Pacing Effects on Manned Assembly Lines

Davis, Louis E.

doi:10.1080/00207546508919974

Cited by 46 publications

(11 citation statements)

References 7 publications

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“…Despite this reservation, the use of inventory buffers remains prevalent even as contemporary practices like JIT/Lean and the Theory of Constraints have experienced widespread adoption. Davis (1966) and Hillier and Boling (1966) began to challenge the use of inventory to buffer the system against variability by introducing the concept of unbalanced production systems. Their research suggests that strategic placement of machines with stochastic process times results in greater output levels and shorter flow times.…”

Section: Drum-buffer-ropementioning

confidence: 99%

Downtime variability: the impact of duration–frequency on the performance of serial production systems

Patti

Watson

2009

International Journal of Production Research

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It is intuitively appealing to measure downtime in terms of total time lost (i.e. frequency times duration). However, the same total downtime can be produced by an infinite number of frequency/duration combinations. This study reports the findings of a computer simulation study on the impact of varying combinations of downtime frequency/duration pairs on performance of serial production systems.The research reported here clearly shows that different downtime frequency/ duration combinations affect serial production lines differently even when the total downtime remains equal. Analysis suggests that long duration, infrequently occurring variation has a much more negative impact on system performance than short duration, frequently occurring variation.

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Section: Drum-buffer-ropementioning

confidence: 99%

Downtime variability: the impact of duration–frequency on the performance of serial production systems

Patti

Watson

2009

International Journal of Production Research

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“…Driven by the differing market requirements over the last years, manufacturing systems have faced broad changes (Yin et al 2018), from the introduction of assembly lines to the costeffectiveness requirements of mass production, the introduction and discussions of balanced and unbalanced lines (Davis 1965), and the establishment of JIT lines based on the "Toyota Production System" (Ono 1988), which aligns production with demand to eliminate waste. Thereby, different production line configurations, such as serial, parallel with or without crossover have been introduced.…”

Section: Literature Reviewmentioning

confidence: 99%

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

et al. 2020

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Manufacturing flexibility improves a firm's ability to react in timely manner to customer demands and to increase production system productivity without incurring excessive costs and expending an excessive amount of resources. The emerging technologies in the Industry 4.0 era, such as cloud operations or industrial Artificial Intelligence, allow for new flexible production systems. We develop and test an analytical model for a throughput analysis and use it to reveal the conditions under which the autonomous mobile robots (AMR)-based flexible production networks are more advantageous as compared to the traditional production lines. Using a circular loop among workstations and inter-operational buffers, our model allows congestion to be avoided by utilizing multiple crosses and analyzing both the flow and the load/unload phases. The sensitivity analysis shows that the cost of the AMRs and the number of shifts are the key factors in improving flexibility and productivity. The outcomes of this research promote a deeper understanding of the role of AMRs in Industry 4.0-based production networks and can be utilized by production planners to determine optimal configurations and the associated performance impact of the AMR-based production networks in as compared to the traditionally balanced lines. This study supports the decision-makers in how the AMR in production systems in process industry can improve manufacturing performance in terms of productivity, flexibility, and costs.

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“…The fact that work time distribution representative of unpaced conditions was found to be positively skewed by Davis (1966) and Dudley (1963), led to the conclusion that the Weibull distribution would be the most appropriate to represent work time in this research.…”

Section: Parameter Descriptionmentioning

confidence: 87%

Predicting the performance of unpaced assembly lines where one station variability may be smaller than the others

Dar-El

Mazer²

1989

International Journal of Production Research

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Predicting the performance of unpaced assembly lines where one station variability may be smaller than the others EZEY M. DAR-ELt and ARIEL MAZERt Reliable predictive formulae are presented for calculating the output efficiency of unpaced assembly lines under two conditions: one, in which all stations in the line exhibit constant variability (Cv); the other, has one of the stations in the line having a lower coefficient of variation (CO VA) than those of the other stations (Cv). The prediction models apply to various combinations of'buffercapacity, processing time variability and operating strategies. Predictions of line performanges are then compared with simulation data to determine their applicability for design purposes.

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Pacing Effects on Manned Assembly Lines

Cited by 46 publications

References 7 publications

Downtime variability: the impact of duration–frequency on the performance of serial production systems

Downtime variability: the impact of duration–frequency on the performance of serial production systems

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

Predicting the performance of unpaced assembly lines where one station variability may be smaller than the others

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