With the increase of market fluctuation, assembly systems moved from a mass production scheme to a mass customization scheme. Mixed model assembly systems (MMASs) have been recognized as enablers of mass customization manufacturing. However, effective implementation of MMASs requires, among other things, a highly proactive and knowledgeable workforce. Hence, modeling the performance of human operators is critically important for effectively operating these manufacturing systems. But, certain cognitive factors have seldom been considered when it comes to modeling process quality of MMASs. Thus, the objective of this paper is to introduce an integrated modeling framework by considering the factors—both intrinsic (such as work experience, mental deliberation time, etc.) and extrinsic (such as task complexity)—that affect the operator’s performance. The proposed model is justified based on the findings presented in the psychological literature. The effect of these factors on process operation performance is also investigated; these performance measures include process quality, throughput, and process capability in regard to handling complexity induced by product variety in MMASs. Two examples are used to demonstrate potential applications of the proposed model.
Issue Section:
Research Papers
1.
Fisher
, M. L.
, Jain
, A.
, and MacDuffie
, J. P.
, 1995, “Strategies for Product Variety: Lessons From the Auto Industry
,” Redesigning the Firm
, B.
Kogut
and E.
Bowman
, eds., Oxford University Press
, New York
, pp. 116
–154
.2.
MacDuffie
, J. P.
, Sethuraman
, K.
, and Fisher
, M. L.
, 1996, “Product Variety and Manufacturing Performance: Evidence From the International Automotive Assembly Plant Study
,” Manage. Sci.
0025-1909, 42
(3
), pp. 350
–369
.3.
Fisher
, M. L.
, and Ittner
, C. D.
, 1999, “The Impact of Product Variety on Automobile Assembly Operations: Empirical Evidence and Simulation Analysis
,” Manage. Sci.
0025-1909, 45
, pp. 771
–786
.4.
Lee
, C. Y.
, and Vairaktarakis
, G. L.
, 1997, “Workforce Planning in Mixed Model Assembly Systems
,” Oper. Res.
0030-364X, 45
, pp. 553
–567
.5.
Bernhard
, W.
, and Schilling
, A.
, 1997, “Simulation of Group Work Processes in Manufacturing
,” Proceedings of the 29th Conference on Winter Simulation
, IEEE Computer Society, pp. 888
–891
.6.
Baines
, T.
, Mason
, S.
, Siebers
, P. O.
, and Ladbrook
, J.
, 2004, “Humans: The Missing Link in Manufacturing Simulation?
,” Simulation Modelling Practice and Theory
, 12
, pp. 515
–526
.7.
Fine
, C. H.
, 1986, “Quality Improvement and Learning in Productive Systems
,” Manage. Sci.
0025-1909, 32
, pp. 1301
–1315
.8.
Wang
, H.
, and Hu
, S. J.
, 2010, “Manufacturing Complexity in Assembly Systems With Hybrid Configurations and Its Impact on Throughput
,” CIRP Annals-Manufacturing Technology
0007-8506, 59
, pp. 53
–56
.9.
Bohlen
, G. A.
, and Barany
, J. W.
, 1976, “A Learning Curve Prediction Model for Operators Performing Industrial Bench Assembly Operations
,” Int. J. Prod. Res.
0020-7543, 14
, pp. 295
–303
.10.
Yelle
, L. E.
, 1979, “The Learning Curve: Historical Review and Comprehensive Survey
,” Decision Sci.
0011-7315, 10
, pp. 302
–328
.11.
Argote
, L.
, and Epple
, D.
, 1990, “Learning Curves in Manufacturing
,” Science
0036-8075, 247
, pp. 920
–924
.12.
Wright
, T. P.
, 1936, “Factors Affecting the Cost of Airplanes
,” J. Aeronaut. Sci.
0095-9812, 3
, pp. 122
–128
.13.
Kini
, R. G.
, 1994, “Economics of Conformance Quality
,” Ph.D. thesis, Carnegie Mellon University, Pittsburgh, PA.14.
Li
, G.
, and Rajagopalan
, S.
, 1998, “Process Improvement, Quality, and Learning Effects
,” Manage. Sci.
0025-1909, 44
, pp. 1517
–1532
.15.
Plamondon
, R.
, and Alimi
, A. M.
, 1997, “Speed/Accuracy Trade-Offs in Target-Directed Movements
,” Behav. Brain Sci.
0140-525X, 20
, pp. 279
–303
.16.
Reed
, A. V.
, 1973, “Speed-Accuracy Trade-Off in Recognition Memory
,” Science
0036-8075, 181
, pp. 574
–576
.17.
Bootsma
, R. J.
, Marteniuk
, R. G.
, MacKenzie
, C. L.
