Coming into today, shares of the auto parts retailer had lost 4.84% in the past month. In that same time, the Retail-Wholesale sector lost 0.65%, while the S&P 500 lost 2.79%. Market participants will be closely following the financial results of Advance ...

Why Advance Auto Parts (AAP) Outpaced the Stock Market Today

Advance Auto Parts has underperformed compared to its competitors O'Reilly and AutoZone over the past decade. However, if we analyze its financial ratios this could be justified. The company's decline may have begun after a significant acquisition made in ...

Advance Auto Parts: Cheap For A Reason And An Uncertain Future

Advance Auto Parts (AAP) closed the most recent trading day at $48.25, moving -0.41% from the previous trading session. This move lagged the S&P 500's daily gain of 0.73%. At the same time, the Dow added 0.

Advance Auto Parts (AAP) Stock Dips While Market Gains: Key Facts

Advance Auto Parts may be undervalued with mixed growth prospects, but the 18 analysts following the company give it an rating of hold. Their target prices range from $42.0 to $98.0 per share, for an average of $68.61. At today's price of $48.88 ...

Have Analysts Taken Advance Auto Parts (AAP)'s Valuation Into Account?

But now, things may be about to get worse again. Jordan Jaskiewicz is getting a taste of the auto parts shortage that has been frustrating car owners and repair shops for three years. "We had a ...

'The supply for everything's at an all-time low': Auto parts shortages worsen as UAW strike drags on

Summary The consumer retail sector is experiencing a trend towards bifurcation, benefiting chains with either exceptional customer experience or the lowest prices. Advance Auto Parts is losing ground to competitors like AutoZone and O'Reilly,

Advance Auto Parts: Another Retailer Stuck Between 'A Rock And A Hard Place'

The consumer retail sector is experiencing a trend towards bifurcation, benefiting chains with either exceptional customer experience or the lowest prices. Advance Auto Parts is losing ground to ...

Retail sales in the United States rose 0.7% in September, more than doubling the growth anticipated by economists in a stronger-than-expected report, the Commerce Department said Tuesday.

U.S. September retail sales rose 0.7% beating expectations

falling short of the Retail-Wholesale sector's loss of 5.07% and the S&P 500's loss of 2.4%. The upcoming earnings release of Advance Auto Parts will be of great interest to investors. The company ...

Advance Auto Parts (AAP) Increases Despite Market Slip: Here's What You Need to Know

Get the most recent info and news about Advance Auto Parts on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

Advance Auto Parts, Inc. provides automotive replacement parts, accessories, batteries, and maintenance items for domestic and imported cars, vans, sport utility vehicles, and light and heavy duty trucks. The company offers battery accessories; belts and hoses; brakes and brake pads; chassis and climate control parts; clutches and drive shafts; engines and engine parts; exhaust systems and parts; hub assemblies; ignition components and wires; radiators and cooling parts; starters and alternators; and steering and alignment parts. It also offers air conditioning chemicals and accessories; air fresheners; antifreeze and washer fluids; electrical wires and fuses; electronics; floor mats, seat covers, and interior accessories; hand and specialty tools; lighting products; performance parts; sealants, adhesives and compounds; tire repair accessories; vent shades, mirrors and exterior accessories; washes, waxes and cleaning supplies; and wiper blades. In addition, the company offers air filters; fuel and oil additives; fuel filters; grease and lubricants; motor oils; oil filters, part cleaners and treatments; and transmission fluids for engine maintenance. Further, it offers battery and wiper installation; engine light scanning checking; electrical system testing; video clinic; oil and battery recycling; and loaner tool program services. Additionally, the company sells its products through its Website. It serves professional installers and do-it-yourself customers. Advance Auto Parts, Inc. operates its stores under the Advance Auto Parts, Autopart International, Carquest, and Worldpac brand names. As of December 28, 2019, it operated 4,877 stores and 160 branches in the United States, Puerto Rico, the U.S. Virgin Islands, and Canada; and served 1,253 independently owned Carquest branded stores in Mexico, the Bahamas, Turks and Caicos, and the British Virgin Islands. The company was founded in 1929 and is based in Raleigh, North Carolina.

