Leveraging supply chain analytics to improve performance

Abstract

Today’s systems capture a tremendous amount of supply chain (SC) related data. To take advantage of this capability firms must transform this information into better decisions and improved performance. The ultimate goal is to transform this mass of unstructured data into useful performance indicators and diagnostics that result in improved service, lower costs and lower inventory. Those firms who can successfully transform data into actionable business intelligence will gain a sustainable competitive advantage. The vision to use SC analytics effectively must include people, process and technology. Integration of the three is critical to maximise results. To begin the journey you must first identify what data is needed and how it should be structured. For example, the Supply Chain Operations Reference (SCOR)1 Model provides a comprehensive set of performance indicators at both the business and operational levels. It links top-level metrics like ‘perfect order fulfillment’ with lower-level measures and diagnostics like ‘production schedule adherence’. Kaplan and Norton’s Balanced Scorecard – Translating Strategy into Action2 helps in translating the firm’s vision and strategy into a set of performance measures. The four perspectives of the scorecard (financial, customer, internal and learning) help to balance long-term and short-term goals. Adapting such things to the individual business model is an important step in the analytics process. BASF has used both of these concepts in performance measurement for many years. BASF, a firm with over 400 SAP plant codes and 85 strategic business units, began the effort to further leverage ‘big data’ with two major projects. The first replaced regional data warehouses with one global business warehouse. Many applications were transferred to the new environment. The second major project (Project ONE) converted all of BASF globally to one version of SAP R3. Like most firms, BASF had implemented SAP R3 over a period of years. Therefore, different regions had different versions, making data and communication difficult or even impossible. BASF was the first such company to transform to one version globally. All the various analytics types (predictive, descriptive, optimising) are applicable to SC management. Selecting the correct analytics and creating detailed definitions need to occur before software programming. This process must be end-user-driven. The technology needs to include ease-of-use as a primary objective. Change management (communication, education, validation) must be included in the project plans, as resistance to such a major transformation is to be expected.