As supply chain risks increase and businesses become more complex and globalized, inventory management challenges are growing exponentially. Inventory Optimization emerged as a key focus area over the next two years for SAPinsiders in our recent research,
Supply Chain Planning in The Cloud. As organizations still struggle with fluctuating demand patterns that developed during the pandemic, defining inventory policies is also becoming more and more challenging. Hence, it is not surprising that inventory management and inventory optimization remain key focus areas. To understand the perspectives of SAPinsiders, we surveyed them on
inventory management and optimization topics from September 2022 to November 2022. One of the key challenges that SAPinsiders highlighted in their responses, was the lack of a robust inventory strategy. While defining an inventory strategy is a comprehensive topic in itself, in this article, we explore a few recommended actions that should be part of building foundation for inventory management strategy for SAPinsiders.
Based on the survey responses, organizations should make the following plans around their inventory management and optimization strategies to build a robust foundation for inventory management:
- Define your inventory strategy with analytics. This is key to building a solid foundation for inventory management. As shown in Figure 1, lack of proper inventory strategy is a key challenge that SAPinsiders are facing. There are several components that define inventory strategy, ranging from inventory valuation and inventory classification to inventory policy design. Analytics can help bring science into defining these elements of your inventory strategy, ensuring that the final strategy that comes together is data-driven and aligns with your corporate strategy as well. An example is inventory classification. Using machine learning algorithms like k-means clustering, you can perform a more robust inventory classification of your SKUs, resulting in not only increased service level but significant cost savings.
- Explore cloud to address data scalability and fragmentation challenges. The power of having access to good, near-real-time data visibility is immense. And it is a critical component of the foundation for inventory management. Lack of proper inventory visibility was among the top pain points with the current portfolio, identified by SAPinsiders. This is also ther eason inventory accuracy was identified as the top metric. The kind of timely and seamless inventory visibility that SAPinsiders desire not only requires near-real time data collection, harmonization and centralization but also scalability and flexibility. And the best way to attain these is by leveraging the cloud. Whether it is cloud-based visibility and planning platforms, or the end-to-end data and analytics infrastructure based in the cloud, the cloud must be a critical component in your inventory visibility modernization journey and hence an intergral part of your inventory strategy. This is also the component of your strategy where you can leverage edge computing and TinyML strategically to add enhanced capabilities to your tracking and tracking data-based planning infrastructure. An ideal configuration is that your fundamental analytics are distributed at the edge, with a centralized algorithm interfacing with the TinyML algorithms on edge devices. This architecture allows you to scale to advanced analytics approaches, like deep learning, at a later stage more easily.
- Integrate visibility with analytics. If you executed your visibility data strategy correctly, there are some advanced analytics methodologies that you can leverage on that data. Examples are reinforcement learning algorithms for inventory management and deep learning for logistics planning, that can also impact and address your associated inventory management challenges (like reducing transportation lead time uncertainty). But if you are not firmly established in the game, the best strategy is to start with elementary analytics methodologies, like descriptive and diagnostic analytics. This approach helps you build a gradual culture of analytics, redefines processes based on learning, and helps establish a foundation for advanced approaches, like prescriptive and predictive analytics and eventually AI- and ML-based algorithms.
- Collaborate to extend beyond your technical infrastructure. While building your own capabilities is critical, a supply chain extends way beyond your own organization. This is where integrating your visibility capabilities with those of your supply chain partners becomes critical. Fortunately, technology and computing power today allow us to move beyond vanilla EDI integrations. Visibility platforms of two different organizations, if integrated prudently, can “talk” effectively with each other.
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