The global manufacturing landscape is in a state of perpetual evolution, confronted by the intricate dance of complex supply chains, fluctuating market demands, and an unyielding pressure for hyper-efficiency. Traditional production planning systems, long the bedrock of manufacturing operations, are increasingly strained by the need for real-time adaptability and the capacity to manage a vast array of variables. This environment has cultivated a critical demand for more intelligent and responsive planning paradigms.
AI is emerging as a profoundly transformative force, holding the promise of significantly enhancing decision-making capabilities, optimizing the allocation of scarce resources, and streamlining operational workflows across the manufacturing value chain. Indeed, traditional manufacturing paradigms often grapple with challenges such as reliance on outdated machinery and processes, alongside high labor costs.
In contrast, AI-based planning systems can leverage sophisticated algorithms to perform real-time data analysis, predict demand shifts with greater accuracy, and dynamically adjust production schedules to changing conditions. This transition represents not merely a technological upgrade but a fundamental reshaping of how manufacturing excellence is pursued and achieved.
At the vanguard of integrating AI into the sophisticated domain of SAP manufacturing solutions is Mahesh Babu MG, a distinguished SAP Supply Chain Manufacturing leader. His career, spanning over 19 years, reflects extensive experience and profound expertise in SAP Manufacturing and Planning solutions, encompassing SAP ECC, SAP APO, and SAP S/4HANA. MG has demonstrated a deep understanding of manufacturing business processes and architectural design across a multitude of industries.
In his current capacity, he directs the SAP Premium Hub CoE Manufacturing and PLM team, a role that underscores his leadership and considerable technical acumen. A certified SAP S/4HANA Production Planning & Manufacturing expert, MG is also the acclaimed author of two editions of the SAP Press book, “PP/DS with SAP S/4HANA” (specifically, the first edition, ISBN 978-1-4932-1872-1, and the second edition).
These comprehensive guides on advanced planning and scheduling, covering critical areas like master data, heuristics, the PP/DS optimizer, and alert monitoring, have been cataloged by the Library of Congress, cementing his status as a preeminent authority in the field.
This unique combination of deep, practical SAP PP/DS knowledge and forward-thinking research into AI augmentation positions him as a vital link between established enterprise systems and the next wave of intelligent technologies. This ensures his insights are both innovative and readily applicable in real-world manufacturing contexts.
Combining his extensive background in SAP PP/DS and his proficiency in HANA prototyping, MG conceptualized and executed a significant behavioral study. This research was designed to meticulously investigate the quality of management decisions when operating under AI-augmented planning systems. The experiment featured a conceptual AI co-pilot, modeled on the capabilities of SAP Joule, integrated directly with PP/DS functionalities.
Within the structured environment of controlled workshops, participating planners were tasked with comparing traditional heuristic-based outputs against AI-driven recommendations. The outcomes of this experiment were compelling: users augmented by the AI co-pilot were able to reach scheduling decisions 30% faster, demonstrated a 20% higher acceptance rate for optimized sequences, and, crucially, contributed to a 12% reduction in late orders. These statistically significant findings robustly underscore how an AI assistant, such as a Joule-like entity, can not only accelerate the planning cycle but also materially enhance the quality of decisions made.
The concept of AI copilots like SAP Joule, which deliver contextual insights and automate tasks via natural language, is central here. Although a standard Joule for PP/DS was not available at the time of the study, MG’s exploration of this paradigm through a conceptual model is particularly insightful. The direct link between AI-augmented decision quality and a 12% reduction in late orders highlights a tangible business outcome, addressing a major pain point in manufacturing related to customer satisfaction and operational costs.
The insights gleaned from this pioneering research have subsequently informed the content of MG’s executive workshops and his various publications. This has further solidified his reputation as an influential thought leader in the rapidly advancing field of AI-driven manufacturing planning.
