Quantifying benefits of conversion to lean manufacturing with discrete event simulation: A case study

Detty, Richard B.; Yingling, Jon C.

doi:10.1080/002075400189509

Cited by 217 publications

(118 citation statements)

References 9 publications

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“…Many examples of this approach combining VSM and simulations are available in literature (Abdulmalek & Rajgopal, 2007;Bernards, van Engelen, Schrauwen, Cramer & Luitjens, 1990;Gurumurthy & Kodali, 2011;Lian & van Landeghem, 2007;McDonald et al, 2002;Narasimhan, Parthasarathy & Narayan, 2007;Wang, Guinet, Belaidi & Besombes, 2009). One of the most popular simulation tools available on the internet is Arena Simulation (Detty & Yingling, 2000;Hammann & Markovitch, 1995;Kelton, 2002). This software has been selected for the present work considering that its diffusion is wide and that its features have been successfully proven in similar studies (Detty & Yingling, 2000;Lian & van Landeghem, 2002).…”

Section: Simulation As Support To Value Stream Mappingmentioning

confidence: 99%

“…One of the most popular simulation tools available on the internet is Arena Simulation (Detty & Yingling, 2000;Hammann & Markovitch, 1995;Kelton, 2002). This software has been selected for the present work considering that its diffusion is wide and that its features have been successfully proven in similar studies (Detty & Yingling, 2000;Lian & van Landeghem, 2002).…”

Section: Simulation As Support To Value Stream Mappingmentioning

confidence: 99%

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Exploring the impact of Lean manufacturing on flexibility in SMEs

Lucherini

Rapaccini

2017

JIEM

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Abstract:Purpose: This paper describes the use of simulation and case-study research to assess flexibility gains induced by the adoption of three Lean Manufacturing practices.Design/methodology/approach: We gather useful material and information about the manufacturing process of a selected Small-Medium Enterprise by adopting a case-research approach. The Value Stream Mapping is the method used for visualizing flows of products and information along the production system. Starting from the current arrangement of the company, computer simulation is used to assess the benefits arising from Cellular Manufacturing, Just-in-Time Delivery by Suppliers, and Single Minute Exchange of Dies. Findings: To investigate the flexibility improvements coming from the introduction of LeanManufacturing, we present a simulation model of the described company on which we performed our analysis. We quantify the flexibility of different configurations according to the new 5-step approach in order to segregate the contribution of different lean techniques. Originality/value:We extend the combined use of Case Research and Computer Simulation to the research on Manufacturing Flexibility within Small-Medium Enterprises. We enhance the knowledge on this under investigated context collecting quantitative field data. Moreover, building on the factorial Design of Experiment, we introduce a new 5-step method to appraise the costbenefit ratio of lean techniques for flexibility. The managerial implication of this research is -919-Journal of Industrial Engineering and Management -https://doi.org/10.3926/jiem.2119 mostly related to the provision of a supporting method for the decision making process propaedeutic to Lean Manufacturing introduction.

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Section: Simulation As Support To Value Stream Mappingmentioning

confidence: 99%

Section: Simulation As Support To Value Stream Mappingmentioning

confidence: 99%

Exploring the impact of Lean manufacturing on flexibility in SMEs

Lucherini

Rapaccini

2017

JIEM

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“…The actual implementation is mostly based on the trial and error principle because the future state is not validated (Marvel & Standridge, 2009;Standridge & Marvel, 2006). As of this lacking validation / prior quantification of developed target states and the associated absence of hard facts through key performance indicators (KPIs), many managers shy away from lean projects at all (Detty & Yingling, 2000;Abdulmalek & Rajgopal, 2007). Moreover, in practice, rarely alternative system designs are derived, but only a single future state pursued (Wolff, 2013).…”

Section: Background and Relevancementioning

confidence: 99%

“…Various authors are addressing that fact by using discrete event simulation to quantify required outcome before implementation (Detty & Yingling, 2000;Wolff, 2013;Lanza & Ude, 2010;McDonald, van Aken, & Rentes, 2002;Standridge & Marvel, 2006;Abdulmalek & Rajgopal, 2007;Azadeh, Seifoory, & Abbasi, 2007;Boerkircher & Gamber, 2010;Brueggemann & Mueller, 2008;Lian & van Landeghem, 2002). Sometimes the efforts for performing such a dynamic quantification (e. g. creating the simulation model) are exceeding the aspired gains.…”

Section: Quantification Of Value Stream Maps Addressing Variabilitymentioning

confidence: 99%

Learning to See Clear: Quantification and Multidimensional Assessment of Value Stream Mapping Alternatives Considering Variability

Roessler

Metternich

Abele

2014

BMR

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The prior quantification and validation of future state maps in lean production and optimization projects mostly is not taken into consideration in the traditional value stream mapping approaches. Furthermore the implementation of future states is based upon the trial and error principle. The effects of proactively changing production systems often are unknown and could underlie vast variations due to the planned outcome. So for many managers hard facts are missing and the uncertainties included in such a value stream optimization project are very high. This prevents a necessary system change accompanied by the adoption of lean methods. Thus in this paper a comprehensive value stream optimization approach is presented which primarily focuses upon chances for prior static and dynamic future state map quantification. Under consideration of parameter variability a downstream multidimensional assessment of possible design alternatives is proposed using a fuzzy decision making method to facilitate transparency in the selection of the most adequate future state map. The method described in this paper will be discussed at an industrial case study.

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“…They used this extra analytical tool to find that lean manufacturing saves "inventory, floor space, transportation, manpower, equipment, lead times, and variability" (Detty and Yingling 2000). Another manufacturing application includes an on-line simulation to increase the flexibility and adaptability of their production line (Drake et al 1995).…”

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confidence: 99%

Integrated optimization and simulation models for the locomotive refueling system configuration problem

Verschelden¹,

Stamm²,

Easton³

2017

2017 Winter Simulation Conference (WSC)

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Locomotives in the U.S. use over 3 billion gallons of fuel each year and faster refueling can increase rail network capacity without the infrastructure cost associated with new terminals or tracks. This thesis introduces the locomotive refueling system configuration problem (LRSCP), which seeks to improve efficiency in refueling yards through new technologies or policies. This research also creates two new methods to solve LRSCP. Based on experimental results, solving either TRIP or SLIP and incorporating the optimal refueling policy improves railyard operations by 10 to 30%. This impact is statistically significant and increases the capacity of a railyard. Additionally, it impacts other important parameters such as time spent in the yard and the maximum queue for the railyard. Furthermore, there is a significant decrease in wasted time and an improvement to railyard efficiency.Implementing either method should increase a railyard's capacity and significantly increase revenue opportunities.iv

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Quantifying benefits of conversion to lean manufacturing with discrete event simulation: A case study

Cited by 217 publications

References 9 publications

Exploring the impact of Lean manufacturing on flexibility in SMEs

Exploring the impact of Lean manufacturing on flexibility in SMEs

Learning to See Clear: Quantification and Multidimensional Assessment of Value Stream Mapping Alternatives Considering Variability

Integrated optimization and simulation models for the locomotive refueling system configuration problem

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