High-level benchmarks often obscure paths to operations improvements. New data and metrics that tap underlying performance dynamics offer better visibility.

Here is a brief excerpt from an article written by Per- Per-Magnus Karlsson, Shruti Lal, and Daniel Rexhausen for the McKinsey Quarterly, published by McKinsey & Company. Brilliantly, they examine opportunities that did not exist before. To read the complete article, check out other resources, learn more about the firm, obtain subscription information, and register to receive email alerts, please click here.

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Consumers want more variety, convenience, and service, increasing pressure on supply-chain executives to generate savings that fund the added costs of complexity and enhanced customer demands. We find that many companies are taking similar actions to improve productivity, with the result a convergence in supply-chain performance, by commonly used benchmarks. Put simply, companies seem to have hit the wall.

Appearances can be deceiving, however. Our work with global consumer-products players across several hundred supply-chain projects shows that when companies mine deeper veins of operational data to create more precise metrics, new paths to improvements appear. Exhibit 1 shows an 11 percent difference between median and top-quartile companies when commonly used cost benchmarks are used. Some of the difference arises from structural factors, such as costs attributable to product variations and demand volatility, and is therefore outside companies’ control. A closer analysis, however—one that filters out these structural differences and uses more granular data to quantify second-level cost components, such as labor staff or transport charges per pallet—shows a much greater potential for improvement. We found similar opportunities for supply-chain services when broad benchmarks, such as case fill rates (indicating order-fulfillment levels), are broken down with more granular data and key performance indicators, such as forecast accuracy.

[Check out Exhibit 1]

How to capture the potential gains from more precise data and a better analysis of the underlying drivers? Exhibit 2 digs deeper into one application involving service improvements. High levels of demand volatility weigh on how well a consumer-packaged-goods company fulfills customer orders. Poor management of order flow leads either to items being out of stock or to costly “safety stock” investments. When we looked at a set of companies with relatively low volatility levels (less than 40 percent of total demand), we found that there was still a significant gap in service levels between top and bottom quartiles, indicating that some of the performance differences stem from how well a company manages the variation. Two benchmarks drawn from a deeper cut of operations data showed that to be the case: one a measure of the accuracy of demand forecasts and the other a measure of the flexibility of production processes. We found that more accurate forecasts of sales volatility resulting from promotional campaigns (levers under management control) accounted for 70 percent of the service differences. More agile production processes allowing companies to adjust rapidly to volatile SKUs explained the remainder of the performance gap.

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Here is a direct link to the complete article.

Per-Magnus Karlsson is a senior expert in McKinsey’s Stockholm office, Shruti Lal is a senior expert in the Chicago office, and Daniel Rexhausen is a partner in the Stuttgart office.

The authors wish to thank Sebastian Gatzer, Volodymyr Opanasenko, and Frank Sänger for their contributions to this article.