PLM Applied to Manufacturing Problem Solving: A Case Study at Exide Technologies

  • Alvaro Camarillo
  • José RíosEmail author
  • Klaus-Dieter Althoff
Part of the Decision Engineering book series (DECENGIN)


This chapter presents a case study of a prototype Knowledge Management system that supports the process of Manufacturing Problem Solving in a multinational company. The prototype system allows capturing and reusing knowledge generated during the resolution of Overall Equipment Effectiveness (OEE) problems in multiple locations at shop floor level. The developed system was implemented in Exide Technologies. The system integrates the 8D method, Case-Based Reasoning (CBR) and Product Lifecycle Management (PLM). The PLM system is used as the source of extended problem context information (i.e. Products, Processes and Resources) that will enrich the similarity calculation of the CBR application. Process Failure Mode and Effect Analysis (PFMEA) is used as the source of the initial set of cases to populate the case-base. From the development perspective, the system comprises a multi-agent architecture based on SEASALT (Shared Experience using an Agent-based System Architecture LayouT) and programmed in Java. The development infrastructure comprises: Eclipse, JADE (Java Agent DEvelopment framework) and AML (Adaptive Mark-up Language) studio. The selected software applications are myCBR and Aras. The prototype system was tested and validated in three main steps with an increasing level of complexity. The results demonstrated the feasibility of the adopted approach. An overall description of the system, results, lessons learned, and recommendations are provided.


Product lifecycle management (PLM) Manufacturing problem solving (MPS) Fault diagnosis Process failure mode and effect analysis (PFMEA) Case-based reasoning (CBR) 


  1. 1.
    Bhamu J, Singh Sangwan K (2014) Lean manufacturing: literature review and research issues. Int J Oper Prod Manag 34(7):876–940CrossRefGoogle Scholar
  2. 2.
    Singh J, Singh H (2015) Continuous improvement philosophy—literature re-view and directions. Benchmarking Int J 22(1):75–119CrossRefGoogle Scholar
  3. 3.
    Koudal P, Chaudhuri A (2007) Managing the talent crisis in global manufacturing: strategies to attract and engage generation Y. A Deloitte Research Global Manufacturing Study, Deloitte ResearchGoogle Scholar
  4. 4.
    Ambos TC, Ambos B (2009) The impact of distance on knowledge transfer effectiveness in multinational corporations. J Int Manag 15(1):1–14MathSciNetCrossRefGoogle Scholar
  5. 5.
    Minbaeva DB (2007) Knowledge transfer in multinational corporations. Manag Int Rev 47(4):567–593CrossRefGoogle Scholar
  6. 6.
    Lundgren M, Hedlind M, Kjellberg T (2016) Model driven manufacturing process design and managing quality. Proc CIRP 50:299–304CrossRefGoogle Scholar
  7. 7.
    Liu DR, Ke CK (2007) Knowledge support for problem-solving in a production process: a hybrid of knowledge discovery and case-based reasoning. Expert Syst Appl 33:147–161CrossRefGoogle Scholar
  8. 8.
    Camarillo A, Ríos J, Althoff KD (2018) Knowledge-based multi-agent system for manufacturing problem solving process in production plants. J Manuf Syst 47:115–127CrossRefGoogle Scholar
  9. 9.
    Camarillo A, Ríos J, Althoff KD (2018) Product lifecycle management as data repository for manufacturing problem solving. Materials 11(8):1469–1488CrossRefGoogle Scholar
  10. 10.
    Riesenberger CA, Sousa SD (2010) The 8D methodology: an effective way to reduce recurrence of customer complaints. In: Proceedings of the world congress on engineering, p 3Google Scholar
  11. 11.
    Richter MM, Weber RO (2013) Case-based reasoning: a textbook. Springer, HeidelbergCrossRefGoogle Scholar
  12. 12.
    Stark J (2015) Product lifecycle management. Springer International PublishingGoogle Scholar
  13. 13.
    VDA (2015) Qualitätsmanagement in der Automobilindustrie – Qualitätsmanagement-Methoden Assessments. Verband der Autoindustrie (VDA)Google Scholar
  14. 14.
    Bach K (2012) Knowledge acquisition for case-based reasoning systems. Ph.D. thesis. University of HildesheimGoogle Scholar
  15. 15.
    Mikos WL, Ferreira JCE, Botura PEA, Freitas LS (2011) A system for distributed sharing and reuse of design and manufacturing knowledge in the PFMEA domain using a description logics-based ontology. J Manuf Syst 30:133–143CrossRefGoogle Scholar
  16. 16.
    Scippacercola F, Pietrantuono R, Russo S, Silva NP (2015) SysML-based and Prolog-supported FMEA. In: 2015 IEEE international symposium on software reliability engineering workshops (ISSREW), pp 174–181Google Scholar
  17. 17.
    Cearley DW, Walker MJ, Burke B (2015) Top 10 strategic technology trends for 2016: at a Glance. GartnerGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alvaro Camarillo
    • 1
    • 2
  • José Ríos
    • 2
    Email author
  • Klaus-Dieter Althoff
    • 3
    • 4
  1. 1.Exide Technologies SASGennevilliersFrance
  2. 2.Mechanical Engineering DepartmentUniversidad Politécnica de MadridMadridSpain
  3. 3.German Research Center for Artificial Intelligence (DFKI)KaiserslauternGermany
  4. 4.Institut für Informatik, University of HildesheimHildesheimGermany

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