Dynamic Data Envelopment Analysis Approach Research Articles

Evaluation of Chinese commercial bank efficiency and influencing factors: a three-stage dynamic data envelopment analysis approach

Evaluation of Chinese commercial bank efficiency and influencing factors: a three-stage dynamic data envelopment analysis approach

Measuring the performance of retailers during the COVID-19 pandemic: Embedding optimal control theory principles in a dynamic data envelopment analysis approach

Traditional retailers (bricks-and-mortar) have been continuously increasing online sales. However, not all retail companies were able to respond to the increasing sales with the same efficiency level as their competitors. This paper aims to propose a dynamic model – incorporating principles of Optimal Control Theory (OCT) into a Data Envelopment Analysis (DEA) model - for measuring the performance of retailing companies’ cost efficiency. It also aims to contribute through the application by investigating the impact of the pandemic on companies from the most prominent developing market in Latin America, Brazil. Twenty-one companies publicly traded in the São Paulo Stock Exchanges (B3) between the third quarter of 2018 (3Q2018) and the third quarter of 2020 (3Q2020) were investigated. Also, six measures - initial inventory cost (IIC), final inventory cost (FIC), net operating income (NOI), cost of goods sold (COGS), cost of the purchased product (CPP), and plant, property, and equipment (PPE) – were considered. In this way, the findings have implications for researchers and practitioners. Practitioners can discover which competitor(s) is (are) adopting the best practices at each operational aspect (e.g., inventory cost). Additionally, the proposed method can be replicated in other markets (developing or not) and for other categories of retailing companies (e.g., small- and middle-sized). Further research directions are presented, and their implications are discussed.

Efficiency evaluation of Asia's cultural tourism using a dynamic DEA approach

Cultural tourism has become important for economic transformation. This study aims to construct a model that assesses the performance of cultural tourism for various tourist destinations in Asia through a dynamic data envelopment analysis (DEA) approach with consideration of various aspects of the development of cultural tourism and the formulation of related government policies. According to the overall efficiency values, Cambodia, China, Hong Kong, and Singapore were the top performers throughout the study period. Moreover, the results obtained with the dynamic DEA model differ significantly from those obtained with the static DEA model. The dynamic framework allows the reallocation of resources over time which directly affects efficiency measurement. Finally, the bootstrapped truncated regression results suggest that tourists value the integration and utilization of resources at the tourist destination in cultural tourism. Raising the awareness of the value of cultural resources is vital for developing synergies between tourism and culture.

A novel dynamic data envelopment analysis approach with parabolic fuzzy data: Case study in the Indian banking sector

Data envelopment analysis (DEA) is a non-parametric approach that measures the efficiency of a decision-making unit (DMU) statically and requires crisp input-output data. However, as a performance analysis tool, DEA overlooks the inter-relationship present among periods, and in many real applications, it is challenging to define the information for variables like customer satisfaction, service quality, etc. in precise form. To fix this, the present paper develops a novel parabolic fuzzy dynamic DEA (PFDDEA) approach that not only measures the system and period fuzzy efficiencies of DMUs by considering the inter-dependence among periods in the presence of undesirable resources but also handles data as parabolic fuzzy numbers (PFNs). It evaluates fuzzy efficiencies in a dynamic environment by distinguishing the role of links as inputs/outputs. In the proposed approach, system fuzzy efficiencies are estimated by solving the proposed PFDDEA models based on the α-cut approach that guarantees the shape of the membership function of the system fuzzy efficiencies obtained at different α-levels as PFNs. Further, an algorithmic approach for measuring period fuzzy efficiencies based on the concept of α-cuts and Pareto’s efficiency is developed that leads to the estimation of the shapes of their membership functions. Finally, a relationship has been derived between upper (lower) bound system efficiency and upper (lower) bound period efficiencies at each α-level. To the best of our knowledge, this is the first attempt that dynamically evaluates fuzzy efficiencies (system and period) of DMUs when the data for the inputs/outputs/links are PFNs. To validate the applicability and robustness of the proposed approach, it is applied to eleven Indian banks for two periods 2019–2020 and 2020–2021, including loss due to non-performing assets (NPAs) as an undesirable output and unused assets as a link between periods. Here, NPAs are the bad loans that cease to generate income for the banks. The findings of the study (i) depict the system and period efficiencies as PFNs, (ii) conclude that the Federal Bank (FB) is the most efficient and Punjab National Bank (PNB) is the least efficient bank in the system and all periods, and (iii) provide implications that are highly valuable for bank experts to consider the impact of NPAs and unused assets for improving underperformed banks. These findings indicate that the proposed PFDDEA approach is highly useful for ranking/benchmarking in a dynamic manner keeping in view the presence of uncertain data variables represented as PFNs.

Healthcare and Welfare Policy Efficiency in 34 Developing Countries in Asia.

The healthcare and welfare policies of nations, as well as the amount of investments put into these areas, vary across countries. Investments in healthcare and welfare have been increasing worldwide which brings the question of assessing the efficiency of these investments. There are, however, difficulties in evaluating the effectiveness of such investments due to differences in countries’ economic development levels and due to the differences in data definition issues. There are only a limited number of studies in the literature that employ consistent and comparable indicators across countries. This study evaluates the healthcare investment efficiency and health competitiveness efficiency of 34 developing countries in Asia using a two-stage dynamic data envelopment analysis approach. Furthermore, we employ a broader measure of indicators on national healthcare and welfare policies and outcomes, in addition to the investment data on healthcare and welfare expenditures. Our findings indicate that the establishment of an investment environment with a consolidated approach and management is an important factor that increases the efficiency of investments in healthcare and welfare sectors. A consistent delivery of the national policy strategy is also crucial for reaching the medium-and long-term targets for each country. For example, if a country establishes healthcare and welfare policies that focus on improving its indicators with low efficiencies, the output will be improved and a better return on investment will be ensured in a long-term perspective.

