The Study of the Cycle Time Improvement by Work‐In‐Process Statistical Process Control Method for IC Foundry Manufacturing

Lin, Yu‐Cheng; Tsai, Chih‐Hung; Li, Rong‐Kwei; Chen, Ching‐Piao; Chen, Hsien‐Ching

doi:10.1108/15982688200800028

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…The papers contained within this cluster demonstrate that issues around bottleneck management are found across a wide range of industry sectors, with general manufacturing (Chhaochhria and Graves 2013), semiconductors, automotive (Nyhuis and Vogel 2006), electronics (Sheu and Chen 2008) and aerospace (Srinivasan, Ebbing and Swearingen 2003) all featuring. Like Lozinski and Glassey (1988), Lin et al (2008) also provide control charts to monitor the bottlenecks while the remaining papers tend to focus on the planning phase only. As with the Production Control cluster, the use of modelling approaches is commonplace.…”

Section: Bottleneck Managementmentioning

confidence: 99%

On the versatility of Little’s Law in operations management: a review and classification using vignettes

Potter

Towill

Gosling

2019

Production Planning & Control

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Despite Little's Law being considered as one of the 'laws' of operations management, evidence of its application in an empirical context is diverse and diffuse. Hence, this paper aims to identify, classify and consolidate published empirical applications of Little's Law in a systematic manner to better understand its versatility. This paper undertakes a systematic literature review of the databases of the five main publishers of operations management journals, plus snowball sampling for additional papers. A final sample of 128 empirical journal articles is identified and categorized. Tactical, medium-term decisions relating to capacity dynamics and operations re-engineering are the most popular categories. To give further insights into versatility, vignettes for each category are developed. The review and vignettes confirm Little's Law as a highly relevant paradigm to operations management decisions due to its empirical versatility across levels, sectors and time domains. The paper suggests four factors to underline the empirical versatility of Little's Law in operations management: applicability, utility, simplicity and visibility.

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Section: Bottleneck Managementmentioning

confidence: 99%

On the versatility of Little’s Law in operations management: a review and classification using vignettes

Potter

Towill

Gosling

2019

Production Planning & Control

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“…A failed attempt showing up at the end is detrimental to the effort exerted as a rejected product item has already absorbed all the costs in its value chain. Therefore, the stabilization of the foundry operations is key to solidifying the work‐in‐progress providing assurance that eventually optimal cycle time rates have been achieved (Lin et al , 2008). Magnesium interacts directly with other blending constituents and thus interferes with essential end‐product mechanical characteristics such as the tensile and yield strength.…”

Section: A Real Case Applicationmentioning

confidence: 99%

Robust quality controlling: SPC with box plots and runs test

Besseris

2013

The TQM Journal

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PurposeThe purpose of this paper is to propose a methodology that may assist quality professionals in assessing process variation with a combination of tools based on simple robust statistics. The technique targets alternative way of screening and detection of common and special causes in individuals' control charts (ICC).Design/methodology/approachThe technique is using the classical box plot to detect and filter out outliers attributed to special causes. Then, the runs test is used to partition the data streak at points where the p‐value exceeds an assigned critical value. The transition between partitions is where the onset of a common cause is suspected.FindingsThe approach presented is supplemented with a case study from foundry operations in large‐scale can‐making operations. It is demonstrated how the magnesium content of an aluminium alloy is trimmed against special causes and then the location of the common causes is identified in a step‐by‐step fashion.Research limitations/implicationsThe proposed method is useful when the collected data do not seem to comply with a known reference distribution. Since it is rare that an initial monitoring of a process will abide to normality, this technique saves time in recycling control charting which point often to misleading assignable causes. This is because the outliers are identified through the box plot one and out.Practical implicationsThe technique identifies and eliminates quickly the off‐shoot values that tend to cause major instability in a process. Moreover, the onset for non‐assignable data points is detected in an expedient fashion without the need to remodel each time the inspected data series or to test against a score of multifarious test rules. The ingredients of the method have been well researched in the past, therefore, they may be implemented immediately without a further need to prove their worth.Originality/valueThe method mixes up two distinctive robust tools in a unique manner to aid quality monitoring to become fortified against data model inconsistencies. The technique is suitable for controlling processes that generate numerical readings. As such, the approach is projected to be useful for industrial as well as service operations.

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“…Purnomo and Wee (2014) proposed a bi-objective mathematical model to maximize the TH. To decrease the CT, Lin et al (2008) developed a formula for standard WIP to set the range of WIP level. Zarifoglu et al (2013) also developed a queueing model to gain an insight into the relationship among CT and lot size.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis and optimization of queueing network production systems considering non-conforming products rework and departure

Zhang

2018

JAMDSM

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During the past decades, the production performance analysis and optimization have played a significant role in queueing network system production planning and operation design for the manufacturing plants, but how to model the optimized production system performance and solve it is facing great challenges. Since the more and more wide use of queueing network, the non-conforming product scenarios such as rework and departure, cause mass random interference. These problems when modelled in the conventional analysis and optimization models are extremely difficult to solve as they are coupled and NP-hard problems. This paper considers a modified M/M/n/m-first come first service (M/M/n/m-FCFS) queueing network approximation model that is developed to maximize utilization and profit by analysing the queue time and queue length of the products under the constraint conditions. A hybrid meta-heuristics is applied to solve the approximation model, both the service rate and buffer allocation of the queueing network system can be determined. Finally, to assess the effectiveness of the proposed method, extensive numerical experimental results from the approximation model are compared to the ARENA simulation, and sensitivity and validation analysis are also conducted, which prove it accurate, believable, and effective.Keywords : Production planning, Operation design, Analysis and optimization, Queueing network, Non-conforming product, Hybrid meta-heuristics, Approximation model IntroductionMost manufacturing companies are facing the challenge of quickly implementing flexible and complex production systems (Walid and Ali, 2002). But the production systems often experience worse performance for the unavoidable variability exists in reality. This phenomenon appears widely in queueing network and multi-product mixed production system. Performance analysis and optimization problems in queueing systems have received considerable attention in recent years, due to their wide applicability in the performance evaluation and optimal design of manufacturing systems and computer networks (Song et al., 1998). Now, the long serial lines analysis and optimization have been well studied using decomposition (Shi and Gershwin, 2014;Damodaran and Hulett, 2012;Cauffriez and Willaeys, 2006) or aggregation (Veeger et al., 2011;Belmansour and Nourelfath, 2008) methods for a long time, and extensive results are obtained. Although many studies have focused on solving the line optimization problems, the studies in which a large number of approaches are currently applied to research the performance of queueing network systems considering the non-conforming products are still extremely limited.Research in the field of performance optimization tends to focus on issue models, such as cycle time (CT) optimization (Gilenson et al., 2015), throughput (TH) optimization (Law et al., 2008), work-in-process (WIP) optimization (Reyes et al., 2013;Qiu, 2005), and buffer allocation optimization (Shihabudin et al., 2015;Nahas, 2014). The published literature...

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The Study of the Cycle Time Improvement by Work‐In‐Process Statistical Process Control Method for IC Foundry Manufacturing

Cited by 6 publications

References 12 publications

On the versatility of Little’s Law in operations management: a review and classification using vignettes

On the versatility of Little’s Law in operations management: a review and classification using vignettes

Robust quality controlling: SPC with box plots and runs test

Performance analysis and optimization of queueing network production systems considering non-conforming products rework and departure

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