Implementing Business Analytics within the Supply Chain: Success and Fault Factors

Abstract

Abstract: Implementing business analytics across a large company is more about understanding that organization⠒s culture, than it is about the actual technology. Understanding an organization⠒s motivation, advantages and roadblocks is imperative for successful implementation and benefit. This research examines both the critical success factors along with the implementation faults of the largest steel producer in North America, and discusses how these cultural factors play out on a large scale during an ERP implementation. First, this research identifies general critical success factors as business plan and vision; change management; communication; ERP team composition, skills and compensation; project management; top management support and championship; and system analysis, selection and technical implementation (Hoon Na and Delgado 2006). Then, general implementation faults are identified as operational problems, motivational problems, knowledge problems and regulatory problems (Mayntz 1997 in Niehaves, Klose, Becker 2006). These theories are applied to the specific case of Nucor Steel. Application is contextualized through a historical perspective, identifying a low‑cost business model, and enormous divisional autonomy as hindrances to the implementation of a common, shared ERP. A timeline of business analytics at the company is given, beginning in 2002, at which point a culture shift occurred though the acquisition of a major competitor. Divisional autonomy at this time, began to be challenged, leading to easier integration of reporting systems and cross‑company data analysis. Then, details are provided as to how this company is making a case for a new, innovative, business model and how it is developing needed expertise in the area of business analytics. Changes in the steel business are requiring companies to move from a low‑cost model to a value‑added model increasing the need for innovation in all areas of the company. These innovations inevitably require the use of more complex data analytics that cut across the entire company, instead