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The purpose of this paper is to apply data envelopment analysis (DEA) techniques to the collective decision-making environment to appraise two-stage production process under different decision preferences.

The authors propose a novel multi-criteria group decision-making approach that uses consensus-strategic data envelopment analysis (CSDEA) to appraise two-stage production process under two different decision strategies, which are efficiency- and fairness-based group decision preferences.

The authors find that the proposed CSDEA model evaluates the performance of the decision-making units (DMUs) not by diminishing other competitors but rather based on group interests of the entire decision set.

The authors extend Li’s two-stage model to cases that consider both intermediate inputs and outputs. The authors address the issue of incorporating collective managerial strategy into multi-criteria group decision-making and propose a novel CSDEA model that considers not only the individual-level performance of a DMU but also the group-level or collective decision strategies.

Two-stage production systems including a processing shop and an assembly stage are widely used in various manufacturing industries. These two stages are usually studied independently which may not lead to ideal results. This paper aims to deal with a two-stage production system including a job shop and an assembly stage.

Some exact methods are proposed based on branch and bound (B&B) approach to minimize the total completion time of products. As B&B approaches are usually time-consuming, three efficient lower bounds are developed for the problem and variable neighborhood search is used to provide proper upper bound of the solution in each branch. In addition, to create branches and search new nodes, two strategies are applied including the best-first search and the depth-first search (DFS). Another feature of the proposed algorithms is that the search space is reduced by releasing the precedence constraint. In this case, the problem becomes equivalent to a parallel machine scheduling problem, and the redundant branches that do not consider the precedence constraint are removed. Therefore, the number of nodes and computational time are significantly reduced without eliminating the optimal solution.

Some numerical examples are used to evaluate the performance of the proposed methods. Comparison result to mathematical model (mixed-integer linear programming) validates the performance accuracy and efficiency of the proposed methods. In addition, computational results indicate the superiority of the DFS strategy with regard to CPU time.

Studies about the scheduling problems for two-stage production systems including job shop followed by an assembly stage traditionally present approximate method and metaheuristic algorithms to solve the problem. This is the first study that introduces exact methods based on (B&B) approach.

Incorporates the concept of quality control in the probabilistic analysis of two‐stage production systems. Provides an integrated framework to consider manufacturing, inspection and rework activities simultaneously. The system is provided with an initial storage of unlimited capacity. Rework of a defective item produced by machine I is done on machine I itself and depends on the type of defect. Explicit expressions for some of the system characteristics have been obtained using the state‐space method and regeneration point technique. All the random variables involved in the analysis are assumed to be arbitrarily distributed (i.e. general).

The purpose of this paper is to develop a simultaneous measurement of overall performance and its two dimensions of efficiency and effectiveness in the case of Chinese construction industry.

A relational two-stage data envelopment analysis (DEA) method, which builds a relationship between component stages and can effectively identify inefficient stages, is developed and applied in order to measure overall performance, efficiency and effectiveness.

The construction industry of the Eastern region in China demonstrated the best results for overall performance, efficiency and effectiveness. The gaps between regions were primarily reflected in differences of pure technical efficiency. Performance indicators in the whole construction industry improved steadily and but could be improved more effectively. The coefficients of variation became smaller and more well-balanced across the whole industry.

Improving overall performance should focus on promoting construction efficiency at the project level and increasing management effectiveness at the company level. Sustainable development policies, which may include large investment and preferential policies, can narrow performance differences among the regions’ construction industries, and ultimately promote overall performance for the whole industry.

The relational two-stage DEA model is further developed in a variable returns-to-scale condition. The developed approach is generic and can provide a pathway for simultaneously measuring performance, efficiency and effectiveness and to recognise competitive advantages for promoting sustainable development.

This chapter analyses the efficiency of African airlines using a two-stage network DEA (Data Envelopment Analysis) model. Network DEA models usually take into account the production process with intermediate inputs derived from the first stage and a second stage that departs from it. This fundamental feature enables one to view the airline production process as a carry-over activity. The analysis covers the 2010–2013 period. The relative efficiency ranks are presented and policy implications are derived.

The banking industry, which is one of the most significant industries when taking into account both deposit sizes and employment statistics in Turkey, is one of the country's primary economic drivers. In this regard, it is highly important to evaluate banks as it is necessary to present to what extent they use their resources efficiently. The main purpose of the study is to analyze the efficiencies of Turkish banks by the two-stage data envelopment analysis (DEA) and Malmquist productivity index (MPI).

