that equips businesses with the tools to enhance transparency, security, efficiency, and innovation in their operations, fostering growth and success. Obyte features Featured Vector Image by / Freepik vectorjuice

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Welcome Founders! See sample interviews here. , Hey Hackers Grubtech has been nominated in HackerNoon's annual awards in Dubai, United Arab Emirates. Startups of the Year Please vote for us here: https://hackernoon.com/startups/asia/asia-dubai-uae Read more about us below to understand why we deserve your vote. Meet Grubtech Grubtech is an end-to-end operating system for omnichannel restaurants and cloud kitchens. Its all-encompassing solution streamlines day-to-day operations with tools and capabilities such as an omnichannel Point of Sale, integration with food aggregators, an interactive kitchen display system, multi-brand menu and recipe management, commercial and operational reporting and analytics, advanced marketing capabilities, a customer management portal, and more. How We're Disrupting/Improving the Software Development Industry Our company has been disrupting and will continue to disrupt the food and beverage industry. For F&B industries, we have developed one platform to streamline and automate your entire kitchen workflows, sync all departments for seamless communication, and spot improvement opportunities - Allowing your business to grow and expand revenue without extra costs. Standing Out from The Crowd With the rise of on-demand food delivery apps, cloud kitchens, and brick-and-mortar restaurants investing in digital transformation, Grubtech is fast emerging as the game-changing technology platform for online food businesses. Its data-driven approach is focused on providing the sector with a suite of end-to-end future-proof solutions and streamlining operations for restaurants, cloud kitchens, and virtual brands so that they can continue to thrive in an ever-changing landscape. Having raised USD 2 million in seed funding, this Dubai-based foodtech startup is spearheading the establishment of the next generation in food and facilitating the expedited activation and scaling of various concepts and brands. Changing customer behaviors and the impact of COVID-19 have made this an even more pressing requirement, and the need for digital disruption in an otherwise tech-scarce sector has never been more important. Our Predictions/Thoughts on the Software Development Industry in 2023 What emerging trends or technologies do you think will have the biggest impact on software development in 2023? Generative AI, ChatGPT, and LLMs. Do you think remote work will continue to be a prevalent trend in the software development industry in 2023 and 2024? Why or why not? We know because of the pandemic that remote work is feasible for not just the software development industry, but many industries, and it has provided opportunities for companies to access a global talent pool without geographical limitations. In the last two years in particular, we have seen many companies moving to a hybrid style of working. I think we will continue to see hybrid work as a key theme in many software companies. Now that both employers and employees can see that hybrid work is effective, and productivity levels have not only been maintained but even exceeded, we will see more demand in companies for this. How do you think the demand for software developers will change in 2023? a. Will there be a shortage or surplus of skilled workers? I think there will still be a demand. While there is an increase in the number of software developers, there have been huge technological advancements in the past couple of years. The rapid pace of technological advancements, including artificial intelligence, machine learning, etc, is expected to continue and these technologies require skilled software developers to design, develop, and maintain the software systems. As well as that organizations across various industries are undergoing digital transformation to stay competitive and meet evolving customer expectations. These companies will need software developers to oversee these changes in their businesses. I think most organizations will see a need to have their own in-house software developers to help support their systems and oversee this new change in business. What company or institution will have the most impact on the industry in 2023? I think Open AI has already had a huge impact on the industry so far this year and will continue to do so. OpenAI's efforts in natural language processing and machine learning research have contributed to the advancement of the field. It has also sparked discussions around the responsible use of AI, ethics, and potential challenges related to bias, privacy, and security. In the past year, we have seen a transformative change in numerous industries, with the increased readiness of companies to incorporate LLMs and other AI solutions into their business operations to streamline processes and operate more efficiently. At Grubtech, we believe in harnessing the power of technology to make a positive impact on the restaurant industry. By leveraging advanced algorithms and real-time data analysis, our system ensures accurate inventory tracking, minimizing overstocking and spoilage. What word defines the state of Software Development in 2023? Innovation, collaboration, and automation. Software development has always been driven by innovation, and this will continue. The software development landscape has increasingly embraced collaboration and teamwork. Automation is becoming increasingly prevalent in software development, with the aim of streamlining processes and improving efficiency. Why we decided to participate in HackerNoon's Startup of the Year awards To offer us increased visibility, networking opportunities, credibility, and potential recognition for our accomplishments. We want to showcase our products and services and gain exposure in the industry. Final Thoughts GrubTech is one of the UAE’s fastest-growing tech startups that enables cloud kitchens and restaurants to successfully manage end-to-end operations using state-of-the-art digital tools and capabilities. GrubTech’s client portfolio has grown rapidly to span fifteen countries, managing millions worth of food orders for large organizations like Delivery Hero’s Talabat (including Expo 2020), and other international food chains like California Pizza Kitchen, Dunkin Donuts, and Paul’s. Grubtech has pioneered omnichannel solutions with the launch of MamasandPapas, and Gap in the GCC, and successfully introduced the region's first two-hour e-commerce delivery promise. We think Grubtech deserves this award because of its cutting-edge innovations and being at the forefront of tech disruption across the GCC regions. Vote for us today!

