Five Strategies for Managing Supply Chain Uncertainty

 

Faced with continuing inflation, manufacturers are seeking strategies to contain per-unit costs while maintaining margins. But geopolitical rivalries and an emerging wave of tariffs are introducing new levels of supply chain uncertainty. Compounding the challenge are information gaps across supply chains that can cost manufacturers lost time, productivity, margin erosion, price reductions, and missed shipment dates.

If one thing is clear in this era of uncertainty, it is that traditional demand-driven supply chain management methods can no longer keep pace with the complexities of supply chain uncertainty. Instead, manufacturers need to adopt adaptive supply chain practices while enabling greater agility, speed and scale in response to unpredictable supply chain constraints.

At the heart of an adaptive strategy is the use of real-time data for a true 360-degree view across supplier networks. The more accurate and reliable the data, the more it can serve as a shock absorber to rapid shifts in demand, availability and pricing. Following are five data-driven strategies that manufacturers can use as a framework for quickly identifying and acting on supply chain uncertainty before it costs valuable time, reduces margin, and delays delivery dates.

• Create an index of supply chain metrics. If not already in place, the first step is to create an index of supply chain metrics that help quantify and track interdependencies across the supply chain. This enables manufacturers to make more informed end-to-end supply chain trade-offs. It is important to select only those metrics that communicate gaps and how fast they are growing. While metrics can be established manually, the most effective indexes are those relying on track-and-traceability applications that can pull information from the same database shared by the enterprise resource planning (ERP), manufacturing execution system (MES), supply chain management (SCM), and pricing software, since they provide a comprehensive and cohesive view of the business and enhance the ability to respond to issues tied to supply chain uncertainty.
• Embed metrics in supply chain processes. Once the metrics have been defined, the next step is to embed them into the organization’s supply chain processes to gain a more multidimensional level of visibility. Manufacturers can leverage their ERP system and a combination of track-and-trace and supply chain integration to embed metrics multiple layers deep into a supply chain and gain greater visibility and control. By embedding metrics in processes, organizations gain the insights into how they can avoid or mitigate potential bottlenecks, including allocations and stock-outs, and gain early warnings of issues within the supply chain that could lead to potential recalls.
• Apply advanced supply chain concepts to understand why gaps occur. Quickly turning data into real-time insights, intelligence, and collaboration helps close costly supply chain gaps. However, the best possible supply chain technique will vary for a given business strategy. Three advanced data-driven supply chain approaches that manufacturers will want to evaluate for their business, if they have not done so already, are vendor managed inventory (VMI), collaborative planning forecasting and replenishment (CPFR), and sales and operations planning (S&OP).
• Measure the gap between supply chain performance and demand-driven forecasts. Often manufacturers will find that their forecasting processes need to be modified to consider how different supply chains are operating today. Demand forecast accuracy (DFA) is one of the core metrics frequently used to measure the gap between actual supply chain performance and demand-driven forecasts. With DFA, organizations can more accurately identify and explain what is needed to absorb supply chain shocks better.
• Assess supply chain decisions using uncertainty metrics instead of accuracy metrics. In the effort to manage inventory, it is critical to go beyond pricing and understand which suppliers will be most likely to deliver required components and materials. Uncertainty metrics assess the risk of a given supplier failing to deliver on schedule, having an incomplete shipment, or delaying or canceling materials deliveries. Many manufacturers use historical data to track past performance, often relying on moving average and projection-based models. Metrics to apply against this data should measure the probability of execution, such as available-to-promise (ATP) and capable-to-promise, enable manufacturers to see which suppliers are meeting commitments regularly.

Absorbing supply chain shocks needs to start with a manufacturer anticipating how the unique risks to their products can be identified and acted on before those risks grow into roadblocks. The five strategies outlined here are being applied by manufacturers today to gain insights via real-time data to mitigate supply chain uncertainty to deliver customers’ orders on time—while ensuring quality and maintaining margins.