Use of simulation in the design of a JIT system

Welgama, Palitha; Mills, R. G. J.

doi:10.1108/01443579510099779

Cited by 26 publications

(10 citation statements)

References 2 publications

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“…Lean operations/manufacturing Dov (1992) Food Batch CS Billesbach (1994) Textile Batch CS Ezingeard and Race (1995) Chemical Batch SIM, CS Welgama and Mills (1995) Pharmaceutical Batch CS Iijima, Komatsu, and Katoh (1996) Food Continuous CM, AR, CS Cook and Rogowski (1996) Chemical Continuous CS Gilmore and Smith (1996) Pharmaceutical Batch AR, CS Jones, Hines, and Rich (1997) Brewing Batch AR, CS Mileham et al (1999) Plastic Batch AR, CS Radnor (2000) Chemical also indicated in the literature that in recent times, use of VSM has been increased in process industries to implement lean (Abdulmalek and Rajgopal 2007;Seth, Seth, and Goel 2008;Jimenez et al 2011;Perez et al 2010;Upadhye, Deshmukh, and Garg 2010;Chowdary and George 2012). Literature also implies that quick changeover techniques are often implemented in batch process industries.…”

Section: Type Of Studymentioning

confidence: 99%

On the adoption of lean manufacturing principles in process industries

Panwar

Nepal

Jain

et al. 2015

Production Planning & Control

110

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Traditionally, the lean paradigm has been applied to discrete manufacturing of items that can be easily put together and taken apart. The process industry, on the other hand, transforms raw materials into cohesive units that are basically blended into a final product with parts that cannot be disassembled and then reassembled. The current lean literature provides numerous commendable examples of theory and practices of lean principles in discrete manufacturing. However, its application in process industry is limited. Furthermore, there is no systematic accounting of the lean literature in this sector, which may have contributed to lesser awareness in the industry. This paper provides a state-of-the-art review of lean manufacturing literature with respect to its applications in process industry. It contributes to the classification of literature in a manner which helps to identify strategies suitable for the adoption of lean concepts in process industry. The paper seeks to synthesise the literature with an emphasis on identifying the scope for lean in process industry and associated benefits. The review also presents an analysis of the lean tools and techniques that have been applied or have potential application in the process industry and the challenges to implement lean. We believe that such a comprehensive review will not only facilitate the adoption of lean in process industry but will also provide agenda for further research by exposing voids in the knowledge base.

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Section: Type Of Studymentioning

confidence: 99%

On the adoption of lean manufacturing principles in process industries

Panwar

Nepal

Jain

et al. 2015

Production Planning & Control

110

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“…In the early days, DES was used to pre-test flow layouts in fully automated manufacturing systems (Wu and Wysk, 1989) or in a low-volume, mixed model Just-in-Time assembly system (Carlson and Yao, 1992). Welgama and Mills (1995) used a simulation approach to address design problems faced by a chemical organisation, changing from a traditional to a Just-in-Time system, considering alternative designs for the Just-in-Time system. DES is widely used and an increasingly popular method for studying the design and operations of manufacturing systems (Rabelo et al, 2005;Detty and Yingling, 2000;Kleijnen, 1995).…”

Section: Discrete Event Simulation (Des)mentioning

confidence: 99%

Predicting performance – a dynamic capability view

Hasegan

Nudurupati

Childe

2018

IJOPM

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PurposeProduction planning and resource allocation are ongoing issues that organisations face on a day-to-day basis. The study addresses these issues by developing a dynamic performance measurement system (DPMS) to effectively re-deploy manufacturing resources, thus enhancing the decision-making process in optimising performance output. The study also explores the development of dynamic capabilities through exploitation of the organisational tacit knowledge. Design/methodology/approachThe study was conducted using 6-stage action research for developing DPMS with real-time control of independent variables on the production lines to study the impact. The DPMS was developed using a hybrid approach of discrete event simulation (DES) and system dynamics (SD) by using the historical as well as live data from the action case organisation. FindingsThrough the development of DPMS and by combining the explicit and tacit knowledge, this study demonstrated an understanding of using cause and effect analysis in manufacturing systems to predict performance. Such a DPMS creates agility in decision making and significantly enhances the decision-making process under uncertainty. The research also explored how the resources can be developed and maintained into dynamic capabilities to sustain competitive advantage.2 Research limitations/implicationsThe present study provides a starting-point for further research in other manufacturing organisations to generalise findings. Originality/valueThe originality of the DPMS model comes from the approach used to build the cause and effect analysis by exploiting the tacit knowledge and making it dynamic by adding modelling capabilities. Originality also comes from the hybrid approach used in developing the DPMS.

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“…Detty and Yingling [12] demonstrated the use of discrete event simulation as a tool to assist organizations with the decision to implement lean manufacturing by quantifying the operational benefits achieved from applying lean principles. Other simulation studies were also conducted to investigate the impact of Just in Time (JIT) and Pull lean principles on improving manufacturing system operational performance [13,14]. Lian and Van Landeghem [15] combined simulation and value stream mapping together with existing data bases of production to develop a tool for assessing lean implementation.…”

Section: Literature Reviewmentioning

confidence: 99%

Cost performance dynamics in lean production leveling

Deif

ElMaraghy

2014

Journal of Manufacturing Systems

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a b s t r a c tBalancing of production systems is one of the main lean manufacturing principles as it reduces in-process storage and related forms of waste. A dynamic systems approach is proposed to investigate challenges of implementing production leveling and associated costs. A lean cell producing at takt time is modeled using system dynamics. The model captures various lean tools influencing production leveling and their implications. Comparative cost analysis between various leveling implementation policies for stochastic demand with multiple products is conducted. Results showed that determining the most feasible leveling policy is highly dictated by both the cost and limitations of capacity scalability. In addition, delivery sequence plans of different products/parts needed to achieve mix leveling and lot sizes affect the feasible production leveling policy while implementing lean principles. The developed model and insights gained from the results can help lean manufacturing practitioners to better decide when and how to implement production leveling as well as determine both production lots sizes and sequence. They also emphasize the importance of cost analysis as assisting decision support tool in the trade-off required between the benefits of different levels of lean policies and their associated cost. .eg (A.M. Deif). sooner or faster than is required by the next process [1]. Overproduction causes additional handling, inspecting, counting and storing costs of those not yet needed products. In addition, with over-production, defects remain hidden in the inventory queues until the downstream process finally uses the parts and quality issues are discovered. Heijunka is the Japanese term for load or production leveling which is the lean manufacturing strategy employed to eliminate over-production. Leveled production attains capacity balance and synchronization of all production operations over time in a manner that precisely and flexibly matches customer demand for the system's products. Ideally, this means producing every product in every shift in quantities equal to demand after smoothing out high frequency random components. Manufacturing processes should be operated at the takt time to achieve level production. The takt time is the target production frequency, based on the rate of sales, to meet customer requirements. Takt time synchronizes the pace of production with the pace of sales. Producing at takt time is achieved through means such as rapid machine setups/changeover, just in time flow and scalable capacity strategies.In the current dynamic business context, leanness assessment has undergone, and is still undergoing, a process of continuous and never-ending evolution [2]. The assessment of leanness impact was usually related to performance metrics that focus on system productivity, cycle and/or lead times and quality improvements. Although previous metrics have direct and indirect impact on the

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Use of simulation in the design of a JIT system

Cited by 26 publications

References 2 publications

On the adoption of lean manufacturing principles in process industries

On the adoption of lean manufacturing principles in process industries

Predicting performance – a dynamic capability view

Cost performance dynamics in lean production leveling

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