Functional and Cellular Layouts of Product Variants: History
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Contributor:
Simon Li
,
Bahareh Eshragh
,
Akposeiyifa Joseph Ebufegha

Due to market competition, manufacturers typically produce their products with different customized features, leading to the production of product variants (or a product family). Since the market trend can change swiftly, the demands of individual product variants can be difficult to predict. Two flexible manufacturing layouts are commonly considered: functional and cellular layouts. While the functional layout is more resilient to demand changes due to better resource pooling, the cellular layout can be more productive on some occasions due to better routing efficiency.

  • product variety
  • cellular layouts
  • functional layouts

1. Introduction

Due to market competition, manufacturers need to serve the diverse needs of customers. This leads to the topic of product variety, which concerns the design and manufacturing of a set of product variants (or a product family) [1]. To manage the complexity of production, modular design is often adopted, wherein product variants of the same family share the same product architecture with different components or modules [2]. Product variants can also share common parts to keep the production cost manageable [3].
Industry 4.0, as an important technological trend, refers to a range of interconnected technologies such as cloud computing, the Internet of Things, and cyber-physical systems, which can support production systems in coping with market challenges [4]. Its potential has been discussed in the contexts of product customization [5], cellular manufacturing [6], mass personalization production [7], and sustainable manufacturing [8]. To deploy new technologies of Industry 4.0, decision-making methods have been proposed to support the selection of digital technologies [9] and sustainable machining process [10].
While the demands for certain product types (e.g., total vehicle sales) can be forecasted, estimating the demands for individual product variants (e.g., sales of a specific vehicle model) is more challenging. Further, it has been identified that the demands of product variants can be negatively correlated [11]. For example, as total vehicle sales remain relatively stable, higher sales of vehicles model ABC could imply lower sales of some other vehicle models. One practical consideration is how the uncertain demands of product variants, which are negatively correlated, can impact the resilience of flexible manufacturing systems in view of their productivity.
Two layouts for flexible manufacturing are common in practice: functional and cellular layouts. Functional layouts focus on grouping machines of similar manufacturing (or machining) functions (e.g., drilling machines are grouped to form a drilling department on the production floor). As work-in-progress parts are free to visit different manufacturing departments, functional layouts allow for flexible routings. They also tend to be more resilient to changes in part demands due to resource pooling [12]. In contrast, cellular layouts form different groups of dissimilar machines (i.e., manufacturing cells), where each group can produce a subset of parts (or part family). Since cells are specialized in producing specific parts, they tend to be more efficient in terms of productivity [13]. However, they are less flexible due to more restricted routings and the loss of resource pooling [12].

