The extent of product variety handled by any manufacturing firm depends on its volume of production (Kamrani et al. 2004). Low-quantity production is usually known as job shop production. The agility embedded in this form of production makes it the most used production system to manage high product variety, although medium-quantity production handles hard product variety using batch production. In this approach, batches of materials are pushed through different stages of production and assembly since batches are manufactured one after the other. There is a fixed time for changeover from one batch to the other. Soft product variety in medium production is handled by cellular manufacturing, where every cell specializes in the production of a given set of similar parts or products, according to the principles of group technology. Each cell is designed
to produce a limited variety of part configurations. Mass production is characterized by high quantity and is dedicated to the manufacture of specific products that have high demands. Mass production can be broadly classified into quantity production and flow line production. Quantity production involves the mass production of single parts on specialized pieces of equipment. Flow line stations involve multiple workstations arranged in sequence, and the products are moved through the sequence with subassemblies assembled at respective locations. Assembly lines of automobile manufacturing are a typical example of flow line manufacturing. The term mixed-model assembly line refers to those assembly lines that can typically handle soft variety in the vehicle assembly operation.
Manufacturing complexity is classified into structural (static) complexity and operational (dynamic) complexity (Frizelle and Woodcock 1995). Structural or static complexity is defined as the expected amount of information necessary to describe the state of a system. Production schedule provides the data to calculate the static complexity of the manufacturing system. Static complexity is measured using the entropy equation:
Hs pij pij j
S
i m
= −
=
= ∑
∑ log2
1 1
(7.7) where
m is the number of resources.
s is the number of scheduled states.
pij is the probability of resource i being in scheduled state j.
Operational or dynamic complexity is defined as the expected amount of informa- tion necessary to describe the state of the system deviating from the schedule due to uncertainty. The calculation involves measurement of the difference between the actual performance of the system and the expected figures in the schedule.
Dynamic complexity is given by
Hd P P p p p pij pij
j n
= − − − − − −
=
(log ) (2 )log (2 ) ( ) log2 1
1 1 1
ss
i
m ∑
∑=1 (7.8)
where
P is the probability of the system being in control.
(1−p) is the probability of the system being out of control.
m is the number of resources.
ns is the number of nonscheduled states.
pij is the probability of resource i being in a nonscheduled state j.
A fair estimate of the cost of increased product variety is often difficult to arrive at because variety incurs many indirect costs that are not clearly understood and are not easy to capture. Costs that are difficult to determine include raw material inventory, work in process inventory, finished goods inventory, postsales service inventory, reduction in capacity due to frequent setups, and cost of increased logistics due to added variety. Figure 7.8 represents the costs generated due to increase level of complexity (Martin and Ishii 1996).
Setup time or the batch size primarily determines the cost of variety in manufacturing. Because of large volumes, mass production has specific machinery that is relatively inflexible for handling product variants. Furthermore, mass production is often characterized by dies that have large setups, which encourage higher lot sizes. This forces mass production to have large batch sizes to minimize the downtime per product. Consequently, this results in larger work in process (WIP), larger floor space, lower quality costs, and lower machinery utilization. Work-in- process inventory costs have a direct relation with respect to the lot size in any manu- facturing setup. With higher work-in-process, the production system drifts toward the push system of production. This is often accompanied by an increase in floor space utilization and increase in internal transportation costs. Large lot sizes increases the quality costs due to repeating errors, the primary reason being the increase in vulner- ability to the unknowingly occurring manufacturing defects. Smaller lot sizes favor lesser part rejection and result in lower quality costs (cost of rejection). Machine uti- lization increases when the lot size increases because of the economy of scale. Thus, when there is a drift toward increase in product variety in mass production, machine utilization suffers. In traditional mass production, frequent setups to accommodate a wide product range also increases the setup, labor costs, and downtime. In sum- mary, mass production is least flexible in handling product variety due to operational inefficiencies and cost increases due to product proliferation.
Plant & Equipment Storage
IT
Excess Plant & Equipment Excess Storage
Additional IT
Materials Labor Utilization
Transportation Overheads
Excess Labor Interest Charges Rectification Costs
Revenue Loss Warranty Payments
Excess Overheads Capital
Revenue Items
Variety Uncertainty
Figure 7.8 Costs generated due to complexity.
Author
Ali K. Kamrani is an Associate Professor of Industrial Engineering. He is also Founding Director of the Design and Free Form Fabrication Laboratory at the University of Houston, USA. He received his BS in Electrical Engineering in 1984, Master of Engineering in Electrical Engineering in 1985, Master of Engineering in Computer Science and Engineering Mathematics in 1987, and PhD in Industrial Engineering in 1991, all from the University of Louisville, Louisville, Kentucky. His research has been motivated by the fundamental application of systems engineering and its application in advanced design and development of complex systems. He is the Editor-in-Chief for the International Journal of Collaborative Enterprise and the International Journal of Rapid Manufacturing.
References
Adat, A. 2005. A Simulation based Methodology to Measure and Analyze the Required Inventory due to Product Proliferation. Master’s thesis, University of Houston.
Aigbedo, H., and Y. Monden. 1996. A simulation analysis for two-level sequence scheduling for just-in-time (JIT) mixed-model assembly lines. International Journal of Production Research 34(11): 3107–24.
Akturk, M.S., and F. Erhun. 1999. An overview of design and operational issues of kanban systems. International Journal of Production Research 37(17): 3859–81.
Anderson, D.M. 1997. Agile Product Development for Mass Customization. How to Develop and Deliver. Chicago: Irwin Professional.
Anwar, F.M., and R. Nagi. 1997. Integrated lot-sizing and scheduling for just-in-time production of complex assemblies with finite set-ups. International Journal of Production Research 35(5): 1447–70.
Baykoc, O.F., and S. Erol. 1998. Simulation modeling and analysis of a JIT production system. International Journal of Production Economics 55(7): 203–12.
Benjaafar, S., J.S. Kim, and N. Vishwanadham. 2004. On the effect of product variety in production-inventory systems. Annals of Operations Research 126(1–4): 71–101.
Calinescu, A., J. Efstathiou, J. Bermejo, and J. Schirn. 1997. Assessing decision making and process complexity in manufacturing through simulation. Proceedings of the 6th IFAC Symposium on Automated Systems Based on Human Skill. IFAC: 159–62.
Calinescu, A., J. Efstathiou, L.H. Huatuco, and S. Sivadasan. 2002. Classes of Complexity in Manufacturing. Research Report, Manufacturing Research Group, Department of Engineering Science, University of Oxford.
Christiansen, K., J. Kjeldgaad, and K. Tvenge. 2003. Shaping Product Portfolios for the Future. Product Management Consultants, Proventia A/S.
Chu, C.H., and W.L. Shih. 1992. Simulation studies in JIT production. International Journal of Production Research 30(1): 2573–86.
Da Silveira, G. 1998. A framework for the management of product variety. International Journal of Operations and Production Management 18(3): 271–85.