Investigating the impact of configuration management on safety and costs in a steel manufacturing plant
- Authors: Nkhuna, Themba
- Date: 2016
- Subjects: Configuration management , Steel industry and trade - South Africa - Management , Manufacturing industries - Management , Project management
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/233760 , uj:23876
- Description: M.Phil. (Engineering Management) , Abstract: Literature shows that Configuration Management is still a challenge in the steel industry and if neglected or inadequate, can result in loss of life and high costs. Consequently organisations are beginning to consider configuration management in their functions. Others are adopting such management procedures so that they will be able to control and manage changes to projects by maintaining the information and documentation on the associated facilities and products, in order to remain competitive and operate efficiently. Based on an assessment of the steel industry and particularly the organisations that the researcher was familiar with, it was established that it appeared that configuration management was not one of the key functions of these organisations. It also appeared from the preliminary assessment that this type of management might be related to certain safety incidents and costs of projects. Therefore the current study was embarked on and sought to establish the impact of the said management on safety and costs in a steel manufacturing plant. Findings from literature on this concept were used to determine its relationship with safety and project costs. In addition to a review of literature, empirical data was collected and interviews were held. Findings confirmed that configuration management had an impact on costs and safety in a steel manufacturing plant. Consequently recommendations were that it was critical if organisations in the steel manufacturing sector were to achieve a zero accident target. Recommendations include a study on the barriers to implementing it.
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A model to improve productivity in steel manufacturing small and medium-sized enterprises : lean six-sigma approach
- Authors: Munyai, Thomas Thinandavha
- Date: 2017
- Subjects: Manufacturing industries - Management , Steel industry and trade - Management , Six sigma (Quality control standard)
- Language: English
- Type: Doctoral (Thesis)
- Identifier: http://hdl.handle.net/10210/269368 , uj:28617
- Description: D.Phil. (Engineering Management) , Abstract: South Africa’s steel manufacturing SMEs are faced by, amongst other challenges, low productivity, where these SMEs are operating below the projected manufacturing output stages. The Global Entrepreneurial Monitor (GEM) for 2015 reported that South Africa is ranked 56 out of 148 countries in terms of competition on productivity worldwide, which is a serious challenge facing steel manufacturing SMEs. This study developed a Lean Six-Sigma model on input resource factors (IRFs) in improving productivity rates for steel manufacturing SMEs situated in Gauteng. The fundamental research objective was to show how Lean Six-Sigma impacts on input resource factors in relation to productivity of steel manufacturing SMEs and to make recommendations to improve productivity of these SMEs. Various Lean Six-Sigma tools were studied to develop an effective Lean Six-Sigma model that will assess the respondents’ productivity performance in their businesses. The Lean Six-Sigma model was viewed the most appropriate tool by integrating various Lean Six-Sigma tools and used as a research method. The research selected mixed method design whereby quantitative was followed by qualitative research method. The results of the study were identified based on the questionnaire, interviews and case study observations. The results were based on the productivity measures; factors influencing productivity of steel manufacturing SMEs; Lean Six-Sigma productivity improvement tools; the extent to which Lean Six-Sigma impact on IRFs in relation to productivity of steel manufacturing SMEs. The Statistical Package for the Social Sciences (SPSS) version 24 and Analysis of a Moment Structures (AMOS) version 23 was used as computer software in order to describe and analyse sets of quantitative data. Secondly, interviews based on experts were conducted and the case study was used to provide additional information on how steel manufacturing operates in terms of the manufacturing process from warehouse through the value chain of material that is converted to products for customer service. Lastly, a case study observation was used to test the application of the LSS DMAIC model in optimising productivity rate in steel manufacturing SMEs. The research found, using exploratory factor analysis study (EFA), that Lean Six-Sigma was the driving force on IRFs such as human capital; technology management; machinery; competitiveness; layout management; finance; government support and location in relation to productivity of steel manufacturing SMEs. Cronbach's alpha was used to measure the construct and alpha coefficient was also applied to describe the reliability of factors extracted from dichotomous and/or multi-point formatted questionnaires or scales. The research found indicates that the score for IRFs is higher and reliable based on coefficient thresholds. Therefore the reliability of the results on IRFs selected are acceptable since the IRFs score exceed the Cronbach’ Alpha coefficients standard score On the other hand, using correlational study, the research reported that IRFs such as government support, competitiveness and layout management impacted on the productivity of steel manufacturing SMEs. This means that, based screening the outliers of the selected IRFs, there is a strong linear relationship between the independent variables selected such as human resources; material; machine; location; layout; finance; management including other factors such as competitiveness and government support and the dependent variable such as productivity improvement within steel manufacturing SMEs. In view of the results found, the research recommends that Lean Six-Sigma model be used to mitigate low productivity in steel manufacturing SMEs.
