Study On The Model Of Manufacture Outsourcing Share Allocation In PC Industy

With the advent of globalization, the world market is becoming more and more competitive and the consumers’demand level is increasing by day and day. As the result, companies are reduced to provide better services and products with low price. In order to achieve this target, companies must make every effort to reduce costs and upgrade their core technology and capabilities. The popularity of the supply chain makes the competition among companies gradually converted to the competition among the supply chains. It is not realistic for a company to take an advantage in today’s competitive environment by using its own resources and strengths. So, many companies combine to form alliance in order to cope with the current competition and risk. Social division of labor is becoming more and more professional and meticulous. Outsourcing born by the chance of current conditions. In order to optimize the allocation of resources and develop core competencies, many companies outsource their noncore businesses to other organizations. Because of the less and less value that PC industry’s manufacturing can add to their companies, the majority of the world’s outstanding PC companies outsource PC’s production to professional manufacturers. As one of the most important form of outsourcing, production outsourcing plays an extremely important role in PC industry. Production outsourcing makes PC brands both save cost and energy which can devoted to their core business.In order to reduce the risk of supply and meet the quantity demand of PC, PC brands often have three or more manufacture outsourcing suppliers. What’s more, PC brands and suppliers’relationship is on the strategic level. PC’s production is according to market forecast, not orders. If PC brands rely on existing production share allocation methods, such as internal meeting and negotiate with suppliers, it is extremely hard for PC brands to take cost of outsourcing, risk of supply disruptions, and other important factors into consideration. What’s more, forecast deviation and budget’s grace also cannot reasonably been taken into consideration.This paper take PC manufacture outsourcing as background, adopting brainstorm methods to determine the distribution share of manufacture related indicators. Then recycle grade proportion method to determine the relative weight of all index, in order to identify the core and basic indicators.In order to quantify the impact that basic indicators make on core indicators, this paper uses Delphi method to score to value the degree of influence that core index scores basic indicators of each supplier make on core indicators. After determining the influence weights basic indicators of each supplier make on core index, this paper calls these influence weights as core indicators’ impact factor.This paper adopts the secondary screening method. Before suppliers entering the production share distribution model, this paper uses Delphi method to quantity requirements of each model’s basic index quantification. Then, this paper selects suppliers that meet the requirements of each model. These suppliers are qualified to enter to the secondary screening mathematical model. Secondary evaluation method takes basic indexes into consideration, making the model more scientific and comprehensive. This paper also simplifies the mathematical model of the secondary screening, making the model more practical. It is very convenient to apply this model to PC industry actual decisions.In this paper, the model of outsourcing to minimize production costs and balance supplier production rate as the objective function, with each type of budget funds, supply and demand balance, the supplier’s minimum order quantity and supplier capacity as constraint conditions. The mathematical model’s objective functions are minimize production costs and balance supplier production rate, taking each model’s budget funds, supply and demand balance, the supplier’s minimum order quantity and supplier capacity as constraint conditions. In order to close to the reality, each models’ budget adopts fuzzy constraints, PC forecasts of volume act as a random variable. This paper uses fuzzy weighted method to turn multiple objective functions into a single objective function. At last, this paper compare company A’s14years share distribution data with the outcome made by mathematical model built in this paper. The contrast shows that the outcome made by this paper reduces PC brands production risk. In the realistic production share allocation situation, the model built by this paper is practical, closer to the reality, and also can support PC brands’ enterprise management.