, and Zaal
, F. T. J. M.
, 1994, “The Speed-Accuracy Trade-Off in Manual Prehension: Effects of Movement Amplitude, Object Size and Object Width on Kinematic Characteristics
,” Exp. Brain Res.
0014-4819, 98
, pp. 535
–541
.18.
Schouten
, J. F.
, and Bekker
, J. A. M.
, 1967, “Reaction Time and Accuracy
,” Acta Psychol.
0001-6918, 27
, pp. 143
–153
.19.
Pachella
, R. G.
, and Pew
, R. W.
, 1968, “Speed-Accuracy Tradeoff in Reaction Time: Effect of Discrete Criterion Times
,” J. Exp. Psychol.
0022-1015, 76
, pp. 19
–24
.20.
Pew
, R. W.
, 1969, “The Speed-Accuracy Operating Characteristic
,” Acta Psychol.
0001-6918, 30
, pp. 16
–26
.21.
Heath
, R. A.
, 1992, “A General Nonstationary Diffusion Model for Two-Choice Decision-Making
,” Mathematical Social Sciences
, 23
, pp. 283
–309
.22.
Busemeyer
, J. R.
, and Townsend
, J. T.
, 1993, “Decision Field Theory: A Dynamic-Cognitive Approach to Decision Making in an Uncertain Environment
,” Psychol. Rev.
0033-295X, 100
, pp. 432
–459
.23.
Diederich
, A.
, 1997, “Dynamic Stochastic Models for Decision Making Under Time Constraints
,” J. Math. Psychol.
0022-2496, 41
, pp. 260
–274
.24.
Shannon
, C. E.
, 1948, “A Mathematical Theory of Communication
,” Bell Syst. Tech. J.
0005-8580, 27
, pp. 379
–423
and 623–656.25.
Woodworth
, R. S.
, and Schlosberg
, H.
, 1954, “Experimental Psychology (Rev. Ed.)
,” New York: Holt
, pp. 492
–527
.26.
Hick
, W. E.
, 1952, “On the Rate of Gain of Information
,” J. Exp. Psychol.
0022-1015, 4
, pp. 11
–26
.27.
Hyman
, R.
, 1953, “Stimulus Information as a Determinant of Reaction Time
,” J. Exp. Psychol.
0022-1015, 45
, pp. 188
–196
.28.
Zhu
, X.
, Hu
, S. J.
, Koren
, Y.
, and Marin
, S. P.
, 2008, “Modeling of Manufacturing Complexity in Mixed-Model Assembly Lines
,” ASME J. Manuf. Sci. Eng.
1087-1357, 130
, p. 051013
.29.
Hu
, S. J.
, Zhu
, X.
, Wang
, H.
, and Koren
, Y.
, 2008, “Product Variety and Manufacturing Complexity in Assembly Systems and Supply Chains
,” CIRP Annals
0007-8506, 57
, pp. 45
–48
.30.
Dehaene
, S.
, 2003, “The Neural Basis of the Weber–Fechner Law: A Logarithmic Mental Number Line
,” Trends Cogn. Sci.
1364-6613, 7
, pp. 145
–147
.31.
Boer
, E. R.
, 2000, “Behavioral Entropy as an Index of Workload
,” Human Factors and Ergonomics Society Annual Meeting Proceedings
, Human Factors and Ergonomics Society, pp. 125
–128
.32.
Cover
, T. M.
, and Thomas
, J. A.
, 2006, Elements of Information Theory
, Wiley
, New York
.33.
Little
, J. D. C.
, 1961, “A Proof of the Queuing Formula L=W
,” Oper. Res.
0030-364X, 9
, pp. 383
–387
.34.
Thomopoulos
, N. T.
, 1967, “Line Balancing-Sequencing for Mixed-Model Assembly
,” Manage. Sci.
0025-1909, 14
, pp. B59
–B75
.35.
Merengo
, C.
, Nava
, F.
, and Pozzetti
, A.
, 1999, “Balancing and Sequencing Manual Mixed-Model Assembly Lines
,” Int. J. Prod. Res.
0020-7543, 37
, pp. 2835
–2860
.36.
Abad
, A. G.
, and Jin
, J.
, 2010, “Complexity Metrics for Mixed Model Manufacturing Systems Based on Information Theory
,” International Journal of nformation and Decision Sciences (IJIDS)
, to be published.37.
Abad
, A.
, and Jin
, J.
, 2010, “Algebraic Expression of System Configurations and Performance Metrics for Mixed Model Assembly Systems
,” Ph.D. dissertation, The University of Michigan, Ann Arbor, MI.Copyright © 2011
by American Society of Mechanical Engineers
You do not currently have access to this content.