Advance Auto Parts

I’ve found that almost anyone I talk to agrees with the statement: There is something wrong with education, particularly higher education. Interestingly enough, everyone also has very different ideas about what the problems are, and how we should solve them. A few popular ideas include: Pay teachers more Make university free Cancel existing student debt Online-first university A couple of weeks ago I was listening to an old podcast where the interviewer was speaking with Austen Allred, the founder of Lambda School (now Bloom University). Austen said that he believed the biggest problem with education is one of the incentives - particularly that . I tend to agree, though , my current side-project, has ended up taking a different path than the one Lambda took. Indiehackers universities don’t care whether or not their students are placed in a good job upon graduation Boot.dev Lambda School has been in the news over the past few years, sometimes good things were reported, sometimes not. I’m not here to gossip, instead, I simply want to look at them as an example of how Austen’s original beliefs on educational incentives ended up panning out. First, let’s talk about where Austen and I agree, and then we’ll get into some of the things I think we can learn from Lambda school, and how we can make education even better. What does it mean to “align incentives”? Let’s look at a simple example using a hypothetical company “Auto Parts United”. Here are a few of their salespeople, all of whom will be guinea pigs trying out 4 new compensation structures. Name Base Commission Explanation Sally 50k 10% Flat 10% commission John 100k 0% No commission George 40k 10%, 20% 10% commission for the first 10 sales, then 20% Taylor 70k 10% capped 10% commission for the first 10 sales, then no commission Based on historical data, the accounting department at “Auto Parts Unlimited” was able to show that if a salesperson made the number of sales, 20 sales each month, then all of these compensation packages would end up paying them the same $100k yearly. As you can probably guess, however, these different compensation structures create different incentives for the salespeople. average very Now, I don’t run a sales organization, but I can tell you with confidence that the “Johns” and “Taylors” of the company aren’t going to be great performers once the new compensation packages are rolled out. They aren’t incentivized to sell! John is incentivized to he is selling so he doesn’t lose his cushy 100k salary. Taylor is incentivized to quickly make 10 sales, then slack off for the rest of the month. appear as if Only “George” and “Sally” have their incentives with those of the company. When the company does well, George and Sally do well. This kind of system is ideal in almost any relationship that can support it. Win-win/lose-lose deals are much more sustainable than any sort of once sided structure. We see examples of this kind of alignment all over the place, though some end up being less aligned than others. For example: aligned Pay-for-what-you-use server costs Percentage-based transaction fees Free returns on e-commerce purchases The problem with university I think it’s safe to say that Austen was right when he pointed out on the podcast that universities aren’t incentivized enough to care about job placement rates. Not only do the institutions have no reason to worry about job placement, but oftentimes the professors themselves consider teaching an afterthought. Many are there primarily to do research, publish papers, and advance the boundaries of their field. How Lambda School approached a new incentive model Lambda was the pioneer of the “income share agreement”, or ISA. The idea is that you do not pay upfront for your tuition. Instead, you get a job upon graduation, you pay the school back a percentage of your wages until you hit a cap. What this does is ensure that Lambda School is incentivized to make sure you get a job upon graduation. if Okay, so far so good. ISAs fixed everything right? If ISAs are so great, why all the fuss about Lambda? If you want to dig up the dramatic history about Lambda School I’ll let you do that, I don’t want to get into the nitty-gritty of he said/she said. Instead, I’ll give you my summary based on the digging I’ve done. I’ve found that most of the more viral complaints boil down to two simple gripes. Some students have vocally complained that the quality of the education has declined over time. Some students consider it to be expensive. Most students end up paying somewhere between $20,000 or $40,000 dollars for the ~6-month program. very If I had to guess about the cause of these problems, I’d say it’s likely that margins ended up being tighter than anticipated, and the bets they made on their student’s placement rates ended up being worse than anticipated. Lambda pays for each student’s education upfront, that student gets a job. If any poor performers are sucking up resources without landing jobs, Lambda is footing the bill. Over time, Lambda gathers data on their costs and realizes they need to charge a lot of money to the best performers to subsidize the cost of those who don’t pay. whether or not This gets interesting because it Lambda is paying for the education of any students who are unable to get jobs. In reality, if Lambda is to continue as a successful business, the high performers end up paying for the tuitions of the underperformers. sounds like To sum all this up, what is Lambda incentivized to do? highly To only allow the highest performers into the program, who ironically are the ones who need it least. To ensure that everyone who goes through the program lands a job paying at least $50k Let’s review what they incentivized to do: are not highly Teach their students in a way that will help them advance post-placement Provide education to those that are less likely to be high performers What other models could be better? Instead of immediately hypothesizing about better solutions, let’s look at a few more competitors in the “learn to code” space, and guess at what incentive structures they might have. Dev Mountain Dev Mountain is a traditional web development boot camp in my area. I expect their product is similar to Lambda in that it’s an in-person web development school. They charge around $10k for a 16-week program. Likely incentivized to: Prepare students for a job, but only so they can keep their marketing “placement rate” high. Keep costs lower so that students can afford them up-front Not likely incentivized to: Create an engaging and motivating experience Care how much students earn in their first job Codecademy Codecademy has a straightforward business model. They charge $19-39 per month at the time of writing for “pro” access to their online courses. Unlike lambda school, their digital content is consumed at scale asynchronously by students, meaning they can get away with lower tuition costs. much Likely incentivized to: Create an engaging experience so that you don’t cancel your subscription Keep the monthly cost low to compete with other digital information products Not likely incentivized to: Prepare students for a job Care how much students earn in their first job FreeCodeCamp FreeCodeCamp is unique on this list - they’re totally free! That said, that doesn’t mean the creators and maintainers don’t have an incentive structure. There is always an incentive mechanism, even if it’s not obvious. Likely incentivized to: Keep costs extremely low Monetize users indirectly (for example via ads, data mining, or something else) Prioritize traffic and engagement over learning outcomes Not likely incentivized to: Prepare students for a job Udemy Udemy is a marketplace, they don’t create their own content, instead, they allow creators to publish their work on the platform and then Udemy takes a cut. Likely incentivized to: Emphasize a of learning content quantity Prioritize content made by teachers who have marketing skills Not likely incentivized to: Prepare students for a job Create a cohesive learning path So what would a better incentive structure look like? First off, let’s address the elephant in the room, I’m a big ‘ol hypocrite at the moment. is my side project, and its business model is basically a carbon copy of Codecademy’s. For now, I’m trying to get the project to a level of revenue where I can afford to go full-time on it. In the meantime, I’m thinking about how a different model could create a better incentive structure. We’ll get there, but we aren’t there yet. Boot.dev Okay, so what might this “better model” look like? Well, we want a platform that is strongly incentivized monetarily to do as many of the following as possible: Create an engaging experience while prioritizing learning outcomes Ensure students land their first coding job Help students continue to be successful in their careers after their first job Prioritize quality over quantity so that the best materials are easily accessible Refrain from serving ads or selling user data Ensure that slower learners can be successful as well Keep overall costs for students as low as possible so that price is never a barrier Free + recruitment fee One plan would be to go the FreeCodeCamp route. We could open up all the learning content for free, then instead of selling ads, I could monetize through job placements. I’d like to do more research on this path, but with the data, I currently foresee some problems: Companies won’t pay to recruit junior talent, only senior talent Boot.dev would be incentivized to create an amazing product for recruiters while education becomes an afterthought There is no monetary incentive to help students who get stuck - they are likely underperformers who won’t be placed Codecademy + Career coaching/tutoring services The big problem with our current model at Boot.dev (the Codecademy model), is that we have no real monetary incentive to help students land a high-paying job, other than to collect some sweet testimonials. We also aren’t incentivized to care about what happens to them after they graduate. In fact, you could even argue that our current model actively doesn’t want them to get a job, because they will be more likely to cancel their subscription. This is the hypothetical model that I think has the most promise: Students pay a small monthly tuition for access to the learning content This ensures that the learning side of the platform remains top-of-mind for us. When I say small, I mean less than it is now. Something like $15/mo. I never want to be turning people away due to a price barrier. A free-if-you-get-placed career coaching funnel for students that complete our materials. The career coaching services and materials would ensure that we care about the career success of our students. We could even offer them to those who are looking for second and third jobs or even just looking to get promoted or snag a raise. It also has the nice side-effect of helping us keep our learning content streamlined and career-applicable because in order to maximize revenue we need to be able to place those students in jobs. Payment for the optional career coaching might be an ISA model, or maybe it’s a flat fee that’s paid back x months after the desired career outcome - I’m not sure. But all in all, I think this be a great path forward for us. could If you have any other ideas or thinks we could be doing something better, let me know on . Also, if you know anyone who’s looking to throw funding at a project with traction, we’re . Twitter happy to chat Also Published Here

