School of Mechanical and Industrial Engineering

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Browsing School of Mechanical and Industrial Engineering by Subject "Acquisition"

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    Modeling Equipment Acquisition with Life Cycle Costing Decision (Case in Ethio-Plastic Industry)
    (Addis Ababa University, 2018-06) Meaza, Endris; Birhanu, Beshah (PhD)
    Improving the quality of an organization’s products and services is fundamental to business success. This requires the engagement of highly skilled human labor as well as financial resources, given the appropriate technological, organizational, institutional and cultural infrastructure. Furthermore, the equipments or machineries in the organization must be able to make the owner more competitive and profitable in terms of lower unit costs. In this regard, the present research is a case study which reveals the challenges related to cost escalation while the equipment selection decision making is done based on the traditional least cost approach. Hence, LCC analysis is performed to pin point where the cost escalation will be. Based on the collected data (cost history of the case machinery), the outcome of the analysis demonstrated that the traditional approaches adopted for equipment selection leads to a biased decision. As a result, the company is incurring a blown up unexpected cost because economical criteria outweigh other factors in their decision making process for equipment selection. The problem with the traditional approach is that it mainly concentrates on the purchasing price of the equipment and often ignores those costs that appear when it becomes operational. Nevertheless, in addition to the purchasing cost, equipment cost is also highly related with operational and maintenance costs. Moreover, those unexpected costs are also related with the unforeseen quality cost drivers. Consequently, quality based life cycle costing framework is developed to solve this problem. The framework is developed customizing/ improving the Fabrycky and Blanchard’s life cycle cost framework integrating it with the quality cost drivers. When the selection is made with LCC analysis, it will be only based on the overall minimum life cycle cost. Generally low quality equipments will have low cost and high quality equipments will have high cost. The new framework will optimize these costs considering quality cost drivers in addition to minimum life cycle cost as a selection criterion in their decision making. Above all, taking into account quality dimensions in the framework may certainly resolve the occurrence of such unexpected cost. Since practical validation of the framework with real data requires a long period of time, at least equal to the life span of the equipment, the researcher tried to validate it via expert opinions; and it is found to be appropriate.