IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i2p746-d311085.html
   My bibliography  Save this article

Optimization of the Product–Service System Configuration Based on a Multilayer Network

Author

Listed:
  • Zaifang Zhang

    (School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China)

  • Darao Xu

    (School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China)

  • Egon Ostrosi

    (Université de Bourgogne Franche-Comté, UTBM, Pôle Industrie 4.0, Pôle ERgonomie et COnception des Systèmes ERCOS/ELLIADD EA4661, 25000 Belfort, France)

  • Hui Cheng

    (Shanghai Aerospace Equipments Manufacturer Co., Ltd., Shanghai 200245, China)

Abstract

Product–service systems (PSS) accelerate the transition of value creation patterns for manufacturing industries, from product design and production to the delivery of overall solution integrating products and services. Existing PSS configuration solutions provide customers with preferable product modules and service modules characterized by the module granularity. Every service module is essentially a whole service flow. However, the performance of the PSS configuration solution is greatly influenced by service details. In summary, this paper studied the configuration optimization of product-oriented PSS using a fine-grained perspective. A multilayer network composed of (i) a product layer, (ii) a service layer, and (iii) a resource layer was constructed to represent the elements (product parts, service activities, resources) and relationships in PSS. Service activities selection and resource allocation were considered jointly to construct the mathematical model of PSS configuration optimization, thus enabling the calculation of optimizing objectives (time, cost, and reliability) under constraints closer to the actual implementation. The importance degree of service activity was considered to improve the performance of service activities with higher importance. Corresponding algorithms were improved and applied for obtaining the optimal solutions. The case study in the automotive industry shows the various advantages of the proposed method.

Suggested Citation

  • Zaifang Zhang & Darao Xu & Egon Ostrosi & Hui Cheng, 2020. "Optimization of the Product–Service System Configuration Based on a Multilayer Network," Sustainability, MDPI, vol. 12(2), pages 1-25, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:2:p:746-:d:311085
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/2/746/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/2/746/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Arnold Tukker, 2004. "Eight types of product–service system: eight ways to sustainability? Experiences from SusProNet," Business Strategy and the Environment, Wiley Blackwell, vol. 13(4), pages 246-260, July.
    2. Johannes Matschewsky & Marianna Lena Kambanou & Tomohiko Sakao, 2018. "Designing and providing integrated product-service systems – challenges, opportunities and solutions resulting from prescriptive approaches in two industrial companies," International Journal of Production Research, Taylor & Francis Journals, vol. 56(6), pages 2150-2168, March.
    3. Dimitris Mourtzis & Sophia Fotia & Nikoletta Boli & Ekaterini Vlachou, 2018. "An approach for the modelling and quantification of PSS customisation," International Journal of Production Research, Taylor & Francis Journals, vol. 56(3), pages 1137-1153, February.
    4. Eppinger, Steven D. & Browning, Tyson R., 2012. "Design Structure Matrix Methods and Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262017520, December.
    5. Yoshiki Shimomura & Yutaro Nemoto & Takatoshi Ishii & Toshiyuki Nakamura, 2018. "A method for identifying customer orientations and requirements for product–service systems design," International Journal of Production Research, Taylor & Francis Journals, vol. 56(7), pages 2585-2595, April.
    6. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
    7. Omodei, Elisa & De Domenico, Manlio & Arenas, Alex, 2017. "Evaluating the impact of interdisciplinary research: A multilayer network approach," Network Science, Cambridge University Press, vol. 5(2), pages 235-246, June.
    8. Jin Shen & John Ahmet Erkoyuncu & Rajkumar Roy & Bin Wu, 2017. "A framework for cost evaluation in product service system configuration," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 6120-6144, October.
    9. Zaifang Zhang & Danhua Xu & Egon Ostrosi & Li Yu & Beibei Fan, 2019. "A systematic decision-making method for evaluating design alternatives of product service system based on variable precision rough set," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1895-1909, April.
    10. H.J. Long & L.Y. Wang & S.X. Zhao & Z.B. Jiang, 2016. "An approach to rule extraction for product service system configuration that considers customer perception," International Journal of Production Research, Taylor & Francis Journals, vol. 54(18), pages 5337-5360, September.
    11. Jiewu Leng & Pingyu Jiang, 2019. "Dynamic scheduling in RFID-driven discrete manufacturing system by using multi-layer network metrics as heuristic information," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 979-994, March.
    12. Marek Szwejczewski & Keith Goffin & Zissis Anagnostopoulos, 2015. "Product service systems, after-sales service and new product development," International Journal of Production Research, Taylor & Francis Journals, vol. 53(17), pages 5334-5353, September.
    13. Li, Qian & Zhou, Tao & Lü, Linyuan & Chen, Duanbing, 2014. "Identifying influential spreaders by weighted LeaderRank," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 47-55.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rahman Dwi Wahyudi & Moses Laksono Singgih & Mokh Suef, 2022. "Investigation of Product–Service System Components as Control Points for Value Creation and Development Process," Sustainability, MDPI, vol. 14(23), pages 1-23, December.
    2. Ana Batlles-delaFuente & Luis Jesús Belmonte-Ureña & José Antonio Plaza-Úbeda & Emilio Abad-Segura, 2021. "Sustainable Business Model in the Product-Service System: Analysis of Global Research and Associated EU Legislation," IJERPH, MDPI, vol. 18(19), pages 1-33, September.

