Design And Implementation Of Service Parts Requirement Forecast System

Service parts, also known as spare parts, are the accessories for after market service. With the keen competition, after market service became one of the key competencies accompanying with additional service profit. However due to the uncertainty and very complicated situation of product failures, service part requirement is always hard to predict. Therefore a specific system solution for service parts requirement forecast will help enterprise to achieve best after market service with limited cost and resources, which will improve the enterprise's competency ultimately.This dissertation was built up on a real business case, and it proposed that an ultimate model of enterprise service parts management is the knowledge management of after market service. Due to the lack of service parts knowledge management, majority commercial software often rely on purely historical data based forecast models, such as linear regression and moving average, etc. Its forecasting capability is quite limited when facing so complicated causes of service parts requirement. Hence this dissertation investigated throughout all related theories that may improve service parts requirement forecast in reliability engineering, statistics and information technologies. Then it introduced a full system solution of service parts requirement forecast and successfully built up the forecast model based on Bayesian Network. The key accomplishments include:1. Through detail analysis of the different requirements in enterprise and considering the existed system architecture, the blue print and new system architecture design of the service parts requirement forecast system have been completed. It is a system with learning capability. And a prototype was finished as well. Its three key modules, "products and service parts life cycle management module", "service parts requirement forecast module" and "forecast assessment module", are elaborated in this article.2. For the core of requirement forecast, it believes that service parts requirement forecast has to be built up on part life cycle after detail investigation of service parts failure modes and causes combining with classic theories in reliability engineering and Bathtub curve. In this dissertation, the life cycle of service part has been divided into 3 periods "Initial failure period","Occasional failure period" and "Nature failure period". While part is in "Nature failure period", the probability of part failure could be calculated according to its life time distribution function. In the meanwhile, according to the attributes of the part, the life time distribution function could be exponential distribution, Weibull distribution, normal distribution or lognormal distribution. However if the part is in "initial failure period" or "occasional failure period", its failure probability may be forecasted via some key factors such as the part's precision attribution or the industry of its end customer.3. In the phase of data modeling, Bayesian Network was chosen as main approach of forecast modeling because there are very clear cause and effect relationships in service parts requirement variation. The forecast model has been successfully built up and its input variants contain not only service parts life time, but also part wear out attribute, part's precision attribute, customized part or not , industry of end customer, location of end customer , any products for backup on customer site, and seasonal factor of the forecast period. The article detail explained the probability table learning approach in the Bayesian Network of the forecast model. It gave detail solution for every key step of node calculation in the forecast model: firming the proper algorithms for parameter estimation of parts life time distribution function that fits system development mostly; explaining the auto selection algorithm of service parts life time distribution function; defining how to classify a part's life cycle through its life time; introducing Native Bayesian Classifier for industry classification of end customers.4. Finally through data sampling from the original enterprise, the dissertation completed accuracy and feasibility verification of the core forecast model.The forecast model proposed by this dissertation has eliminated the fatal weakness of traditional service parts requirement forecast models which generally ignore the consequence of part attributes and other repair requirements causes in service parts requirement forecast. In accompany with a satisfactory forecast result, this article provides enterprise a complete system prototype of service parts knowledge management.