Browsing by Author "Appollis, Logan-Lee Miche"

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    An implementation framework for statistical process control in small to medium-sized enterprises: A South African context
    (Stellenbosch : Stellenbosch University, 2019-12) Appollis, Logan-Lee Miche; Van Dyk, Wynand; Matope, Stephen; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: A fundamental trait of our everyday environment is that no subsequent event is precisely repeatable. Despite the best effort of manufacturing entities, the probability is low that two consecutive batches of material will have exactly the same characteristics. Variation is inherent to system-based processes as a combination of people, materials, methods, machines and the environment, can contribute to a natural randomness in processes. The implementation of Statistical Process Control (SPC) is driven by the desire to be more proactive as the reactiveness of an inspection-based quality control system is unreliable, costly and time-consuming. SPC is commonly overlooked due to a lack of awareness of the potential benefits and it commonly fails due to an unclear objective and ill-constructed implementation plan. This study is an intervention which surveys existing SPC implementation publications. The study identifies strengths and weaknesses of existing published literature, highlighting key areas of SPC implementation. The study further focuses on the organisational and methodological critical success factors (CSFs), which would be relevant to South African small to medium-sized enterprises (SMEs), utilising the identified CSFs and deficiencies to develop a framework for the sustainable and effective implementation of SPC in manufacturing SMEs. The research methodology consists of three phases, which are the literature review, framework development and the validation of the framework as a case study using participatory action research. The literature survey was performed as a random literature survey coupled with a systematic literature review of SPC implementation frameworks. The research branches into implementation strategies and methodologies used for other continuous improvement initiatives. According to the reviewed frameworks the most commonly identified gaps are a lack of focus on: (1) measurement system capability; (2) process prioritisation; (3) identification of critical to quality characteristics; (4) training and education; (5) validation of the framework; (6) step-by-step procedure with a logical flow and (7) problem-solving. A total of 81% of the articles mentioned training and education as a critical success factor, and 69% of the same reviewed articles also mentioned management commitment as a critical success factor, in contrast to the 13% which mentioned statistical thinking. This study contributes to the domain of quality management and continuous improvement by addressing a tangible issue in a specific manufacturing organisation in which a previous attempt on implementing SPC failed. The study addresses the lack of substance, which current literature offers regarding strategic approaches on the implementation of SPC in smaller organisations with limited resources.
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    Using failure modes and effects analysis as a problem-solving guideline when implementing SPC in a South African chemical manufacturing company
    (Southern African Institute for Industrial Engineering, 2020-05-29) Appollis, Logan-Lee Miche; Van Dyk, Wynand A.; Matope, Stephen
    ENGLISH ABSTRACT: Quality management has ceased to be an operational extra, and has become imperative to doing business in a saturated market environment with demanding clients. It is now a significant component of holistic operational management. The ultimate aim remains customer satisfaction, using the most effective techniques to ensure the most efficient and cost-effective process. Statistical process control (SPC) and statistical quality control have been widely used in quality management to enhance process performance by reducing process variation. However, SPC can only be effective if implemented with a structured problem-solving tool. The diagnostic ability of statistics, coupled with a failure modes and effect analysis (FMEA) as the problem-solving methodology during an out-of-control action plan, creates an environment conducive to improving processes and empowering employees. The failure modes and effect analysis was used to identify and eliminate sources of variation, which led to a reduction in process variation by 63 per cent and a decrease in defects by 160 961 per million opportunities.