Sparse Inverse Covariance Estimation: A Data Mining Technique To Unravel Holistic Patterns Among Business Practices In Firms

Firms are seeking ways to improve managerial decision making in order to enhance operational performance. However, the complexities underlying business processes often mean that operational performance depends on a multitude of factors. Yet, at times the number of empirical cases is rather limited. This presents the challenge of discerning meaningful patterns among a large number of variables that can then be used to derive generalized frameworks and mental models for decision making. In this article, we tackle this challenge with an extension of Sparse Inverse Covariance Estimation (SICE), a novel data mining technique, to address decisions in Operations and Supply Chain Management. We conduct a simulation study to validate the effectiveness of this extension in improving the accuracy and stability of pattern detection. We then apply it to an empirical dataset that is characterized by high dimension, low sample size, and lack of multivariate normal distribution. Our study pioneers the application of SICE in Operations and Supply Chain research. We also extend SICE with bootstrapping. The extended SICE is an effective technique for mining a complex empirical dataset and is a valuable aid for decision support.