Estimating customer service levels in automated multiple part-type production lines: An analytical method

Senanayake, C.D.; Subramaniam, Velusamy

doi:10.1016/j.cie.2012.09.002

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…From the productivity rate result in (8) and (9), (7) which is the actual productivity rate is used as guideline to check which mathematical model can provide more accurate result to actual result and the percentage error is calculated in (10) and (11). Regarding result of (10) and (11), it presents the percentage error when compared to actual productivity rate result. Productivity rate with different station failure rate d.f.r is just 3.37% of percentage error while productivity rate with average station failure rate a.f.r obtained is 39.78% of percentage error when compared to actual productivity rate ac .…”

Section: Calculation and Comparisonmentioning

confidence: 99%

“…This method is considered mathematical and statistical analysis method since it involves mathematical model application and statistical analysis method [9,10]. Due to the high complexity of applying the mathematical model in (4) in industry, the demand for less complicated, convenient, and effective engineering mathematical and statistical analysis method is strongly needed for the automated flow line productivity rate [11].…”

Section: Introductionmentioning

confidence: 99%

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Engineering Mathematical Analysis Method for Productivity Rate in Linear Arrangement Serial Structure Automated Flow Assembly Line

Sin

Usubamatov

Fairuz

et al. 2015

Mathematical Problems in Engineering

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Productivity rate (Q) or production rate is one of the important indicator criteria for industrial engineer to improve the system and finish good output in production or assembly line. Mathematical and statistical analysis method is required to be applied for productivity rate in industry visual overviews of the failure factors and further improvement within the production line especially for automated flow line since it is complicated. Mathematical model of productivity rate in linear arrangement serial structure automated flow line with different failure rate and bottleneck machining time parameters becomes the basic model for this productivity analysis. This paper presents the engineering mathematical analysis method which is applied in an automotive company which possesses automated flow assembly line in final assembly line to produce motorcycle in Malaysia. DCAS engineering and mathematical analysis method that consists of four stages known as data collection, calculation and comparison, analysis, and sustainable improvement is used to analyze productivity in automated flow assembly line based on particular mathematical model. Variety of failure rate that causes loss of productivity and bottleneck machining time is shown specifically in mathematic figure and presents the sustainable solution for productivity improvement for this final assembly automated flow line.

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Section: Calculation and Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Engineering Mathematical Analysis Method for Productivity Rate in Linear Arrangement Serial Structure Automated Flow Assembly Line

Sin

Usubamatov

Fairuz

et al. 2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…However, in order to exploit these technological features, an optimized system design needs to be generated. Multi-product manufacturing systems have been analyzed by [9][10][11][12][13][14][15] among others. In [10] and [11] in particular, the authors analyzed systems having automated machines with multiple failure modes.…”

Section: Introductionmentioning

confidence: 99%

An Approximate Analytical Method to Evaluate the Performance of Multi-product Assembly Manufacturing Systems

2015

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The automotive industry is characterized by the continuous and frequent adoption of cutting edge technologies in manufacturing. The introduction of new technology often entails a system level investigation to determine whether it meets expected production requirements. Many system design alternatives are usually considered at this stage, and a fast performance evaluation tool is desired to quickly obtain the optimum system configuration. In this paper, we introduce an analytical tool that was developed for this specific purpose. The investigated manufacturing system is a multi-product, automated, assembly production line composed of unreliable machines decoupled by finite buffers. Each product is an assembly of two components that are produced in sub-assembly lines before being assembled at an assembly machine. The performance of this system needs to be evaluated for different configurations that arise mainly due to the integration of a remote laser welding technology. This new technology promises, among other advantages, a much faster assembly than existing technologies. This makes it feasible to share assembly among many products with different possible production policies. The processing machines including the assembly machine are liable to fail randomly in operation thereby necessitating an evaluation method capable of accounting for stochastic behavior.To solve this problem, we develop a decomposition-based approximate analytical method. We use the continuous material flow approximation, which allows to model machines having deterministic but inhomogeneous processing times as in the investigated real system. We also account for multiple machine failure modes and introduce new techniques to incorporate the assembly of multiple products with different policies of switching between products. Comparison with simulation results shows the good accuracy of the model in estimating system throughput and work-in-progress. The model has been successfully utilized to evaluate the performance of an automotive door assembly manufacturing system and the main results of this case study are reported.

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Service level enhancement in newsvendor model

Aldaihani

Darwish

2016

Computers & Industrial Engineering

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Estimating customer service levels in automated multiple part-type production lines: An analytical method

Cited by 5 publications

References 24 publications

Engineering Mathematical Analysis Method for Productivity Rate in Linear Arrangement Serial Structure Automated Flow Assembly Line

Engineering Mathematical Analysis Method for Productivity Rate in Linear Arrangement Serial Structure Automated Flow Assembly Line

An Approximate Analytical Method to Evaluate the Performance of Multi-product Assembly Manufacturing Systems

Service level enhancement in newsvendor model

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