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August 10, 2003

The Smartest Link

What can BI do to transform your organization's supply chain from a costly morass into a competitive strength that works in sync with strategic objectives? Here are eight key areas where BI can have the greatest effect

by Sudhi Sinha

Supply chain management (SCM), always a key to evolving industry and commerce, is now a major strategic differentiator. The heat is on SCM implementations across all industries to achieve cost efficiency, zero latency, and greater agility. However, for SCM's strategic evolution to continue, IT managers must tackle the challenges posed by business demand to leverage supply chain data for strategic decision-making.

In a typical supply chain, data and information are spread across multiple application silos, often owned by a range of business collaborators, partners, and other players in the process. Software solutions in the marketplace promise to give you a single window on this colossal universe of data and information. However, experience shows that these products bring with them limitations and proprietary dimensions.

Can BI applications, built on data warehouses, offer a solution? BI systems have shown that they can help SCM with quicker responsiveness to unplanned customer requirements. BI can help companies become more agile so they can adapt their supply chain capacity more dynamically and precisely to fit evolving strategic objectives for optimal cost structure and eliminate waste in processes.

In this article, I'll discuss eight key areas in SCM where BI can be applied to its greatest effect for leveraging integrated data and information.

Product Definition

Corporations generally consider product definition as an activity managed by their engineering and marketing departments. However, the supply chain organization is also an important stakeholder. Less product replication can make a big difference in streamlining the supply chain and coordinating it with inventory management, forecasted demand, and the purchase of materials and assembly items. BI can help companies proactively determine common product denominations for effective materials planning. BI analysis can also guide organizations toward optimal "kitting" strategies.

At one organization, a BI analysis recognized a pattern of material overstocking to support sales of items included in kits and sold individually. The study revealed that poor planning of warehouse space and labor was due to kitting. The company decided to use virtual kitting, where the external interfaces to the company's business systems continued to recognize kit transactions, while internal systems translated the kits into their component parts. The company used a single BI system to broker the translation.

Inventory Management

Inventory management in terms of cost and volume reduction is the essence of SCM. Physical inventory moves through (at least) the vendor's warehouse, the delivery system from the vendor to the company, the receiving company's warehouse, its production lines to finished goods inventory, and, finally, to the distribution channels. Additionally, virtual inventory is often hidden in the company's purchase and sales orders. These inventory points are typically spread over multiple physical locations and disparate IT systems. Such expansive data distribution thwarts accurate inventory visibility.

A data mart can help bring about inventory data integration. Inventory volume is a function of quantity (fact) with respect to time, unit of measure, and location (dimensions). If cost (another fact) is added to the scope, it can be defined with the additional parameters of supply source and unit cost. Both these measures should remain fairly stable over time and serve well as two additional dimensions. Analysts can synthesize any number of new inventory locations into the data mart because all the dimensions — time, location, unit of measure, supplier, and cost — can be defined for the additional locations.

For managing safety inventory, a sophisticated inventory data mart can be instrumental. With data mining, your organization can uncover trends in demand uncertainty, stock fill lead-time, supply lead-time variability, and product availability requirements, and link those trends to safety inventory.

Demand and Forecasting

A primary objective of most ERP implementations is to unify the demand and forecast of materials and provide a single view for material requirements. However, diversity in business processes across operating units of a corporation, as well as the variety of partnering systems in the supply chain, can stymie efforts to reach the goal of a unified materials requirements view. Moreover, popular application solutions generally can't correlate demand for end products to that for raw materials.

Once again, BI (notably, advanced statistical and data mining tools) can come to the rescue by bringing together sales-order and purchase-order data, and by examining it for correlation and trends. You could further integrate order data with comprehensive inventory data to assess actual material requirements. A unified material requirements view at a holistic level would enable a corporation to plan demand and forecasting much more accurately — and thereby dynamically allocate supply based on sudden demands. Effective aggregate planning could go a long way toward maximizing profit by balancing capacity, inventory, and stock outage costs.