Selecting Advanced Analytics in Manufacturing: A Decision Support Model

41 Pages Posted: 23 Sep 2022

See all articles by Rafael Lorenz

Rafael Lorenz

ETH Zurich

Mathias Kraus

University of Erlangen-Nuremberg-Friedrich Alexander Universität Erlangen Nürnberg

Hergen Wolf

ETH Zurich

Stefan Feuerriegel

LMU Munich

Torbjørn Netland

ETH Zürich - Department of Management, Technology, and Economics (D-MTEC)

Date Written: September 11, 2022

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 customised 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.

Keywords: advanced analytics, data envelopment analysis, decision model, manufacturing

Suggested Citation

Lorenz, Rafael and Kraus, Mathias and Wolf, Hergen and Feuerriegel, Stefan and Netland, Torbjorn, Selecting Advanced Analytics in Manufacturing: A Decision Support Model (September 11, 2022). Available at SSRN: https://ssrn.com/abstract=4215912 or http://dx.doi.org/10.2139/ssrn.4215912

Rafael Lorenz

ETH Zurich

Mathias Kraus

University of Erlangen-Nuremberg-Friedrich Alexander Universität Erlangen Nürnberg ( email )

Schloßplatz 4
Erlangen, DE Bavaria 91054
Germany

Hergen Wolf

ETH Zurich

Stefan Feuerriegel (Contact Author)

LMU Munich ( email )

Geschwister-Scholl-Platz 1
Munich, 80539
Germany

HOME PAGE: http://www.ai.bwl.lmu.de

Torbjorn Netland

ETH Zürich - Department of Management, Technology, and Economics (D-MTEC) ( email )

ETH-Zentrum
Zurich, CH-8092
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
20
Abstract Views
174
PlumX Metrics