Manufacturing productivity improvement using effectiveness metrics and simulation analysis

Huang, Samuel H.; Dismukes, John P.; Shi, Jingping; Su, Qi; Razzak, Mousalam A.; Bodhale, Rohit; Robinson, Derek

doi:10.1080/0020754021000042391

Cited by 146 publications

(105 citation statements)

References 7 publications

Supporting

Mentioning

101

Contrasting

Unclassified

Order By: Relevance

“…In this category, the included costs of spare parts, and consumable for maintenance activities. Spare parts cost includes all the replacement parts for the machinery in the aspect of mechanical, electrical, and fluid systems [34]. Expenses relating to machinery hardware such gasket, bearing, lubricants, cooling agent, screw, bolts and nuts, O-rings and others are categorized as consumable cost [35].…”

Section: Key Operating Parameters Of Small Scale Production Plantmentioning

confidence: 99%

Modelling Production Performance of Small Scale Production Plant

Lee¹,

Karunakaran²,

Djubair³

et al. 2018

IJAEMS

View full text Add to dashboard Cite

show abstract

Section: Key Operating Parameters Of Small Scale Production Plantmentioning

confidence: 99%

Modelling Production Performance of Small Scale Production Plant

Lee¹,

Karunakaran²,

Djubair³

et al. 2018

IJAEMS

View full text Add to dashboard Cite

show abstract

“…By conducting a simulation, using accurate model will reduce and eliminate the risk associated with their new investment. Many of researchers [1][2][3][4][5][6] try to use different simulation approaches to support this claim. Most of the industries based on discrete events nature.…”

Section: Introductionmentioning

confidence: 99%

Productivity improvement using discrete events simulation

Hazza

Elbishari

Ismail

et al. 2018

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

Abstract. The increasing in complexity of the manufacturing systems has increased the cost of investment in many industries. Furthermore, the theoretical feasibility studies are not enough to take the decision in investing for that particular area. Therefore, the development of the new advanced software is protecting the manufacturer from investing money in production lines that may not be sufficient and effective with their requirement in terms of machine utilization and productivity issue. By conducting a simulation, using accurate model will reduce and eliminate the risk associated with their new investment. The aim of this research is to prove and highlight the importance of simulation in decision-making process. Delmia quest software was used as a simulation program to run a simulation for the production line. A simulation was first done for the existing production line and show that the estimated production rate is 261 units/day. The results have been analysed based on utilization percentage and idle time. Two different scenarios have been proposed based on different objectives. The first scenario is by focusing on low utilization machines and their idle time, this was resulted in minimizing the number of machines used by three with the addition of the works who maintain them without having an effect on the production rate. The second scenario is to increase the production rate by upgrading the curing machine which lead to the increase in the daily productivity by 7% from 261 units to 281units.

show abstract

“…In addition, for a more efficient handling of logistical tasks, many companies rely on common concepts and methods, such as just-in-time (JIT) and/or just-in-sequence (JIS) delivery. Scientific research provides suitable approaches and efficiency metrics to evaluate the efficiency of individual and/or a group of manufacturing facilities adequately [1][2][3][4]. Furthermore, the existing literature provides appropriate indicators and performance measurement systems for efficiency assessment of JIT-and/or JIS-deliveries [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

In-Plant Logistics Efficiency Valuation Using Discrete Event Simulation

Seebacher¹,

Winkler²,

Oberegger³

2015

Int. j. simul. model.

View full text Add to dashboard Cite

The purpose of this paper is to present an applicable approach for the valuation of in-plant logistics efficiency. Therefore, we developed a time-based efficiency concept that considers all relevant time losses when executing production logistics processes. The occurring delays are captured in real-time using modern auto-ID-technologies. For the valuation and improvement of in-plant logistics efficiency, we propose to use simulation modelling to investigate the cause-effect relations in the production system in advance. Thus, a discrete event simulation model has been constructed to model the workflow of a production system with discrete manufacturing processes and its in-plant logistics processes. The aim of the provided simulation study is to prove the functionality and to verify the applicability of the current approach in business practice.

show abstract

Manufacturing productivity improvement using effectiveness metrics and simulation analysis

Cited by 146 publications

References 7 publications

Modelling Production Performance of Small Scale Production Plant

Modelling Production Performance of Small Scale Production Plant

Productivity improvement using discrete events simulation

In-Plant Logistics Efficiency Valuation Using Discrete Event Simulation

Contact Info

Product

Resources

About