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Productivity improvement of a small to medium, custom-manufacturing factory

Mini Dissertation (B Eng. (Industrial and Systems Engineering))--University of Pretoria, 2018.

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Main Author: Moolman, Tanisha
Format: Thesis
Language:English
English
Published: University of Pretoria. Faculty of Engineering, Built Environment and Information Technology. Dept. of Industrial and Systems Engineering 2019
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access_status_str Open Access
author Moolman, Tanisha
author_browse Moolman, Tanisha
author_facet Moolman, Tanisha
author_sort Moolman, Tanisha
collection Thesis
dc_rights_str_mv © 2018 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.
description Mini Dissertation (B Eng. (Industrial and Systems Engineering))--University of Pretoria, 2018.
format Thesis
id oai:repository.up.ac.za:2263/68355
institution University of Pretoria (South Africa)
language English
English
last_indexed 2026-06-10T12:39:57.392Z
license_str Other — see source repository
provenance_str_mv Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository
publishDate 2019
publishDateRange 2019
publishDateSort 2019
publisher University of Pretoria. Faculty of Engineering, Built Environment and Information Technology. Dept. of Industrial and Systems Engineering
publisherStr University of Pretoria. Faculty of Engineering, Built Environment and Information Technology. Dept. of Industrial and Systems Engineering
record_format dspace
source_str UPSpace — University of Pretoria Institutional Repository
spelling oai:repository.up.ac.za:2263/68355 Productivity improvement of a small to medium, custom-manufacturing factory Moolman, Tanisha Mini-dissertations (Industrial and Systems Engineering) Mini Dissertation (B Eng. (Industrial and Systems Engineering))--University of Pretoria, 2018. This report addresses productivity shortcomings at PG Aluminium (PGA) and how to address those challenges in order to improve productivity. The main problem identi ed through a business process analysis (BPA), value analysis, and work sampling study was labour productivity, as the factory's productivity is at a low of 36% when the time spent on value-adding activities are measured against the total time available for production. Productivity shortcomings can be addressed by implementing Lean principles, such as the Just-In-Time (JIT) tool and techniques. JIT, amongst other things focus on the elimination of wastes. Seven wastes are classi ed by JIT of which three of the seven were identi ed in PGA's factory. They were \waiting", \unnecessary motion", and \unnecessary inventory". These three wastes are directly hampering the ow of the fabrication process. The three wastes are therefore addressed in this study by improving the current hardware picking process, as well as through the design of a scheduling model to increase the ow of the process in the factory. The hardware picking process was analysed in more depth by doing a Business Process Analysis (BPA) which highlighted areas for improvement in the picking process. A simpli ed version of the scheduling model was designed using linear modelling principles and Python software. The model aims to produce as many products as possible in the shortest amount of time. Using the time study data collected for the scheduling model, a hypothetical individual performance measurement tool (IPMT) was designed that PGA can use to compare worker performance to expected performance. The aim is to improve the overall ow of the factory by improving labour productivity so that ultimately the business process can be optimised. The scheduling model, hardware picking process improvement suggestions, and the IPMT were evaluated using the evaluation methods suggested by Manson (2006). Amongst other evaluation methods, a simulation model was designed using simulation software (AnyLogic), which was used to evaluate the e ect of the scheduling model and the hardware picking process improvements. From the analysis it was determined that the overall hardware picking time can decrease by up to 33.8% if the suggested improvements are implemented. The results of the scheduling model using the simulation indicates that the overall dead time (which translates into work-inprogress) can be reduced by 46.3%. From this study it is therefore clear that the overall productivity at PGA can be improved by implementing an improved hardware picking system as well as a scheduling model. 2019-02-01T09:46:40Z 2019-02-01T09:46:40Z 2018 2018 Mini Dissertation http://hdl.handle.net/2263/68355 en en © 2018 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. PDF application/pdf University of Pretoria. Faculty of Engineering, Built Environment and Information Technology. Dept. of Industrial and Systems Engineering
spellingShingle Mini-dissertations (Industrial and Systems Engineering)
Moolman, Tanisha
Productivity improvement of a small to medium, custom-manufacturing factory
title Productivity improvement of a small to medium, custom-manufacturing factory
title_full Productivity improvement of a small to medium, custom-manufacturing factory
title_fullStr Productivity improvement of a small to medium, custom-manufacturing factory
title_full_unstemmed Productivity improvement of a small to medium, custom-manufacturing factory
title_short Productivity improvement of a small to medium, custom-manufacturing factory
title_sort productivity improvement of a small to medium custom manufacturing factory
topic Mini-dissertations (Industrial and Systems Engineering)
url http://hdl.handle.net/2263/68355
work_keys_str_mv AT moolmantanisha productivityimprovementofasmalltomediumcustommanufacturingfactory