The Problem with Education is a Problem of Incentives

El problema de la educación es un problema de incentivos

Le problème de l'éducation est un problème d'incitations

शिक्षा की समस्या प्रोत्साहन की समस्या है

教育の問題はインセンティブの問題

O problema da educação é um problema de incentivos

Vấn đề Giáo dục là Vấn đề Khuyến khích

教育问题是激励问题

Machine Learning and data analytics have shown a pronounced effect on various aspects of the commercial world and industries. Enterprises are using innovation in the field of data analytics and machine learning to design better marketing campaigns. It also helps generate pricing and customer-centric recommendations and even plan more effective financial budgets. However, the rapid advancements of technological innovation is coupled with an equally swift evolution of the business-to-business (B2B) sales channel. Data has now become the bread and butter for a large number of big and small companies, a trend which will only continue to grow. The power of advanced analytics if tapped efficiently can help in improving B2B sales. How has the B2B sales channel evolved over time? A McKinsey&Company report on marketing and sales suggests that customers insist on getting competitive off-the-shelf products along with more complex and customized solutions. They also expect extensive levels of B2B sales support and expertise in the industry and the products from the sales force. Companies are investing in attempts beyond selling their products, in ways like risk-sharing and service-level agreements by customers. This is to ensure that they stay committed to providing real value. Moreover, companies now need to develop multi-channel sales programs to enable customer targeting in a more effective way. For instance, a logistics and transportation partner can focus on the simpler sales while dedicated sales managers will have to focus on the challenging customers to meet their requirements and provide extensive B2B sales support at all times. Furthermore, companies servicing large customers are devising new and cost-effective ways to interact with customers and provide sales support to them. For example, in multinational customer and seller networks, customers now communicate over the telephone or through Web and video conferences. This enables sales reps to plan and spend more time on high-value face-to-face selling activities, such as developing complex interactions with current clients and mining for new clients and customers. B2B enterprises of all scales are adopting ways of business-to-consumer (B2C) retailers like by using customer data to predict their behavior, drive higher sales, and strengthen customer relationships. Amazon How has the evolving B2B sales channel started using advanced analytics? Most B2B , customer behavior and health of their overall operations. However, the conventional metrics like revenue and profit margin do not provide a complete insight into the performance of a company. Since there is a lag in the calculation of such performance metrics from the time of actual events, companies now need to design better performance metrics. Real-time metrics enable analyses of fast-evolving industries in shorter sprints of time-period. companies use analytics to monitor and analyze product quality and performance in the market Real-time analytics is becoming more and more popular with all B2B companies. They are designing alternative measures like when the customer first engages with the sales team or digital content, their perception of the same and the time when they get back to the company for the actual contract or their first purchase. Such metrics can give one real-time insight into a company’s performance instead of delayed information. for analytics in the B2B space has enabled real-time insights on a company’s performance. It has also ensured that the enterprises receive real-time recommendations on B2B sales and purchase decisions. Retail companies like Tesco and Walmart are using advanced analytics to make more informed decisions on product portfolio optimization and product pricing decisions while procuring goods from their suppliers. Use of Artificial intelligence and advanced machine learning algorithms Advanced analytics can be used across industries Statisticians and researchers have tailored a lot of mathematical algorithms for special use-cases in the B2B retail industry. For example, managers can predict the performance of newly introduced products on the basis of similar products in the past. This would help the managers decide if they want to store the particular product in their stores or not. Furthermore, companies use analytics for designing marketing strategies like cross-selling, up-selling, product-bundling and targeted advertisements for higher customer penetration. Such algorithms are useful across B2B insurance, retail, manufacturing, and even technology industry. General Motors, a US-based automotive company has a large network of dealerships throughout the world which procures aftermarket auto-parts from the manufacturing giant on the basis of the customer sentiment towards the products. In this scenario, advanced analytics can help optimize storage and purchases for the dealerships and hence, maximize profit for both the dealerships and the OEM (original equipment manufacturer). Beyond product and customer-base optimization, B2B companies can use advanced analytics for effective lead generation and scoring. This is by far, the most valuable use-case for companies that want to grow their customer base rapidly. Many companies are increasingly using historical market information to view their sales prospects. Besides cold calling and mails, companies use advanced analytics to customize PoVs and . sales pitches for the targeted customer Some success stories of using advanced analytics in B2B sales , caters to the needs of mining, quarrying, oil and gas companies across the globe. It has invested resources in creating a sound data analytics infrastructure. Instead of drawing insights from paper and white-boards, they have digitized all their data in the form of decision boards and recommendation engines. Data provided by its customers, along with the objectives of the mining projects and blasts, conditions of the equipment and the site, the exact techniques and products used in the blast, and the outcome achieved is used for creating an analytics framework. Orica Limited, an Australia-based supplier of commercial explosives and blasting systems The pre-blast modeling and post-blast measurement and analyses are then packaged in a user-friendly online system called . This allows mining customers to simulate and analyze blast outcomes given the information like the geological site data, drilling equipment data and the desired outcome. This has not only helped Orica optimize the mining processes but has also led to them using data analytics for industry-specific intelligent recommendations. This is one example of how analytics can improve the performance of an unconventional industry. Blast IQ If that is not impressive enough, Monsanto Growth Ventures (MGV), the venture capital arm of the Monsanto Company used data analytics to enhance its chemicals-business with data and analytics-based services. Post-acquisition of The Climate Corporation in 2013, they together assembled a database using IoT infrastructures like soil sensors and field experiments. Monsato then incorporated the findings in a suite of farmer advisory services. Since then, it has continued to develop and retain a loyal customer base with additional mobile applications and advisory services. The use of analytics in a primitive industry, agriculture is evidence enough that advanced analytics can help any B2B business model grow and advance in a more informed and sustainable manner. Summary Advanced analytics has not only changed the perception of large businesses towards technology, but it has also reformed the way these enterprises measure their own performance. Organizations are now more receptive to technology and data-driven decisions. This trend is still in its growth-stage and will continue to take over more lines of industries over time.

