Abstract
The rapid development of Industry 4.0 technologies creates significant prospects to improve supply chain performance. However, choosing the best technology for specific supply chain goals remains challenging due to various available tools, diverse organisational needs, and changing market demands. The challenge is greater for SMEs with limited resources. This study therefore develops a decision-making framework to assist Gauteng-based manufacturing SMEs in analysing and prioritising Industry 4.0 technologies to select with a view to improving supply chain performance. A convergent mixed-method approach was employed, effectively integrating quantitative and qualitative studies to simultaneously address various research objectives. For the quantitative aspect, a questionnaire was administered, allowing for the collection of numerical data that could be statistically analysed. The qualitative component involved engaging in semi-structured interviews, thus enabling in-depth conversations that captured rich, nuanced insights from participants. The survey revealed that the Gauteng-based manufacturing SMEs experience similar inefficiencies within all the supply chain phases based on the SCOR model (Planning, Sourcing, Producing, Delivering, Returning and Enabling). The survey confirmed the need for these SMEs to optimise supply chain performance, and the analysis further revealed that planning inefficiencies significantly impact supply chain performance.
A systematic literature review revealed Industry 4.0 technologies that are applicable to optimise these supply chain phases, i.e. in the planning phase, which became the focal point of the study. The review findings revealed that 4IR technologies such as simulation, machine learning, digital twin and the Internet of Things were the critical few technologies that can optimise supply chain planning activities such as forecasting, production planning and control and scheduling. These Industry 4.0 technologies are equally capable of optimising the supply chain planning phase. Therefore, the developed technology selection framework utilised AHP with expert input to determine and rank factors which manufacturing SME decision-makers need to consider when selecting these technologies. This was achieved through the analysis of the interview data, which revealed ten (10) factors, including strategic alignment and cost ranking first with a weight of 0.16, security, functionality, integration, data analytics and ease of use with a weight of 0.14, 0.11, 0.1, 0.09, and
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0.08 respectively. These were followed by after-sales support and training with an equal weight of 0.06 and scalability with a weight of 0.04. Thereafter, the development of the decision-making framework for the selection of 4IR technologies (which could be used by Gauteng-based SME decision-makers) was based on the amalgamation of the quantitative and qualitative research findings. The framework was shared with decision-makers in the manufacturing SMEs first to validate and confirm the ten factors and their rank, and secondly, to assess the framework's usability through the System Usability Scale (SUS). With a response rate of 90%, all the decision-makers (100%) considered the framework acceptable, with scores ranging from 77.5 -100. This indicates that the developed technology selection framework decisively aligns with the critical factors that Gauteng-based decision-makers prioritise in their technology selection decisions. It also confirms that the framework is usable for making technology selection decisions in manufacturing organisations, therefore achieving the study's main objective.