Supply chain leaders constantly seek opportunities to improve supply chain performance—in part by leveraging supply chain benchmarking data to evaluate improvements that other leading organisations have achieved.

A supply chain benchmarking project is successful when it collects the right data, analyses them logically and leverages them as one of many inputs to drive investments in supply chain improvement initiatives.

The first step in supply chain benchmarking is the collection of benchmarking data and the comparison with competitors/competition.

Once the comparison is established, the next step is to identify opportunities for improving supply chain performance.

Once a supply chain benchmarking project is complete, it is important to analyse the results to understand how the interdependencies influence your functional trade-offs. By identifying the probable causes for poor performance—versus looking at supply chain metrics in isolation—you can gain insights on which key levers improve supply chain effectiveness. These insights are invaluable inputs to determining what actions will deliver the greatest returns.

One set of supply chain metrics that can exhibit a strong relationship is forecast error, finished goods inventory and perfect order fulfilment. If the ability to forecast is poor, it is reasonable to expect that the ability to deliver orders on time and in full (OTIF) to customers will be impacted. Additionally, if forecasting capabilities are weak, suppliers may also struggle to provide on-time performance that could also impact stock availability, leading to lower customer service.

A second supply chain benchmark example involves these supply chain metrics: supplier on-time, supplier quality and inventory levels. Often as a result of poor supplier relationship management, long lead times, insufficient capacity or ineffective demand forecasting, supplier performance is impacted. When supplier performance is uncertain, companies often take the decision to buffer with inventories. Higher raw materials and finished goods inventories are also usually accompanied by high inventory obsolescence (or write-offs), particularly for companies with short product life cycles. In some cases, poor supplier performance may also have a negative impact on plant use.

A third supply chain benchmark example involves these supply chain metrics: forecast error, plant use and inventory. In response to unpredictable surges in demand, supply chains might make short-term changes in the production plan to accommodate urgent orders. This typically results in lower plant use and often higher inventory levels to serve as a buffer.

The Gartner Hierarchy of Supply Chain Metrics identifies the 17 critical metrics to focus on across the end-to-end supply chain: forecast accuracy; perfect order; SCM cost; cash to cash; accounts payable; accounts receivable; inventory total; supplier quality; supplier on-time; raw materials inventory; purchasing costs; direct material costs; cost detail; production schedule variable; plant use; WIP and finished goods inventory; order cycle time and perfect order detail.

A supply chain scorecard is a concise set of metrics derived from the key drivers of supply chain operation, which makes them easy to understand and actionable. The best scorecards have around 12 supply chain metrics. Cross-functional partners value the supply chain metrics, and as metrics become outdated, they are filtered out of the supply chain scorecard. Supply chain metrics are retained for the sake of continuity, not for immediate business concerns.