How Advanced Data Analytics is Impacting B2B Sales

Blockchain technology, which has already disrupted the financial services and supply chain industries, has at last arrived in the auto industry. We will look at some potential applications of blockchain in the automotive industry along with a few real life examples. So, sit tight ! By this time , you must have read hundreds of articles about what is blockchain and what are its advantages. So, lets not waste our time on that again and come to the point directly. Let’s talk about what blockchain can do for the automotive industry. Since the advent of cars, the automotive industry has always embraced technological advances, but the pace of change has accelerated so rapidly that auto original equipment manufacturers (OEMs) worldwide are struggling to keep up. Automotive industry is changing, and blockchain will make a significant impact on that change. Believe me, blockchain will be the next big thing, and it will not skip the automotive industry. Over the past year or so, stakeholders in the auto industry have been looking into how blockchain technologies can be applied in businesses and are looking to launch major initiatives. The Automotive industry is a complex ecosystem with multiple parties involved in the design, production, distribution, marketing, selling, finance and servicing of vehicles. Outlined below are some example scenarios: AUTOMOTIVE INTERACTIONS With the rise of autonomous cars and millions of devices communicating with each other. There is a need that these interactions and transactions exist on a immutable database of shared, secure, and highly permissioned access. A shared ledger between automotive OEMs, parts distributors, dealers, service mechanics, insurance providers, and others could support the ability for parts or equipment within a car to autonomously sense its own needs. For instance, a car could advise the driver about the need for repair, contact remote users for updates or nearby suppliers for replacement parts, negotiate pricing and appointments for service and repairs, authenticate the proper technician, and process the respective payment for services. which uses blockchain as a shared ledger to log all car repair and maintenance history in one place. The next pilot, it says, will focus on vehicle-based micro-transactions essential to integrating the IoT with the exchange of value. The latest example of this blockchain transparency across interactions is launched by Renault. The French automaker is piloting a digitized car maintenance program , , as both nodes (car and tollbooth) have ethereum nodes, which use smart contracts to trigger a machine-to-machine (M2M) transaction. Oaken Innovation recently demonstrated the idea of a blockchain-enabled tollbooth , in which Tesla cars automatically pay as they pass through toll booths PARTS AUTHENTICATION Phony auto parts that are genuine in appearance frequently get into the supply chain and land in dealer service centers. These parts often fail very soon after the installment and in turn causing damage to the automaker and part supplier brands. To combat these frauds, automakers have been using a number of layers of anti-fraud technologies and operations, but somehow phony parts were always capable of seeping into the supply chain. Blockchain can address this problem with the creation of distinctive ID for every part, together with immutable timestamps from when the part is created. These tags connect to the blockchain and can be set into the part to add a level of authenticity protection. Information on authenticity can be made accessed by the person who owns the vehicle to confirm which parts have been changed out, to the manufacturer to more efficiently perform recalls, or to the service center to signal when a part has arrived at its usage limit. The Digital Twin platform is designed to give every product its own a “digital twin” for authentication purposes. A German utility, innogy SE , has launched ‘The Digital Twin’ platform to nurture this usage of blockchain. MANUFACTURER, SALES and SERVICE FINANCE A car manufacturer must wait several weeks or months before it receives payment for a shipment of vehicles from an importer/distributor/dealer. With multiple parties controlling different steps in the chain it is a heavy flow of paperwork. A Blockchain based system would enable greater transparency of accurate information between the different parties, faster processing of export/import and banking documentation and ultimately reduce the settlement period. An auto finance provider generally has no idea of the real driving behaviour of its car drivers or their vehicle’s service history. A Blockchain based system would enable driving patterns and service events to get sent to a shared ledger that all parties have access to. Transparency of information about a vehicle’s real wear and tear would help the auto finance provider to more accurately gauge residual value of the vehicle. Typical auto financing includes a host of verification steps to which blockchain could be applied for efficiency gains. use case. Indian automotive OEM Mahindra , created a blockchain incubator focused specifically on the automotive financing SUPPLY CHAIN A manufacturing plant must coordinate effectively with multiple tiered suppliers, 3rd party logistics and transportation companies to ensure timely delivery of parts and optimised inventory levels. The automotive supply chain is incredibly complex, consisting of numerous types of parts, hardware, software, and firmware suppliers, distributors, dealers, regulatory agencies, insurance companies, and more. A Blockchain based system would enable greater transparency of accurate information between the different parties; improving just-in-time logistics, reducing erroneous orders and raising inventory turns. Various blockchain technologies could also be applied along multiple phases. Security, identity, and permissioning impact anti-counterfeit measures in the supply chain; smart contracts, oracles, and custom code support security and compliance adherence; payments and contracts transfer to expedite reconciliation of the deal and the transfer of money and assets from one party to another. Blockchain technology sends information over a network of independent computers, known as a distributed ledger, intended to ensure that the transaction is secure and ownership rights over the data/property are protected. TRI believes blockchain may create transparency and trust among users, reduce risk of fraud and reduction or elimination of transaction costs, such as fees or surcharges applied by third party institutions The Toyota Research Institute (TRI) announced today that it is exploring blockchain and distributed ledger technology for use in the development of a new mobility ecosystem that could accelerate development of autonomous driving technology. VEHICLE TRACKING A common, unethical car dealer practice is to sell a showroom vehicle and then not report the sale to the bank, which had provided a loan for the vehicle. This practice allows a dealer to have working capital to bridge payroll and suppliers. If the bank catches on, it will take immediate action against the dealer. However, because the car is sold legally, the bank has no claim on the vehicle. At the same time, the financing to the dealer has been spent and must be written off.With transparent access to data, the asymmetry in information that allows this practice to happen goes away. Blockchain provide instant transparency and gains value as more stakeholders opt-in. It incorporates telematics data that monitors where and how a car has been driven, primarily in situations where the engine is worked hard. By using the car history, artificial intelligence algorithms can accurately predict the resale value of the car at any given time. Being capable of tracking a vehicle also means better enforcement of environmental laws and auditing of manufacturers. Vehicle tracking also give manufacturers the chance to sell value-added services to customers. The Carpass project is designed to facilitate vehicle tracking through a “digital wallet” that tracks a vehicle’s “life events.” ELECTRIC VEHICLES As electric cars become more prevalent, it will require a new machine-to-machine (M2M) infrastructure that includes energy providers, charging station owners, automakers, parts suppliers and car owners. No one enterprise can manage the intricacy to unite stakeholders, and no single entity should possess the platform, which would create a monopoly. , a subsidiary of German energy conglomerate RWE, announced that it has launched hundreds of blockchain-powered charging stations for electric cars across Germany through its e-mobility startup venture . The new e-mobility solution allows owners of electric cars to charge their vehicles at any of the new charging stations set up by innogy Innovation Hub’s startup venture Share&Charge, as well as already existing charging points by making digital payments in euros using the Share&Charge app. This app is the world’s first e-mobility community platform that uses blockchain technology. Innogy SE Share&Charge AUTONOMOUS RIDE-SHARING Ride-hailing services such as Lyft and Uber are already reinventing the way we use our vehicles. With a few swipes on an app, a driver picks you up in their car and takes you to your destination. Blockchain could help make this vision a reality. This blockchain-based system facilitates the sharing of not just rides but vehicle ownership. Eventually, it could help owners manage entire fleets of autonomous vehicles. The system would automatically settle payments on whatever basis the owners agree upon. In August 2017, consulting firm Ernst & Young (EY) announced the launch of Tesseract. NOTABLE MENTIONS and have gathered together to form the Mobility Open Blockchain Initiative (MOBI). The association will explore further blockchain use and study how its technology can make the industry of global transportation less expensive, safer and more widely accessible to citizens around the world. Companies ranging from IBM and Context Labs to Ford and BMW announced their collaboration at this year’s Futures Blockchain Summit in Dubai In February they , on their official blog, that in collaboration with Berlin-based start-up XAIN they are testing blockchain applications directly in vehicles. Porsche praises themselves as the first company in the industry that tested an implementation of blockchain into their systems. announced In February 2017. Daimler AG, famous by their brand Hyperledger is a collaborative cross-industry effort created to advance blockchain technology. Mercedes-Benz, joined Hyperledger organization as their premium member. If we consider that every car has on average 3 to 5 owners during its lifetime, it’s clear that 80% of car owners don’t know anything about their cars. The startup said they will gather as much information about car history as possible, from different sources, and put it into the blockchain registry. That way if you want to buy a used car you will always be able to check car’s history. CarVertical is working on blockchain-based solution which solves a problem with non existing transparency about car history. IBM and Maersk have recently to create an industry-wide global trade digitization solution built on Blockchain to transform global, cross-border supply chains. announced a collaboration WHAT WILL THE FUTURE BRING While these applications of blockchain technology certainly have strong cases for changing many aspects of the automotive industry, innovators are really only just beginning to scratch the surface of blockchain application in the automotive sector. There are endless possibilities, some of which have already been conceptualized–blockchain incentives for more eco-friendly driving, blockchain-based automotive insurance and financing, blockchain-based automotive titles, blockchain for remotely locking and unlocking vehicles–the list goes on and on. With Blockchain proving to be the future of security, and with cars essentially becoming IoT devices, the fusing of both together seems only natural. It makes perfect sense for leading car manufacturers to be the first in line for this cutting-edge technology and undoubtedly blockchain will play an important role in the future of the automotive industry. sources: , https://www.azom.com/ https://www.tractica.com/

