Supply Chain Metrics that Measure Up

Building and leveraging a metrics framework to drive supply chain performance


By Tejas Faldu and Srikanth Krishna

With rapidly increasing competition and changing market forces, supply chain performance management (SCPM) is a critical area for consumer packaged goods (CPG) companies to help sustain and gain competitive advantage in the CPG space by enabling an agile, lean and efficient customer-oriented supply chain. A robust SCPM infrastructure is crucial to realizing the benefits of various collaborative initiatives.

However, CPG companies need to address key issues before making significant investments in SCPM initiatives. Management must ensure enterprise-wide awareness and understanding of strategic and financial objectives along with the financial impact of supply chain performance on overall corporate performance. Processes and roles in the supply chain need to be mapped to key metrics to determine performance. There must also be a mechanism in place to periodically review actual supply chain performance and redefine performance measures in the changing business context. Further, an integrated single view of supply chain performance across functions and hierarchies is essential to help senior management quickly determine the causes of supply chain failures.

This article offers a step-by-step guide to defining, building and leveraging an effective "metrics framework" that can turn supply chain performance management into a potent tool.

Developing the Framework

A balanced set of metrics, aligned to various supply chain functional areas — demand planning, customer management, warehouse management, etc. — needs to be identified to address decision-making requirements. These metrics should be mapped to the processes of each supply chain function, the overall business strategy and the roles responsible for executing these processes.

1. Establish the right metrics
When choosing a specific set of metrics, a company must consider the four basic characteristics that make metrics effective: reliability, validity, accessibility and relevance.

  • Reliability refers to the consistency of the metric used to measure a given process. As long as the circumstances governing the process do not change, the metric should return a fairly consistent value.
  • A valid metric is one that measures the concept in a specific business context.
  • Further, for metrics to count, they must be easily accessible, i.e., retrieved with reasonable effort and cost.
  • Finally, metrics need to be relevant so that the functions or people concerned can relate to the information and make intelligent and proactive decisions.

2. Link metrics to overall strategic objectives
To tie metrics to the larger strategic goals, it is important to determine the strategic objectives to be used to evaluate the supply chain (e.g. customer satisfaction, enterprise profitability, etc.). This must be followed by building a related supply chain metrics hierarchy under each of these strategic objectives. The hierarchy starts with high-level metrics — suggestive of the overall health of the supply chain — and moves to mid-level and lower-level metrics that are more tactical or operational in nature.

3. Create a detailed metrics bank
Building a detailed metrics bank entails a multi-step process to create an exhaustive set of related metrics that associates metrics with each supply chain process and maps the metric to the role that is directly accountable and responsible for its measurement and performance. In addition to identifying the class of the metric based on the information and process health insight it conveys, it is important to build high-level interdependencies of metrics based on common knowledge and understanding of basic processes. The process must also identify the various dimensions to enable a comprehensive view of the metric in addition to determining how metrics will need to be computed. Further, the process needs to determine the frequency at which the metrics will be measured — this will also be governed by the granularity of available data and the cost-benefits associated with a certain measurement frequency.

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