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Factors affecting the performance of a manufacturing supply chain and the impact of the factors on the supply chain and the organization
- Authors: Mazibuko, Siphesihle
- Date: 2017
- Subjects: Business logistics , Business logistics - Management , Manufacturing industries - Management , Production scheduling
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/269803 , uj:28666
- Description: M.Phil. (Engineering Management) , Abstract: Supply chain (SC) is an integrated process of various business functions used, to source raw material, add value to the raw material, and deliver the product to the customer. The common element to all manufacturing entities is the control of material flow, value adding (manufacturing) processes, and distribution channels to customers. With the ever-increasing competitive environment, many manufacturing companies around the world, including those in South Africa, experience a shortfall in the desired outcomes. As a result a number of organizations have looked at a possible mechanism to put in place to maintain or gain the competitive advantage, or to maintain or increase market share. Industry maturity saw a number of organizations implement the use of Supply Chain Management (SCM) in order to try to minimize costs, increase profits, and meet customer expectations, i.e. on time in full delivery (OTIFD) of a product or service against contractual or negotiated dates and in accordance with the customer requirements (Quality). This research examined the factors that affect the performance (on-time delivery) of a manufacturing supply chain. Two research questions had to be addressed to reach this goal:- (i) What are the factors affecting the on-time-delivery of the supply chain? (ii) What is the impact of these factors on the supply chain and the organization? In addition to the data collected from literature, two other data sets were collected for this research. Operational data were collected from a performance management tool (Qlikview) and the other set of data was collected through interviews with the use of a questionnaire. Both sets of data were analysed to identify any common patterns when it comes to the factors that affect the performance of supply chain. The results of the analysis suggest that the factors that contribute the most to poor supply chain performance are social factors. There is also an indication that there is a relationship between internal deliveries and customer deliveries...
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Integrating physical asset management and facilities management operation and its benefits to the manufacturing industries
- Authors: Phala, Mamohlokwe Julia
- Date: 2019
- Subjects: Facility management , Manufacturing industries - Management
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/417765 , uj:35394
- Description: Abstract: There is a thin line separating the functions of Physical Asset Management (PAM) from Facilities Management (FM). However, if these two functions are integrated at the strategic, tactical and operational levels, the critical assets and facilities necessary for effective business operations will be adequately looked after in a systematic approach. This will facilitate the efficient operation of production processes, improved product, which translate into achieving competitive advantages in the respective industry. The focus of this research is to explore the added value, to the manufacturing industry, if the functions of PAM and FM are integrated and the effects in achieving the strategic objectives of the industry. The research methodology adapted is the multiple sites case study of manufacturing industries, using the instruments of survey, interview and focus group sessions as tools for data collection. The sample encompasses the manufacturing industries, professional bodies and experts from academia. The findings revealed that the majority of the manufacturing industries operate parallel structures for PAM and FM resulting in duplication of functions and resources. These ultimately are negatively affecting the strategic objectives of the industries and the efficiency in the production system and product. Therefore, the progressive integration of both functions was observed as the panacea to achieving the strategic objectives of the industry, in the most cost-effective manner. , M.Phil. (Engineering Management)
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