Production flow analysis explained

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 (1 if a given product requires processing in a given machine, 0 otherwise)

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

b _{ip} , rank order clustering^[1] is an algorithm characterized by the following steps:

1. For each row i compute the number

\sum ^{m} _{p =1} b _{ip} * 2 ^{m-p}

1. Order rows according to descending numbers previously computed
2. For each column p compute the number

\sum ^{n} _{i =1} b _{ip} * 2 ^{n-i}

1. Order columns according to descending numbers previously computed
2. If on steps 2 and 4 no reordering happened go to step 6, otherwise go to step 1
3. Stop

Similarity coefficients

Given a binary product-machines n-by-m matrix, the algorithm proceeds^[2] by the following steps:

1. Compute the similarity coefficient

s _{ij} = n _{ij} /(n _{ij} + u) for all with n _{ij} 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.

1. Group together in cell k the tuple (i*,j*) with higher similarity coefficient, with k being the algorithm iteration index
2. Remove row i* and column j* from the original binary matrix and substitute for the row and column of the cell k,

s _{rk} = max(s _{ri*} ,s _{rj*} )

1. 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.

Notes and References

1. King, J. R., Machine-component grouping in production flow analysis: an approach using a rank order clustering algorithm, International Journal of Production Research, Vol.18 1980 http://www.tandfonline.com/doi/abs/10.1080/00207548008919662#.UeAI5eGLe1E
2. Adapted from MCauley, Machine grouping for efficient production, Production Engineer 1972 https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=04913845