Catálogo de publicaciones - libros

In this chapter, we introduced two methods for measuring efficiency change over time: “window analysis” and “Malmquist index” using non-parametric DEA models.

In this chapter, we have covered two subjects; economies of scope and capacity utilization. 1. Economies of scope concerns the problem whether any significant difference in efficiency exists between specialized and diversified firms. The specialized firms produce different (specialized) products, whereas the diversified firms produce all of the concerned products. We extended this model to comparisons of two business models where both models utilize common inputs for producing common outputs but their business styles are different. 2. Capacity utilization deals with the situation where some input values are fixed and cannot be easily altered while some are flexibly alterable. Thus, we divided inputs into fixed and variable inputs, and studied the effect on efficiency caused by the variable inputs according to whether relaxation of variable inputs improves the efficiency of DMU under evaluation. We analyzed two aspects of capacity utilization; (1) technical and (2) price-based. 3. Example applications were presented to illustrate uses of both these approaches.

In this chapter we have introduced a consensus-making method in a multiple criteria environment using a combination of DEA and cooperative game theory. It is demonstrated that both DEA max and min games have the same Shapley value. Problems like the one exemplified in this chapter are usually solved by means of (among others) conventional custom a single criterion decision or a fixed weights rule. These approaches are not always “rational.” The proposed scheme has diverse applications in areas, such as, cost or burden sharing in international organizations, e.g., United Nations, NATO, UNESCO and so forth, research grant allocation to applicants by a foundation, and resource distribution for R&D. These problems are multifaceted and should be solved in a cooperative frame-work. The DEA game proposed in this chapter can be a promising method for solving these important problems and will open a new dimension to cooperative game theory.

Observed data for use in DEA may suffer from non-controllable environmental effects and statistical noise. Hence, detaching these external effects from data and uncovering the true managerial efficiency are crucial for evaluating DMUs’ performance. In this chapter, we have covered these subjects and demonstrated a case study dealing with Japanese banking using a three-stage approach.