    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. Mario Fargnoli & Nicolas Haber & Massimo Tronci, 2022. "Case Study Research to Foster the Optimization of Supply Chain Management through the PSS Approach," Sustainability, MDPI, vol. 14(4), pages 1-19, February.
    2. Nicolas Haber & Mario Fargnoli, 2021. "Sustainable Product-Service Systems Customization: A Case Study Research in the Medical Equipment Sector," Sustainability, MDPI, vol. 13(12), pages 1-20, June.
    3. Wei, Bo & Liu, Jie & Wei, Daijun & Gao, Cai & Deng, Yong, 2015. "Weighted k-shell decomposition for complex networks based on potential edge weights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 277-283.
    4. Sheikhahmadi, Amir & Nematbakhsh, Mohammad Ali & Shokrollahi, Arman, 2015. "Improving detection of influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 833-845.
    5. Chen, Yahong & Li, Jinlin & Huang, He & Ran, Lun & Hu, Yusheng, 2017. "Encouraging information sharing to boost the name-your-own-price auction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 108-117.
    6. Zhou, Ming-Yang & Xiong, Wen-Man & Wu, Xiang-Yang & Zhang, Yu-Xia & Liao, Hao, 2018. "Overlapping influence inspires the selection of multiple spreaders in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 76-83.
    7. Raja, Jawwad Z. & Frandsen, Thomas & Mouritsen, Jan, 2017. "Exploring the managerial dilemmas encountered by advanced analytical equipment providers in developing service-led growth strategies," International Journal of Production Economics, Elsevier, vol. 192(C), pages 120-132.
    8. Zhang, Jun-li & Fu, Yan-jun & Cheng, Lan & Yang, Yun-yun, 2021. "Identifying multiple influential spreaders based on maximum connected component decomposition method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    9. Xu, Shuang & Wang, Pei, 2017. "Identifying important nodes by adaptive LeaderRank," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 654-664.
    10. Ma, Qian & Ma, Jun, 2017. "Identifying and ranking influential spreaders in complex networks with consideration of spreading probability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 312-330.
    11. Johannes Matschewsky, 2019. "Unintended Circularity?—Assessing a Product-Service System for its Potential Contribution to a Circular Economy," Sustainability, MDPI, vol. 11(10), pages 1-27, May.
    12. Sheikhahmadi, Amir & Nematbakhsh, Mohammad Ali & Zareie, Ahmad, 2017. "Identification of influential users by neighbors in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 517-534.
    13. Jackson, Canek & Pascual, Rodrigo & Mac Cawley, Alejandro & Godoy, Sergio, 2023. "Product–service system negotiation in aircraft lease contracts with option of disagreement," Journal of Air Transport Management, Elsevier, vol. 107(C).
    14. Zhu, Hengmin & Yin, Xicheng & Ma, Jing & Hu, Wei, 2016. "Identifying the main paths of information diffusion in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 320-328.
    15. Li, Kai & Zhu, Hengmin & Zhang, Yihan & Wei, Jing, 2022. "Dynamic evaluation method on dissemination capability of microblog users based on topic segmentation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    16. Farsi, Maryam & Erkoyuncu, John Ahmet, 2021. "An agent-based approach to quantify the uncertainty in Product-Service System contract decisions: A case study in the machine tool industry," International Journal of Production Economics, Elsevier, vol. 233(C).
    17. Liu, Ying & Tang, Ming & Zhou, Tao & Do, Younghae, 2016. "Identify influential spreaders in complex networks, the role of neighborhood," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 289-298.
    18. Wen, Tao & Jiang, Wen, 2019. "Identifying influential nodes based on fuzzy local dimension in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 332-342.
    19. Fei, Liguo & Zhang, Qi & Deng, Yong, 2018. "Identifying influential nodes in complex networks based on the inverse-square law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1044-1059.
    20. Tomohiko Sakao & Abhijna Neramballi, 2020. "A Product/Service System Design Schema: Application to Big Data Analytics," Sustainability, MDPI, vol. 12(8), pages 1-22, April.

    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:gam:jsusta:v:12:y:2020:i:2:p:746-:d:311085. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.