A stochastic data envelopment analysis approach for multi-criteria ABC inventory classification

ABSTRACT

One of the common methods for classifying inventory items is ABC classification approach. In many cases, the data might be stochastic. In the current study, using stochastic data envelopment analysis model, we present a new approach to categorize inventory items given stochastic data and nature of criteria. Then, a new stochastic mixed integer programming model is proposed to forecast classes of the new inventory items. The proposed stochastic mixed integer programming model does not impose subjective judgment on the classification of inventory items and can be used for multi-group classification. The developed approach can classify inventory items and forecast the class of new items with both qualitative and quantitative criteria. The applicability of developed stochastic data envelopment analysis and stochastic mixed integer programming models is demonstrated by a case study.

KEYWORDS:

• Inventory classification
• stochastic data envelopment analysis
• stochastic discriminant analysis
• stochastic mixed integer programming

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Mohammad Tavassoli

Mohammad Tavassoli is a scholar and teacher in the area of Industrial Engineering (IE). He received his BS in Industrial Engineering from Islamic Azad University, Khorramabad Branch, in Iran. He also received his MA as a top researcher (2013) in Industrial Management from Islamic Azad University, Karaj Branch, in Iran. He obtained his PhD in Industrial Management from Esfahan University, Department of Management in Iran. He has published several refereed papers in many prestigious journals such as Journal of Sustainable Production and Consumption, Annals of Operations Research, Air Transport Management, International Journal of Electrical Power & Energy Systems, International Journal of Mathematics in Operational Research, International Journal of Applied Management Science, International Journal of Math Model Algorithm, International Journal of Management Science and Engineering Management, International Journal of Science and Technology and International Journal of Operational Research Society of India. His research interests include supply chain management, operational management, and data envelopment analysis.

Reza Farzipoor Saen

Reza Farzipoor Saen has published over 192 refereed papers in ABDC and ABS ranked journals. His h indices in Google Scholar and Scopus are 41 and 37, respectively. He has 24 years of industrial and consultation experience. Reza is on the list of top 2% of scientists in the world, ranked by Stanford University (2020; 2021).