The model has implications for participant sport managers. The model highlights that sport participation motivation is the antecedent of consumer engagement (such as mastery and weight), sport participants engage on three dimensions (cognitive, emotional, and behavioural), and the outcomes of consumer engagement are word-of-mouth and re-participation intention. The model provides a complete understanding of the process of consumer engagement in participant sports. The paper seeks to fill that gap by developing a conceptual model on consumer engagement in participant sports. However, in the field of participant sports, there still lacks research on consumer engagement. This is because of the outcomes associated with consumer engagement such as increased sales, enhanced competitive advantage, consumer loyalty, and the development of mutual beneficial relationships between organisations and consumers. The topic of consumer engagement has been gaining the attention of both academics and practitioners. However, contrary to popular belief, there are still environmental difficulties to be overcome, to enable large scale adoption. Results show that there are significant economic gains to be had by properly identifying spare parts, and consequently optimizing management strategies. Its application is demonstrated using a case from the paper and pulp industry. It includes a multi-criteria classification system aimed at quickly preselecting suitable candidates and environmental/cost models to facilitate strategy definition. The model is structured in 4 phases: Characterization preselection from the operational perspective technological preselection strategy definition. This paper’s objective is to contribute to the large-scale adoption of additive manufacturing, by proposing, explaining, and demonstrating, a decision-support model, designed to identify spare parts suitable for AM, and support the definition of new and optimized warehouse management strategies. Although interesting for demonstration proposes, to fully integrate additive manufacturing technologies in spare parts management, practitioners need comprehensive tools, based on top-down approaches, capable of quickly analysing large samples of spare parts. The common practice is to identify parts using a bottom-up approach, heavily reliant on the knowledge of people directly involved in maintenance activities. However, large-scale implementation in complex industries remains a challenge, in big part due to the generalized lack of data, necessary to identify spare parts suitable for AM. The opportunities to shorten supply chains, open supplier markets, improve response times, and ultimately optimize stocks, generate interest in adopting additive manufacturing (AM) in spare parts’ supply chains. In the case of the item with exponential distribution, the behavior is non-linear, turning asymptotic for those service levels higher than 0.62, whereas for the item with Poisson distribution, the cost variation has been linear Likewise, for the item with Poisson distribution, the cost behavior has been considered non-linear, as the reorder point has been located at intermediate values of the range studied depending on the service level.Īlso, it has been assumed that the cost variation with service level has been different for both items. In this case, it is considered a linear relationship for high and low service levels for the item with exponential distribution, and a parabolic relationship for intermediate levels, and those reorder points located at extreme values of the range under study, depending on the cost structure of stockouts and inventory maintenance. It is evident that the variation of the minimum inventory cost as a function of the reorder point, has been different for both items. The estimation process explores carefully what happens to the reorder point and inventory cost when the service level changes. It has been considered several important factors like account stockouts and safety stock maintenance. This paper aims the estimation of lead time, and the reorder point that produce minimum inventory cost, using the Exponential and Poisson distributions for each of the corresponding products under study, when a service level is required.
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