Production flow analysis


In operations management and industrial engineering, production flow analysis refers to methods which share the following characteristics:
  1. Classification of machines
  2. Technological cycles information control
  3. Generating a binary product-machines matrix
Methods differ on how they group together machines with products. These play an important role in designing manufacturing cells.

Rank order clustering

Given a binary product-machines n-by-m matrix, rank order clustering is an algorithm characterized by the following steps:
  1. For each row i compute the number
  2. Order rows according to descending numbers previously computed
  3. For each column p compute the number
  4. Order columns according to descending numbers previously computed
  5. If on steps 2 and 4 no reordering happened go to step 6, otherwise go to step 1
  6. Stop

Similarity coefficients

Given a binary product-machines n-by-m matrix, the algorithm proceeds by the following steps:
  1. Compute the similarity coefficient for all with being the number of products that need to be processed on both machine i and machine j, u comprises the number of components which visit machine j but not k and vice versa.
  2. Group together in cell k the tuple with higher similarity coefficient, with k being the algorithm iteration index
  3. Remove row i* and column j* from the original binary matrix and substitute for the row and column of the cell k,
  4. Go to step 2, iteration index k raised by one
Unless this procedure is stopped the algorithm eventually will put all machines in one single group.