Abstract
D.Phil. (Engineering Management)
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.