Grubtech - Revolutionizing UX With Disruptive Tech 

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In the bustling world of retail, the grocery sector has long been at the forefront of innovation. From automated checkouts to personalized recommendations, advancements have continually shaped the way we shop for our daily essentials. Whether we personally embark on shopping trips or not, there is always someone designated to procure necessary grocery items. For years, the grocery sector has remained largely unchanged - with minor changes such as self-checkout - and for those of us who hate it, there never seems to be a light at the end of the miserable grocery shopping tunnel. However, Artificial Generative Intelligence (AGI) has emerged as a game-changer, bringing unprecedented possibilities to the grocery industry. How AGI Is Transforming the Grocery Sector This cutting-edge technology holds the potential to transform every aspect of the grocery sector, from supply chain management to customer experience. Efficient inventory management has always been a cornerstone of successful grocery stores and with AGI, retailers will be empowered to optimize their inventory by analyzing vast amounts of data, including historical sales, weather patterns, and even social media trends. By leveraging machine learning algorithms, this technology enables accurate demand forecasting, reducing instances of overstocking or understocking. As a result, grocery stores can optimize their supply chain, minimize waste, and ensure that customers find the products they desire. The grocery industry relies heavily on complex supply chain networks, involving numerous suppliers, distributors, and retailers. AGI revolutionizes this process by introducing intelligent automation. It enables real-time tracking of inventory, monitors delivery schedules, and identifies potential bottlenecks. By analyzing transportation data, weather conditions, and traffic patterns, AI-powered systems can provide dynamic route optimization, ensuring timely and cost-effective deliveries. This streamlining of supply chain operations not only reduces costs but also enhances overall efficiency and customer satisfaction. One of the most significant advantages of AGI lies in its ability to provide personalized shopping experiences. By analyzing customer data, including purchase history, preferences, and demographics, AI algorithms can generate personalized recommendations tailored to individual shoppers. These recommendations extend beyond simple product suggestions, encompassing meal planning, recipe ideas, and even dietary restrictions. With AI-powered chatbots and virtual assistants, grocery retailers can offer real-time customer support, answer queries, and assist in navigating the store, enhancing the overall shopping experience. Through diverse methods like visual search, product tagging, and image recognition, generative AI holds the potential to guide customers toward novel and relevant products they might not have otherwise considered. Recent reports highlight how certain businesses are actively exploring ways to integrate this technology into their e-commerce platforms. An example of this is Instacart, which aims to leverage a chatbot-powered search engine capable of answering users' food-related inquiries by utilizing data from its extensive network of 75,000 grocery stores. The groundbreaking ability to automatically recommend recipes based on the available stock could potentially revolutionize the e-grocery industry by helping stores upsell products that would otherwise go unnoticed by consumers. Pricing is a critical factor that influences consumer behavior and AGI now assists grocery retailers in devising smarter pricing strategies by analyzing market trends, competitor pricing, and customer demand patterns. This brilliant technology enables dynamic pricing, where prices can be adjusted in real-time based on factors such as time of day, seasonality, or even individual customer preferences. By optimizing prices, grocery stores can strike the right balance between profitability and customer satisfaction, fostering customer loyalty and driving sales. AGI also helps in maintaining food quality and safety which is paramount in the grocery sector. AGI ensures the integrity of products throughout the supply chain. By analyzing data from various sources, including quality control inspections, supplier certifications, and consumer feedback, AI algorithms can identify potential risks and deviations in real-time. This proactive approach enables prompt actions such as product recalls or quality improvements, safeguarding consumer health and trust. Generative AI is not just a technological advancement; it is a transformative force reshaping the grocery industry. With its immense potential to optimize inventory management, streamline supply chains, personalize shopping experiences, devise intelligent pricing strategies, and enhance food safety, AGI holds the key to unlocking a new era of efficiency and customer-centricity. As retailers embrace this cutting-edge technology, they will gain a competitive edge, driving innovation, and redefining the grocery shopping experience for consumers worldwide. The future of the grocery sector has arrived, and it is powered by AGI.

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