In past few classes of Business Analytics, a common question is asked from all of us: Can a combination of variables be used to predict group membership? Ultimately, answer turns out to be positive when we are exposed to a multi-application tool called Discriminant Function Analysis.
Discriminant Analysis can be used for multiple applications in multiple fields to get multiple interpretations. This puts my focus on how it can be useful in making major termination decisions during Research and Development (R&D) projects. Most of the companies in eastern world focus on selection of R&D projects, neglecting dynamic and stochastic considerations for changing implementation process of R&D projects. They seldom make timely termination decisions for ongoing R&D projects. As a result, the successful commercialization ratio of R&D projects in eastern countries is much lower than in western countries.
Discriminant analysis at each stage can be useful in determining the variables which are best predictor of success or failure for an R&D project. The major variables which may influence project termination decisions are:
· Degree of freedom at work
· Degree of urgency in the project development
· Degree of transparency of critical decisions about the project
· Degree to which chance events influence an R&D project
· Expected probability of commercial success
· Expected probability of technical success etc.
To illustrate this, a study was conducted in China focusing on the reasons behind wrong timing of termination of R&D projects in various firms. Around 41 variables were grouped into six categories related to: the R&D project team, the market for the project output, the resources for the development of the project, the technology, the priority of the project, and the commercial goals.
Following this, a questionnaire was designed for project leaders to elucidate the termination decision for R&D projects at their manufacturing firms. Most of the firms surveyed were representative of their industries, and engaged in R&D and technological innovation activities. As a result, twelve variables among 41 were found to have more significant discriminating strength on the success or failure of R&D projects at three evaluation points (Initial, middle and final stage). It can be inferred that such analysis can comprehensively measure the risk that an ongoing R&D project faces.
To summarize, techniques like Discriminant Analysis facilitate integration of quantitative methods with qualitative ones, and thus is very helpful for project managers in taking termination decisions based on supported data rather than solely relying on personal operating experiences.
Prashant Pandey - 13155
Group - Operations 1