Parameters Investigation of Mathematical Model of Productivity for Automated Line with Availability by DMAIC Methodology

Sin, Tan Chan; Usubamatov, Ryspek; Hamzas, Mohd Fidzwan B. Md. Amin; Wai, Low Kin; Yao, Teh Keat; Bahari, M. S.

doi:10.1155/2014/206717

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…Each level in the process is essential to achieve the optimum expected outcomes ("Study and Analysis the Wastage Reduction of Fluorescent Powder in CFL 23 W in Philips Pvt Ltd Mohali, Using Six Sigma Methodology," 2016). On the other hand, PACE Prioritization Matrix which was formed by Karen Martin Group, is a LEAN engineering tool established to explore the productivity parameters for increasing the production efficiency (Sin et al, 2014).…”

Section: Methodsmentioning

confidence: 99%

Developing a novel based productivity model by investigating potential bounds of production plant: A case study.

Hussain

2019

Int. J. Prod. Manag. Eng.

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Productivity level is based on reliability impression which is the rudimentary aspect of automatic assembly line for continuous production. Productivity forecasting is a professional tool helping to enhance production system and attain the client petition by using precise model. Due to mechanisms complexity of assembly lines, analysis of failure factors contributes a significant role for investigating potential bounds that require analytial approach for comparison the current and proposed model of productivity effects. The issues related to production losses need additional space for improvement of productivity model which may not present a close comparison between the current and proposed model of productivity. Through this paper the development of a novel based productivity model is attempted to extant comparisons for availability of assembly line workspaces of an automobile tire manufacturing plant in Pakistan. For investigating the potential bounds of productivity losses, DMAIC and PACE techniques are used. It is verified that novel productivity model yielded better results of 3.358 % errors showing its accuracy as compared to real productivity level at different workspaces. Different rates of production losses followed by the workspaces blockage timing issues are effectively discussed with engineering maintainable explanation for productivity enhancement within the tire curing assembly line.

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Section: Methodsmentioning

confidence: 99%

Developing a novel based productivity model by investigating potential bounds of production plant: A case study.

Hussain

2019

Int. J. Prod. Manag. Eng.

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“…In the present mining scenario, mining companies employ a holistic view to incorporate both technical and site-specific factors to measure the basis of their asset optimization efforts since impressive asset management practices not only enhance the economic performance of a mine but also results in much greater engagement and motivation of the work force [8] [9] [10]. A great number of researches have been conducted for the past several decades to measure the influence of various factors on mine productivity but the majority of the researches have focused on the segregated fields such as mine safety [11] [12], reliability of mining equipment [13] [14] maintenance operation delays [15] and delays due to site specific factors such as high methane concentration, strata failure and inundation [5] [16] [17]. However, none of these studies addressed the issue of the interrelationship between the causal variables and their direct or indirect effect on productivity.…”

Section: Coal Produced In Tonnes Per Week Productivitymentioning

confidence: 99%

“…Productivity of longwall mining operations turns out to be a critical measure of performance of the emerging means of underground coal production [5]. There are several methods used across the industry to measure the productivity [6].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Relationship of Some Causal Factors Associated with Productivity of Longwall Mining Using Structured Equation Modeling

Talan¹,

Bhattacherjee²

2021

JMMCE

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Enhancement of productivity optimization is steadily gaining the priority in mining companies especially in the underground coal mining industry which faces a daunting task to balance marginal profit generation with a comparatively high cost of production, volatile market price and rapid grade variation. This paper is aimed to analyze some of the causal factors both technical and site specific which are directly or indirectly impacting the productivity of the longwall coal mining system such as downtime of equipment system in longwall panel, overloading of conveyors and bin, preventive maintenance, gas management practices and injury severity rate. Structured Equation Modelling (SEM) was used to study the causal relationships between the abovementioned factors and mine productivity. The equipment considered for analysis included shearer, armored face conveyor, crusher, bridge stage loader, chock supports, main gate drive, gate conveyor, hydraulic pumps and crusher. SEM was applied to relate the correlations existing among these causal variables in order to assess their direct or indirect impact on mining productivity. Based on the data extracted over a period of 10 months which included the extraction of 2 longwall panels, the study revealed a significant negative causal relationship between injury severity rate (p < 0.01) and equipment downtime (p = 0.001), with mine productivity. However, preventive maintenance delay time, gas management delay time and conveyor overloading delay time were found to have an insignificant direct influence on mine productivity but indirectly modify it through a significant mediator relationship with equipment downtime. This information would assist mine management to take proper preventive measures.

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“…Ocampo & Pavón (2012) performed a comparative analysis of the DMAIC methodology and the steps taken to develop a simulation model and propose a five-step methodology that incorporates both tools to analyze different scenarios. Chan et al (2014) used the DMAIC methodology and PACE prioritization matrix (Priority, Action, Consider, and Eliminate) to improve and robust a mathematical model of productivity, they raise that productivity is one of the most important criteria in automated lines.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a system dynamics model based on Six Sigma methodology

2017

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A dynamic model to analyze the complexity associated with the manufacturing systems and to improve the performance of the process through the Six Sigma philosophy is proposed. The research focuses on the implementation of the system dynamics tool to comply with each of the phases of the DMAIC methodology. In the first phase, define, the problem is articulated, collecting data, selecting the variables, and representing them in a mental map that helps build the dynamic hypothesis. In the second phase, measure, model is formulated, equations are developed, and Forrester diagram is developed to carry out the simulation. In the third phase, analyze, the simulation results are studied. For the fourth phase, improving, the model is validated through a sensitivity analysis. Finally, in control phase, operation policies are proposed. This paper presents the development of a dynamic model of the system of knitted textile production knitted developed; the implementation was done in a textile company in southern Guanajuato. The results show an improvement in the process performance by increasing the level of sigma allowing the validation of the proposed approach.Keywords: Six Sigma, DMAIC, system dynamics, knitting. RESUMENSe propone un modelo dinámico para analizar la complejidad asociada a los sistemas de producción y mejorar el desempeño de los procesos a través de la filosofía Seis Sigma. La investigación se enfoca en la implementación de la herramienta de dinámica de sistemas para dar cumplimiento a cada una de las fases de la metodología DMAIC. En la primera fase, definir, se articula el problema, recolectando los datos, seleccionando las variables y representándolas en un mapa mental que ayuda a construir la hipótesis dinámica. En la segunda fase, medir, se formula el modelo, se desarrollan las ecuaciones y se elabora el diagrama de Forrester para llevar a cabo la simulación. En la tercer fase, analizar, se estudian los resultados obtenidos de la simulación. Para la cuarta fase, mejorar, se valida el modelo a través de un análisis de sensibilidad. Finalmente en la fase de control, se proponen políticas de operación. En este trabajo se desarrolló un modelo dinámico del sistema de producción textil de tejido de punto; la implementación se realizó en una empresa textil del sur de Guanajuato. Los resultados muestran un aumento en el desempeño del proceso incrementando el nivel de sigma, lo que valida el enfoque propuesto.Palabras clave: Seis sigma, DMAIC, dinámica de sistemas, tejido de punto.

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Parameters Investigation of Mathematical Model of Productivity for Automated Line with Availability by DMAIC Methodology

Cited by 3 publications

References 13 publications

Developing a novel based productivity model by investigating potential bounds of production plant: A case study.

Developing a novel based productivity model by investigating potential bounds of production plant: A case study.

Assessment of Relationship of Some Causal Factors Associated with Productivity of Longwall Mining Using Structured Equation Modeling

Development of a system dynamics model based on Six Sigma methodology

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