Optimizing Supply Chain Efficiency with Data-Driven Decisions

Challenge

Navigating Complexity in Manufacturing Portfolios with a Unified Optimization Tool

In the ever-evolving consumer products landscape, companies must continuously develop new flavors and product formats to meet shifting consumer preferences. Once a new product is conceptualized, manufacturing plans must be created based on a complex set of variables—including forecasted demand, material availability, production capacity, shelf life, and cost factors. 

The primary challenge was to identify an optimal strategy for production and portfolio management that could align with real-time business priorities, enhance asset utilization, and reduce operating expenses, while also supporting long-term capital planning. 

This effort was complicated by several key hurdles. There was no connected, scalable tool available to effectively manage production portfolios and scheduling decisions. The company also needed the ability to react quickly and accurately to changing market dynamics and global supply chain disruptions. Its expansive manufacturing network, spanning multiple product categories and facility capabilities, added another layer of complexity. Moreover, the existing solutions in use were only capable of handling small product portfolios and lacked the resolution necessary to optimize critical metrics like margin at the item-line level. 

Solution

Building a Scalable Optimization Platform on SAP BTP

This advanced tool allows for experimentation with complex production portfolios, enabling rapid scenario simulation and analysis. The solution supports trade-off evaluation between supply, demand, and commercial planning teams—enabling faster, data-driven decision-making across the enterprise.

Key features of the solution include: : 

• A scalable mathematical optimization framework that evaluates the financial and operational impact of various production decisions across multiple network

• A centralized data module that standardizes manufacturing attributes, integrates ML-driven data validation, and enables stakeholder oversight 

• A robust UI application built using SAP BTP’s Kyma runtime, with interfaces developed in SAP Fiori, SAP Business Application Studio, and SAP Analytics Cloud 

• Integration of operations research models, machine learning for data quality enhancement, and automation for continuous insights generation 

The solution empowers teams to build future-focused models around innovation SKUs and emerging categories, including the company’s expansion into salty snacks, by providing a quantifiable view of production trade-offs. 

Results

Realizing Value Through Informed, Agile Decision-Making

Each manufacturing network presents its own set of complexities—ranging from changeovers and productivity variances to waste management, labour planning, and utilization constraints. The cost of this complexity is dispersed across the value chain and manifests differently in each network. 

With the implementation of the new optimization platform, the business now can map entire manufacturing networks, run multiple what-if scenarios, evaluate production and commercial decisions quantitatively, and deliver optimal solutions in real time. For example, the platform revealed that eliminating a product with $9 million in annual sales would free up capacity for a key innovation item projected to generate $13 million in sales. This decision alone would improve network utilization by 3% and unlock $17 million in additional portfolio revenue. 

The end-to-end solution, built natively on SAP BTP, also enables instant deployment across manufacturing planning teams, standardizes the use of Python for machine learning and optimization modelling, and lays a structured foundation for future data science partnerships and analytics expansion. 

Conclusion

Building a Connected, Resilient Supply Chain of the Future

This initiative has redefined how supply chain decisions are made—transforming fragmented, siloed processes into a unified, real-time decision-making framework. By establishing a shared data structure and integrating supply, demand, and commercial planners into a single optimization environment, the organization can now collaboratively define problems, model solutions, and reach consensus faster than ever before. 

More than just a technology upgrade, this solution represents a cultural shift toward a more agile, data-driven approach to managing complexity. It supports smarter capital investment decisions, optimizes manufacturing capacity, and paves the way for continuous innovation—driving sustained business growth in a dynamic and competitive market.