Cellular Production in Manufacturing

Group Technology

Overview.  Cellular manufacturing systems often use the concept of group technology (GT) in their basic design.  The relative juxtaposition of machine part characteristics are important in order to develop a logical relationship that facilitates organizational grouping.  Cellular manufacturing is the quintessential expression of this grouping and guides the design of cellular production (Singh, 1993, p. 284; Rajagopalan, & Batra, 1975, p. 567).  By the decomposition of manufacturing systems into cells, manufacturing processes gain unique efficiencies not otherwise appreciated (Singh, 1993, p. 284).  For instance, using GT, parts may be identified by characteristics which enable them to be produced efficiently by the best grouping of machine processing order possible to reduce their time in manufacturing (Singh, 1993, p. 284).  When viewed in this way the efficiencies gained may come through by reduction of work-in-process, reduced setup times, reduced tool requirements, and reduced lead time, generating efficient control of manufacturing operations (Singh, 1993, p. 284).  The expectation is value-added product quality and enhanced productivity (Singh, 1993, p. 284).

Using classification and appropriate coding systems.  Classification and coding systems are developed through the relationship of part design or part material attributes which guide processing order however, manufacturing specific attributes can also influence this system (Singh, 1993, p. 285).  Examples of similar attributes that can influence the development of part families are specific machining techniques such as cutting, drilling, extruding and cold forming.  Others are casting metals or layup and curing of advanced composite materials like Kevlar and graphite. 

Machine Grouping.  Machine-component grouping provides further refinement in cellular design (Singh, 1993, p. 285).  For instance, certain drilling processes require a specific order of machine tools in order to create a part properly. For instance, a sequential increase in drilling tool sizes used to create a properly dimensioned hole using a step increasing size of drills.  Machine-component grouping ensures that a proper production flow (Singh, 1993, p. 285) through those steps occur in the correct order.

Similarity Coefficient.  The basis behind similarity coefficient methods is to delineate measures of similarity between machines and their machining capabilities.  Based on these similarities, part families are created via subsequent machining processes thus creating an efficient production methodology (Singh, 1993, p. 286).

Heuristics.  Mathematical and heuristic methods form a sequential method of forming part families in the first stage of manufacturing then they implement a cost based mathematical

programming methodology to assign machines to part families in logical manufacturing cells (Singh, 1993, p. 286).

Pattern Recognition.  Pattern development using knowledge based systems and expert systems integration, optimizations qualities as it pertains to material handling capabilities, technological requirements and machine capacity and machine dimensions for the basis to create production cells (Singh, 1993, p. 287).

Fuzzy Clustering.  Fuzzy clustering develops unique solutions where issues of ambiguity in cell formation problems exists (Singh, 1993, p. 287).  Since many day-to-day manufacturing problems stem from machine cell operator availability, fuzzy multi-criteria decision making can enhance workflow by preventing interruptions as they occur (Azadeh, Nazari-Shirkouhi, Hatami-Shirkouhi, & Ansarinejad, 2011, p. 329).  In this way, efficient use of available qualified machine operators are incorporated into the overall production system planning logic (Azadeh et al., 2011, p. 329).

Neural Network Approach.  Neural network based approaches deploy an artificial intelligence process through mathematical models (Singh, 1993, p. 287).  As Artificial Neural Networks develop and are implemented into cell formations, unique and efficient processes take place (Singh, 1993, p. 287).  Some of these solutions control the best order of processes involved in batch production as well as the mixing of batches (Rajagopalan et al., 1975, p. 567) as inputs of demand and machine availability change. 

Conclusion

            Conventional manufacturing systems found in traditional manufacturing organizations have led designers to rely on GT to overcome many new age problems that exceed the limitations of traditional manufacturing methods (Saxena, & Jain, 2011, p. 11).  Due to the flexibility of mathematical algorithms, solutions beyond the scope of traditional manufacturing methods include, in part, to lot splitting between cells, work load balancing, alternative process routing, and more (Saxena et al., 2011, p. 31).  Additionally, GT also impacts worker performance and job satisfaction (Huber, & Hyer, 1985, p. 214) likely through successful job completion and a 33% increase in employee output over traditional methods (Huber et al., 1985, p. 216).  The basic idea is to obtain the least costly production method in the shortest time possible (Meredith, & Shafer, 2013, p. 66) in order to increase profits and promote customer satisfaction though fast service and fast time-to-market.

Insightfully yours,

Crystal Majdak, Co-Founder

Robert Majdak Sr., Co-Founder

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References

Azadeh, A., Nazari-Shirkouhi, S., Hatami-Shirkouhi, L., & Ansarinejad. A. (2011). A unique fuzzy multi-criteria decision making: Computer simulation approach for productive operators’ assignment in cellular manufacturing systems with uncertainty and vagueness, The International Journal of Advanced Manufacturing Technology. 56(1-4), 329-343.

Huber, V. L., & Hyer, N. L. (1985). The human factor in cellular manufacturing. Journal of  Operations Management, 5(2), 213–228.

Meredith, J. R., & Shafer, S. M. (2013). Operations management for MBAs (5th ed.). Hoboken, NJ: John Wiley & Sons, Inc. ISBN: 9781118369975.

Rajagopalan, R., & Batra, J. L. (1975). Design of cellular production systems. International Journal of Production Research, 13(6), 567.

Saxena, L. K., & Jain, P. K. (2011). Dynamic cellular manufacturing systems design—a

comprehensive model. The International Journal of Advanced Manufacturing Technology, 53(1-4), 11-34.

Singh, N. (1993). Design of cellular manufacturing systems: An invited review, European Journal of Operational Research. 69(3), 284–291.