The contemporary manufacturing sector, especially within North America, grapples with a confluence of pressures. These include persistent global supply chain disruptions, escalating operational costs, and an urgent demand for enhanced agility and operational visibility. In this challenging environment, the digital twin has emerged as a pivotal technology, offering a virtual replica of physical assets and processes.
This allows for real-time monitoring, sophisticated scenario simulation, and continuous process optimization, thereby enabling manufacturers to navigate complexities with greater precision. The adoption of this technology is rapidly accelerating, with the North American digital twin market poised for substantial growth. Specifically, the supply chain digital twin market in this region is forecast to achieve a valuation.
At the forefront of such innovative implementations is Mahesh Babu MG. A SAP Supply Chain Manufacturing leader with over 19 years of extensive experience in SAP manufacturing and planning solutions spanning SAP ECC, SAP APO, and SAP S/4HANA, MG currently directs the SAP Premium Hub CoE Manufacturing and PLM team. He spearheaded a cross‑functional team to deploy a live digital twin of a multi‑site manufacturing network.
This was achieved by extending SAP S/4HANA production planning and detailed scheduling (PP/DS) with SAP business technology platform (BTP) services, ingeniously integrating real‑time Internet of Things (IoT) data and advanced HANA data models. The results were striking: "Within six months of go‑live, this framework delivered a 25% reduction in planning cycle times and a 15% boost in resource utilization, demonstrating clear ROI for North American manufacturers."
These figures are noteworthy, aligning with or even surpassing industry reports on digital twin advantages, some of which indicate development time reductions of up to 50% and operational cost savings of around 30%.
The rapid realization of such significant ROI within a mere six months effectively counters the common perception that digital twin initiatives are invariably protracted and excessively resource-draining. With a well-defined strategy and the appropriate technological platforms, substantial value can be unlocked swiftly.
The synergy between established SAP solutions like S/4HANA PP/DS and agile cloud platforms such as BTP proves to be a potent catalyst for sophisticated innovations like digital twins, highlighting a crucial trend where the combination, rather than a single technology, drives success.
MG's professional endeavors are sharply focused on "helping manufacturing industries in North America to optimally leverage SAP supply chain manufacturing solutions to improve their manufacturing planning and scheduling operations." His practical guidelines, distilled from his leadership at the CoE and his comprehensive SAP Press book, "PPDS with SAP S/4HANA," delineate effective methodologies for prototyping "what‑if" scenarios, embedding BTP extensions into standard operational workflows, and continuously optimizing production sequencing to ensure sustained value.
Motivation and SAP Strategy Alignment
The impetus for developing a digital twin solution for multi-site supply chain planning stemmed from a critical operational deficiency: the lack of real-time labor availability data within the SAP S/4HANA production planning and detailed scheduling (PP/DS) horizon.
MG explains, "The real-time labor availability in the recent future, which is within the PPDS scheduling horizon, is critical for scheduling manufacturing orders accurately, considering labor availability. Such a feature is not available in the native solution." This gap directly impacted the accuracy of manufacturing schedules, consequently affecting production efficiency and the ability to meet customer commitments reliably.
The motivation was, therefore, not driven by technological novelty but by a pressing business requirement for more precise and dynamic planning inputs. To address this, the SAP Business Technology Platform (BTP) was identified as the ideal enabler. BTP's inherent strengths in application development, integration, data and analytics, and automation provided the necessary tools to construct a digital twin of actual labor availability.
This involved integrating data from a variety of site-specific manufacturing execution systems (MES) and human resources (HR) systems. MG notes, "As there are a variety of HR and manufacturing execution systems involved, BTP served as the anchor to integrate all these systems, harmonize data, and integrate back to PPDS. For all extension requirements, SAP recommends side-by-side extensions in BTP when in-app extensibility cannot solve complex requirements." This approach aligns perfectly with SAP's overarching strategy, which encourages the use of BTP for side-by-side extensions while fostering rapid innovation on a flexible platform.
BTP's capacity to serve as an "anchor" for integrating diverse legacy systems is particularly valuable, as large manufacturing enterprises often operate within complex, heterogeneous IT landscapes. The SAP BTP integration platform is specifically designed for such scenarios, enabling the creation of a unified data layer. Furthermore, this innovative extension supports the strategic importance of PP/DS as SAP's core solution for advanced manufacturing planning and detailed scheduling.