MG’s professional endeavors are sharply focused on empowering manufacturing industries across North America to optimally harness the capabilities of SAP Supply Chain manufacturing solutions. The ultimate aim is to significantly improve their manufacturing planning and scheduling operations. The manufacturing sector, especially in North America, is currently navigating a period of profound transformation, largely propelled by widespread digitalization and an urgent need to build more resilient and agile supply chains.
The adoption of AI is widely recognized as a pivotal enabler in this ongoing shift, with a substantial percentage of companies actively exploring or already implementing AI technologies within their operations. MG’s work directly confronts the practical challenges and unlocks the opportunities inherent in this evolving industrial landscape. North America stands as a significant market for SAP SCM consulting services, with numerous large manufacturing enterprises actively seeking specialized expertise to navigate these changes.
The conceptual nature of the “SAP Joule” in PP/DS for MG’s study points to a proactive stance, exploring AI’s future potential even before standard solutions are universally available. This is a critical approach for organizations aiming to stay ahead of rapid technological advancements.
Addressing traditional PP/DS challenges with AI-driven insights
Traditional Production Planning and Detailed Scheduling (PP/DS) systems, while robust, present inherent challenges that prompted an exploration into AI augmentation. MG notes, “In traditional PPDS, the planners and schedulers rely on automated background execution planning and scheduling algorithms and the optimizers in PPDS to resolve planning and scheduling issues related to the manufacturing of finished products and assemblies. However, not all such planning and scheduling problems can be solved by the planning/scheduling algorithms and/or the optimizer.” This gap often necessitates considerable manual intervention.
Planners dedicate significant time to sifting through alerts generated by interactive transactions, identifying problems such as potential delays in sales order deliveries due to capacity unavailability on an assembly line, or days’ supply calculations falling below critical thresholds. In these instances, planners manually reprioritize manufacturing orders or create new ones to mitigate issues. Traditional production planning methodologies often struggle with aspects like demand variability, a lack of real-time operational visibility, and inherent inflexibility when faced with dynamic changes, all of which can exacerbate these manual efforts.
The objective of the conceptual study was shaped by these existing limitations and the emerging potential of AI. “SAP Joule is a co-pilot that understands the business semantics in the connected cloud ERP system. For PPDS, there are no Joule capabilities available in the standard solution as of now. So, this conceptual study focuses on the impact and benefits of a co-pilot/chatbot-enabled production planning and detailed scheduling,” MG explains.
The core use case was to train an AI model using historical PP/DS alert data and the corresponding resolution actions taken by planners. This approach aimed to automate decision-making for the more frequently observed and often time-consuming alerts within the Alert Monitor within the PP/DS system and enhance metrics such as lead times and on-time delivery by meticulously considering resource and component availability.
However, its effectiveness often hinges on heuristics and an alert monitor for managing exceptions, requiring planners to manually intervene for alerts like “days’ supply calculation results in a value which is under a defined threshold” or “delay in delivery of a sales order driven by non-availability of capacity.”
The vision for an AI co-pilot, capable of understanding business context and interacting through natural language, directly addresses the manual burden associated with these common alerts. This automation is anticipated to yield significant efficiency gains, a frequently cited advantage of AI in the manufacturing sector.
The study’s focus on training an AI with planner actions suggests a method to codify and scale the valuable tacit knowledge currently held by experienced planners, potentially standardizing best practices and accelerating learning for newer team members. This forward-looking exploration into co-pilot capabilities, even in the absence of a standard PP/DS Joule solution, provides a crucial perspective for organizations developing long-term AI strategies.
The pivotal role of HANA prototyping in designing AI-augmented experiments
The technical architecture of MG behavioral experiment was significantly shaped by his expertise in SAP HANA prototyping. This proficiency was critical in establishing the foundational data structures necessary for the study. MG states, “HANA prototyping expertise is leveraged to create artifacts to capture the details of the alerts and the actions taken by the planner in HANA models, which can be later used to train the AI model.”
These artifacts, primarily HANA tables and views, were meticulously designed to record the nuances of alerts generated within the PP/DS system and the subsequent corrective actions implemented by human planners. This data capture mechanism was event-driven, ensuring that the information collected was a dynamic reflection of ongoing planning activities.
