Selecting advanced analytics in manufacturing: a decision support model

Abstract

Advanced analytics offers new means by which to increase efficiency. However, real-world applications of advanced analytics in manufacturing are scarce. One reason is that the management task of selecting advanced analytics technologies (AATs) for application areas in manufacturing is not well understood. In practice, choosing AATs is difficult because a myriad of potential techniques (e.g. diagnostic, predictive, and prescriptive) are suitable for different areas in the value chain (e.g. planning, scheduling, or quality assurance). It is thus challenging for managers to identify AATs that yield economic benefit. We propose a multi-criteria decision model that managers can use to select efficient AATs tailored to company-specific needs. Based on a data envelopment analysis, our model evaluates the efficiency of each AAT with respect to cost drivers and performance across common application areas in manufacturing. The effectiveness of our decision model is demonstrated by applying it to two manufacturing companies. For each company, a customized portfolio of efficient AATs is derived for a sample of use cases. Thereby, we aid management decision-making concerning the efficient allocation of corporate resources. Our decision model not only facilitates optimal financial allocation for operations in the short-term but also guides long-term strategic investments in AATs.