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The purpose of this paper is to calculate the bank efficiency of removing potential risks that are hidden from the extreme portfolio of bank’s assets and further compare the differences and causes of bank’s efficiency and potential risk level between China’s representative banks and OECD representative banks in 2011-2015.

Based on the weight-limited DEA model, this paper calculates the bank’s efficiency and further compares the differences between China’s representative banks and OECD representative banks by using commercial banks’ transnational data.

By analyzing US representative banks’ data, the authors find that the excessive expansion of the scale of banks’ investment for the non-real economy shrinks after the bubble burst and would not improve the efficiency of banks immediately. The OECD representative banks rather prefer to extreme asset portfolio so that the potential risks gradually increase, while there is a diminishing effect on investments in non-real economies to improve bank efficiency. On the other hand, China’s representative banks have the signs of reducing investment in the real estate market, but the existence of the bubble in the market led to a lagged effect on the impact of adjustment of bank asset portfolio on efficiency.

This paper has practical significance for commercial banks to improve efficiency and reduce credit risks. This is conducive to the implementation of targeted supervision by the banking supervision department.

Based on the lesson that the financial crisis created by the real estate bubble burst in the USA in 2008 and the financial market active guidance of the developed economies, faced with the reality of Chinese real estate market bubble rising and the continuous improvement of Chinese financial market, this paper compares the differences between representative banks in China and OECD, and explores the causes by using the cross-country data of commercial banks.

By adjusting the weight of the input variables in the efficiency measurement, quantifying the risk is often overlooked by the changes in bank efficiency. This potential risk is caused by the bank’s investment preferences in the non-real economy represented by real estate and tradable financial assets.

The operations research method of data envelopment analysis (DEA) shows promise for assessing radiotherapy treatment plan quality. The purpose of this paper is to consider the technical requirements for using DEA for plan assessment.

In total, 41 prostate treatment plans were retrospectively analysed using the DEA method. The authors investigate the impact of DEA weight restrictions with reference to the ability to differentiate plan performance at a level of clinical significance. Patient geometry influences plan quality and the authors compare differing approaches for managing patient geometry within the DEA method.

The input-oriented DEA method is the method of choice when performing plan analysis using the key undesirable plan metrics as the DEA inputs. When considering multiple inputs, it is necessary to constrain the DEA input weights in order to identify potential plan improvements at a level of clinical significance. All tested approaches for the consideration of patient geometry yielded consistent results.

This work is based on prostate plans and individual recommendations would therefore need to be validated for other treatment sites. Notwithstanding, the method that requires both optimised DEA weights according to clinical significance and appropriate accounting for patient geometric factors is universally applicable.

DEA can potentially be used during treatment plan development to guide the planning process or alternatively used retrospectively for treatment plan quality audit.

DEA is independent of the planning system platform and therefore has the potential to be used for multi-institutional quality audit.

To the authors’ knowledge, this is the first published examination of the optimal approach in the use of DEA for radiotherapy treatment plan assessment.

This chapter examines the impact of recent airline consolidations in the United States on the technical efficiencies of the airlines involved. Data envelopment analysis (DEA) is used to assess the efficiencies, and the consolidations examined are those that occurred among major network carriers between 2005 and 2013. The airline production process is conceptualized as the transformation of labor, fuel, and fleet-wide seating capacity into available seat-miles, or, under an alternative model specification, into user value, as measured by the airline’s operating revenue. Efficiency is conceptualized in terms of minimizing the airline’s usage of the three inputs, given its output level. The analysis seeks to determine whether the airlines that consolidated were more efficient, post-consolidation, than they were prior to consolidation, compared to airlines that did not enter into consolidations. Although there are limitations owing to the small number of airlines in the dataset, the chapter finds no evidence that the consolidations enhanced the efficiencies of the airlines involved, relative to the efficiencies of the airlines that did not enter into consolidations.

The purpose of this paper is to model the tradeoffs among fatalities, CO₂ emissions and value generated by the truck transportation portion of supply chains with the goal of determining if efforts to reduce CO₂ emissions increase transportation‐related fatalities.

The joint production of CO₂, fatalities, and truck transport value in the 50 US states during 2002‐2007 is modeled using data envelopment analysis. The directional output distance function is estimated under two assumptions: strong and weak disposability of CO₂ emissions. This provides the means of calculating shadow prices that estimate the cost of reducing CO₂ emissions.