SAP HANA and its capabilities are well-suited for such real-time data processing and analytics, providing an ideal platform for capturing the dynamic event data essential for training a responsive AI model. This aligns with the broader trend of leveraging ML and AI in SAP data analysis to enhance real-time data analysis and facilitate predictive modeling.
The practical application of this HANA-based data capture is further illustrated by a specific scenario MG describes: “For example, for a level 3 assembly in manufacturing alerts are raised to notify the planner on a delay, the planner resolved by executing a planning heuristics manually to regenerate plan, as at this level there are planning very frequent failures.
So this alert, along with the nature of the product/location, combined with the actions taken, are captured using the HANA artifacts such as tables and views updated based on events happening in the PPDS system.” This detailed example underscores how operational data, including specific alert types, contextual information about the product and location, and the precise resolution steps undertaken by planners, can be systematically collected and structured.
This process of creating HANA artifacts is a direct application of data preparation techniques vital for effective machine learning model training and addresses the fundamental need for high-quality, comprehensive data to fuel robust AI performance. Recent developments, such as data extraction for PP/DS for SAP S/4HANA covering order details, operations, and resource capacity, indicate the industry’s move towards facilitating such data extraction for analytical purposes, similar to what MG prototyped.
The choice to focus the prototype on a level 3 assembly with very frequent planning failures suggests a strategic selection of a high-impact area where AI could demonstrate significant value by addressing a persistent pain point, a best practice in AI use-case identification.
Furthermore, capturing not just alerts and actions but also the nature of the product/location points to the creation of a rich, contextual dataset. Such multi-dimensional data is invaluable for training sophisticated AI models capable of understanding nuances beyond simple alert classifications, leading to more accurate and contextually relevant AI-driven recommendations.
Crafting robust comparisons: Participant and scenario selection for AI decision support studies
The design of the controlled workshops in MG’s study placed a strong emphasis on meticulous participant and scenario selection to ensure a robust and meaningful comparison between traditional human-driven planning decisions and those augmented by the conceptual AI co-pilot. Given the focused nature of the study, specific choices were made to enhance the validity of the findings.
MG clarifies this by stating, “As this study’s scope is controlled and limited to two alert types (delay in order fulfilment and over utilization of capacities), the roles selected were production supervisor for finished goods and scheduling supervisor for the finished goods assembly lines.” This deliberate limitation of scope to two critical alert types and their corresponding relevant supervisory roles allowed for a deeper, more controlled analysis of AI’s impact on specific, yet crucial, planning tasks.
The selection of production supervisor for finished goods and scheduling supervisor for the finished goods assembly lines as participant roles is a critical aspect of the experimental design. These roles are directly and routinely involved in managing the types of alerts under investigation—order fulfilment delays and capacity over-utilization. This alignment ensures that the tasks presented during the workshop are ecologically valid, meaning they closely mirror the real-world responsibilities and challenges faced by the participants.
The scenarios, centered on delay in order fulfillment and overutilization of capacities, address common and highly impactful problems in the manufacturing sector. These issues have significant financial and operational ramifications, making their potential mitigation through AI a compelling proposition for businesses. The use of controlled workshops is a standard and effective methodology in behavioral science for comparing human performance under different conditions, such as with and without AI decision support tools.
The “controlled” nature of these workshops helps to isolate the specific impact of the AI tool by minimizing the influence of extraneous variables. Furthermore, for a robust comparison in studies evaluating human versus AI decision-making, scenarios must be designed in such a way that normative or optimal decisions can be objectively identified. The alert types chosen by MG typically have established “better” or “worse” resolution pathways, allowing for a clear benchmark against which both human and AI-augmented decisions can be assessed. This careful pairing of specific alert types with relevant supervisory roles enhances the realism of the experiment and, consequently, the applicability of its findings to actual organizational structures and operational workflows.