SAP PP/DS provides robust functionalities like constrained planning and detailed scheduling, which are significantly amplified by the real-time data feeds from the BTP-hosted digital twin. By enhancing PP/DS with these capabilities, the solution not only addresses current needs but also aligns with SAP's future roadmap for the product, ensuring a sustainable and forward-looking approach to supply chain optimization. This strategic architectural decision to leverage BTP for extensions yields long-term benefits that extend beyond the immediate project's scope, ensuring system integrity and facilitating future upgrades.
Orchestrating Innovation: Leading Cross-Functional Teams for BTP-enhanced PP/DS
The traditional method of managing labor capacities within PP/DS involved manual updates to master data, a process inherently prone to errors and delays. "The traditional solution requires manual update of the available capacities for labor resources within the PPDS master data, which is error-prone," MG states. Bridging the gap between these conventional PP/DS processes and the innovative BTP extensions necessitated a highly collaborative, multi-disciplinary approach.
MG structured and led his cross-functional team by fostering close cooperation between various stakeholders. This involved engaging directly with "plant/site-specific shop floor managers to understand how their labor availability is maintained in their HR and MES systems." Simultaneously, the technology teams were tasked with assessing the integration capabilities of these diverse legacy systems with BTP, primarily through the SAP BTP Integration Suite.
A critical aspect of this leadership was ensuring alignment with production planners and schedulers to define the appropriate level of data granularity. For instance, labor resources in PP/DS are often maintained at a pool level, such as "Electrical" or "Plumbing," and the digital twin is needed to feed data in a compatible and meaningful format. This meticulous attention to detail extended to master data modifications within PP/DS.
MG elaborates, "The manufacturing master data such as the routing and the work center definitions to hold the labor capacity as a secondary finite capacity in addition to the main machine on which the manufacturing order is executed, needed to bridge the gap from a master data perspective." This decision to define labor as a "secondary finite capacity" is a nuanced yet crucial technical detail.
It allows the PP/DS planning engine to accurately constrain schedules based on both labor and machine availability simultaneously, reflecting shop floor realities more faithfully and leading to more achievable production plans. This demonstrates a sophisticated application of SAP S/4HANA Manufacturing for planning and scheduling.
The success of this project underscores that such innovations depend significantly on effective change management and stakeholder buy-in across diverse functional areas, from the shop floor to IT and planning departments. Introducing a digital twin that alters fundamental processes like labor capacity management requires careful navigation of existing workflows and a clear demonstration of value to all involved.
The project also highlighted the necessity for teams with blended skill sets, capable of understanding both the intricate business processes governing labor management and the technical architecture underpinning system integration and master data configuration.
This holistic approach, addressing people, process, and technology, was fundamental to the project's achievements. MG also points out the broader applicability of the developed concepts: "The same concept can also be applied to actual machine work centers, to update their real-time efficiency and adjust the master data in PPDS accordingly to schedule the future manufacturing orders considering this constraint." This foresight into extensibility further showcases a strategic and well-rounded leadership approach.
Powering the Digital Twin: Integrating Real-Time IoT Data Streams
The digital twin developed by MG's team relies on two distinct streams of real-time data. "The labor availability integration is integrated from the HR/MES systems to BTP," MG clarifies. "But for the efficiency of the equipment and machinery, the data is made available via IoT data streams. These IoT data streams periodically update BTP with their current run rate/ efficiency."
This architectural distinction, catering to the different natures of human-reported labor data and machine-generated sensor data, demonstrates a nuanced understanding of data sources and optimal integration patterns. The integration of IoT data for equipment efficiency followed a multi-step process. Real-time sensor data from machinery is initially captured by an IoT Edge platform.
Such platforms, like SAP Edge Services or comparable solutions, are crucial for pre-processing data locally. From the edge, data is transmitted, often via an IoT Gateway (with modern implementations using SAP BTP Integration Suite to manage this connectivity), to a central service cockpit. Subsequently, SAP Cloud Platform Internet of Things services are employed to handle the incoming data streams and device messages efficiently. This data, specifically containing the efficiency metrics of equipment and machines, is then processed using IoT Message Processing services and stored in a SQL database within the BTP environment.
A key aspect of this integration is the transformation of raw sensor data into actionable intelligence suitable for planning systems. MG explains the logic: "The average efficiency of the previous 7 days is considered to calculate the mean efficiency of the equipment and machines. This calculated efficiency is updated in the PPDS resource master data for the next 4 weeks as the efficiency of the PPDS resource." This intermediate processing step—calculating a 7-day rolling average—is vital for smoothing out short-term fluctuations and converting granular sensor data into a meaningful metric that PP/DS can directly utilize.
