Optimization of machine downtime in the plastic manufacturing

Nwanya, Stephen C.; Udofia, J.I; Ajayi, Oluseyi O.

doi:10.1080/23311916.2017.1335444

Cited by 29 publications

(16 citation statements)

References 8 publications

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“…Some case studies have proved that successfully implementing TPM brings out invaluable impacts to the overall performance of the organization or a company. TPM has shown significant improvements of the ranges 30-40% improvement in Overall Equipment Effectiveness, a 45% improvement in manufacturing output, 55-75% reduction in accidents as well as 70-80% reduction in defects & rework, 15% reduction in power costs as well as 75% reduction in breakdowns, downtimes [31][32][33].…”

Section: Iv1 Tpm Implementation Frameworkmentioning

confidence: 99%

Framework for total productive maintenance for an SME

Gwangwava

Baile

Dikgale

et al. 2021

ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)

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Total Productive Maintenance (TPM) is a worldwide maintenance program for plant equipment and machinery. It is a proactive maintenance that optimizes Overall Equipment Effectiveness (OEE) to get rid of the six (6) big losses. The research was inspired by observations made over a six (6) months period at Company X, a Small and Medium Enterprise (SME). Observations made were that the SME produced a lot of defective items, some requiring reworks, the SME had poor maintenance plans, poor relationship between departments, resulting in low morale of workers, frequent breakdowns of machines, as well as low production rate. The main objective of the research was to design a framework that would identify and address the aforementioned problems, resulting in an optimized OEE rate. The research data was obtained through various methodologies, including observation, questionnaire as well as interviews among the company employees -technicians, operators, and maintenance engineers. The data was analysed using Microsoft excel performance dashboards as well as TPM templates. Based on the findings, a framework was designed and developed that sought to address the aforementioned problems at the company. The study improved the OEE of machines and processes through the implementation of TPM approach at Company X.

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Section: Iv1 Tpm Implementation Frameworkmentioning

confidence: 99%

Framework for total productive maintenance for an SME

Gwangwava

Baile

Dikgale

et al. 2021

ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)

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“…Also, a low availability of the machines is identified by stops and technical failures that generate waste in the different processes involved in the operation of the machine [4]. On the other hand, it is considered important to reduce the downtime of a machine that is preceded by a shutdown, which has a considerable effect on improving productivity and decreases negative impacts such as waste generation [24].…”

Section: Literature Reviewmentioning

confidence: 99%

Model to reduce waste in the production of labels in Peruvian companies of the plastic sector by applying Autonomous Maintenance, Kanban and Standardization of work

Flores¹,

Valenzuela²,

Viacava³

et al. 2020

Proceedings of the 18th LACCEI International Multi-Conference for Engineering, Education, and Technology: Engineering, Integrat

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This work presents the problem of excessive waste of labels that is generated in the printing of these. The printing process is the key process of the company, since it transforms the plastic rolls together with the inks and an error in the procedures causes the loss of the material entered and money. This drives to propose solutions through Lean techniques in the production of labels. In general, the studies are based on other lines in the plastic sector, such as pipes, bags, containers, among others. To address this problem, the Lean Manufacturing methodology adapted to a cycle of continuous improvement will be used, forming a solution proposal model, with techniques such as support 5s, the application of Autonomous Maintenance, Kanban and Standardization of Work. Based on a pilot test, a waste reduction of 7.78% and an improvement in the indicators that are involved in the investigation are obtained.

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“…Modern manufacturing systems consist of advanced engineering schemes with elevated technology that ought to operate for 24 h in a day, 7 days in a week and 365 days in a year (Gupta et al, 1988;Windmark et al (2012); Peng and Zhu, 2017;Tsarouhas and Besseris, 2017). By emphasising the significance of bottleneck machines in production lines, authors have called for more attention to the mean downtime per year of equipment in engineered systems (see also Fuerst et al, 1991;Fox et al, 2008;Al-Chalabi et al, 2014;Koussaimi et al, 2016;Ossai et al, 2016;Nwanya et al, 2017). As noted by Windmark et al (2012), manufacturing downtime in cells, production lines or sole machines exhibit complicated features in statistical behaviour and should be studied for their frequency and duration of the display.…”

Section: Introductionmentioning

confidence: 99%

Maintenance downtime evaluation in a process bottling plant

Inyiama

Oke

2020

IJQRM

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PurposeDowntime is a process parameter that substantially impacts on the operating hours and results in production losses, thus motivating maintenance engineers to control process plants. Notwithstanding, the impacting nature of process equipment failure on the operating hours in bottling plants remains inadequately examined. In this paper, the cause-and-effect analysis was used to establish the root cause of the downtime problem and Pareto analysis employed to justify the greatest opportunities for improvement in reducing downtime and increasing reliability levels. Weibull analysis is then conducted on the industrial setting. Novel aspect ratios are proposed.Design/methodology/approachUsing the Weibull failure function of machines as a principal facilitator to produce failure predictions, the downtime behaviour of a process plant was modelled and tested with practical data from a bottling process plant. This research was conducted in a Nigerian process bottling plant where historical data were examined.FindingsThe analysis of the results shows the following principal outcome: First, the machines with the highest and least downtime values are 2 and 5, respectively, with correspondingly mean values of 22.83 and 4.39 h monthly. Second, the total downtime 92.05 and 142.14 h for the observed and target downtime, with a coefficient of determination of 0.5848 was recorded. Third, as month 1 was taken as the base period (target), all the machines, except M5 had accepted performance, indicating proper preventive maintenance plan execution for the bottling process plant. Availability shows a direct relationship between the failure and uptime of the machines and the downtime impacts on production. Two machines had random failure pattern and five machines exhibited a wear-out failure pattern and probably due to old age and wear of components in the machines.Originality/valueThe major contribution of the paper is the Weibull modelling in a unique application to a bottling plant to avoid current practices that use reliability software that is not easily accessible.

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Optimization of machine downtime in the plastic manufacturing

Cited by 29 publications

References 8 publications

Framework for total productive maintenance for an SME

Framework for total productive maintenance for an SME

Model to reduce waste in the production of labels in Peruvian companies of the plastic sector by applying Autonomous Maintenance, Kanban and Standardization of work

Maintenance downtime evaluation in a process bottling plant

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