An ANP Model to Generate Performance Indicators for Manufacturing Firms Under a Balanced Scorecard Approach

Abstract

This paper presents a method that supports a process for the generation of performance indicators for manufacturing areas within companies. The existing literature in the area of performance management presents frameworks to guide the selection of performance indicators. Particularly, the literature shows the use of multicriteria decision making methods (MCDM) for selecting indicators from a set of predefined ones. This paper goes beyond by developing a method to support the generation of the performance indicators for the manufacturing area. This process is supported by the Analytic Network Process (ANP). The network model, which is based on a Balanced Scorecard (BSC) framework, includes nodes that are grouped into five clusters: long-term objectives, strategic business units, critical success factors, manufacturing decision areas, and human resource management. The proposed method consists of the following major steps: assigning weights to the manufacturing decision areas, diagnosing these areas, and generating performance indices ranked from high to low. The proposed method allows managers to define performance indicators for the manufacturing area that are aligned with the company’s long-term strategic objectives. This is done by the use of an ANP model that captures the complex relationships that exist between the various strategic objectives of the strategy map of a company. As an illustration, an application in a company that produces pork-based food is described. The managers found that the proposed method was easy to understand and easy to follow, and that it was useful for defining performance measures.

Keywords:

• Analytic Network Process
• Performance Indicators
• Manufacturing Strategy

EMJ Focus Areas:

• Organizational & Performance Assessment

Acknowledgements

The authors would like to thank the support of the Direction of Science and Technological Research (Project DICYT N° 061817QL) and the Department of Industrial Engineering, University of Santiago of Chile.

Declaration of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Notes on contributors

Luis E. Quezada

Luis E. Quezada, MSc, PhD, is a professor at the Department of Industrial Engineering of the University of Santiago of Chile. He holds a BSc degree from the University of Chile and a PhD and MSc degree from the University of Nottingham, UK. His research interests include strategic management, operations management and multi-criteria analysis. He is member of the Board of the International Foundation for Production Research (IFPR).

Daniel E. Aguilera

Daniel E. Aguilera, MSc, holds a Bachelor and a MSc degree in Industrial Engineering from the University of Santiago of Chile, Chile. He worked on the project as part of his studies of MSc in Industrial Engineering. Currently, he works as a Fixed Income Portfolio Manager at Banco de Credito e Inversiones (BCI), Chile.

Pedro I. Palominos

Pedro I. Palominos, MSc, PhD, is a professor at the Department of Industrial Engineering and Director of the Smart City Program of the University of Santiago of Chile. He holds a PhD in Industrial Engineering from Technical University of Catalonia, Spain and MS degrees in Federal University of Rio de Janeiro, Brazil. His research interests include operations management, scheduling, smart cities and multi-criteria analysis.

Astrid M. Oddershede

Astrid M. Oddershede, MSc, PhD, is a professor at the Department of Industrial Engineering of the University of Santiago of Chile. She holds a MSc degree from the University of Toronto, Canada and a PhD degree from the University of Newcastle, UK. Her research interests include health management systems and multi-criteria analysis.