Blockchain Writing a New Chapter for Automotive Industry

Traditionally, developers gain knowledge gradually when working on projects over time. I’m not referring to general knowledge such as a programming language. Instead, I am referring to project-specific knowledge, including the many decisions made while planning a project. This includes product decisions (what to implement, what not to implement, and why) and design decisions (what architecture to use, how to split the system into its components, and how the components interact). There is, of course, more specific knowledge that accumulates either when developing code or reading it to understand something - for example, during debugging. Knowledge about how the code is constructed, what parts are essential, and how the data flows in the system is gradually accumulated. is key here because in the mind of the developers as they develop their system, this knowledge is an inseparable part of the craft of programming. Gradually Not surprisingly, this knowledge is hard to transfer. For example, when new developers join the team, they need to quickly learn all kinds of things from the design decisions through the specific implementation details; when a bug occurs, we need to find the cause and make a fix; when performing a code review or basically whenever we interact with code we haven’t written ourselves, we need to get into the mind of the developer who originally has originally written the code. Our existing methods for sharing knowledge about software are fundamentally broken, and we need to prioritize sharing this knowledge efficiently and effectively. In this post, I will explain why and what can be done about it. Why Knowledge-Sharing Methods Are Broken Knowledge-sharing methods are broken for two fundamental reasons: knowledge is scattered, and documentation is outdated. Knowledge is Scattered Where is that “knowledge” we want to transfer stored? Well, in the mind of the developers, of course. But then, when it is transferred, it is also stored somewhere. Some knowledge is found within the code itself, and some isn’t. Design decisions, for example, are hard to deduce from the code. Furthermore, in the code itself, you can’t find the reasons that made the developers not implement things in a certain way. Knowledge about code can be transferred in oral conversations and, in this instance, not stored at all. They can be conveyed on recorded video calls, documents stored on shared folders or Wiki pages, code comments, README files, Slack conversations, emails, and more. This scattered knowledge makes it extremely hard to find; most of the time, you don’t even know where to look for it - assuming there is any relevant documentation to begin with. Documentation is Outdated Even when the relevant knowledge is documented, it is often outdated. This is the result of the nature of software; it quickly evolves. As developers create new features and fix bugs, the code constantly changes, and they simply don’t remember to update the relevant documentation or bother looking for it. Time is always of the essence and locating and updating the documentation takes precious time that could be devoted to writing more code. Developers No Longer Trust Documentation It’s a vicious cycle: when developers stumble on a document, they often lose trust in the documentation. Because once you find outdated documentation more than a couple of times, you are likely to assume that it’s probable that other docs are outdated as well. So why would you, as a developer, bother to read the documentation? And of course, if it’s not valuable to read the documentation, it’s understandable why developers would not justify or prioritize the effort of writing the docs in the first place. As a result, developers spend less time creating high-quality documentation, leading to other developers reading the documentation and not getting value from it, which leads to a stronger belief that there is no value in writing documentation at all. Sometimes, developers try to avoid the problem of outdated documentation in advance - by writing only high-level documents describing general flows and architecture, as these are not liable to change frequently. However, this leaves many gaps. A lot of the important knowledge that simply cannot be covered by high-level documents is never documented. Knowledge Should Be Found And Consumed At The Right Time Even if we assume that all knowledge relevant to a software project exists in some written form and is centralized in a single place, it is not practical to just read all the relevant documentation once you join a new project. When, for example, should you read each document, and do you know when it’s the right time? Swimm’s Knowledge Management Solution With Swimm, information is located and consumed in two forms. Developers get a high-level overview of the project they enter upon entrance using our Swimm Playlists. Using Swimm’s IDE plugins, developers find the relevant information just in time, when they need it, when trying to understand a part of the code that has relevant documentation, or when writing code where existing documentation can facilitate. Swimm analyzes the changes introduced to the code on every PR. When the changes are minor, we Auto-sync them, track the relevant code parts, and update the documentation accordingly. When breaking changes are introduced, we mark them for the developer to manually update the documentation as part of the PR - when the information is fresh - as the breaking change has just been introduced. Knowledge sharing is transforming teams Knowledge sharing is transforming for developer teams with the use of code-coupled documentation. Learn more about Swimm’s knowledge management documentation platform and join our growing community of Swimm users around the world.

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Moving to the upcoming WebGPU means more than just switching graphics APIs. It's also a step towards the future of web graphics. But this migration will turn out better with preparation and understanding — and this article will get you ready. Hello everyone, my name is Dmitrii Ivashchenko and I'm a software engineer at MY.GAMES. In this article, we'll discuss the differences between WebGL and the upcoming WebGPU, and we'll lay out how to prepare your project for migration. Content Overview Timeline of WebGL and WebGPU The current state of WebGPU, and what's to come High-level Conceptual Differences Initialization • WebGL: The Context Model • WebGPU: The Device Model Programs and Pipelines • WebGL: Program • WebGPU: Pipeline Uniforms • Uniforms in WebGL 1 • Uniforms in WebGL 2 • Uniforms in WebGPU Shaders • Shader Language: GLSL vs WGSL • Comparison of Data Types • Structures • Function Declarations • Built-in functions • Shader Conversion Convention Differences Textures • Viewport Space • Clip Spaces WebGPU Tips & Tricks • Minimize the number of pipelines you use. • Create pipelines in advance • Use RenderBundles Summary Timeline of WebGL and WebGPU , like many other web technologies, has roots that stretch back quite far into the past. To understand the dynamics and motivation behind the move towards WebGPU, it's helpful to first take a quick look at the history of WebGL development: WebGL The desktop version of OpenGL debuts. OpenGL desktop (1993) : This was the first stable release of WebGL, based on OpenGL ES 2.0, which was itself introduced in 2007. It provided web developers with the ability to use 3D graphics directly in browsers, without the need for additional plugins. WebGL 1.0 (2011) : Introduced six years after the first version, WebGL 2.0 was based on OpenGL ES 3.0 (2012). This version brought with it a number of improvements and new capabilities, making 3D graphics on the web even more powerful. WebGL 2.0 (2017) In recent years, there has been a surge of interest in new graphics APIs that provide developers with more control and flexibility: : Created by the Khronos group, this cross-platform API is the "successor" to OpenGL. Vulkan provides lower-level access to graphics hardware resources, allowing for high-performance applications with better control over graphics hardware. Vulkan (2016) : This API was created by Microsoft and is exclusively for Windows and Xbox. D3D12 is the successor to D3D10/11 and provides developers with deeper control over graphics resources. D3D12 (2015) : Created by Apple, Metal is an exclusive API for Apple devices. It was designed with maximum performance on Apple hardware in mind. Metal (2014) The current state of WebGPU, and what's to come Today, WebGPU is available on multiple platforms such as Windows, Mac, and ChromeOS through the Google Chrome and Microsoft Edge browsers, starting with version 113. Support for Linux and Android is expected in the near future. Here are some of the engines that already support (or offer experimental support) for WebGPU: : Full support for WebGPU. Babylon JS : Experimental support at the moment. ThreeJS : In development, but with very promising prospects. PlayCanvas : Very early and experimental WebGPU support was announced in version 2023.2 alpha. Unity : Officially supports WebGPU, making it one of the pioneers in this area. Cocos Creator 3.6.2 : currently supported in v113+ for Windows, macOS, and ChromeOS only. Construct Considering this, transitioning to WebGPU or at least preparing projects for such a transition appears to be a timely step in the near future. High-level Conceptual Differences Let's zoom out and take a look at some of the high-level conceptual differences between WebGL and WebGPU, starting with initialization. Initialization When starting to work with graphics APIs, one of the first steps is to initialize the main object for interaction. This process differs between WebGL and WebGPU, with some peculiarities for both systems. WebGL: The Context Model In WebGL, this object is known as "context", which essentially represents an interface for drawing on an HTML5 canvas element. Obtaining this context is quite simple: const gl = canvas.getContext('webgl'); The context of WebGL is actually tied to a specific canvas. This means that if you need to render on multiple canvases, you will need multiple contexts. WebGPU: The Device Model WebGPU introduces a new concept called "device". This device represents a GPU abstraction that you will interact with. The initialization process is a bit more complex than in WebGL, but it provides more flexibility: const adapter = await navigator.gpu.requestAdapter();
const device = await adapter.requestDevice();