The proactive updating of PP/DS resource master data with these projected efficiencies allows the planning system to be forward-looking rather than merely reactive. "The BTP integration Suite is leveraged for this integration, and standard APIs (function modules) in PPDS were called to update the resource master data," MG adds, highlighting the use of standard SAP tools for robust connectivity.
This entire process ensures that future manufacturing orders are scheduled against more realistic capacity expectations, directly influencing the accuracy of delivery promises and optimizing resource utilization, key components of the demonstrated ROI. The real-time data collected from various sources is crucial for effective digital twin manufacturing, forming the backbone of any effective digital twin.
Navigating Complexity: Overcoming Technical Hurdles in SAP S/4HANA PP/DS and BTP Customization
The implementation of this advanced digital twin solution, despite leveraging powerful platforms like SAP S/4HANA PP/DS and BTP, was not without its technical challenges. These hurdles underscore that such projects often require deep technical expertise and innovative problem-solving. Two primary challenges emerged during the customization process.
The first significant hurdle involved the integration of diverse IoT sensors. MG explains, "As there were a variety of sensors in the machines and chemical reactors, the standard normalized set of communication frames was not sufficient." This issue of handling a wide range of industrial IoT sensor data is common, especially in industrial environments with legacy equipment.
To overcome this, "IoT edge platform SDK was leveraged to develop custom adapters to create custom networking adapters. These custom adapters, which were created by reusing the existing logical blocks, enabled seamless integration with the defined IoT services for messaging." The use of an SAP IoT Edge Platform SDK provided the necessary flexibility, but it also demanded specialized development skills to create these custom adapters, highlighting that "out-of-the-box" compatibility is not always a given with the vast array of industrial sensors.
The second major challenge arose within the PP/DS system itself. "Within PPDS, when these resource efficiencies are being updated and as the system is a global system with 24X7 operations, there were locking conflicts, which failed in some manual and automatic PPDS manufacturing order scheduling executions," MG recounts. Such locking conflicts are a known issue in high-transaction, multi-user database systems, particularly those with in-memory components like SAP's liveCache.
The team's solution was decisive: "An enhancement was implemented in PPDS to activate hard merge, which merges the order scheduling data with brute force to merge to the PPDS liveCache, which holds the scheduling data." This "hard merge" functionality, likely a specific mechanism within HANA or liveCache, forcefully applies the updates.
While effective in resolving the immediate locking issues and ensuring the timely update of crucial efficiency data in a 24/7 operational context, such a "brute force" approach implies a carefully considered trade-off, balancing the need for system availability and data freshness against the potential risks of overriding standard concurrency controls if not implemented with a thorough understanding of the underlying database architecture and its consistency mechanisms. These experiences demonstrate that realizing the full potential of digital twins often involves navigating and resolving intricate technical issues at multiple layers of the solution stack.
Quantifying Success: Metrics for Planning Cycle Time Reduction and Resource Utilization Uplift
The success of the digital twin initiative was rigorously measured against key performance indicators crucial to manufacturing efficiency: planning cycle times and resource utilization. The project delivered a remarkable 25% reduction in planning cycle times and a 15% uplift in resource utilization, metrics that directly translate to improved customer service, reduced operational costs, and increased throughput.
The primary driver for the 25% reduction in planning cycle time was the enhanced accuracy of order promise dates. MG elaborates, "The main business challenge that warranted this innovation was that the planning cycle times were very high, as the order promise dates were never accurate. As the efficiency of the shopfloor is not accurately maintained in the master data and stays static, customer orders were promised with unrealistic delivery dates." This inaccuracy led to frequent sales order rescheduling during backorder processing, which in turn inflated the overall planning cycle.
"With this innovative solution, as the real-time efficiency of the shopfloor is used to project the near future efficiency of manufacturing, the sales order promising dates were accurate, resulting in a 25% reduction in the overall planning cycle time." This improvement not only streamlines the planning department's workload, reducing firefighting, but also significantly enhances customer satisfaction by providing reliable delivery commitments.
The 15% increase in resource utilization stemmed from addressing inefficiencies in scheduling across multiple work centers with finite capacities. In traditional scenarios, static efficiency assumptions in PP/DS master data could lead to misalignments if actual shop floor efficiencies fluctuated.
MG explains, "If one of the workcenter capacity's efficiency increases or decreases in the shopfloor, this will result in correct scheduling of the preceding or succeeding manufacturing steps. So by accurately integrating the real-time efficiency of all work centers involved in the manufacturing process, that resulted in an uplift of 15% in the overall resource utilization."
