Manufacturers are facing a demanding combination of rising costs, supply uncertainty, and pressure to improve output. In the National Association of Manufacturers’ third-quarter 2026 outlook survey, respondents expected raw material and other input costs to increase 5.0% over the following twelve months. Freight rates were a challenge for 77.3% of respondents, while 74.1% cited fuel costs.
These pressures make production decisions more consequential. Materials purchased too early can tie up working capital. A late response to a shortage can disrupt a schedule. Unnecessary processing and handling can add expense to an already constrained operation.
For Mahendrakumar Kalal, a Texas-based SAP manufacturing specialist, researcher, and author, these are practical problems for enterprise technology to address. His work combines more than thirteen years of SAP experience with research into predictive production planning, digital twins, and secure integration. Its focus is on improving the decisions manufacturers can control within their operations, even when external costs and supply conditions remain uncertain.
Closing the Gap Between Digital Ambition and Execution
Deloitte’s 2025 Smart Manufacturing and Operations Survey, covering 600 executives, found that 92% of respondents believed smart manufacturing would be the main driver of competitiveness over the next three years. Yet the same survey identified implementation obstacles involving resources, change management, skills, and technology readiness.
The workforce findings are particularly relevant to production planning. Forty-six percent of respondents reported moderate to significant challenges filling planning and scheduling roles, and 48% reported similar difficulties filling production and operations management positions.
Kalal’s research addresses part of this gap by examining how analytical tools can support planning tasks. Predictive models can help identify patterns and flag conditions requiring attention. Their practical role is to give planners a stronger basis for judgment, with recommendations connected to material availability, production requirements, and enterprise records.
Making Production Demand Meaningful at the Cell Level
Since joining O.C. Tanner in January 2024, Kalal has contributed to SAP manufacturing initiatives that connect enterprise planning with production execution. A central example is custom cell-based material requirements planning.
In a factory organized around manufacturing cells, material and plant information must be linked to the production unit responsible for the work. Without that context, planners may need additional manual coordination to translate system requirements into executable assignments.
Kalal’s approach incorporates cell information into demand consolidation and planned-order processing, organizing requirements by material, plant, and production cell. Preserving that information as demand progresses into production orders helps maintain continuity between the enterprise plan and the manufacturing floor.
This work targets a specific source of planning friction. Clearer assignments can support coordination and visibility; the extent of any reduction in inventory, delays, or planning effort depends on the implementation and must be measured within the operation.
Using Digital Twins to Examine Planning Alternatives
Kalal’s intellectual-property work includes the German-registered utility model A Digital Twin System for Cell-Based Autonomous Production Consolidation in ERP-Controlled Discrete Manufacturing. A second German utility model concerns digital twin-triggered reconfiguration of material requirements planning.
These concepts address a challenge that becomes more important under supply uncertainty: understanding the consequences of a change before applying it to a production plan. A digital representation of manufacturing conditions can provide a basis for evaluating alternative allocations and planning responses.
The direction of Kalal’s research is toward connecting that representation with ERP requirements and decision support. For example, a proposed application could evaluate how a material delay affects cell assignments before recommending a planning adjustment. Such capabilities require validation against actual constraints and controlled authorization before changes reach live operations.
Kalal reports independent, non-exclusive adoption of elements of his cell-based planning approach by multiple organizations. The scope and results of individual applications are documented in the respective organizational adoption and implementation records.
Applying Predictive Research to Inventory and Throughput
Rising input costs increase the importance of avoiding unnecessary inventory while maintaining production continuity. Kalal’s IEEE conference paper, Lean-Driven SAP Production Planning Optimization Using Machine Learning for Inventory and Throughput Efficiency, examines a SAP-inspired analytical framework using manufacturing-process data.
The study combines lean-oriented measures, machine learning, and methods for interpreting model predictions. Its research contribution concerns analytical modeling; it does not establish that every manufacturer implementing the framework will achieve the same operational outcome.
His paper, Predictive Production Planning in Advanced Manufacturing Using SAP PP and Analytics, examines predictive methods for forecasting, resource allocation, and production decisions. Another conference publication, Integrating SAP PP and SAP Digital Manufacturing for Lean Production Optimization, addresses the relationship between planning and digital execution.
Together, these studies provide a research basis for developing planning support that can anticipate requirements, identify performance patterns, and connect recommendations with enterprise processes. Useful evaluation measures include forecast error, schedule adherence, inventory levels, and the time planners spend resolving exceptions.
Removing Friction from Existing Workflows
Kalal’s operational projects also address improvements that can be made within established systems. At O.C. Tanner, a delivery-processing initiative changed when shipping-related validation occurred to address a dependency affecting delivery creation.
Another initiative focused on suppressing unnecessary packing-list printing for designated manufacturing cells while retaining applicable exceptions. The work coordinated rules across LIDS, SAP Process Orchestration, and SAP.
These projects address internal processing effort and document waste. They cannot control freight prices or fuel costs, but they can target avoidable work within fulfilment. That distinction matters when manufacturers are evaluating improvements under cost pressure: benefits should be linked to the process actually changed.
Building Security into More Connected Manufacturing
More responsive planning depends on reliable information moving between applications. An incorrect transaction or an unauthorized interface action can undermine the quality of a recommendation and disrupt execution.
Kalal’s journal article, Secure SAP Manufacturing Integration Threat Modeling and Mitigation for RFC, IDoc, and API Communication, examines risks affecting core SAP communication mechanisms and discusses layered protective measures.
His broader research direction connects planning intelligence with dependable integration. For applications that recommend or execute changes, relevant controls include restricted access, validated inputs, traceable decisions, and human review appropriate to the consequences of the action.
Developing Knowledge for Practical Adoption
Kalal’s journal publications also include A Data-Driven Framework for Lean Production Optimization in SAP-Integrated Manufacturing Systems and Machine Learning-Enabled Production Planning for Intelligent SAP Manufacturing Systems. His book, INTELLIGENT SAP MANUFACTURING: AI, Digital Twins, and Autonomous Production Planning, brings these interests into a broader discussion of enterprise manufacturing.
His background combines a mechanical engineering degree from Gujarat University, a master’s degree in industrial management from Texas A&M University–Kingsville, and an MBA from Campbellsville University. His SAP assignments have included support for Boston Scientific, AbbVie, Merck, and other organizations.
The next challenge for the approaches examined in his research is operational validation: establishing whether recommendations improve planning performance, remain understandable to users, and operate reliably within existing systems. His implemented SAP projects provide practical experience for examining those questions.
For manufacturers facing higher costs and constrained planning resources, the relevance of this work lies in specific decisions: how demand is assigned, how exceptions are evaluated, and how approved changes are executed. Those are the points where Kalal’s research and manufacturing experience meet.
