Bottlenecks in Production Lines with Rework: A Systems Approach

Biller, Stephan; Li, Jingshan; Marin, Samuel P.; Meerkov, Semyon M.; Zhang, Liang

doi:10.3182/20080706-5-kr-1001.02520

Cited by 10 publications

(3 citation statements)

References 5 publications

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“…(Fig.2 n -number of distributed machines. [4,5,6] The solution is based on the assumption that the transport cost per unit distance is the same between all machine workplaces. In the given example the saving of material flows is 10.8%.…”

Section: Modification Of the Triangle Net Methodsmentioning

confidence: 99%

Programming System for the Applications of Deployment Methods in an Engineering Company

Komák

Králik

2014

Scientific Proceedings Faculty of Mechanical Engineering

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INTRODUCTIONEvery company engaged in manufacturing continually attempts to make their production more effective. One of the ways to streamline production is to minimize transport costs and the reduction of excess time in inter-operational transport. ANALYSIS OF PRODUCTION SYSTEM IN TERMS OF PRODUCTION DISPOSITIONTo effectively organize machine workplaces, there are several methods of deployment. Their selection is often a complicated and time consuming process. If there are a large number of deployed elements, we may not even succeed in achieving the desired results. Therefore, there is an effort to develop computer systems that would apply the methods in computer code and thereby replace humans in solving such problems. In properly applied computer technology, we can achieve precise results. These issues are part of the structure of the disposition of production (Fig. 1). [2,3] Figure 1: Added elements to the structure of the production disposition 9

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Section: Modification Of the Triangle Net Methodsmentioning

confidence: 99%

Programming System for the Applications of Deployment Methods in an Engineering Company

Komák

Králik

2014

Scientific Proceedings Faculty of Mechanical Engineering

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“…Machine breakdown (Wazed et al, 2010); (Nakata et al, 1999); (Chiang et al, 2001); (Riezebos et al, 2003); (Roser et al, 2002(Roser et al, , 2002b; (Leporis and Zedenka, 2010); (Liu et al, 2004); (Sui-Hua, 2005); (Hwang and Singh, 1998); (Kuo et al, 1996); (Biller et al, 2010); (Chiang et al, 2000); (Chiang et al, 1998); (Yano, 1987); (Identifying Bottleneck Cells with Goal Programming, 2008); (Sengupta et al, 2008); (Roser et al, 2002(Roser et al, , 2002b; (Gunasekaran et al, 1998) Quality variation (Wazed et al, 2010); (Biller et al, 2010); (Chiang et al, 2000); (Chiang et al, 1998); (Ben-Daya and Rahim, 2003); (Identifying Bottleneck Cells with Goal Programming, 2008) Process variation (Wazed et al, 2010); (Jiang et al, 2003); (Sharda and Bury, 2010); (Grosfeld-Nir and Gerchak, 2004); (Biller et al, 2008); (Biller et al, 2010); (Ben-Daya and Rahim, 2003); (Identifying Bottleneck Cells with Goal Programming, 2008); (Gunasekaran et al, 1998) Inspection error and time (Wazed et al, 2010); (Darwish and Ben-Daya, 2007); (Ben-Daya and Rahim, 2003); (Identifying Bottleneck Cells with Goal Programming, 2008) Set-up a...…”

Section: Bottleneck Variable Referencementioning

confidence: 99%

A study on the factors causing bottleneck problems in the manufacturing industry using principal component analysis

Ongbali

Afolalu

Oyedepo

et al. 2021

Heliyon

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There are a myriad of bottleneck variables that constrict the overall manufacturing capacity and make improvement decisions complex. Consequently, this study aims to identify and analyse the copious variables to pinpoint the key variable factors that influence and turn the manufacturing elements into bottleneck problem to prioritize process improvement effort. The study is limited to identifying and analysing the numerous bottleneck variables to gain insight into how much each of the variables influences the process output via the manufacturing elements. The 76-bottleneck variables abstracted from the literature were used to craft a structured questionnaire that were administered to respondents in the manufacturing industry whose size was determined at a 95% confidence level and 5% error margin respectively. The 95% confidence level is chosen to ensure adequate representation of the population and to validate the data for the study. The respondents' scores were collated into ( m x n ) data matrix which served as input variable into the factor analysis model. StatistiXL software was then employed to evaluate the data matrix. The trivial variables were discarded and 19 factors with eigenvalues (λ ˃ 1) were extracted and creatively labelled for interpretation. The result established that the “Process capability index” is the principal bottleneck factor that loaded 25% of the variables studied. The principal variables in the cluster include Equipment failure = -0.832, Operations = -0.780, Material unavailability = -0.811, and Market demand = -0.739 among others. Similarly, Manufacturing process restraint, Resources, Weather, Communication, Logistics, and Line dedication are other key factors by the magnitude of their respective variables’ factor loadings such as Random event = 0.812, Raw materials flow = -0.834, Process technology = 0.878, and Random environmental factors among other variables. Although bottleneck problems vary from one manufacturing system to another, the problems identified and the solutions presented in this study are generic and the improvement effort should focus on addressing the principal variables while not neglecting the middling and weakling variables.

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“…By ignoring the smaller terms, equations (24) and (25) provide estimates of partial derivatives of P (g ss ) with respect to λ ij and µ ij , respectively. Therefore, by comparing these estimates, the largest ∂P (gss) Numerical experiments have been carried out to justify the bottleneck indicators introduced above.…”

Section: Remarkmentioning

confidence: 99%

Improving quality in flexible manufacturing systems: A bottleneck transition approach

Wang

Arinez

et al. 2010

2010 IEEE International Conference on Automation Science and Engineering

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In this paper, a Markov chain model is introduced to study product quality in flexible manufacturing systems with batch productions. Using this model, we introduce good and defective states as the cycles where good quality and defective parts are produced, respectively. Then the product quality is a function of the transition probabilities characterizing the changes among good and defective states. To improve product quality, identifying the transition that has the largest impact on quality is important, and such transition is referred to as the quality bottleneck transition (BN-t). Analytical expressions to quantify the sensitivity of quality with respect to transition probabilities are derived, and indicators to identify the BN-t based on the data collected on the factory floor are developed. Through extensive numerical experiments, it is shown that such indicators have high accuracy in identifying the correct bottlenecks and can be used as an effective tool for quality improvement effort. Finally, a case study at an automotive paint shop is presented to illustrate the applicability of the method.

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Bottlenecks in Production Lines with Rework: A Systems Approach

Cited by 10 publications

References 5 publications

Programming System for the Applications of Deployment Methods in an Engineering Company

Programming System for the Applications of Deployment Methods in an Engineering Company

A study on the factors causing bottleneck problems in the manufacturing industry using principal component analysis

Improving quality in flexible manufacturing systems: A bottleneck transition approach

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