Measuring the impact: Key metrics for evaluating AI-augmented planning decisions
To rigorously evaluate the impact of the AI co-pilot, MG prioritized a set of key metrics focusing on decision speed, the quality of AI-recommended action sequences, and tangible operational outcomes like order timeliness. For measuring how quickly decisions could be made with AI assistance, the study focused on the AI’s responsiveness.
MG explains, “The decision speed is measured by the latency between the input prompt to the chatbot to the production of the initial response. For example, for a prompt, ‘how many high priority delayed order alerts are present for the products I’m responsible for,’ the chatbot will use AI to understand the prompt and trigger backend PPDS actions to generate alerts and respond with the number of alerts.”
This metric directly quantifies the AI’s ability to quickly process natural language queries, interact with the underlying PP/DS system, and furnish planners with relevant information, a critical factor in fast-paced manufacturing environments. Such responsiveness aligns with common AI assistant performance measures.
The quality of the AI’s suggestions, specifically the acceptance of its proposed action sequences, was assessed using a sophisticated measure. “Contextual precision was used to measure sequence acceptance to evaluate the actions triggered by AI, comparing it with a manual sequence of actions with the same prompt,” MG states. This approach moves beyond a simple binary acceptance or rejection of an AI’s advice.
Finally, order timeliness, a crucial manufacturing KPI often measured by manufacturing KPIs, was a key outcome measure, reflected in the 12% reduction in late orders reported in the study’s angle. AI model validation techniques were applied to ensure the reliability and accuracy of these metrics, including verifying chatbot responses for relevance and correctness. The combination of these metrics—speed, quality of decision/action sequence, and operational outcome—provides a holistic framework for evaluating the AI co-pilot’s effectiveness, ensuring that improvements in one area do not come at the expense of others.
The example prompt for decision speed also highlights the envisioned sophistication of the AI, capable of understanding natural language, user context (e.g., “products I’m responsible for”), and triggering complex backend system interactions. This points towards the advanced capabilities of future planning assistants.
Unforeseen dynamics: Planner interactions with AI suggestions versus traditional heuristics
The interaction between human planners and AI-driven decision support systems can often reveal unexpected dynamics, and MG’s study provided valuable insights in this regard. One of the key observations centered on the profound influence of training data characteristics on the AI’s recommendations.
MG notes, “With the Joule-like modeled chatbot, the quality and pattern of the training data hugely impacted the AI suggestions. In case of the capacity overload cases, the training data consisted of scenarios where the orders that caused the overload were rescheduled to the following week.”
This initial training regimen led to a specific, albeit suboptimal, AI behavior. The paramount importance of training data quality is a well-established principle in AI development; deficiencies in data can lead to inaccurate predictions and flawed decision-making.
This reliance on observed patterns in the training data led to an interesting scenario. MG elaborates, “But when capacity is still available within the same week, the AI suggestion was to reschedule the order to the following week; this was mitigated by introducing the capacity check as an action before proposing the rescheduling action in the training data.”
This AI behavior, where it suggested a less optimal solution due to biases in its training, exemplifies how AI models can learn unintended patterns if the training data isn’t sufficiently comprehensive or representative of all relevant decision-making logic.
Such behavior, if uncorrected, could negatively impact planner trust and lead to poor operational decisions. The mitigation strategy—enriching the training data to include an explicit capacity check—reflects an iterative refinement process common in machine learning model development.
This experience highlights the challenge of embedding “common sense” or implicit contextual rules that human planners intuitively apply, such as checking for more immediate capacity availability before suggesting a longer deferral.
AI models learn from patterns, and if the training data does not fully encapsulate all desired decision logic, the AI will exhibit these gaps. The ability to diagnose why the AI made a suboptimal suggestion (due to training data patterns) was crucial for its correction, underscoring the value of transparency and explainability in AI systems, which are vital for building user trust and enabling effective human oversight. This iterative loop, where AI behavior influences potential user interaction and the anticipation of negative interaction due to AI flaws, drives AI refinement, which is critical for developing practical and reliable AI decision support tools.
