How to Measure Supply Chain Performance

The appropriate metrics to manage and measure the success of a company’s operation vary significantly by industry, by individual company, and by the scale of the business. What does not vary, however, is the universal need of all companies to employ a well-structured, hierarchical framework to organize and manage their metrics.

The absence of a cohesive framework to house metrics greatly increases the likelihood that a company’s performance measurement system (PMS) will provide inadequate management support, and that resources will be wasted developing duplicative, unaligned and even conflicting metrics.

There are a number of well-known models and frameworks for operations, logistics and supply chain management. Two of the most prominent are the SCOR model and the Balanced Scorecard, and the interested reader is referred to these.

Figure 1 depicts an integrated hierarchical supply chain performance measurement system. The framework contains three levels (the strategic, tactical and operational), and within each level, it has both external and internal measures. In this PMS framework, it is the scale of an operation or activity that a particular metric monitors which determines its place in the hierarchy.

Figure 2 provides additional insight on how this hierarchical PMS framework works, displaying sample external and internal metrics for a distribution organization at each level of the hierarchy. The external metrics measure outputs and/or services that flow across the supply chain and evaluate some aspect of serving the customer. The internal metrics have an “inward” focus; and as shown in Figure 2, they evaluate how efficiently the overall distribution organization and each of its sub-functions operates.

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How can Lean Six Sigma help Machine Learning?

Note that this article was submitted and accepted by KDnuggest, the most popular blog site about machine learning and knowledge discovery.

I have been using Lean Six Sigma (LSS) to improve business processes for the past 10+ year and am very satisfied with its benefits. Recently, I’ve been working with a consulting firm and a software vendor to implement a machine learning (ML) model to predict remaining useful life (RUL) of service parts. The result which I feel most frustrated is the low accuracy of the resulting model. As shown below, if people measure the deviation as the absolute difference between the actual part life and the predicted one, the resulting model has 127, 60, and 36 days of average deviation for the selected 3 parts. I could not understand why the deviations are so large with machine learning.

After working with the consultants and data scientists, it appears that they can improve the deviation only by 10%. This puzzles me a lot. I thought machine learning is a great new tool to make forecast simple and quick, but I did not expect it could have such large deviation. To me, such deviation, even after the 10% improvement, still renders the forecast useless to the business owners.

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External Insights Critical to Effective Supply Chain Performance

Traditional forecasting models that leverage historical data to predict future performance are the tools used by most supply chain executives to plan critical functions, yet these predictions are frequently inaccurate. In fact, research from KPMG International, in cooperation with the Economist Intelligence Unit, shows that most quarterly forecasts are off by 13 percent—meaning that supply chain managers are basing their decisions for ordering materials and scheduling distribution on erroneous projections. The result can mean surpluses or shortages, potentially costing companies millions either way.

There is a better way to anticipate supply chain demands—one that can vastly improve projections, and decrease the discrepancies between forecasting and reality, therefore helping supply chain executives perform their jobs more effectively. Few companies take into account macroeconomic factors, global manufacturing activity, consumer behavior, online traffic, weather data, etc. when making business projections. Yet companies that do identify leading performance indicators using such external data earn more than a 5 percent higher return on equity than those that use only internal metrics. Leveraging external factors, in addition to internal performance measures, is proven to result in more accurate, effective forecasts. Not to mention that improving forecast accuracy can represent huge bottom-line benefits. For a billion dollar manufacturing company, for example, improving forecast accuracy and overall return on equity even 1 percent can equal a $3 million increase in net income.

Forecasting accuracy, improved through external factors, benefits multiple business functions—from financial operations (shareholder value) to human resources (adequate staffing) to marketing (product innovation)—but is especially impactful on the supply chain management function.

Improves Inventory Management

Improved forecast accuracy using external drivers equates to reduced inventory management costs, ultimately improving bottom-line profit. By accounting for external factors, companies can see a 10 to 15 percent improvement in forecast accuracy, significantly decreasing the cost of excess inventory. By ordering raw materials based on correct projections, supply chain managers no longer have to worry about discounts necessary to move excess inventory or the cost of warehousing excess materials because they are ordering accurately from the start.

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