In 1991 “Terminator 2” revolutionized the use of computers for digital effects or computer-generated imagery (CGI) in movies (Wikipedia.org, 2006). Costs were high and few saw digital effects to grow beyond a niche of few scenes in a few movies (Marr & Kelly, 2006). Fifteen years later, digital effects are widely considered a key success factor. For example, in 2005 all top five grossing movies, including Harry Potter and Star Wars sequels, relied extensively on digital effects (Box Office Mojo, 2006). Many industry observers foresee a similar success of computational methods in business intelligence analytics (BIA) and decision support (Davenport, 2006; Heingartner, 2006; March, 2005; Kimbrough, 2003). Just as CGI has not diminished the value of story telling, so will computational BIA not reduce the value of an experienced decision-maker but it will enhance it. After all, very few decision-makers make the right decision based on gut instinct all the time. Computational BIA and decision support is seen as yet another example of how a rapid increase in processing power and advances in software design have enabled embedding of more and more business activities in software routines. This process has been referred to as “softwarization” (Schlueter Langdon, 2003). Softwarization has expanded far beyond enterprise resource planning, billing, and customer relationship management. Today, few passengers and pilots seem to mind that computers land their jet liners. This article presents two examples of how advanced BIA can improve precision of managerial and executive- level decision-making. Both examples follow a multi-step approach derived from research science that explicitly addresses instrument validation. Both examples are taken from the auto industry, a global, complex, and important industry in developed countries. The first example uses traditional methods but combines them creatively to increase analytical precision. It is focused on lifecycle aging and baseline sales estimation to improve the return on marketing investments (RoMI) using gap analysis and ordinary least squares estimation. While the first example is essentially a backwards-looking analysis, the second one is forwardlooking. The second application simulates the impact of a disruptive event, a recession on profitability of automakers and their suppliers. It takes advantage of complex adaptive system modeling techniques and agent-based software implementation methods.
Key Terms in this Chapter
Emergent Behavior: Refers to the way complex systems and patterns arise out of a multiplicity of relatively simple interactions.
Multi-Agent System (MAS): A system that is composed of several agents, collectively capable of reaching goals that are difficult to achieve by an individual agent or monolithic system.
Strategic Simulation: The imitative representation of the functioning of a strategic system by means of the functioning of another.
Business Intelligence Analytics (BIA): The method of logical analysis, which includes use of causal models, mathematical functions, and metrics, related to company operations.
Complex Adaptive System (CAS): A system that is complex in that it is diverse and made up of multiple interconnected elements and adaptive in that it has the capacity to change and learn from experience.