Tangible benefits: The operational and financial impact of reducing late orders with AI
The 12% reduction in late orders achieved in MG’s conceptual study translates into significant and multifaceted benefits for manufacturing organizations, spanning both operational efficiencies and financial improvements. Traditional approaches to resolving order delays, especially those stemming from shop-floor disruptions, are often cumbersome and reactive.
As MG describes, “In traditional planning scenarios, when a delay for a sales order is caused by shop floor delays to manufacture the products at the scheduled duration, the nightly background runs will have to be executed to schedule the backlogged manufacturing orders to the future available capacity slots. Then the backorder processing for sales orders will have to be executed to calculate the new sales order promise dates.”
Furthermore, managing high-priority sales orders frequently demands manual intervention by planners to reschedule other manufacturing orders, a time-consuming and potentially error-prone process.
The AI-augmented chatbot approach, as explored in the study, offers a paradigm shift towards more agile and automated resolution. MG highlights this contrast: “With the chatbot-based approach, the planner can simply ask the chatbot if any of the high-priority sales orders are delayed and instruct to resolve the issue, which in turn leverages AI capabilities to trigger corresponding rescheduling actions automatically.” This capability directly addresses a major source of inefficiency and cost.
A 12% decrease in late orders represents a substantial operational enhancement. Industrially, late deliveries and backorders are known to inflate operational costs through expedited shipping and overtime, diminish customer satisfaction, leading to reduced retention, and can even cause reputational damage. Consequently, reducing late orders yields improved resource utilization and smoother production flows.
The financial implications are equally compelling, with potential for direct cost savings (poor on-time delivery can account for nearly 10% of costs), increased revenue from enhanced customer loyalty, and healthier profit margins. Research from entities like the Aberdeen Group has shown a correlation between higher on-time delivery rates and improved project profitability, with best-in-class firms significantly outperforming others in this metric.
Moreover, the automation of rescheduling for critical, high-priority orders not only lessens the planner’s direct workload but, more importantly, ensures that strategic business objectives, such as fulfilling key customer orders, are addressed with consistency and efficiency.
This minimizes the revenue risk associated with these vital accounts, showcasing how AI can safeguard critical income streams and bolster important customer relationships by ensuring prioritized and effective handling of urgent fulfillment challenges. The shift from reactive, batch-oriented rescheduling to proactive, AI-driven resolution signifies a fundamental enhancement in planning agility. This enables manufacturers to respond to disruptions more rapidly and thereby minimize their cascading negative impacts across the supply chain.
From experiment to expertise: Shaping executive workshops and thought leadership with AI insights
The compelling outcomes from MG’s behavioral experiment, particularly the demonstrated gains in planner efficiency and the enhancement of decision quality, have become foundational elements in shaping his executive workshops and influential writings, including his authoritative SAP Press books.
A central tenet of his message to industry leaders and practitioners is the transformative potential of AI to enable a “manage by exception” paradigm in manufacturing planning. As MG observes, “A typical planner or scheduler manages thousands of materials within their area of responsibilities, and their spending time to resolve the more common and trivial planning/capacity alerts in PPDS takes most of their time.”
This extensive involvement in routine alert resolution often detracts from more strategic activities.
The introduction of AI-driven tools offers a clear pathway to alleviate this burden. MG emphasizes the critical shift in focus that AI facilitates: “When their attention and focus are needed for more complex alerts and managing the business. This AI-driven manufacturing planning drives the businesses and their planning team towards the goal of ‘Do not manage the system, just manage the real exceptions.’”
This philosophy, where AI systems adeptly handle common and less critical alerts, empowers human planners to dedicate their expertise and cognitive bandwidth to navigating genuinely complex scenarios, managing significant business exceptions, and undertaking strategic planning initiatives.