2. Functional and Cellular Layouts

The difference between functional and cellular layouts lies in their principles of organizing (or grouping) machines on a production floor. While the functional layout focuses on grouping machines with similar manufacturing (or machining) functions, the cellular layout forms different groups of dissimilar machines (i.e., manufacturing cells), where each group can produce a subset of parts (or part families). According to the review by [14], the comparison of functional and cellular layouts has been studied since at least the 1970s. As a new concept back then, early studies tried to identify and explore the potentials of the cellular layout [13][15]. When the concepts and practices of group technology and cellular manufacturing became better known, researchers conducted comparison studies between functional and cellular layouts using simulations and empirical data. The review by [14] summarized the trade-off in performance metrics between the two layouts from prior studies. Though there is no consensus on all performance aspects, they generally identify some patterns. First, the cellular layout tends to perform better with shorter setup and inter-station move times. In contrast, the functional layout tends to work well with shorter wait times in machine queues and flow times.
Following the review paper by [14], several comparison studies can be found in the literature. Huq et al. [16] examined how lot sizes and setup time reductions affect the performance of both layouts. Their study emphasized the need for setup time reduction to make the cellular layout competitive, while large lot sizes tend to narrow the throughput performance of two layouts. Assad et al. [12] examined the trade-off between pooling loss (due to the splitting of machine resources) and setup time reduction, justifying the need for setup time reduction in the cellular layout to compensate its pooling loss. Pitchuka et al. [17] analyzed the effects of four factors (i.e., processing time, setup time, batch size, and part arrival) on the queue times of both layouts and verified that the partitioning of part production into cells can reduce the variability of processing time and part arrival. This benefit of variability reduction could compensate the disadvantage of the pooling loss of the cellular layout in some cases.
In addition, researchers have started to extend their considerations beyond the traditional scope of investigations, such as quantification of operational cost (including setup cost, work-in-progress inventory cost, quality cost, and setup time reduction cost) [18], the use of the Taguchi method (or design of experiments) for comparative study [19], and the implementation of pull production control through “Constant Work-in-Progress” (CONWIP) in contrast to kanban [20].
Early papers have identified that changes in part demands can alter the relative performance of functional and cellular layouts [21][22][23]. Seifoddini and Djassemi [24] characterized the percentage of product mix change and showed that the cellular layout tends to perform weakly with increasing product mix variations. Djassemi [25] followed a similar characterization of changes for a system with flexible cross-trained operators and obtained a similar observation that the cellular layout tends to be more sensitive to demand changes. Jithavech & Krishnan [26] interpreted the uncertainty of product demands as a type of risk, which can affect a layout’s efficiency. Thus, demand forecasts and multi-period information have been considered for designing cellular layouts [27][28][29][30].
In the context of uncertain demands of product variants, both functional and cellular layouts can be considered as comparable choices, offering various levels of flexibility in manufacturing with the trade-off of productivity. However, it is important to note that layout decisions cannot be easily reversed, as layout changes can entail high costs and delays.  These layout decisions can become difficult when the demands of product variants are uncertain. Thus, the resilience of facility layouts subject to uncertain demands is an important element for sustainable manufacturing. The investigation of product demands and facility layout performance can support us to understand how layout decisions can make the production system more sustainable in response to market changes.