const context = canvas.getContext('webgpu');
context.configure({
   device,
   format: 'bgra8unorm',
}); One of the advantages of this model is that one device can render on multiple canvases or even none. This provides additional flexibility; for example, one device may control rendering in multiple windows or contexts. Programs and Pipelines WebGL and WebGPU represent different approaches to managing and organizing the graphics pipeline. WebGL: Program In WebGL, the main focus is on the shader program. The program combines vertex and fragment shaders, defining how vertices should be transformed and how each pixel should be colored. const program = gl.createProgram();
gl.attachShader(program, vertShader);
gl.attachShader(program, fragShader);
gl.bindAttribLocation(program, 'position', 0);
gl.linkProgram(program); Steps for creating a program in WebGL: : The source code for shaders is written and compiled. Creating Shaders : Compiled shaders are attached to the program and then linked. Creating Program : The program is activated before rendering. Using Program : Data is transmitted to the activated program. Data Transmission This process allows for flexible graphics control, but can also be complex and prone to errors, especially for large and complex projects. WebGPU: Pipeline WebGPU introduces the concept of a "pipeline" instead of a separate program. This pipeline combines not only shaders but also other information, which in WebGL, is established as states. So, creating a pipeline in WebGPU looks more complex: const pipeline = device.createRenderPipeline({
 layout: 'auto',
 vertex: {
   module: shaderModule, entryPoint: 'vertexMain',
   buffers: [{
     arrayStride: 12,
     attributes: [{
       shaderLocation: 0, offset: 0, format: 'float32x3'
     }]
   }],
 },
 fragment: {
   module: shaderModule, entryPoint: 'fragmentMain',
   targets: [{ format, }],
 },
}); Steps to create a pipeline in WebGPU: : The shader source code is written and compiled, similar to how it's done in WebGL. Shader definition : Shaders and other rendering parameters are combined into a pipeline. Pipeline creation : The pipeline is activated before rendering. Pipeline usage While WebGL separates each aspect of rendering, WebGPU tries to encapsulate more aspects into a single object, making the system more modular and flexible. Instead of separately managing shaders and rendering states, as is done in WebGL, WebGPU combines everything into one pipeline object. This makes the process more predictable and less prone to errors: Uniforms Uniform variables provide constant data that is available to all shader instances. Uniforms in WebGL 1 In basic WebGL, we have the ability to set variables directly through API calls. uniform : GLSL uniform vec3 u_LightPos;
uniform vec3 u_LightDir;
uniform vec3 u_LightColor; : JavaScript const location = gl.getUniformLocation(p, "u_LightPos");
gl.uniform3fv(location, [100, 300, 500]); This method is simple, but requires multiple API calls for each variable. uniform Uniforms in WebGL 2 With the arrival of WebGL 2, we now have the ability to group variables into buffers. Although you can still use separate uniform shaders, a better option is to group different uniforms into a larger structure using uniform buffers. Then you send all this uniform data to the GPU at once, similar to how you can load a vertex buffer in WebGL 1. This has several performance advantages, such as reducing API calls and being closer to how modern GPUs work. uniform : GLSL layout(std140) uniform ub_Params {
   vec4 u_LightPos;
   vec4 u_LightDir;
   vec4 u_LightColor;
}; : JavaScript gl.bindBufferBase(gl.UNIFORM_BUFFER, 1, gl.createBuffer()); To bind subsets of a large uniform buffer in WebGL 2, you can use a special API call known as . In WebGPU, there is something similar called dynamic uniform buffer offsets where you can pass a list of offsets when calling the API. bindBufferRange setBindGroup Uniforms in WebGPU WebGPU offers us an even better method. In this context, individual variables are no longer supported, and work is done exclusively through buffers. uniform uniform : WGSL [[block]] struct Params {
   u_LightPos : vec4<f32>;
   u_LightColor : vec4<f32>;
   u_LightDirection : vec4<f32>;
};
[[group(0), binding(0)]] var<uniform> ub_Params : Params; : JavaScript const buffer = device.createBuffer({
  usage: GPUBufferUsage.UNIFORM,
  size: 8
}); Modern GPUs prefer data to be loaded in one large block, rather than many small ones. Instead of recreating and rebinding small buffers each time, consider creating one large buffer and using different parts of it for different draw calls. This approach can significantly increase performance. WebGL is more imperative, resetting global state with each call, and striving to be as simple as possible. WebGPU, on the other hand, aims to be more object-oriented and focused on resource reuse, which leads to efficiency. Transitioning from WebGL to WebGPU may seem difficult due to differences in methods. However, starting with a transition to WebGL 2 as an intermediate step can simplify your life. Shaders Migrating from WebGL to WebGPU requires changes not only in the API, but also in shaders. The WGSL specification is designed to make this transition smooth and intuitive, while maintaining efficiency and performance for modern GPUs. Shader Language: GLSL vs WGSL WGSL is designed to be a bridge between WebGPU and native graphics APIs. Compared to GLSL, WGSL looks a bit more verbose, but the structure remains familiar. Here's an example shader for texture: : GLSL sampler2D myTexture;
varying vec2 vTexCoord;
void main() {
  return texture(myTexture, vTexCoord);
} : WGSL [[group(0), binding(0)]] var mySampler: sampler;
[[group(0), binding(1)]] var myTexture: texture_2d<f32>;
[[stage(fragment)]]
fn main([[location(0)]] vTexCoord: vec2<f32>) -> [[location(0)]] vec4<f32> {
  return textureSample(myTexture, mySampler, vTexCoord);
} Comparison of Data Types The table below shows a comparison of the basic and matrix data types in GLSL and WGSL: Transitioning from GLSL to WGSL demonstrates a desire for stricter typing and explicit definition of data sizes, which can improve code readability and reduce the likelihood of errors. Structures The syntax for declaring structures has also changed: GLSL: struct Light {
  vec3 position;
  vec4 color;
  float attenuation;
  vec3 direction;
  float innerAngle;
  float angle;
  float range;
}; WGSL: struct Light {
  position: vec3<f32>,
  color: vec4<f32>,
  attenuation: f32,
  direction: vec3<f32>,
  innerAngle: f32,
  angle: f32,
  range: f32,
}; Introducing explicit syntax for declaring fields in WGSL structures emphasizes the desire for greater clarity and simplifies understanding of data structures in shaders. Function Declarations : GLSL float saturate(float x) {
	return clamp(x, 0.0, 1.0);
} : WGSL fn saturate(x: f32) -> f32 {
  return clamp(x, 0.0, 1.0);
} Changing the syntax of functions in WGSL reflects the unification of the approach to declarations and return values, making the code more consistent and predictable. Built-in functions In WGSL, many built-in GLSL functions have been renamed or replaced. For example: Renaming built-in functions in WGSL not only simplifies their names, but also makes them more intuitive, which can facilitate the transition process for developers familiar with other graphics APIs. Shader Conversion For those who are planning to convert their projects from WebGL to WebGPU, it's important to know that there are tools for automatically converting GLSL to WGSL, such as **[Naga](https://github.com/gfx-rs/naga/)**, which is a Rust library for converting GLSL to WGSL. It can even work right in your browser with the help of WebAssembly. Here are Naga supported end-points: Convention Differences Textures After migration, you may encounter a surprise in the form of flipped images. Those who have ever ported applications from OpenGL to Direct3D (or vice versa) have already faced this classic problem. In the context of OpenGL and WebGL, textures are usually loaded in such a way that the starting pixel corresponds to the bottom left corner. However, in practice, many developers load images starting from the top left corner, which leads to the flipped image error. Nevertheless, this error can be compensated by other factors, ultimately leveling out the problem. Unlike OpenGL, systems such as Direct3D and Metal traditionally use the upper-left corner as the starting point for textures. Considering that this approach seems to be the most intuitive for many developers, the creators of WebGPU decided to follow this practice. Viewport Space If your WebGL code selects pixels from the frame buffer, be prepared for the fact that WebGPU uses a different coordinate system. You may need to apply a simple "y = 1.0 - y" operation to correct the coordinates. Clip Spaces When a developer faces a problem where objects are clipped or disappear earlier than expected, this is often related to differences in the depth domain. There's a difference between WebGL and WebGPU in how they define the depth range of the clip space. While WebGL uses a range from -1 to 1, WebGPU uses a range from 0 to 1, similar to other graphics APIs such as Direct3D, Metal, and Vulkan. This decision was made due to several advantages of using a range from 0 to 1 that were identified while working with other graphics APIs. The main responsibility for transforming your model's positions into clip space lies with the projection matrix. The simplest way to adapt your code is to ensure that your projection matrix outputs results in the range of 0 to 1. For those using libraries such as gl-matrix, there is a simple solution: instead of using the function, you can use ; similar functions are available for other matrix operations. perspective perspectiveZO if (webGPU) {
	// Creates a matrix for a symetric perspective-view frustum
  // using left-handed coordinates
  mat4.perspectiveZO(out, Math.PI / 4, ...);
} else {
  // Creates a matrix for a symetric perspective-view frustum
  // based on the default handedness and default near
  // and far clip planes definition.
  mat4.perspective(out, Math.PI / 4, …);
} However, sometimes you may have an existing projection matrix and you can't change its source. In this case, to transform it into a range from 0 to 1, you can pre-multiply your projection matrix by another matrix that corrects the depth range. WebGPU Tips & Tricks Now, let's discuss some tips and tricks for working with WebGPU. Minimize the number of pipelines you use. The more pipelines you use, the more state switching you have, and the less performance; this may not be trivial, depending on where your assets come from. Create pipelines in advance Creating a pipeline and using it immediately may work, but this is not recommended. Instead, create functions that return immediately and start working on a different thread. When you use the pipeline, the execution queue needs to wait for pending pipeline creations to finish. This can result in significant performance issues. To avoid this, make sure to leave some time between creating the pipeline and first using it. Or, even better, use the variants! The promise resolves when the pipeline is ready to use, without any stalling. create*PipelineAsync device.createComputePipelineAsync({
 compute: {
   module: shaderModule,
   entryPoint: 'computeMain'
 }
}).then((pipeline) => {
  const commandEncoder = device.createCommandEncoder();
  const passEncoder = commandEncoder.beginComputePass();
  passEncoder.setPipeline(pipeline);
  passEncoder.setBindGroup(0, bindGroup);
  passEncoder.dispatchWorkgroups(128);
  passEncoder.end();
  device.queue.submit([commandEncoder.finish()]);
}); Use RenderBundles Render bundles are pre-recorded, partial, reusable render passes. They can contain most rendering commands (except for things like setting the viewport) and can be "replayed" as part of an actual render pass later on. const renderPass = encoder.beginRenderPass(descriptor);