New data envelopment analysis models for assessing sustainability Part 1: A dynamic data envelopment analysis approach

Article published in Expert Systems on 08 March 2020, available open access at: https://doi.org/10.1111/exsy.12548.

A New Robust Dynamic Data Envelopment Analysis Approach for Sustainable Supplier Evaluation

Supplier selection is one of the intricate decisions of managers in modern business era. There are different methods and techniques for supplier selection. Data envelopment analysis (DEA) is a popular decision-making method that can be used for this purpose. In this paper, a new dynamic DEA approach is proposed which is capable of evaluating the suppliers in consecutive periods based on their inputs, outputs, and the relationships between the periods classified as desirable relationships, undesirable relationships, and free relationships with positive and negative natures. To this aim various social, economic, and environmental criteria are taken into account. A new method for constructing an ideal decision-making unit (DMU) is proposed in this paper which differs from the existing ones in the literature according to its capability of considering periods with unit efficiencies which do not necessarily belong to a unique DMU. Furthermore, the new ideal DMU has the required ability to rank the suppliers with the same efficiency ratio. In the concerned problem, the supplier that has unit efficiency in each period is selected to construct an ideal supplier. Since it is possible to have more than one supplier with unit efficiency in each period, the ideal supplier can be made with different scenarios with a given probability. To deal with such uncertain condition, a new robust dynamic DEA model is elaborated based on a scenario-based robust optimization approach. Computational results indicate that the proposed robust optimization approach can evaluate and rank the suppliers with unit efficiencies which could not be ranked previously. Furthermore, the proposed ideal DMU can be appropriately used as a benchmark for other DMUs to adjust the probable improvement plans.

Environmental impact efficiency of natural gas combined cycle power plants: A combined life cycle assessment and dynamic data envelopment analysis approach

The energy sector is still dominated by the use of fossil resources. In particular, natural gas represents the third most consumed resource, being a significant source of electricity in many countries. Since electricity production in natural gas combined cycle (NGCC) plants provides some benefits with respect to other non-renewable technologies, it is often seen as a transitional solution towards a future low‑carbon power generation system. However, given the environmental profile and operational variability of NGCC power plants, their eco-efficiency assessment is required. In this respect, this article uses a novel combined Life Cycle Assessment (LCA) and dynamic Data Envelopment Analysis (DEA) approach in order to estimate –over the period 2010–2015– the environmental impact efficiencies of 20 NGCC power plants located in Spain. A three-step LCA+DEA method is applied, which involves data acquisition, calculation of environmental impacts through LCA, and the novel estimation of environmental impact efficiency (overall- and term-efficiency scores) through dynamic DEA. Although only 1 out of 20 NGCC power plants is found to be environmentally efficient, all plants show a relatively good environmental performance with overall eco-efficiency scores above 60%. Regarding individual periods, 2011 was –on average– the year with the highest environmental impact efficiency (95%), accounting for 5 efficient NGCC plants. In this respect, a link between high number of operating hours and high environmental impact efficiency is observed. Finally, preliminary environmental benchmarks are presented as an additional outcome in order to further support decision-makers in the path towards eco-efficiency in NGCC power plants.

How to evaluate sustainability of supply chains? A dynamic network DEA approach

PurposeThe purpose of this paper is to develop a novel network and dynamic data envelopment analysis (DEA) model for evaluating sustainability of supply chains. In the proposed model, all links can be considered in calculation of efficiency score.Design/methodology/approachA dynamic DEA model to evaluate sustainable supply chains in which networks have series structure is proposed. Nature of free links is defined and subsequently applied in calculating relative efficiency of supply chains. An additive network DEA model is developed to evaluate sustainability of supply chains in several periods. A case study demonstrates applicability of proposed approach.FindingsThis paper assists managers to identify inefficient supply chains and take proper remedial actions for performance optimization. Besides, overall efficiency scores of supply chains have less fluctuation. By utilizing the proposed model and determining dual-role factors, managers can plan their supply chains properly and more accurately.Research limitations/implicationsIn real world, managers face with big data. Therefore, we need to develop an approach to deal with big data.Practical implicationsThe proposed model offers useful managerial implications along with means for managers to monitor and measure efficiency of their production processes. The proposed model can be applied in real world problems in which decision makers are faced with multi-stage processes such as supply chains, production systems, etc.Originality/valueFor the first time, the authors present additive model of network-dynamic DEA. For the first time, the authors outline the links in a way that carry-overs of networks are connected in different periods and not in different stages.

Evaluation of the results of a production simulation game using a dynamic DEA approach

Assessing the performance of teams participating in simulation games is a special area of performance evaluation, because not only the results achieved by the teams, but also the characteristics of the learning process are evaluated. In this paper, data envelopment analysis (DEA) is applied to the evaluation of the results of teams competing in a production simulation game used during a Master’s degree program. An output oriented, slack-based DEA model with constant returns to scale is proposed. The slack-based measure is modified to avoid the problems of negative data. A comparison of the results of the static and the dynamic DEA models reveals how the dynamic approach can help to explore the characteristics of the evolution of efficiency over time. Some general conclusions concerning the application of dynamic DEA models in a simulation environment are also presented.