The Application of Multidimensional Scaling for Recognising Similarities and Production Planning

  • Manfred Auch
Conference paper


Multidimensional scaling is a method for representing objects as points in a space. The similarity between 2 objects is represented by the distance between these 2 points. Thus the similarities within a set of objects can be recognized. One further advantage of multidimensional scaling is that ordinal scale level is sufficient for collecting data i.e. the exact value of the Objects’ characteristics need not be known. The rank order is sufficient and this simplifies data collecting considerably. Thus multidimensional scaling proves itself to be widely superior to cluster analysis which has been used up to now for classifying and recognizing similarities. This is proved by practical examples where multidimensional scaling was used for recognizing families of parts in order to design cellular manufacturing systems, for creating groups of products when designing assembly areas, for grouping working places according to tasks and stress criteria when working out design recommendations etc.


Distance Matrix Multidimensional Scaling Stress Group Cellular Manufacturing Design Recommendation 
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-Verlag Berlin Heidelberg 1985

Authors and Affiliations

  • Manfred Auch
    • 1
  1. 1.Fraunhofer-Institut für Arbeitswirtschaft und Organisation (IAO)StuttgartDeutschland

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