The supply chain is a vast and expansive process. It has many links, the number varying from one industry to another, from one company to another, from one combination of partners to another. The expanse and unpredictable nature of supply chains presents a challenge to anyone trying to build an analytic application for supply chain intelligence (SCI). One approach to SCI focuses on suppliers and issues with their supplies; another focuses on procurement issues like purchase prices. These provide good visibility into the supply side of the chain, but tend to ignore the demand side.

In manufacturing companies, most supply chain issues relate to the production of a product. Thus, manufacturers need to analyze their supply chain with a focus on life cycle stages of a product. On the supply side of the chain, raw materials, components and subassemblies are analyzed in the context of the products they complete, not as isolated supplies. On the demand side, many quality and compatibility issues are unknown until a product enters life cycle stages involving distribution and use by end consumers.

Data warehousing for many manufacturers means collecting data from dozens of IT sources on both supply and demand sides of a chain, as well as from IT systems that control the production process. The information must be organized to represent specific life cycle stages of specific product. Hence, a goal of SCI is to represent a complete product life cycle – from suppliers' suppliers to customers' customers – so that events at different life cycle stages can be related to discover their effects on the three attributes that most manufacturers obsess over: product quality, cost and production yield.

Product life cycle analysis is the only way to accomplish the following analytic tasks with any accuracy:

  • Identification of a component-level defect with the ability to trace back to its root cause such as a specific configuration of commodity components.
  • A closed loop that allows field quality and reliability to provide input into design decisions, thus enabling a company to address quality issues in the design process and avoid warranty-related costs in later product life cycle stages.
  • Monitoring of life cycle events so the SCI application can notify managers of out-of-parameter events and their possible ramifications.
  • Total understanding of product and component "flows" through supply, operations, logistics, demand, and customer service processes.

THE HURWITZ TAKE: Many manufacturers have attempted to accomplish these tasks with their enterprise resource planning (ERP) or supply chain management (SCM) systems. But these are transactional systems that lack the appropriate data model for product life cycle analysis. SCI is a natural extension of ERP and SCM – not a replacement – because it extends ERP and SCM by providing an analytic component that can increase manufacturing performance by increasing yields, lowering supply costs and ensuring product quality.

Hurwitz Group notes that many manufacturers are frustrated by plateaus in performance. These companies should consider analytic applications as a way to rise above a plateau. Evaluators should look for analytic applications that provide a comprehensive view of an entire supply chain, but with a focus on product life cycle analysis.

Editor’s Note: For a related discussion, read Philip Russom's article "Increasing Manufacturing Performance Through Supply Chain Intelligence" in DM Review's September, 2000 issue online.

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