This dynamic adjustment ensures that operations are scheduled more tightly and realistically, minimizing idle time and maximizing the output from existing assets. Such an uplift can have cascading benefits, including improved cost absorption and potentially deferring or reducing the need for capital expenditure on new equipment.
These quantified achievements align well with industry reports on digital twin benefits, where improvements in promise fulfillment and cost reductions are commonly cited. The fundamental shift from static to dynamic, near real-time master data for efficiency was the cornerstone of these impressive results, illustrating a paradigm change in how planning systems can be intelligently fed with current operational realities.
Prototyping the Future: A "What-if" Scenario in the Digital Twin
A core strength of digital twin technology lies in its ability to conduct "what-if" scenario analysis, allowing businesses to test different operational strategies and predict outcomes in a risk-free virtual environment. MG provided two illustrative scenarios prototyped within their digital twin, showcasing its practical value in informing shop-floor decision-making. These scenarios are not mere theoretical exercises but are directly linked to real-world operational variables that have significant financial and service-level consequences.
The first scenario addressed labor availability fluctuations. MG described a common situation: "For example, during summer break, the total available shop floor labor capacity dips by 30% (pre-approved vacation plan) on a specific production line."
He contrasted the outcomes: "Without considering this, the standard solution would still assume 100% capacity is still available in June/July and scheduled manufacturing orders. When June/July approaches, the actual production quantity is 30% less. This leads to changes in sales order delivery promise dates, attracting penalties and customer dissatisfaction. With this innovative solution, as the labor availability is accurately considered for scheduling manufacturing orders, the first promised sales orders delivery dates can be honored." This capability is crucial for building resilience against labor availability challenges.
The second scenario focused on a chemical processing line, specifically the impact of catalyst bed depletion on reactor efficiency. "In a chemical processing line, the reactor has a catalyst bed, which depletes with time and the type of chemical processing (temperature/pressure conditions)," MG explained. In the standard approach, reactor efficiency is a static value in the PP/DS master data.
"To manufacture 1 ton of a chemical blend takes 16 hours in standard operating conditions. But when the catalyst bed is depleted by 50%, for the same 1 Ton of chemical blend would take 32 hours." The digital twin transforms this: "With this innovation, we receive the sensor data from the reactor to the operating conditions (temperature/ pressure) and estimate the efficiency of the catalyst bed. This calculated/projected efficiency is updated in the PPDS master data to accurately calculate the lead time of the manufacturing orders."
The tangible benefit is significant: "This increases the utilization of the packaging lines, as the blend lines are accurately scheduled, avoiding unproductive stages of the packaging lines (as the packaging lines can be leveraged to package other products while waiting for the reactor process to complete)." This example demonstrates optimizing complex, dynamic processes through simulation.
In both scenarios, the digital twin acts as an intelligent bridge, translating low-level operational data (vacation plans, sensor readings) into high-level planning parameters (available capacity, resource efficiency) that PP/DS can directly consume. MG further suggested an advanced application: "This can be further enhanced by leveraging PPDS simulation (inactive) planning versions to further project the efficiency based on the orders scheduled in the future to estimate the life of the catalyst bed."
This illustrates a sophisticated, multi-layered simulation capability—using the BTP digital twin for current state assessment and PP/DS simulation for future projections based on planned orders, moving towards true predictive asset lifecycle management.
Best Practices From the Field: Embedding BTP Extensions into Standard Manufacturing Workflows
Drawing from his extensive CoE leadership experience and the insights compiled in his SAP Press book, MG outlined several best practices for embedding SAP BTP extensions into standard manufacturing workflows, particularly for industries leveraging SAP S/4HANA PP/DS. He noted, "Manufacturing industries such as food & beverages, consumer products, chemicals, heavy equipment/machinery, aerospace & defense, and automotive require PPDS capabilities to manage their manufacturing planning and scheduling operations." These sectors often encounter complex scheduling scenarios where these capabilities are foundational.
However, MG acknowledged that "Based on the specific requirement of these industries, there would be gaps in the standard PPDS solution, which can be mitigated by leveraging BTP side-by-side extensions." This approach aligns with the broader strategy of maintaining a clean core in S/4HANA while enabling innovation through BTP.
These "gaps" are often not deficiencies in core planning algorithms but rather needs for specific business process orchestrations, industry-unique compliance steps, or integrations with specialized legacy systems.