This entry is adapted from the peer-reviewed paper 10.3390/su152014946

References

  1. ElMaraghy, H.; Schuh, G.; ElMaraghy, W.; Piller, F.; Schönsleben, P.; Tseng, M.; Bernard, A. Product variety management. CIRP Ann. 2013, 62, 629–652.
  2. Tseng, M.M.; Jiao, J.; Merchant, M.E. Design for Mass Customization. CIRP Ann. 1996, 45, 153–156.
  3. Hu, S.J. Evolving Paradigms of Manufacturing: From Mass Production to Mass Customization and Personalization. Procedia CIRP 2013, 7, 3–8.
  4. Yin, Y.; Stecke, K.E.; Li, D. The evolution of production systems from Industry 2.0 through Industry 4.0. Int. J. Prod. Res. 2018, 56, 848–861.
  5. Pech, M.; Vrchota, J. The Product Customization Process in Relation to Industry 4.0 and Digitalization. Processes 2022, 10, 539.
  6. YounesSinaki, R.; Sadeghi, A.; Mosadegh, H.; Almasarwah, N.; Suer, G. Cellular manufacturing design 1996–2021: A review and introduction to applications of Industry 4.0. Int. J. Prod. Res. 2022, 61, 5585–5636.
  7. Wang, Y.; Ma, H.-S.; Yang, J.-H.; Wang, K.-S. Industry 4.0: A way from mass customization to mass personalization production. Adv. Manuf. 2017, 5, 311–320.
  8. Enyoghasi, C.; Badurdeen, F. Industry 4.0 for sustainable manufacturing: Opportunities at the product, process, and system levels. Resour. Conserv. Recycl. 2021, 166, 105362.
  9. Medić, N.; Anišić, Z.; Lalić, B.; Marjanović, U.; Brezocnik, M. Hybrid fuzzy multi-attribute decision making model for evaluation of advanced digital technologies in manufacturing: Industry 4.0 perspective. Adv. Prod. Eng. Manag. 2019, 14, 483–493.
  10. Celent, L.; Mladineo, M.; Gjeldum, N.; Crnjac Zizic, M. Multi-Criteria Decision Support System for Smart and Sustainable Machining Process. Energies 2022, 15, 772.
  11. Swaminathan, J.M.; Tayur, S.R. Managing Broader Product Lines through Delayed Differentiation Using Vanilla Boxes. Manag. Sci. 1998, 44 Pt 2, S161–S172.
  12. Assad, A.A.; Kramer, S.B.; Kaku, B.K. Comparing functional and cellular layouts: A simulation study based on standardization. Int. J. Prod. Res. 2003, 41, 1639–1663.
  13. Wemmerlöv, U.; Hyer, N.L. Cellular manufacturing in the U.S. industry: A survey of users. Int. J. Prod. Res. 1989, 27, 1511–1530.
  14. Agarwal, A.; Sarkis, J. A review and analysis of comparative performance studies on functional and cellular manufacturing layouts. Comput. Ind. Eng. 1998, 34, 77–89.
  15. Burbidge, J.; Dale, B. Planning the introduction and predicting the benefits of group technology. Eng. Costs Prod. Econ. 1984, 8, 117–128.
  16. Huq, F.; Hensler, D.A.; Mohamed, Z.M. A simulation analysis of factors influencing the flow time and through-put performance of functional and cellular layouts. Integr. Manuf. Syst. 2001, 12, 285–295.
  17. Pitchuka, L.N.; Adil, G.K.; Ananthakumar, U. Effect of conversion of functional layout to a cellular layout on the queue time performance: Some new insights. Int. J. Adv. Manuf. Technol. 2006, 31, 594–601.
  18. Agarwal, A. Partitioning bottleneck work center for cellular manufacturing: An integrated performance and cost model. Int. J. Prod. Econ. 2008, 111, 635–647.
  19. Jerbi, A.; Chtourou, H.; Maalej, A.Y. Comparing functional and cellular layouts using simulation and Taguchi method. J. Manuf. Technol. Manag. 2010, 21, 529–538.
  20. Li, J.-W. Simulation study of coordinating layout change and quality improvement for adapting job shop manufacturing to CONWIP control. Int. J. Prod. Res. 2010, 48, 879–900.
  21. Gaither, N.; Frazier, G.V.; Wei, J.C. From Job Shops to Manufacturing Cells. Prod. Inventory Manag. J. 1990, 31, 33–37.
  22. Morris, J.S.; Tersine, R.J. A Simulation Analysis of Factors Influencing the Attractiveness of Group Technology Cellular Layouts. Manag. Sci. 1990, 36, 1567–1578.
  23. Seifoddini, H. A probabilistic model for machine cell formation. J. Manuf. Syst. 1990, 9, 69–75.
  24. Seifoddini, H.; Djassemi, M. Determination of a flexibility range for cellular manufacturing systems under product mix variations. Int. J. Prod. Res. 1997, 35, 3349–3366.
  25. Djassemi, M. A simulation analysis of factors influencing the flexibility of cellular manufacturing. Int. J. Prod. Res. 2005, 43, 2101–2111.
  26. Jithavech, I.; Krishnan, K.K. A simulation-based approach for risk assessment of facility layout designs under stochastic product demands. Int. J. Adv. Manuf. Technol. 2010, 49, 27–40.
  27. Das, K.; Abdul-Kader, W. Consideration of dynamic changes in machine reliability and part demand: A cellular manufacturing systems design model. Int. J. Prod. Res. 2011, 49, 2123–2142.
  28. Safaei, N.; Tavakkoli-Moghaddam, R. Integrated multi-period cell formation and subcontracting production planning in dynamic cellular manufacturing systems. Int. J. Prod. Econ. 2009, 120, 301–314.
  29. Sharifi, S.; Chauhan, S.S.; Bhuiyan, N. A dynamic programming approach to GA-based heuristic for multi-period CF problems. J. Manuf. Syst. 2014, 33, 366–375.
  30. Venkatadri, U.; Elaskari, S.M.; Kurdi, R. A multi-commodity network flow-based formulation for the multi-period cell formation problem. Int. J. Adv. Manuf. Technol. 2017, 91, 175–187.
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