Planning of manufacturing networks using an intelligent probabilistic approach for mass customised products

ORIGINAL ARTICLE

Abstract

Manufacturers around the globe are presented with the evident need to successfully capture and efficiently satisfy the increasing demand towards highly customised products. The trend for higher levels of customisation increases the operational costs, affects delivery times and worsens the environmental footprint of production. Moreover, the feasible alternative manufacturing network configurations increase together with the exploding product variety and the large pool of cooperating suppliers. The proposed research work describes an intelligent method that utilises three adjustable control parameters and can be used for the identification of efficient globalised manufacturing network configurations capable of carrying out the production of mass customised products. The decision support system presented allows the generation of alternative manufacturing network configurations and their evaluation, through a set of multiple conflicting user-defined criteria of cost, time, quality and environmental impact. The suggested approach, which is implemented into a web-based software tool, is investigated through a probabilistic analysis for guiding the decision-maker when selecting the values of the adjustable control parameters, in order to obtain high-quality manufacturing network designs. The applicability of the method is validated through a real-life pilot case, using data acquired from an automotive manufacturer.

Keywords

Manufacturing network design Mass customisation Decision-making Intelligent algorithm Decentralised manufacturing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mourtzis D, Doukas M (2014) The evolution of manufacturing systems: from craftsmanship to the era of customization. Design and Management of Lean Production Systems. In: Modrak V, Semanco P (eds) IGI Global, ISBN13: 9781466650398Google Scholar
  2. 2.
    Mourtzis D, Doukas M, Psarommatis F, Giannoulis C, Michalos G (2014) A web-based platform for mass customisation and personalisation. CIRP J Manuf Sci Technol 7(2):112–128CrossRefGoogle Scholar
  3. 3.
    Chryssolouris G (2006) Manufacturing systems—theory and practice, 2nd edn. Springer, New YorkGoogle Scholar
  4. 4.
    Gu P, Hashemian M, Nee AYC (2004) Adaptable design. CIRP Ann Manuf Technol 53/1:539–557CrossRefGoogle Scholar
  5. 5.
    Mourtzis D, Doukas M (2012) Decentralized manufacturing systems review: challenges and outlook. Logistics Research, Springer, ISSN: 1865–0368, doi: 10.1007/s12159-012-0085-x
  6. 6.
    Hu SJ, Ko J, Weyand L, El Maraghy HA, Kien TK, Koren Y, Bley H, Chryssolouris G, Nasr N, Shpitalni M (2011) Assembly system design and operations for product variety. CIRP Ann 60(2):715–733CrossRefGoogle Scholar
  7. 7.
    Pandremenos J, Paralikas J, Salonitis K, Chryssolouris G (2009) Modularity concepts for the automotive industry: a critical review. CIRP J Manuf Sci Technol 1(3):148–152CrossRefGoogle Scholar
  8. 8.
    Caux C, David F, Pierreval H (2006) Implementation of delayed differentiation in batch process industries: a standardisation problem. Int J Prod Res 44(16):3243–3255CrossRefMATHGoogle Scholar
  9. 9.
    Weber TA (2008) Delayed multi-attribute product differentiation. Decis Support Syst 44(2):447–468CrossRefGoogle Scholar
  10. 10.
    Helms M, Ahmadi M, Jih W, Ettkin L (2008) Technologies in support of mass customisation strategy: exploring the linkages between e-commerce and knowledge management. Comput Ind 59(4):351–363CrossRefGoogle Scholar
  11. 11.
    Nepal B, Monplaisir L, Famuyiwa O (2012) Matching product architecture with supply chain design. Eur J Oper Res 216(2):312–325MathSciNetCrossRefGoogle Scholar
  12. 12.
    Michalos G, Makris S, Papakostas N, Mourtzis D, Chryssolouris G (2010) Automotive assembly technologies review: challenges and outlook for a flexible and adaptive approach. CIRP J Manuf Sci Technol 2(2):81–91CrossRefGoogle Scholar
  13. 13.
    Hu SJ, Zhu X, Wang H, Koren Y (2008) Product variety and manufacturing complexity in assembly systems and supply chains. CIRP Ann Manuf Technol 57(1):45–48CrossRefGoogle Scholar
  14. 14.
    Mourtzis D, Doukas M, Psarommatis F (2014) Design of manufacturing networks for mass customisation using an intelligent search method. Int J Comput Integr Manuf. doi:10.1080/0951192X.2014.900867, Published online: 24-03-2014Google Scholar
  15. 15.
    Mourtzis D, Doukas M, Psarommatis F (2013) Design and operation of manufacturing networks for mass customisation. CIRP Ann Manuf Technol 63/1:467–470CrossRefGoogle Scholar
  16. 16.