The authors aim to analyze both the efficiency and productivity of Turkish banks by two-stage DEA and the MPI, which enable decomposing into sub-sections of production processes. Hence, more detailed insight into the Turkish banking system can be presented through two-stage efficiency and production approaches.

DEA results indicate that two out of three state-owned banks achieved resource efficiency while none of the investigated banks performed profit efficiency throughout the investigated period. Besides, average resource efficiency is found higher than average profit efficiency in Turkish banks. MPI results reveal that both technological and technical improvement prospects exist for Turkish banks.

The original contribution of this paper is to employ two-stage DEA and the MPI, which reflect both the static and dynamic performance of the Turkish banking sector. In this regard, this study aims to be a pioneer by both reflecting the static and dynamic performance analysis of Turkish banks.

A comparative evaluation of push, pull and a combination of push‐pull production strategies for a multi‐product two‐stage production system is presented. A mathematical model is developed to determine EBQ and optimal batch splitting and is applied to a case of a manufacturing company producing hard disk parts. The results obtained are compared with the existing practice. It is shown that a push policy results in the highest total system cost while the pull system – the least, with the hybrid policy in‐between. It is recommended that a switchover to optimal hybrid push‐pull policy is called for from the present non‐optimal push policy. Cost reduction potential for such a switchover is estimated.

The banking industry plays a key role in China's financial industry. In the past decade, the speed of the development of China's commercial banks has gradually declined. Commercial banks with different ownership structures also have certain differences in terms of operating efficiency, and their monetary policies are often different. Therefore, the authors study the impact of ownership structure on the efficiency of commercial banks under different monetary policies. This study also provides relevant reference opinions with regard to the healthy, sustainable and stable development of China's banking industry.

This paper mainly uses the two-stage data envelope analysis (DEA) model under meta-frontier and group frontier to study the deposit and loan efficiency changes of 16 banks from 2007 to 2014 under ownership structure heterogeneity. Furthermore, the model introduces the balance parameters between deposits and loans, in order to realize the mathematical abstraction description of macro-monetary policy.

First, based on bank efficiency analysis, the paper finds that most banks' loan efficiency is higher than their deposits. Second, the paper concludes that different monetary policies have little effect on bank deposit and loan efficiency, while ownership heterogeneity has a significant impact on bank performance. Finally, through the decomposition of the sources of inefficiency in bank performance, this paper finds that management and technology are two factors that affect the inefficiency of banks.

The authors work contributes to the existing literature in the following ways: First, to the best of the authors’ knowledge, this is the first attempt to use the DEA model to study the relationship between monetary policies and bank supply chain efficiency. The results may provide additional managerial implications for the banking industry from the perspective of monetary policies. The result is helpful in terms of explaining how and why banks should strengthen risk management, as well as how to deal with non-performing loans in management terms and finally, why banks should make financial technology innovations in technology terms.

This paper aims to identify the relationship between efficiency and profitability by using data from Greek meat processing companies over the period 1994‐2007.

The relationship of efficiency and profitability is studied, by applying a new performance decomposition model. This method is capable of making valid and consistent inferences about the performance of a two‐stage production system, as well as the main sources of inefficiencies within a company.

A poor performance over the study period is observed in the sample companies. The low performance is mainly due to the low profitability. The results do not confirm the existence of a positive strong correlation between efficiency and profitability. The companies that have the capability of producing their products with the best practices are not always capable of generating the maximum profits.

The need for the improvement of performance has two aspects: first, it is a demand for the effective use of resources, and simultaneously, it is an urgent requirement for the generation of profits. According to the study findings, the long‐term survival of firms in our sample seems to require adopting mainly profitability‐enhancing strategies.

This paper provides one of the first evaluations of performance focusing on efficiency and profitability, by applying an innovative performance decomposition approach that has not yet been applied in Greek industries.

Production is defined as the mission of creating wealth (economic goods and services) from a variety of resources (human and non‐ human) by adding values (intrinsic and extrinsic) through transformation (physical and conceptual) so as to derive utilities (form, place, time, economic, non‐economic). This mission is organised through a system. Basically, what a production system looks like is as Fig.1. It is basically the flow of various resources that defines the nature and characteristic of the production system.