The “manage by exception” principle is a well-regarded approach in operations management, focusing attention on deviations that truly require expert intervention, and AI serves as a powerful enabler for this. MG’s experimental results, such as the 30% faster decision-making and 12% reduction in late orders, provide concrete, data-backed evidence that transforms abstract discussions about AI’s potential into tangible demonstrations of value. This empirical grounding is particularly persuasive in executive workshops, aiding leaders in their AI investment decisions.
Moreover, real-world thought leadership plays a crucial role in the manufacturing sector. MG’s work, therefore, not only contributes to academic understanding but also serves as a practical tool for advocacy and education, bridging the gap between AI’s potential and executive comprehension and buy-in. This redefinition of the planner’s role—from a system operator to a strategic problem solver—is a crucial aspect of AI integration, necessitating new skills and a shift in organizational mindset, topics likely central to MG’s educational outreach.
Strategic adoption: Guiding organizations towards AI-augmented SAP PP/DS environments
Based on the findings from his research, MG provided strategic advice for organizations contemplating the integration of AI-augmented planning systems within their existing SAP PP/DS landscapes. The initial, albeit limited, study demonstrated a clear business benefit. As MG states, “With this limited study and experiment, the benefit to the business was calculated at a 12% increase in on-time fulfilment of customer orders.”
This quantifiable improvement serves as a strong starting point and a testament to AI’s potential. Building on this, a primary recommendation is to initiate AI adoption by targeting high-impact use cases where the return on investment is evident and measurable. This aligns with broader AI adoption strategies that advocate for starting with clear business objectives and well-defined applications that offer tangible value.
MG suggests a specific avenue for valuable expansion: “If further use cases, such as analyzing the planning and optimizer job logs to identify planning and scheduling errors and leveraging AI to automate the resolution of more common errors, will bring in a lot of value to the businesses.”
This counsel points towards an incremental adoption strategy. Analyzing planning and optimizer job logs is a strategically astute next step because these logs represent rich, structured data sources readily available within SAP systems.
Tapping into this existing data for AI initiatives can lower the initial barrier to entry for further AI exploration, offering a pragmatic path to scale AI benefits. Automating the resolution of common errors identified in these logs reinforces the “manage by exception” philosophy. This not only frees up planners but also reduces the workload on IT support and system administrators, allowing them to concentrate on more complex systemic issues or enhancements, thereby broadening AI’s efficiency impact beyond the planning department.
The initial success of the 12% improvement in on-time fulfillment acts as a crucial proof-of-concept. The advice to tackle further use cases implies a strategy of demonstrating tangible value early and then leveraging that success to champion and guide broader AI adoption within the organization—a key principle for effective change management and strategic scaling. Organizations should also ensure robust data quality and governance, as these are critical prerequisites for any successful AI deployment within SAP environments. While MG’s advice focuses on use case expansion, the underlying SAP context, including SAP’s strategy for scaling Enterprise AI, remains an important consideration for long-term success.
The journey of integrating AI into manufacturing planning, as illuminated by the work of MG, reveals a pathway toward significantly enhanced operational capabilities. His behavioral experiment, demonstrating that a conceptual AI co-pilot for SAP PP/DS can improve decision speed by 30% and reduce late orders by 12%, offers compelling evidence of AI’s practical value. More profoundly, these findings champion a shift in the planner’s role towards a “manage by exception” model.
By automating the handling of routine alerts and common error resolutions, AI empowers skilled planners to dedicate their expertise to complex, strategic challenges that truly require human ingenuity. This synergy between human intellect and artificial intelligence is not a distant theoretical concept but an achievable reality, offering manufacturing industries, particularly those in North America, leveraging SAP solutions, a route to greater efficiency, improved agility, and a stronger competitive posture.
The pioneering research and strategic insights provided by experts like MG are crucial in navigating this transformation, ensuring that the adoption of AI is both impactful and aligned with the evolving demands of modern manufacturing. The continued evolution of intelligent manufacturing planning will undoubtedly be shaped by this increasing collaboration. Here, human oversight guides AI’s power to unlock new levels of performance and innovation.