Economies of Scope in Network-Structured Production Systems

Authors

https://doi.org/10.48314/ijorai.v2i2.91

Abstract

Methods for determining economies of scope in Data Envelopment Analysis (DEA) have been widely developed to investigate and examine the effects of product or service diversification on production costs. However, these techniques are not sufficiently efficient for analyzing economies of scope in systems with network structures, including network-structured production systems. The major reason for this inefficiency is the neglect of the internal structure of production units and the disregard of their intermediate products. Therefore, in this paper, while introducing the concept of economies of scope and reviewing the conventional DEA-based models for its calculation, a new method is proposed which, by taking into account the internal structure of units, is capable of evaluating the performance of virtual diversified firms formed through the integration of specialized firms with network structures. One of the important features of the proposed method is that it creates an opportunity for cooperation between the analyst and the decision-maker (manager). This cooperation facilitates the adoption of appropriate decisions aimed at reducing production costs, such as the merger of specialized firms for the joint production of products and services, or the elimination of a product from the production cycle in order to specialize the production process. By considering intermediate products, the proposed approach performs more accurately in comparison with conventional DEA methods, and planning based on it is consequently more reliable. The inefficiency of traditional DEA approaches regarding this concept is demonstrated through a numerical example in comparison with the proposed method.    

Keywords:

Data envelopment analysis, Network-structured production systems, Intermediate products, Economies of scope

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Published

2026-06-13

How to Cite

Zeinalzadeh Ahranjani, L. . (2026). Economies of Scope in Network-Structured Production Systems. International Journal of Operations Research and Artificial Intelligence , 2(2), 80-90. https://doi.org/10.48314/ijorai.v2i2.91

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