The authors' findings indicate that the transfer of resources to the reduction of CO₂ emissions will result in a statistically significant increase in fatalities and a statistically significant decrease in value of transport from truck transport.

The model presented is based on secondary data from the Federal Highway Statistics Series, the Fatality Analysis Reporting System, and the Bureau of Economic Analysis.

The model developed demonstrates tradeoffs among sustainability‐related variables.

The model presented in the paper uses shadow prices to assess sustainability‐related tradeoffs in supply chains. While this method has been used in other fields, this is its first use in supply chain studies.

The purpose of this paper is to outline and demonstrate a method for screening and selection of potential portfolio companies (PCs) during the screening phase in corporate venture capital.

The use of the data envelopment analysis (DEA) enables the consideration of individual, heterogeneous and multidimensional decision criteria in portfolio selection and the preceding screening process by the investor.

The result of this method is a relative ranking of the PCs, with all the PCs considered serving as peer group. A weighting of individual criteria is not necessary because it is part of the functionality of DEA. The authors validate the proposed approach in a case study and show that it can be well combined with other models and theoretical frameworks.

The method is particularly useful in two cases. First, if a highly specialized investor wishes to use a variety of individual selection criteria for portfolio selection. Second, if an investor only has insufficient (financial) data on potential PCs, but still wants to make a (pre-) selection based on observable (qualitative) characteristics. This model helps to make consistent, intersubjectively comprehensible decisions based on valid decision criteria and helps to optimize the decision-making process in the context of portfolio selection in CVC.

This method allows the systematic selection of an attractive group from a large number of potential PCs, based on observable characteristics and taking into account individual strategic investment objectives, without having to make assumptions about underlying distributions or weights of decision criteria.

The purpose of this paper is to determine the criteria weights of outsourcing and their key role in ranking outsourcing suppliers.

A new hybrid multi-criteria decision-making approach merges three tools, namely, decision making trial and evaluation (DEMATLE), fuzzy analytic network process (FANP) and ordinal/cardinal data envelopment analysis (DEA) model. Afterwards, experts’ opinions were gathered from a Pipe and Fittings company. Finally, their opinions were incorporated in three-stage approach for outsourcing suppliers’ selection.

The findings of this study show that among the selective criteria for outsourcing, business development, focus on basic activities and order delays are the three most important criteria. Also, the proposed approach ranks suppliers to facilitate decision making for selection.

The number of suppliers, selection criteria and the number of members of the respondents’ team have been identified as some of the limitations of the present study.

The study has significant and practical implications for the managers and for the organizations which have to choose top suppliers, particularly in the case of dealing with numerous and qualitative/quantitative criteria.

This paper proposed a new three-stage approach that incorporates outputs of previous as inputs of next stage to increasing results accuracy. Also, it showed that by incorporating results of FANP method into DEA model, key role of experts’ opinions as a qualitative and quantitative criteria can be caused by increasing flexibility of decision process.

The role of business logistics for a water distribution company in Central Asia has become a major concern. As the marketing environment is getting more and more competitive, the company is forced to focus on the efficiency of its supply chain management operations both by improving customer service, increasing its profitability and productivity. The purpose of this paper is to report upon the designing of a responsive supply chain for water distribution in Central Asia.

A logistic plan to satisfy customer requirement for water distribution in a Central Asian city subject to satisfactory service levels both in the number of distribution centers (DCs) and truck delivery schedule is outlined in this paper. The logistics plan includes repositioning the DCs in relation to the customer location for efficient distribution. The problem is formulated as truck delivery schedule using a new algorithm where single distribution centre is converted into a multiple warehouse location problem. The problem is solved using WINQSB software. Further, the current DCs are appraised with the software and suggested possible new locations for convenience.

The application part of this case study consists of identifying water DCs in city limits. By developing improved distribution and logistics management, the study aims at economical operations, convenient zonal distributions, and responsive SCM characteristics. To this end, a spatial distribution plan and route sequencing solution is developed for water distribution.

The paper shows how to improve logistic network that results in cost savings, convenient zonal distributions, and responsive SCM operations. To this end, a spatial distribution plan and route sequencing is developed for water distribution.