Tariffs, Inventory Optimization, And Digital Business Planning

Martin Barkman

Part 4 of 6 in the “Rethinking Digital Business Planning” series

The recent tariffs going back and forth between the United States and China, Canada, Mexico, and the European Union are having a real-world impact on supply chain managers and business planners. Some observers suggest that companies in the global economy are stockpiling raw materials and other goods in anticipation of higher prices and stockouts.

The strategy is clear: Secure inventory now to ensure ongoing high levels of service and availability for customers in the future. Companies, however, can only stockpile so much. Eventually, the realities of maxed-out storage capacity and rising inventory carrying costs will make themselves felt.

The balance challenge

While today’s tariff situation may represent an extreme set of circumstances, it highlights in dramatic fashion an enduring challenge for supply chain managers and business planners: Strike the right balance between inventory carrying targets and customer service levels.

On the one hand, companies need to hold inventory to minimize lead times, assure supply in times of potential scarcity, achieve economies of scale, and ensure availability and service levels. On the other hand, they typically want to avoid inventory to reduce the cost of capital, reserve shelf space, or minimize the risks of theft, fire, and perishability.

Digital business planning and MEIO

Today’s global companies do not need a tariff war to teach them the advantages of optimizing inventory levels. Companies have long pursued optimization by various means including multi-echelon inventory optimization (MEIO).

The idea behind MEIO is that inventory optimization at the individual warehouse level returns only limited advantage. With supply networks that include warehouses at the central, regional, and local levels, as well as upstream production operations, optimization at one level only is, at the very least, suboptimal. It may, in fact, be detrimental, causing stockouts and other problems at various levels of the distribution network.

MEIO takes the entire supply network into account, optimizing inventory levels across all distribution and production centers with the help of IT. With a digital business planning approach – one that emphasizes demand-driven planning, improved responsiveness, the holistic participation of supply chain partners, and strategic agility – companies can balance inventory enterprise-wide to meet customer needs while keeping costs down.

Digital business planning works with MEIO to standardize digital supply chain processes and support inventory planning at the tactical and strategic levels. Insights generated by advanced analytics such as machine learning can help planners set target levels for individual warehouses while also gaining insights into the drivers of demand. Ultimately, this helps minimize cost not only for inventory but for production and distribution as well.

Demand-driven MRP

Demand-driven MRP (DDMRP), also supported by digital business planning, is an alternative approach to managing the flow of supply through the supply network. Defined and promoted by the Demand Driven Institute, DDMRP can help organizations drive significant savings in inventory and customer service.

DDMRP adopts a “position, protect, and pull” approach. First, planners set strategic inventory decoupling points throughout the supply network to buffer against volatility and compress lead times. Next, based on historical and forecast information, they determine the appropriate levels of inventory to carry at each decoupling point, setting buffers that protect the supply chain from outages and disruptions. Finally, inventory is pulled from buffer to buffer based on actual demand.

This approach can be distinguished from “classic MRP,” where supply is generally planned to forecast. In a complex digital economy with higher volatility demand and practical limits to forecasting accuracy, DDMRP can provide a more stable material flow, resulting in a more favorable inventory position and/or improved service level for many SKUs.

With massive volumes of data and machine learning algorithms to detect patterns of demand and inventory movements, companies can develop better profiles for buffer stock positions throughout the enterprise. Refined over time for greater precision, these algorithms can help companies achieve their inventory optimization goals even in the context of growing supply chain complexity and demand uncertainty.

Trusted data

From the perspective of digital business planning, underpinning these methods of inventory optimization is a solid governance foundation that allows companies to operate according to trusted data. Based on this data, companies can run self-regulating and adaptive inventory processes. These processes facilitate collaboration across all supply chain players, improve demand responsiveness, and the increased strategic agility needed to keep pace in the face of ever-growing supply chain complexity.

For more information, see the IDC info brief: Digital Business Planning is at the Heart of Supply Chain Transformation.

Martin Barkman

About Martin Barkman

Martin Barkman is Senior Vice President and Global Head of Solution Management for Digital Supply Chain at SAP. In this role, he leads the strategy and go-to-market for the SAP Digital Supply Chain solution portfolio, which encompasses software for R&D, engineering, supply chain planning, manufacturing, logistics, and asset management. Martin joined SAP in 2013 following SAP’s acquisition of SmartOps Corporation, where he served as Executive Vice President of Global Operations and then the Chief Executive Officer. Martin earned his B.S. in Chemical Engineering with Distinction from Cornell University and later an MBA in Finance as a Palmer Scholar from the Wharton School at The University of Pennsylvania.