IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v188y2017icp86-104.html

Improving green flexibility through advanced manufacturing technology investment: Modeling the decision process

Author

Listed:
  • Bai, Chunguang
  • Sarkis, Joseph

Abstract

Globalization and greening are two major trends manufacturing. Each trend has increased organizational and supply chain risk and uncertainty. Advanced manufacturing technology (AMT) are resources that can competitively aid modern industry in this volatile and complex environment. Thus, the evaluation, selection and implementation of more environmentally conscious AMT is important for meeting global requirements, especially with respect to environmental sustainability. Although a wide variety of methods to support AMT selection and evaluation processes exist, important aspects including green flexibility performance and psychological characteristics of decision makers under risk and uncertainty are missing. This paper presents a novel method for general investment appraisal of AMT, but especially introducing the context of green flexibility within manufacturing organizations. This paper aims to (1) develop effective green flexibility measures for manufacturing firms, incorporating various economic and environmental flexibility types, (2) introduce a hybrid possibility multiple criteria decision model for AMT evaluation and ranking integrating neighborhood rough set theory and cumulative prospect theory based on the three-parameter interval grey number, and (3) investigate the application of the proposed method in an illustrative case example to help manufacturing practitioner and researchers understand how to investigate various AMTs in this decision environment. Various advantages and disadvantages of the methodology are introduced. The results are evaluated with theoretical, methodological and managerial implications identified. This paper sets the foundation for significant future research in green manufacturing flexibility in an AMT environment.

