Abstract
This chapter investigates data envelopment analysis (DEA) in the context of supply chain management. DEA, a non-parametric method for assessing productive efficiency with many inputs and outputs, has considerably impacted research and practical implementation. It enables performance analysis across organizations with complicated input-output interactions. DEA provides user-friendly and customizable criterion weighting, simplifies analysis by eliminating the need for production function calculation, and delivers comprehensive efficiency measurements. This chapter examines existing research on the use of DEA in supply chains to assess present practices, recent breakthroughs, and techniques critically. This chapter addresses the central research question, “What are the latest advancements and methodologies in applying DEA to the supply chain?” The findings of this study add to the understanding of current practices at the confluence of DEA and supply chain management, which is critical in today's complex corporate context.
TopIntroduction
A non-parametric way of measuring the productive efficiency of procedures with many inputs and outputs is called data envelopment analysis (Liu et al., 2013). Since its inception in 1978, the contemporary version of Data Envelopment Analysis (DEA) has significantly contributed to advancements in both research and practical implementation (Sıcakyuz, 2023). DEA is a way of analyzing the performance traits of various organizations where multiple inputs and outputs make comparisons more difficult. It is essentially a linear programming methodology (Mahmoudi et al., 2020). With this approach, various inputs and outputs are combined and transformed into a single efficiency indicator. This method first establishes an “efficient frontier” made up of a group of DMUs that demonstrate best practices, and then it gives the efficiency level to other non-frontier units based on how close they are to the efficient frontier (Fotova Čiković & Lozić, 2022; Liu et al., 2013).
The Data Envelopment Analysis (DEA) method has several benefits. First off, it gives criteria weights automatically, improving the effectiveness of the ranking process. Second, DEA makes the analysis simpler by doing away with the need to estimate a production function and its corresponding assumptions. Thirdly, it evaluates each observation against an efficient border that has been optimized, giving a thorough efficiency measurement. Additionally, DEA is renowned for being straightforward and user-friendly, enabling reliable decision-making based on actual data. Finally, it provides flexibility by simultaneously supporting a range of inputs, outputs, and measurement criteria (Rezaei & Adressi, 2015).
According to research, the DEA approach is primarily applied in the following five research fields: agriculture, finance, supply chains, transportation, and public policy. An important part of ensuring the smooth and effective operation of supply chain processes is played by industrial engineering, a specialized branch of engineering. It includes a broad range of methods, strategies, and guidelines targeted at streamlining operations, boosting output, and eventually strengthening supply chain networks' overall performance and competitiveness. Industrial engineering is a crucial part of contemporary supply chain management because of its multidimensional approach, which includes elements like logistics, quality assurance, operations management, and resource allocation (Fotova Čiković & Lozić, 2022).
The overarching objective of this chapter is to conduct a comprehensive review and analysis of existing research papers within the realm of Data Envelopment Analysis (DEA) and its application in the context of supply chains. By scrutinizing these papers, we aim to present a precise and critical assessment of the current state of practices in this field. This endeavor seeks to shed light on the latest advancements, methodologies, and insights derived from the body of literature by asking the question, what are the latest advancements and methodologies in applying DEA to supply chain management in current literature? And ultimately offering a valuable contribution to the understanding and evaluation of contemporary practices at the intersection of DEA and supply chain management.
Key Terms in this Chapter
Supply Chain Management (SCM): The management of the flow of goods and services, involving the movement and storage of raw materials, work-in-process inventory, and finished goods from the point of origin to the point of consumption.
Performance: The measurement of how well an organization, such as a supply chain, achieves its objectives and goals. Performance in supply chains can include metrics like delivery speed, cost reduction, and sustainability.
Supply Chain: A network between a company and its suppliers to produce and distribute a specific product to the final buyer. It includes all processes involved in the production and distribution of goods.
Productive Efficiency: A state where a system, such as a supply chain, cannot produce more of one good without producing less of another, given fixed inputs. DEA is used to measure this efficiency.
Data Envelopment Analysis (DEA): A non-parametric method used to evaluate the efficiency of different decision-making units, such as organizations or businesses, by comparing multiple inputs and outputs.
Efficiency: In the context of supply chain management, it refers to the effectiveness with which resources are used to produce outputs. High efficiency means achieving maximum output with minimum input.
Sustainability: In supply chain context, it refers to conducting operations in a way that is environmentally friendly, socially responsible, and economically viable over the long term.
Resilience: The ability of a supply chain to effectively respond to and recover from disruptions, such as natural disasters or market changes. This includes adaptability and flexibility in operations.