Reconfiguring Process Plans: A New Approach to Minimize Change

  • A. Azab
  • Hoda A. ElMaraghy
  • S. N. Samy
Part of the Springer Series in Advanced Manufacturing book series (SSAM)


In a customer driven market, the increasing number of product variants is a challenge most engineering companies face. Unpredictable changes in product design and associated engineering specifications trigger frequent changes in process plans, which often dictate costly and time consuming changes to jigs, fixtures and machinery. Process Planning should be further developed to cope with evolving parts and product families, increased mass customization and reduced-time-tomarket. Agility and responsiveness to change is important in process planning. The current methods do not satisfactorily support this changeable manufacturing environment. They involve re-planning or pre-planning, where new process plans are generated from scratch every time change takes place, which results in production delays and high costs due to consequential changes and disruptions on the shop floor. The obvious cost, limitations and computational burden associated with the re-planning/pre-planning efforts are avoided by the developed methods. A novel process planning concept and a new mathematical programming model have been developed to genuinely reconfigure process plans to optimize the scope, extent and cost of reconfiguration and to overcome the complexity and flaws of existing models. Hence, process planning has been fundamentally changed from an act of sequencing to that of insertion. For the first time, the developed methods reconfigure process plans to account for changes in parts’ features beyond the scope of original product families. A new criterion in process planning has been introduced to quantify the extent of resulting plan changes and their downstream implications. The presented method was shown to be cost effective, time saving, and conceptually and computationally superior. This was illustrated using two case studies in different engineering domains. The developed hypothesis and model have potential applications in other disciplines of engineering and sciences.


Machine Tool Process Planning Work Piece Product Family Precedence Constraint 
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.


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Copyright information

© Springer London 2009

Authors and Affiliations

  • A. Azab
    • 1
  • Hoda A. ElMaraghy
    • 1
  • S. N. Samy
    • 2
  1. 1.Department of Industrial & Manufacturing Systems EngineeringUniversity of WindsorWindsorCanada
  2. 2.Intelligent Manufacturing Systems (IMS) CenterUniversity of WindsorWindsorCanada

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