BTP is ideally suited for these "last-mile" enhancements. For instance, "in aerospace & defense, there can be approvals needed to confirm the changed production schedule. In such cases, BTP build process automation or event mesh can create events for a custom workflow in BTP before releasing the manufacturing orders with the changed schedule." Services like SAP build process automation allow for the creation of automated approval workflows, while SAP Event Mesh can facilitate asynchronous communication to trigger these workflows or notify other systems of schedule changes.
The adoption of BTP for such extensions offers significant advantages in terms of agility and speed of innovation. BTP's low-code/no-code capabilities and pre-built content can empower business users or functional consultants to design and implement necessary workflows, reducing dependence on scarce specialized developer resources and accelerating the rollout of improvements.
MG emphasized the synergy: "In my SAP Press book, I covered about the strategy settings and tools which can be used to accurately schedule manufacturing orders, with the power of PPDS and the innovation capabilities possible within BTP, there are a lot of opportunities to improve the efficiency and accuracy of manufacturing shop floor operations." This underscores that successful BTP extensions are built upon a solid foundation of correctly configured and understood core PP/DS functionalities. BTP complements, rather than replaces, the need for deep expertise in the underlying SAP planning system.
The Evolving Landscape: The Future of Digital Twins in SAP-Driven Supply Chain Planning and Guidance for Adopters
Looking ahead, MG envisions an expanding role for digital twins in SAP-driven supply chain planning, extending far beyond the initial scope of shop floor capacity management. He asserts, "BTP and IoT digital twins are a powerful tool set. Apart from the above study for accurate shopfloor capacity management, the digital twins can be leveraged to track and update PPDS to quality holds, projection of scraps to accurately define safety stock inventory levels, monitoring real-time manufacturing floor space usage to project future space requirements, etc."
These future use cases point towards the development of a more holistic, end-to-end digital representation of the manufacturing environment, integrating aspects of quality management, inventory optimization, and intralogistics.
This evolution aligns with industry trends where digital twins are increasingly incorporating AI, machine learning, and potentially blockchain for enhanced predictive accuracy and supply chain transparency. SAP's strategy strongly supports this direction, with SAP BTP providing the platform for integrating these intelligent technologies. The combination of intelligent technologies with robust planning systems like PP/DS is key.
MG firmly believes that "BTP IoT digital twins combined with the power of PPDS would help manufacturing industries differentiate themselves within their eco systems." This differentiation arises not just from internal efficiencies but from the ability to offer superior responsiveness, reliability, and innovation in serving customers and collaborating with suppliers. For organizations embarking on their digital twin journey, MG's experience and broader industry advice suggest a pragmatic, phased approach.
It is indeed a journey, not a one-off project. Key steps include clearly defining objectives and identifying specific pain points to address, mapping existing processes, assets, and data sources, and fostering cross-functional collaboration from the outset.
Starting with a manageable scope, proving value, and then scaling the solution, while ensuring robust data governance and quality, and choosing the right technology stack that allows for integration of IoT data and advanced analytics are also crucial. By following such guidance, organizations can navigate the complexities of digital twin implementation, manage risks effectively, and iteratively build solutions that deliver tangible business value, ultimately leading to more resilient and competitive supply chain operations.
The successful implementation led by MG, which integrated a sophisticated digital twin with SAP S/4HANA PP/DS through SAP BTP extensions, stands as a compelling testament to the transformative potential of these technologies in modern manufacturing. Achieving a 25% reduction in planning cycle times and a 15% boost in resource utilization within six months offers North American manufacturers a clear, quantifiable example of ROI.
This initiative, born from the practical necessity of gaining real-time visibility into labor and equipment efficiency, effectively demonstrates how the intelligent fusion of IoT data and advanced analytics within core planning processes can overcome long-standing operational challenges.
The best practices and innovative problem-solving approaches shared by MG, enriched by his leadership at SAP's CoE and his authoritative publications, not only highlight a path to success but also underscore the strategic alignment of such endeavors with SAP's evolving technology roadmap. This case serves as a practical blueprint, demystifying digital twin implementation in an SAP environment and showcasing how to translate complex technological capabilities into concrete business benefits.
As supply chains worldwide continue to navigate an environment of increasing complexity and an unyielding demand for greater resilience and agility, the intelligent and strategic application of digital twin technology, as exemplified in this project, offers a clear trajectory. It enables manufacturing organizations to not only address current pressures but also to proactively sculpt a future characterized by enhanced efficiency, adaptability, and competitive strength.
The journey towards a fully digitized, interconnected, and self-optimizing supply network is an ongoing evolutionary process, and pioneering efforts such as these provide critical insights and inspiration, illuminating the path for broader industry transformation and a fundamental shift towards dynamic, data-driven operational paradigms.
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