Elsevier

Computers & Industrial Engineering

Volume 135, September 2019, Pages 1224-1238
Computers & Industrial Engineering

Assessing sustainability of supply chains: An inverse network dynamic DEA model

https://doi.org/10.1016/j.cie.2018.11.009Get rights and content

Highlights

  • We develop a network dynamic SBM model to assess sustainability of supply chains.

  • An inverse DEA model with network and dynamic structure is proposed.

  • Our proposed model produces new sets of inputs and outputs.

Abstract

In this paper, we assess sustainability of supply chains by data envelopment analysis (DEA). DEA is a popular and beneficial method to assess efficiency of decision making units (DMUs). We introduce a network dynamic DEA model to assess sustainability of supply chains in multiple periods. Furthermore, we introduce an inverse network DEA model in dynamic context. Given results of case study, sustainable supply chains invest on triple bottom-lines of sustainability.

Introduction

Sustainable supply chain management is defined as strategic, transparent integration and achievement of an organization’s social, environmental, and economic goals in systemic coordination of key inter-organizational business processes for improving long-term economic performance of individual company and its supply chains (Mentzer et al., 2002). Sustainability in supply chain was started by Drumwright, 1994, Murphy et al., 1994. Environmental and social responsibilities should be considered in supply chains (Azadnia, Saman, & Wong, 2015).

Wittstruck and Teuteberg (2012) assume triple bottom-lines (social, economic, and environmental factors) of sustainability of supply chains as triple pillars of a house. To assess sustainability of supply chains, we use network dynamic data envelopment analysis (DEA). Charnes, Cooper, and Rhodes (1978) invented DEA and is a method to assess efficiency of supply chains (Kumar, Jain, & Kumar, 2014). Given multi-stage nature of supply chains, we use network DEA which has been introduced by Färe and Grosskopf (1996). Tone and Tsutsui, 2009, Tone and Tsutsui, 2014 measured efficiency of each division of decision making unit (DMU) by network DEA. Izadikhah and Farzipoor Saen, 2018, Badiezadeh et al., 2018 utilized network DEA model to assess sustainability of supply chains. However, in this paper, we incorporate dynamic structure into our proposed network DEA model. A couple of researchers have introduced dynamic DEA models to evaluate DMUs over time (Färe & Grosskopf, 1997). Tone and Tsutsui (2010) measured DMU’s efficiency in each period by a dynamic slacks-based measure (SBM) model. We utilize inverse DEA model to deal with investment on each triple bottom-line of sustainability.

In inverse DEA model, decision maker changes inputs/outputs for each DMU, then inverse model calculates proper outputs/inputs without changes in current efficiency score of DMU under evaluation (DMUo) (Lertworasirikul, Charnsethikul, & Fang, 2011). Inverse DEA model was proposed, for the first time, by Wei, Zhang, and Zhang (2000). Inverse DEA models can be used in resource allocation (Hadi-Vencheh & Foroughi, 2006) and investment analysis (Lertworasirikul et al., 2011) problems. Kalantary, Farzipoor Saen, and Toloie Eshlaghy (in press) proposed an inverse DEA model to assess sustainability of supply chains. They utilized input (output)-oriented range adjusted measure (RAM) model. However, in proposed model of Kalantary et al. (in press), inputs and outputs’ changes should not violate from their ranges. In this paper, this issue is resolved. This paper has following contributions:

  • For the first time, we develop a network dynamic SBM model to assess sustainability of supply chains.

  • For the first time, an inverse DEA model with network and dynamic structure is proposed.

  • Our proposed inverse DEA model changes inputs and outputs and produces new sets of inputs and outputs.

To the best of our knowledge, there is no paper on inverse network dynamic DEA model to assess sustainability of supply chains. The objective of this paper is to propose a network dynamic SBM model to assess sustainability of supply chains. Furthermore, an inverse network dynamic SBM model is proposed to check whether or not balanced investment on each triple bottom-line of sustainability has occurred.

Structure of this paper is organized as follows: In Section 2, literature review is given. In Section 3, we develop our inverse DEA model. In Section 4, case study is presented. We discuss results and managerial implications in Section 5. Conclusions are explained in Section 6.

Section snippets

Sustainable supply chain management

Environmental awareness of societies and consumers are increased (Zhang, Wang, & You, 2015). Companies are under pressure to consider environmental issues (Govindan, Khodaverdi, & Jafarian, 2013). Eskandarpour, Dejax, Miemczyk, and Péton (2015) analyzed 87 papers in the field of sustainable supply chain network design. Companies have different reasons to apply sustainable principles in their supply chains (Carbone, Moatti, & Wood, 2012). Zhu and Sarkis (2007) emphasized on higher business

Basic SBM model and inverse SBM model

General output-oriented model is as follows (Wei et al., 2000):Maxp0s.t:jnxijλjxio,i=1,...,mjnyrjλjyrop0,r=1,...,lδ1(jNλj+δ2(-1)δ3v)=δ1λj,v0,i,j,r(j=1,...,n)

In model (1), xio and yro are ith input and rth output of DMUo, respectively. There are m inputs and l outputs. Also, there are n DMUs. In model (1), po is optimum objective function value. Parameters δ1, δ2, and δ3 are 0 and 1. Model (1) can be a CCR (δ1=0), BCC (δ1=1 and δ2=0), non-increasing (δ1=δ2=1 and δ3=0), or non-decreasing (

Case study

To validate proposed models, a case study is presented. Iran Dairy Industries (IDI) Co. is a leading dairy manufacturer in Iran. IDI Co. has 13 factories across Iran. IDI wishes to assess overall, divisional, and term efficiencies of 13 factories. Table 2 summarizes criteria of assessment. Dataset dates back to 2012–2015 (see Table 3 in Appendix A). Each factory has 3 stations including processing, packing, and distribution. Structure of each factory is depicted in Fig. 1. Table 4 shows results

Results and managerial implications

Table 3, Table 5 are results of models (26), (27), respectively. Given Table 4, following points are presented:

  • 1.

    There are 5 efficient factories in which their efficiency scores are unity; i.e., North-West 2, South, Central 1, Capital, North-West 3, and North-East.

  • 2.

    North 1 has the worst efficiency score.

  • 3.

    West 2 has a decreasing trend over 4 years. Central 2 has an increasing trend.

  • 4.

    South West and North 1 have a fixed trend throughout 4 years.

  • 5.

    As is seen in Table 4, all factories have almost a stable

Conclusions

Customers’ environmental awareness influences competition among companies (Lertworasirikul et al., 2011, Liu et al., 2012). Furthermore, greening sensitivity of customers is another factor which affects organization’s profit (Ghosh & Shah, 2015). Sustainable supply chain has three pillars including environmental performance, economic performance, and social performance (Wittstruck & Teuteberg, 2012) which should be addressed (Li, 2013).

We developed an inverse network dynamic DEA model to

Acknowledgments

Authors would like to appreciate constructive comments of two anonymous Reviewers.

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