renderPass.setPipeline(renderPipeline);
renderPass.draw(3);

renderPass.executeBundles([renderBundle]);

renderPass.setPipeline(renderPipeline);
renderPass.draw(3);

renderPass.end(); Render bundles can be executed alongside regular render pass commands. The render pass state is reset to defaults before and after every bundle execution. This is primarily done to reduce the JavaScript overhead of drawing. GPU performance remains the same regardless of the approach. Summary Transitioning to WebGPU means more than just switching graphics APIs. It's also a step towards the future of web graphics, combining successful features and practices from various graphics APIs. This migration requires a thorough understanding of technical and philosophical changes, but the benefits are significant. Useful Resources & Links: WebGPU — All of the cores, none of the canvas From WebGL to WebGPU in Construct by Alain Galvan Raw WebGPU tutorial by Brandon Jones WebGPU Best Practices WebGL + WebGPU Meetup - July 2023 Link

Migrating from WebGL to WebGPU

WebGL থেকে WebGPU-তে স্থানান্তরিত হচ্ছে

Migration von WebGL zu WebGPU

Migrando de WebGL a WebGPU

Migration de WebGL vers WebGPU

WebGL से WebGPU पर माइग्रेट किया जा रहा है

WebGL から WebGPU への移行

WebGL에서 WebGPU로 마이그레이션

Migrando de WebGL para WebGPU

Переход с WebGL на WebGPU

WebGL'den WebGPU'ya geçiş

Di chuyển từ WebGL sang WebGPU

从 WebGL 迁移到 WebGPU

Legacy automakers and electric vehicle companies are in a race for innovation. With multiple household brands now hoping to deliver on their electric vehicle (EV) promises, furthering investments into electrifying their line-ups, and - a wealth of new job opportunities are being developed alongside the surging growth. manufacturing better, faster, and more environmentally-friendly cars The combination of growing consumer demand, partnered with more progressive electric vehicle policies that aim to direct capital investment towards auto manufacturers is helping to fast-track the transition to electric and battery-operated vehicles across nearly every corner of the world. While regions such as the United States, Europe, and China are spearheading the EV transition, key changes in policy and political investment are helping advance technological development, boosting manufacturing capacity, and assisting in crafting more efficient trade agreements between various partners. The introduction of several policies in recent years, including the Inflation Reduction Act (IRA), and the Bipartisan Infrastructure Bill, have already helped generate new job opportunities and future possibilities for the U.S. EV industry. However, these key moments were only further building on top of an already growing industry that has experienced steady investment flowing from both the private and public sectors. Now, manufacturers are rushing to fill open positions, in a historically demanding job market that has already undergone tremendous transformation in recent years. The steady demand for new opportunities Following the pandemic, and the aftermath thereof, which later triggered perhaps the biggest shift in labor market conditions, and would be known as the - overall labor conditions have remained historically positive despite the presence of sticky inflation and eye-watering interest rates that were last seen during the Bush Administration. Great Resignation Experts predict that the current change in consumer behavior, and the shift towards owning a battery-operated-vehicle (BEV) could help result in surging job growth in the auto industry. The current prediction estimates that if BEVs are able to rise to a robust 50% of domestic auto sales by 2023, the automotive industry could within the decade. generate an additional 150,000 new jobs Yet, this wouldn’t be the first time that legacy automakers and EV companies would add to the growing and highly dynamic U.S. job market. In a by the Environmental Defense Fund and WSP, automakers have added more than 179,000 new EV jobs and over $165 billion in new electric vehicle investments during the last eight years. report Other claim that the amount of new jobs added by the EV investments is higher, with more than 188,000 new jobs generated in recent years, according to Blue Green Alliance Foundation and Atlas Public Policy's data. sources However, looking at this from a more diplomatic perspective, the U.S. Bureau of Labor Statistics, which doesn't necessarily specify how many new EV jobs have been added over the last decade or so, revealed that four key areas will continue to see increased demand in the coming years. Those areas include the design and development of electric vehicles; installation and maintenance of charging infrastructure; production of batteries; and automotive production jobs. Although these segments stretch beyond traditional labor market categories, new possibilities would only become more possible through direct investment in the wider auto industry, and better policies that can help secure the United States as a leader in the global EV industry. New employment opportunities require (some) losses While the auto industry might be experiencing tremendous change due to improved manufacturing policies and surging capital investment, unfortunately, growth doesn't come without cutting some losses throughout the process. Despite all the efforts, there remains a level of risk many experts are unable to clearly identify over the long term. There has been however some indication that policy inaction could lead to increased job losses spread across the auto industry, with some experts claiming more than due to poor policy management, and failure to improve investment opportunities. 75,0000 jobs could be lost by 2030 Stunted growth could stem from various factors, including slowing economic conditions, ineffective supply chain management, and failure to regain market dominance. This year already has seen one of these factors playing out on the broader marketplace, with big-tech companies, including Amazon, Microsoft, Meta, and Oracle to name just a few cutting their employee headcount due to slower client demand and rising prices. Throughout the year, more than , across various companies, departments, and vacancies were handed a pink slip, some often laid off via a virtual company call, or even being replaced by more efficient and productive artificial intelligence (AI) systems. 249,000 tech employees Auto workers haven’t been completely shielded from these events either. Last year, several big companies, including General Motors (GM), Volkswagen, Lucid Group, Ford, and Argo AI announced cutting their headcounts in an attempt to stabilize their balance sheets and restructure their forward-looking strategy. Some due to their transition towards EV manufacturing in recent years. Ford cut more than 8,000 jobs last year, in an attempt to focus more on its EV business segment. automakers have laid off employees General Motors wasn’t in the same boat, but treaded through similar waters, announcing that it would limit hiring of new positions, and rather focus on filling critical needs in positions that could help further boost its presence in the auto market and more importantly, the EV industry. Some electric vehicle companies are feeling similar tension rising up against the surface as workers begin to demand better pay, against a backdrop of surging materials and operational costs due to ineffective supply chains and geopolitical tension in Europe and the Middle East. Chinese-based EV startup, Nio, recently that due to slower sales growth and waning demand, the company will look to cut nearly 10% of its workforce. Earlier in the year, word surfaced that Rivian, an American EV automaker, was planning to as it looked towards restructuring its long-term strategy and regain market share due to the EV price war taking place between major automakers such as GM and announced cut around 6% of its workforce Tesla. Being laid off isn’t the only thing that prospective auto workers will need to be concerned about. In recent months, the United Auto Workers (UAW) Union, a labor union that represents auto workers in the U.S. and parts of southern Ontario, Canada, embarked on what was perhaps considered one of the biggest labor strikes in recent decades. In mid-September this year, roughly at three of the biggest manufacturing plants, including at General Motors, Ford, and Stellantis Jeep assembly plants. 