Component selection system for green supply chain

With the differences of customization attributes, the changes of the implementing stages of rules and different selling countries, the contents of the check value of RoHS (Restriction of the use of certain Hazardous Substance in electrical and electronic equipment) which each enterprise has to comply with and which is more complicated than component selection operation for general products are different. Under such complicated productive production, it is a major test for business decision makers to maintain the most effective operational efficiency and the lowest cost. This study puts forward a set of solutions which integrate group technology and neural network against component selection of green supply chain management (GSCM). First, neural networks are used for grouping products with the similar customized need against the value of the check item of each part of orders. Next, according to the information of existing inventory within the enterprise, available parts are selected and the total production costs are calculated to effectively reduce the complexity of the planning of the production lines for production manager. The above operation mode is established to be an information system of a component selection for green supply chain. Finally, data is analyzed to account for the using situation to ensure the availability of practical operation of the system based on the case of a company.