How to evaluate sustainability of supply chains? A dynamic network DEA approach
| DOI | https://doi.org/10.1108/IMDS-09-2016-0389 |
| Date | 16 October 2017 |
| Published date | 16 October 2017 |
| Pages | 1866-1889 |
| Author | Vahid Shokri Kahi,Saeed Yousefi,Hadi Shabanpour,Reza Farzipoor Saen |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
How to evaluate sustainability of
supply chains? A dynamic
network DEA approach
Vahid Shokri Kahi
Department of Management and Accounting, Karaj Branch,
Islamic Azad University, Karaj, Islamic Republic of Iran
Saeed Yousefi
Pegah Distribution Company,
Karaj, Islamic Republic of Iran
Hadi Shabanpour
Karaj Branch, Islamic Azad University,
Karaj, Islamic Republic of Iran, and
Reza Farzipoor Saen
Department of Industrial Management, Karaj Branch,
Islamic Azad University, Karaj, Islamic Republic of Iran
Abstract
Purpose –The 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/approach –A 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.
Findings –This 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/implications –In real world, managers face with big data. Therefore, we need to
develop an approach to deal with big data.
Practical implications –The 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/value –For 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.
Keywords Data envelopment analysis, Sustainable supply chain management, Efficiency evaluation,
Food supply chain, Dynamic DEA, Network DEA, Slacks-based measure
Paper type Research paper
Industrial Management & Data
Systems
Vol. 117 No. 9, 2017
pp. 1866-1889
Emerald Publishing Limited
0263-5577
DOI 10.1108/IMDS-09-2016-0389
Received 22 September 2016
Revised 20 November 2016
7 January 2017
4 March 2017
Accepted 7 March 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
© Vahid Shokri Kahi, Saeed Yousefi, Hadi Shabanpour and Reza Farzipoor Saen. Published by
Emerald Publishing Limited. This article is published under the Creative Commons Attribution
(CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this
article ( for both commercial & non-commercial purposes), subject to full attribution to the original
publication and authors. The full terms of this licence may be seen at: http://creativecommons.org/
licences/by/4.0/legalcode
Authors would like to appreciate helpful comments of two anonymous reviewers.
1866
IMDS
117,9
1. Introduction
Supply chain is a purposeful combination of interconnected and interdependent
organization. Although, units of supply chains are separated legally, they are connected
by flow of material, information, and finance. Providing the best combination of resources
via outsourcing is the main issue of supply chain management (SCM) in modern business
enterprises (Ageron et al., 2012). Price, quality, flexibility, and supplier reputation are
economic criteria in SCM evaluation (Bai and Sarkis, 2010).
Nowadays, sustainability factors play a critical role in long-term achievement of SCM;
accordingly, purchasing process becomes more complicated with social and environmental
pressures. As Govindan et al. (2013) addressed, over the past decades, due to rapid reduction of
natural resources, concerns over wealth inequality and corporate social responsibility,
sustainability has become important for researchers and scholars. In other words, there are
pressures which force SCM to focus not only on economic but also on social and environmental
criteria (Dyllick and Hockerts, 2002). Thus, sustainable supply chain may be of great importance
attributable to surging environmental conservation and societal prosperity while reinforcing
economic intention of organization. To do so, companies should conserve resources, optimize
processes, uncover product innovations, save costs, increase productivity and promote corporate
values by managing and improving environmental, social, and economic performances across
supply chains (Seuring and Müller, 2008). On the other hand, there are several sustainability
criteria which complicate SCM’s evaluation. This means that decision-makers encounter some
discretionary/free and even contradictory criteria while evaluating sustainability of SCM.
Dual-role links, inputs, desirable and undesirable outputs are some of the main criteria.
In an accurate appraisal of supply chain, interactions among suppliers should be taken
into consideration. To evaluate sustainability of SCM, dealing with multiple criteria has
been one of the significant concerns in preceding models (Yousefi et al., 2016). To deal with
multiple criteria, this paper develops a novel network-dynamic data envelopment analysis
(DEA) model. Wong et al. developed two DEA models including technical efficiency and cost
efficiency models to explain application of DEA in measuring supply chain performance.
Traditional DEA models measure relative efficiency of decision-making units (DMUs)
(Charnes et al., 1978; Tavassoli et al., 2014).
However, one of the most growing criticisms in these sorts of models is that the DMUs
are assessed in a specific period. Given fluctuations in performance of DMUs during several
periods, considering just a specific period is insufficient in comprehensive efficiency
evaluation. In dynamic DEA presented by Tone and Tsutsui (2010), the DMUs’efficiencies
are assessed during several periods. However, dynamic DEA does not deal with DMUs’
internal structures. Network DEA (NDEA) deals with DMUs with internal structures
(Yu and Lin, 2008). Tone and Tsutsui (2014) proposed network-dynamic DEA model in
which the DMUs were assessed in multiple periods with internal interactions. Their model is
based upon slack-based measure (SBM) model.
Objective of this paper is to improve the dynamic DEA model proposed by Tone and Tsutsui
(2014). To this end, we develop a novel dynamic DEA model with network structure in which, for
the first time, the overall carry-overs of a network in period (t) enter next period (t+1). Note that
Tone and Tsutsui (2014) connected multiple stages in different periods. In Tone and
Tsutsui (2014), the carry-overs of each stage inside period (t) enter the next period (t+1)
separately and independently. However, in our proposed approach, the overall values of
carry-overs for each network are obtained and enter the next period. In other words, Tone and
Tsutsui (2014) connected the stages to each other (among periods) but we connect each network
(as a whole) to other networks. Hence, as it is shown in Figure 1, we consider each period (t)asa
network which enters next period (t+1) by carry-overs. Here, in addition, free links among
periods are defined in terms of desirable and undesirable carry-overs. Figure 1 displays our novel
network dynamic DEA structure. Note that all notations in Figure 1 are defined in Section 3.
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A dynamic
network DEA
approach
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