    Wu C, Barnes D, Rosenberg D, Luo X (2009) An analytic network process-mixed integer multi-objective programming model for partner selection in agile supply chains. Prod Plan Control Manag Oper 20(3):254–275CrossRefGoogle Scholar
  17. 17.
    Threatte K, Graves SC (2001) Tactical shipping and scheduling at polaroid with dual lead-times. Innovation in Manufacturing Systems and Technology. url:http://hdl.handle.net/1721.1/4043
  18. 18.
    Tan PS, Lee S, Goh AES (2012) Multi-criteria decision techniques for context-aware B2B collaboration in supply chains. Decis Support Syst 52(4):779–789CrossRefGoogle Scholar
  19. 19.
    Wu C, Barnes D (2011) A literature review of decision-making models and approaches for partner selection in agile supply chains. J Purch Supply Manag 17(4):256–274CrossRefGoogle Scholar
  20. 20.
    van der Vorst J, Tromp SO, van der Zee DJ (2009) Simulation modelling for food supply chain redesign: integrated decision making on product quality, sustainability and logistics. Int J Prod Res 47(23):6611–6631CrossRefMATHGoogle Scholar
  21. 21.
    Persson F, Olhager J (2002) Performance simulation of supply chain designs. Int J Prod Econ 77(3):231–245CrossRefGoogle Scholar
  22. 22.
    Schneeweiss C (2003) Distributed decision making in supply chain management. Int J Prod Econ 84(1):71–83MathSciNetCrossRefGoogle Scholar
  23. 23.
    Biswas S, Narahari Y (2004) Object oriented modelling and decision support for supply chains. Eur J Oper Res 153(3):704–726CrossRefMATHGoogle Scholar
  24. 24.
    Zhang L, You X, Jiao J, Helo P (2009) Supply chain configuration with co-ordinated product, process and logistics decisions: an approach based on Petri nets. Int J Prod Res 47(23):6681–6706CrossRefMATHGoogle Scholar
  25. 25.
    Yao J, Liu L (2009) Optimization analysis of supply chain scheduling in mass customization. Int J Prod Econ 117(1):197–211CrossRefGoogle Scholar
  26. 26.
    Liang TF (2011) Application of fuzzy sets to manufacturing/distribution planning decisions in supply chains. Inf Sci 181(4):842–854CrossRefMATHGoogle Scholar
  27. 27.
    Mula J, Peidro D, Madroñero MD, Vicens E (2010) Mathematical programming models for supply chain production and transport planning. Eur J Oper Res 204(3):377–390CrossRefMATHGoogle Scholar
  28. 28.
    Kanda AA, Deshmukh SG (2008) Supply chain coordination: perspectives, empirical studies and research directions. Int J Prod Econ 115(2):316–335CrossRefGoogle Scholar
  29. 29.
    Pero M, Abdelkafi N, Sianesi A, Blecker T (2010) A framework for the alignment of new product development and supply chains. Supply Chain Manag Int J 15(2):115–128CrossRefGoogle Scholar
  30. 30.
    Salvador F, Rungtusanatham M, Forza C (2004) Supply-chain configurations for mass customization. Prod Plan Control Manag Oper 15(4):381–397CrossRefGoogle Scholar
  31. 31.
    Beamon BM (1999) Measuring supply chain performance. Int J Oper Prod Manag 19(3):275–292CrossRefGoogle Scholar
  32. 32.
    Jacobs FR, Bendoly E (2003) Enterprise resource planning: developments and directions for operations management research. Eur J Oper Res 146(2):233–240Google Scholar
  33. 33.
    Mourtzis D, Doukas M, Psarommatis F (2012) A multi-criteria evaluation of centralized and decentralized production networks in a highly customer-driven environment. CIRP Ann Manuf Technol 61/1:427–430CrossRefGoogle Scholar
  34. 34.
    Mourtzis D, Doukas M, Psarommatis F (2012) Design and planning of decentralised production networks under high product variety demand. Procedia CIRP 45th CIRP CMS 2012 3: 293–298. doi:10.1016/j.procir.2012.07.051
  35. 35.
    Michalos G, Makris S, Mourtzis D (2012) An intelligent search algorithm-based method to derive assembly line design alternatives. Int J Comput Integr Manuf 25(3):211–229CrossRefGoogle Scholar
  36. 36.
    Mourtzis D, Doukas M, Psarommatis F (2013) Environmental impact of centralised and decentralised production networks in the era of personalisation. Robust Manufacturing Control. Springer Berlin Heidelberg, ISBN 978-3-642-30748-5, Chapter 27, doi:10.1007/978-3-642-30749-2_26
  37. 37.
    EPA (2010) url:www.epa.gov
  38. 38.
    Chryssolouris G, Lee M (1994) An approach to real-time flexible scheduling. Int J Flex Manuf Syst 6(3):235–253CrossRefGoogle Scholar
  39. 39.
    Zhou G, Min H, Gen M (2002) The balanced allocation of customers to multiple distribution centers in the supply chain network: a genetic algorithm approach. Comput Ind Eng 43(1–2):251–261CrossRefGoogle Scholar
  40. 40.
    Saber AY, Venayagamoorthy GK (2010) Efficient utilization of renewable energy sources by gridable vehicles in cyber-physical energy systems. IEEE Syst J 4(3):285–294CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and AeronauticsUniversity of PatrasPatrasGreece

Personalised recommendations