Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

Sustainability Assessment in Concrete Industry Using Stochastic Data Envelopment Analysis

Document Type : Research Paper

Authors
Fatemeh Shoae Shargh 1
Mansour Soufi 1
Alireza Amirteimoori 2
Mehdi Fadaei 1
1 Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran
2 Department of Applied Mathematics, Rasht Branch, Islamic Azad University, Rasht, Iran
Abstract
The aim of this research is to develop a stochastic data envelopment analysis (SDEA) model to assess the sustainability of active companies of concrete industry in Guilan province based on the sustainability indicators. At the first step, economic, social, and environmental indicators for evaluating the companies were identified through the literature review. At the next step, these indicators were evaluated using the fuzzy Delphi method and more important indicators were identified. These indicators, were categorized into the controllable, uncontrollable, desirable and undesirable inputs and outputs for assessing concrete companies. Finally, a new stochastic DEA model was developed for sustainability assessment of concrete companies in Guilan province in both linear and nonlinear equations. In the proposed models, errors are incorporated into the model via a stochastic component, which is specified by considering the constraints probabilistically. Based on the results of implementing the presented model, at an alpha level of 0.1, the companies Darvishan and Zahmatkesh, at an alpha level of 0.3, the companies Darvishan, Takht-e-Jamshid, and Zahmatkesh, and finally at an alpha level of 0.5, only the company Darvishan are sustainable. So, the only sustainable efficient company is Darvishan which should be considered as a benchmark for other units for achieving sustainability efficiency.
Keywords
Stochastic DEA
Undesirable Outputs
Uncontrollable Inputs
Subjects
Ahi, P., & Searcy, C. (2015). Assessing sustainability in the supply chain: A triple bottom line approach. Applied Mathematical Modelling, 39(10–11), 2882-2896.
Ahmadi, H. B., Kusi-Sarpong, S., & Rezaei, J. (2017). Assessing the social sustainability of supply chains using Best Worst Method. Resources. Conservation and Recycling, 126, 99-106.
Amirteimoori,A., Sahoo, B.K., Charles, V., & Mehdizadeh, S. (2022). Stochastic Data Envelopment Analysis. International Series in Operations Research & Management Science, in: Stochastic Benchmarking, Chapter 1, 55-76, Springer.
Bozorgi Gerdvisheh, F., Soufi, M., Amirteimoori, A., & Homayounfar, M. (2023). Efficiency Analysis of Banking Sector in Presence of Undesirable Factors Using Data Envelopment Analysis. Advances in Mathematical Finance and Applications, 8(2), 589-604.
Carissimi, M. C., Creazza, A., & Colicchia, C. (2023). Crossing the chasm: investigating the relationship between sustainability and resilience in supply chain management. Cleaner Logistics and Supply Chain, 7, 100098.
Chen, R.-H., Lin, Y., & Tseng, M.-L. (2015). Multicriteria analysis of sustainable development indicators in the construction minerals industry in China. Resources Policy, 46(1), 123-133.
Engida, T. G., Rao, X., Berentsen, P. B. M., & Oude Lansink, A. G. J. M. (2018). Measuring corporate sustainability performance – the case of European food and beverage companies. Journal of Cleaner Production, 195, 734-743.
Feil, A.A., de Quevedo, D.M., & Schreiber, D. (2015). Selection and identification of the indicators for quickly measuring sustainability in micro and small furniture industries. Sustainable Production and Consumption, 3, 34-44.
Geyi, D. G., Yusuf, Y., Menhat, M. S., Abubakar, T., & Ogbuke, N. J. (2020). Agile capabilities as necessary conditions for maximising sustainable supply chain performance: An empirical investigation. International Journal of Production Economics, 222, 107501.
Haghighi, S. M., Torabi, S. A., & Ghasemi, R. (2016). An integrated approach for performance evaluation in sustainable supply chain networks (with a case study). Journal of Cleaner Production, 137, 579-597.
Homayounfar, M., Amirteimoori, A., & Toloie-Eshlaghy, A. (2014). Production planning considering undesirable outputs-A DEA. International Journal of Applied Operational Research, 4(3), 1-11.
Huang, Z., & Li, S.X. (2001). Stochastic DEA Models With Different Types of Input-Output Disturbances. Journal of Productivity Analysis, 15, 95-113.
Hussain, J., Kui, Z., Khan, A., Akhtar, R., Ali, R., & Yin, Y. (2023). Proposing a sustainable investment index for measuring economic performance and sustainability: A step toward clean and affordable energy. Sustainable Energy Technologies and Assessments, 60, 103564.
Izadikhah, M., & Farzipoor Saen, R. (2016). Evaluating sustainability of supply chains by two-stage range directional measure in the presence of negative data. Transportation Research Part D: Transport and Environment, 49, 110-126.
Khodakarami, M., Shabani, A., Farzipoor Saen, R., & Azadi, M. (2015). Developing distinctive two-stage data envelopment analysis models: An application in evaluating the sustainability of supply chain management. Measurement, 70, 62-74.
Land, K. C., C. A. Knox Lovell, & Thore, S. (1993). Chance-Constrained Data Envelopment Analysis. Managerial and Decision Economics, 14(6), 541–554.
Nikolaou, I. E., Tsalis, T. A., & Evangelinos, K. I. (2019). A framework to measure corporate sustainability performance: A strong sustainability-based view of firm. Sustainable Production and Consumption, 18, 1-18.
Nozari, H., & Ghahremani-Nahr, J. (2021). Provide a framework for implementing agile big data-based supply chain (case study: FMCG companies). Innovation management and operational strategies, 2(2), 128-136.
Olesen, O., & Petersen, N. (2000): Foundation of chance constrained data envelopment analysis for Pareto-Koopmann efficient production possibility sets. In: Proc. International DEA Symposium 2000, Measurement and Improvement in the 21st Century, The University of Queensland, 313-349.
Olesen, O., & Petersen, N. (2002). The Use of Data Envelopment Analysis with Probabilistic Assurance Regions for Measuring Hospital Efficiency. Journal of Productivity Analysis, 17(1), 83-109,
Piya, S., Shamsuzzoha, A., & Khadem, M. (2019). An approach for analysing supply chain complexity drivers through interpretive structural modelling. International Journal of Logistics Research and Applications, 23(4), 311–336.
Piya, S., Shamsuzzoha, A., & Khadem, M. (2022). Analysis of supply chain resilience drivers in oil and gas industries during the COVID-19 pandemic using an integrated approach. Applied Soft Computing, 121, 108756.
Qorri, A., Gashi, S., & Kraslawski, A. (2022). A practical method to measure sustainability performance of supply chains with incomplete information. Journal of Cleaner Production, 341, 130707.
Sarker, M. R., Ali, S. M., Paul, S. K., & Munim, Z. H. (2021). Measuring sustainability performance using an integrated model. Measurement, 184, 109931.
Soufi, M., & Amirteimoori, A. (2019). Providing a New Targeting Model in a Centralized Decision Making Environment with a Multi-Component Network Structure. Journal of Operational Research and Its Applications, 16 (1), 93-115.
Vinodh, S., & Girubha, R. J. (2012). PROMETHEE based sustainable concept selection. Applied Mathematical Modelling, 36, 5301-5308.

  • Receive Date 19 December 2022
  • Revise Date 12 April 2023
  • Accept Date 21 June 2023