Suggested Citation

  • Bai, Chunguang & Sarkis, Joseph, 2017. "Improving green flexibility through advanced manufacturing technology investment: Modeling the decision process," International Journal of Production Economics, Elsevier, vol. 188(C), pages 86-104.
    • Handle: RePEc:eee:proeco:v:188:y:2017:i:c:p:86-104
    DOI: 10.1016/j.ijpe.2017.03.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527317300725
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2017.03.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Streimikiene, Dalia & Balezentis, Tomas & Krisciukaitienė, Irena & Balezentis, Alvydas, 2012. "Prioritizing sustainable electricity production technologies: MCDM approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3302-3311.
    2. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    3. Arijit Bhattacharya & Prasanta Kumar Dey & William Ho, 2015. "Green manufacturing supply chain design and operations decision support," International Journal of Production Research, Taylor & Francis Journals, vol. 53(21), pages 6339-6343, November.
    4. Kulak, Osman & Kahraman, Cengiz, 2005. "Multi-attribute comparison of advanced manufacturing systems using fuzzy vs. crisp axiomatic design approach," International Journal of Production Economics, Elsevier, vol. 95(3), pages 415-424, March.
    5. Tang, Christopher & Tomlin, Brian, 2008. "The power of flexibility for mitigating supply chain risks," International Journal of Production Economics, Elsevier, vol. 116(1), pages 12-27, November.
    6. Myint, San & Tabucanon, Mario T., 1994. "A multiple-criteria approach to machine selection for flexible manufacturing systems," International Journal of Production Economics, Elsevier, vol. 33(1-3), pages 121-131, January.
    7. Gwo-Hshiung Tzeng & Chi-Yo Huang, 2012. "Combined DEMATEL technique with hybrid MCDM methods for creating the aspired intelligent global manufacturing & logistics systems," Annals of Operations Research, Springer, vol. 197(1), pages 159-190, August.
    8. Moon, Karen Ka-Leung & Yi, Candace Ying & Ngai, E.W.T., 2012. "An instrument for measuring supply chain flexibility for the textile and clothing companies," European Journal of Operational Research, Elsevier, vol. 222(2), pages 191-203.
    9. Vicente Cuñat & Maria Guadalupe, 2009. "Globalization and the Provision of Incentives inside the Firm: The Effect of Foreign Competition," Journal of Labor Economics, University of Chicago Press, vol. 27(2), pages 179-212, April.
    10. Bai, Chunguang & Dhavale, Dileep & Sarkis, Joseph, 2016. "Complex investment decisions using rough set and fuzzy c-means: An example of investment in green supply chains," European Journal of Operational Research, Elsevier, vol. 248(2), pages 507-521.
    11. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    12. Roh, James & Hong, Paul & Min, Hokey, 2014. "Implementation of a responsive supply chain strategy in global complexity: The case of manufacturing firms," International Journal of Production Economics, Elsevier, vol. 147(PB), pages 198-210.
    13. Shang, Jen & Sueyoshi, Toshiyuki, 1995. "A unified framework for the selection of a Flexible Manufacturing System," European Journal of Operational Research, Elsevier, vol. 85(2), pages 297-315, September.
    14. Yang, Chung-Shan & Lu, Chin-Shan & Haider, Jane Jing & Marlow, Peter Bernard, 2013. "The effect of green supply chain management on green performance and firm competitiveness in the context of container shipping in Taiwan," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 55(C), pages 55-73.
    15. Bai, Chunguang & Sarkis, Joseph, 2013. "A grey-based DEMATEL model for evaluating business process management critical success factors," International Journal of Production Economics, Elsevier, vol. 146(1), pages 281-292.
    16. Kaluszka, Marek & Krzeszowiec, Michał, 2012. "Pricing insurance contracts under Cumulative Prospect Theory," Insurance: Mathematics and Economics, Elsevier, vol. 50(1), pages 159-166.
    17. Lowe, Antony & Ridgway, Keith & Atkinson, Helen, 2000. "QFD in new production technology evaluation," International Journal of Production Economics, Elsevier, vol. 67(2), pages 103-112, September.
    18. Chunguang Bai & Joseph Sarkis, 2013. "Green information technology strategic justification and evaluation," Information Systems Frontiers, Springer, vol. 15(5), pages 831-847, November.
    19. Talluri, Srinivas & Paul Yoon, K., 2000. "A cone-ratio DEA approach for AMT justification," International Journal of Production Economics, Elsevier, vol. 66(2), pages 119-129, June.
    20. Boonman, H.J. & Hagspiel, V. & Kort, P.M., 2015. "Dedicated vs product flexible production technology: Strategic capacity investment choice," European Journal of Operational Research, Elsevier, vol. 244(1), pages 141-152.
    21. Bai, Chunguang & Sarkis, Joseph, 2010. "Integrating sustainability into supplier selection with grey system and rough set methodologies," International Journal of Production Economics, Elsevier, vol. 124(1), pages 252-264, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wu, Yunna & Xu, Chuanbo & Zhang, Ting, 2018. "Evaluation of renewable power sources using a fuzzy MCDM based on cumulative prospect theory: A case in China," Energy, Elsevier, vol. 147(C), pages 1227-1239.
    2. Belles-Sampera, Jaume & Merigó, José M. & Guillén, Montserrat & Santolino, Miguel, 2013. "The connection between distortion risk measures and ordered weighted averaging operators," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 411-420.
    3. Martina Nardon & Paolo Pianca, 2019. "Behavioral premium principles," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 42(1), pages 229-257, June.
    4. Dubey, Vivek Kumar & Chavas, Jean-Paul & Veeramani, Dharmaraj, 2018. "Analytical framework for sustainable supply-chain contract management," International Journal of Production Economics, Elsevier, vol. 200(C), pages 240-261.
    5. Tiantian Mao & Jun Cai, 2018. "Risk measures based on behavioural economics theory," Finance and Stochastics, Springer, vol. 22(2), pages 367-393, April.
    6. Martina Nardon & Paolo Pianca, 2019. "Insurance premium calculation under continuous cumulative prospect theory," Working Papers 2019:03, Department of Economics, University of Venice "Ca' Foscari".
    7. Siddharth Shankar Rai & Sunil Girib, 2019. "Assessment of supply chain agility in the Indian garment industry," Asian Journal of Empirical Research, Asian Economic and Social Society, vol. 9(10), pages 265-280, October.
    8. Jian-Peng Chang & Heng-Xin Ren & Luis Martínez & Witold Pedrycz & Zhen-Song Chen, 2024. "Requirement-driven supplier selection: a multi-criteria QFD-based approach under epistemic and stochastic uncertainties," Annals of Operations Research, Springer, vol. 342(2), pages 1079-1128, November.
    9. van den Bergh, J.C.J.M. & Botzen, W.J.W., 2015. "Monetary valuation of the social cost of CO2 emissions: A critical survey," Ecological Economics, Elsevier, vol. 114(C), pages 33-46.
    10. Shuang Yao & Donghua Yu & Yan Song & Hao Yao & Yuzhen Hu & Benhai Guo, 2018. "Dry Bulk Carrier Investment Selection through a Dual Group Decision Fusing Mechanism in the Green Supply Chain," Sustainability, MDPI, vol. 10(12), pages 1-19, November.
    11. Shoji, Isao & Kanehiro, Sumei, 2016. "Disposition effect as a behavioral trading activity elicited by investors' different risk preferences," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 104-112.
    12. Jonathan Meng & Feng Fu, 2020. "Understanding Gambling Behavior and Risk Attitudes Using Cryptocurrency-based Casino Blockchain Data," Papers 2008.05653, arXiv.org, revised Aug 2020.
    13. Daniel Fonseca Costa & Francisval Carvalho & Bruno César Moreira & José Willer Prado, 2017. "Bibliometric analysis on the association between behavioral finance and decision making with cognitive biases such as overconfidence, anchoring effect and confirmation bias," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1775-1799, June.
    14. Jos'e Cl'audio do Nascimento, 2019. "Behavioral Biases and Nonadditive Dynamics in Risk Taking: An Experimental Investigation," Papers 1908.01709, arXiv.org, revised Apr 2023.
    15. Francesco GUALA, 2017. "Preferences: Neither Behavioural nor Mental," Departmental Working Papers 2017-05, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    16. Bin Zou, 2017. "Optimal Investment In Hedge Funds Under Loss Aversion," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(03), pages 1-32, May.
    17. Wiafe, Osei K. & Basu, Anup K. & Chen, En Te, 2020. "Portfolio choice after retirement: Should self-annuitisation strategies hold more equities?," Economic Analysis and Policy, Elsevier, vol. 65(C), pages 241-255.
    18. Nicholas Barberis, 2012. "A Model of Casino Gambling," Management Science, INFORMS, vol. 58(1), pages 35-51, January.
    19. Lovric, M. & Kaymak, U. & Spronk, J., 2008. "A Conceptual Model of Investor Behavior," ERIM Report Series Research in Management ERS-2008-030-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    20. Goytom Abraha Kahsay & Daniel Osberghaus, 2018. "Storm Damage and Risk Preferences: Panel Evidence from Germany," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(1), pages 301-318, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:188:y:2017:i:c:p:86-104. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.