Exploration into The Effect of The Real Life Production Factors in The Assessment of Cellular Manufacturing System

Abstract

Cellular Manufacturing (CM) is a production philosophy that operates in view of  the Group Technology (GT) morality. CM offers a positive impact in the terms of enhancing the quality and increasing the productivity. One of the earlier and essential stages in the CM is known as a Feasibility Assessment (FA). FA considers as an evaluation stage and its results consider as a prediction results for the next design stage called Cell Formation (CF). The output of the FA includes the predicted number of machine cells, the decision of applying or not the CM and the quality of the expected solution. Most of the previous studies focused on studying the influence of the real life production features on the second stage (CF) and recorded significant results. However, an attempt was carried out in the current paper to study the influence of the real life production features on the first stage FA. For this purpose, 19 data sets, two Similarity Coefficients (SCs) based on the real life production features known as production volume and batch size were selected. The results of these two features compared with the results of one well known General Purpose Similarity Coefficient (GPSC) known as Jaccard. Jaccard works based on using only (0,1) matrix as an input data. The output of the current research referred that there is no significant influence of the real life production features on the FA, where 84% of data sets produced the same number of machine cells by using all the three different types of SCs. However, (16%) of datasets created different solutions Thus, Datasets based on (0,1) matrix and (GPSC), (Jaccard) are sufficient to use in the FA to predict the number of machine cells.