13,000 auto workers dropped tools After nearly two months, the UAW and auto manufacturers that would help significantly increase the wages of some plant and assembly workers, further improve retirement benefits, boost pay for temporary workers, and reduce the minimum years required to receive higher pay benefits. managed to reach a deal For legacy automakers, this would perhaps be yet another defining moment in their history, however, for electric vehicle companies, that are still trying to gain a foothold in the auto industry, and having to struggle through the growth pains that come with any new market. The possibility of better wages and improved employee benefits are often overshadowed by the opportunity to become part of something that provides a sense of innovation, and a force of reckoning that’s still trying to navigate through various economic and political challenges. Looking for a job? You’ll need to be an engineer first With the wider auto industry witnessing significant changes, and with many automakers hoping to capitalize on consumer demand, progressive policies, and better investment opportunities, many of them have changed their tune regarding the type of employees they are now hiring to fill the need for critical skills that can help elevate their business growth. As the EV industry continues to develop and breach into new markets, employability has become increasingly competitive, and perhaps increasingly difficult for college graduates and job seekers hoping to land a job in such a multifaceted industry. While there are dozens, even hundreds of new job openings being posted by some of the biggest EV makers in the business, the qualifications required to even be considered for one of these jobs, never mind surviving the rigorous corporate hiring process, has become seemingly outlandish to some extent. The most , depending on the vacancy, often require applicants to hold either a Bachelor's Degree, or an equivalent degree in the field of engineering, design, development, or supply chain management. basic requirements Other, more critical jobs will require applicants to have a necessary qualification, in either electrical, chemical, mechanical, or robotics engineering. That’s not to mention, that some of these jobs will also require applicants to either have a portfolio that showcases their work or a minimum amount of years of working experience to be considered. Keep in mind that in the U.S., nearly with an engineering major will either drop out of college or change their major before graduating. The reason for this high figure? Well, one survey found that students are often ill-prepared for the complexity of the work, and many become overwhelmed with the amount of work and requirements needed to make it to graduation. Additionally, some students have indicated that a lack of success, support, and interest in the field are some of their reasons for changing majors or dropping out. 50 percent of students For less production-orientated positions, such as aftercare service and maintenance, job requirements can even be more stringent, coupled with a laborious hiring process. Due to the strong demand from manufacturers and service centers, and the high competition of entry, many applicants often find it increasingly challenging to land a maintenance job within their first few months of being qualified. With cars becoming more eclectic, new components and inner workings are being introduced, which often requires existing maintenance employees to undergo additional training, or already have the necessary qualifications to service and maintain electric cars. Landing a job is the easy part, even if it doesn’t feel that way. Keeping up with the massive amount of work is another challenge many employees often have to come to terms with. While there may be a short supply for specific jobs, many employees often find themselves having to manage several projects at once, or even doing multiple jobs simultaneously due to the inability to fill open positions. Other leading factors that could also make the job more demanding, and often leading to greater levels of exhaustion include longer working hours, demanding work environment, and lack of flexibility. On top of this, many employees, both on the production floor and in the corporate office have to deal with increased job urgency due to surging competition, and the ability to scale at a much quicker rate. Various studies and surveys have indicated that a culture of urgency in the workplace can lead to increased levels of employees feeling stressed and anxious, only further increasing burnout among employees. For many people, when the going gets tough, they simply quit and move on to the next thing, leaving automakers even more short-staffed. The doom cycle then only begins to repeat itself, as employees become increasingly burned out, many job dissatisfaction, leading many to quit or resign, further causing a higher employee turn around, and more pressure on existing employees to complete projects. will experience higher levels of Working in such a dynamic and multi-dimensional industry was never expected to be easy, yet many people continue to flock towards these opportunities for better pay and wages. show that some Tesla employees make a minimum of $80,000 per year, with a maximum making around $235,000 annually. The average salary for a Tesla employee was around $149,351 per year, making the median $145,000, with roughly 39.1% of Tesla employees making between $150,000 and $199,999. Records Other that many EV companies have been bumping up salaries to attract younger talent, with some mid-level jobs seeing a nearly 100% increase in pay, while senior management has received a steady, yet robust 50% pay increase. research suggests If you want to make money, you will need to work for it, that’s for sure. Finishing Thoughts New job seekers are taking better advantage of the increased opportunities that have been presented by the booming electric vehicle industry. However, the job doesn’t come easy, and demand is high, often creating stiff competition in the labor market. More importantly, future employees who want to work at some of the most innovative companies in the auto industry will need to hold a dedicated degree or qualification, which also doesn’t come easy. While no job is without a sense of urgency or stress, working on some of the most exciting projects that will help reshape the future of the global automotive market is perhaps something worth consideration. Although the journey leading up isn’t easy and requires a tremendous amount of investment, there’s truly something rewarding about working with all-electric cars that will help drive the next generation.

ADVANCE AUTO PARTS

advanceautoparts.com

10K+ employees

Since 1932

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ADVANCE AUTO PARTS

EVERGREEN INDEX #1339

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ADVANCE AUTO PARTS

advanceautoparts.com

10K+ employees

Since 1932

COMPANY RANKING

ADVANCE AUTO PARTS

EVERGREEN INDEX #1339

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