JEDI: Just-in-Time Execution and Distribution Information Support System for Automotive Stamping Operations



Stamping is one of the most complex operations in the automotive supply chain, providing over 400 end items to dozens of assembly plants and service facilities. This operation consists of a complex network of blankers, presses, and subassemblies. Stamping is affected by much variability, such as unexpected machine and tool down time, quality concerns, and customer requirement fluctuations. These facilities typically run a tight schedule, and supply chain visibility is a critical factor in efficient operations. The data pertaining to operations is distributed across several systems including material requirements planning (MRP), plant floor automation, and logistics management. As a result, decision makers are faced with too much data and not enough information. This leads to time loss and effort spent in consolidating and comprehending the data. This chapter describes the Just-in-time Execution and Distribution Information (JEDI) system that collects and integrates relevant data from a set of disparate systems and generates a set of spreadsheet models that represent the stamping production and supply chain status. JEDI not only presents the information in an intuitive way, but also provides what-if analysis capability and decision support for scheduling and distribution.


Supply Chain Electronic Data Interchange Assembly Plant American National Standard Institute Material Requirement Planning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We gratefully acknowledge the invaluable contributions from many people at Ford Stamping, Material Planning & Logistics, Information Technology, and Research & Advanced Engineering. Specifically, we would like to thank John Batey, Edward Zvoch, James Higgins, Craig Morford, Mark Catri, and Robert Quick from Stamping, Rich Davidson, Kasey Kasemodel, and Serguei Vassiliev from IT, and Giuseppe Rossi from Research & Advanced Engineering. We would also like to thank Gloria Chou from Research & Advanced Engineering for providing study results on how pending cycle parts effect premium freight.


  1. 1.
    Barlatt A, Cohn A, Gusikhin O (2007) A hybridization of mathematical programming and search techniques for integrated operation and workforce planning. In: proceedings of the 2007 IEEE international conference on systems, man and cybernetics, pp 632–637Google Scholar
  2. 2.
    Barlatt A, Cohn A, Gusikhin O (2008) A hybrid approach for solving shift-selection and task-sequencing problems. Lect Notes Comput Sci 5015:288–292Google Scholar
  3. 3.
    Barlatt A, Cohn A, Gusikhin O (2010) A hybridization of mathematical programming and dominance-driven enumeration for solving shift-selection and task- sequencing problems. Comput Oper Res 37(7):1298–1307CrossRefGoogle Scholar
  4. 4.
    Barlatt A, Cohn A, Gusikhin O, Fradkin Y, Morford C (2009) Using composite variable modeling to achieve realism and tractability in production planning: an example from automotive stamping. IIE Trans 41:421–436Google Scholar
  5. 5.
    Fodor M, Gusikhin O, Tseng E, and Wang, W (2009) Integration of mobile RFID and inertial measurement for indoor tracking of forklifts moving containers. In Proceedings of the Third Workshop on Intelligent Vehicle Control and Intelligent Transportation Systems, pages 120–129, Milan, Italy.Google Scholar
  6. 6.
    Ford (2002) 830 planning schedule with release capability. Ford Motor Company.
  7. 7.
    Ford (2003) 866 production sequence. Ford Motor Company.
  8. 8.
    Ford (2008) 862 shipping schedule. com/GEC/edispecs/edispecs.asp. Ford Motor Company.
  9. 9.
    Grubar K (2002) Eaton corporation electronic data interchange (EDI) standards: 862 shipping schedule, Version 4010.
  10. 10.
    Grubar K (2006) Eaton corporation electronic data interchange (EDI) standards: 830 material release / forecast, Version 4010.
  11. 11.
    Gusikhin O, Caprihan R, Stecke K (2007) Least in-sequence proba- bility heuristic for mixed-volume production lines. Int J Prod Res 46(3):647–673CrossRefGoogle Scholar
  12. 12.
    Gusikhin O, Rossi G (2005a) The Knowledge Gap in Enter- prise Information Flow, chapter Improving Automotive Supplier Operations through Information Logistics, pp. 81–90. Jonkoping UniversityGoogle Scholar
  13. 13.
    Gusikhin O, Rossi G (2005) Well-connected. APICS Perform Advant 15(2):32–35Google Scholar
  14. 14.
    MEMA (1997) EDI transaction comparison, ANSI vs EDIFACT. MEMA Technology Council
  15. 15.
    Nicol M (1996) Reaching your customers and suppliers: electronic data interchange- EDI. Manufact-Line: NIST/Michigan Manufacturing Technology Center for Michigan Small and Medium-sized Manufacturers, 2Google Scholar

Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  1. 1.Ford Research & Advanced EngineeringDearbornUSA

Personalised recommendations