Store performance evaluation: A case study on BPDB'S shikalbaha power plant local store
MetadataShow full item record
Power plant store keeps in stock the items that are of high value and critical to the power plant operation and maintenance. Useful working life of a power plant varies with the type of power plant used. Factors for example long working life of the power plant, uniqueness of the spare parts, dependency on foreign manufacturer for spare parts, rapid change of technology, fluctuations of the currency rate, long procurement lead time, bureaucracy in the procurement process etc influence the power plant to keep in stock the valuable items for long time. The purpose of this research is to identify the factors that are affecting the performance of local store and their impact on power plant operation and also to identify the most important areas of storage system that require immediate improvement. Data were collected using the questionnaire with a sample size of 31. The respondents were asked to response in four areas of storage system and they were store facility, record keeping system, staff performance and store security system. Officials of the power station who are directly or indirectly involved with the operation of the power plant store as well as operation of the power plant provided their opinion. Data were analyzed using frequency analysis, reliability test, descriptive analysis, correlation analysis, regression analysis and chi-square test. Results revealed that the store is suffering from poor store capacity, poor record keeping system and poor employee performance. Store security is comparatively better as store security is involved with the power plant security as a whole. However the store has no alarming system, no security signs and hitting and air-conditioning system. Regression analyses showed significant relationships between store performance and three of the five attribute factors. Chi-square analyses also showed significant relationship between store performance and three of the five attribute factors.