Journal of Business Logistics · 2013 · 159 citations · 4 references
ComputingData ScienceSupply Chain ManagementCustomer Relationship ManagementForecastingPredictive AnalyticsPredictive ModelingStrategic ForecastingPredictive Analytics AdoptionPredictive Supply Chain AnalyticsSupply Chain Forecasting
Predictive analytics is transforming diverse fields—from sports and epidemiology to forecasting and customer relationship management—and is increasingly applied by manufacturers, retailers, software firms, and consultants to enhance supply chain management and logistics. The authors propose a 2 × 2 model to integrate predictive analytics into theory development, outlining research questions that the community should pursue and emphasizing the need for dialogue to advance these topics. The model is built on the premise that predictive analytics is largely atheoretical, and the authors discuss how the maker movement’s shift in producer roles amplifies the value of big data within supply chains. The 2 × 2 framework reveals that while the discipline has followed a single theory‑development path, predictive analytics can serve as a key component of a comprehensive process, and addressing the identified questions is crucial for both academia and practitioners seeking precise, efficient strategies.
Predictive analytics is impacting many diverse areas, ranging from baseball and epidemiology to forecasting and customer relationship management. Manufacturers, retailers, software companies, and consultants are creatively discovering new applications of big data using predictive analytics in supply chain management and logistics. In practice, predictive analytics is generally atheoretical; however, we develop a 2 × 2 model to explain the role of predictive analytics in the theory development process. This 2 × 2 model shows that in our discipline we have traditionally taken one path to theory development, but that predictive analytics can be a salient component of a comprehensive theory development process. The model points to a number of research questions that need to be addressed by our research community. These questions are not just highly relevant to the academic community but also in urgent need of answers to help practitioners execute the right strategies with greater precision and efficiency. We also discuss how one disruptive trend, the maker movement, changes the nature of who the producers are in the supply chain, making big data even more valuable. As we engage in higher levels of dialogue we will be able to make meaningful progress addressing these vital research topics.
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