Study on Risk Identification and Evaluation of Manufacturing Green Products R & D
|School||Shandong University of Science and Technology|
|Course||Management Science and Engineering|
|Keywords||Green products risk identification risk assessment regression analysis factor analysis RBF neural network|
Green and environmental protection is the subject of today’s society. Countries in the world pay more and more attention to environmental and resource issues. The rapid development of manufacturing sector is the important reasons of global resource depletion and environmental degradation. So how to reduce resources consumption and environmental pollution has become one of the major problems which the manufacturing sector faces in 21st century. Development manufacturing green products can make ecological environment and socio-economic connection to a coordinated development of the organic whole. It can fundamentally solve the environmental problems of manufacturing industry. At present, under the strong government guidance and the trends of environmental protection, many companies have invested substantial resources developing green products, but the failure rate is very high. It’s a tremendous loss of business. Therefore, the development of green products needs scientific and reasonable risk management which is helpful for scientific and reasonable decisions of green product development. It can reduce the risk and obstacles of developing products, enhance competitiveness of enterprises.This paper study on green products such as machinery, household appliances products. Based on research results of previous study on risk management, product development, green product development risks, green manufacturing, first, define the concept of manufacturing green products and risk combined with the specific situation of China, analysis of the risk characteristics of the green product development, utilization the flow chart and expertise to identification green product development risk, obtained 55 potential risk factors. Next, investigate the related manufacturing enterprises; determine 23 key risk factors which can cause a risk of green product development, and constructing the evaluation index system of risk though this. Once more, established RBF neural network model for risk assessment, indicating its good generalization performance and accuracy by comparing the BP neural network model, and use this model to evaluate a company’s R & D comprehensive risk and sensitivity analysis to prove its effectiveness and practicality. Finally, discuss organizational structure and the basic process of risk management of manufacturing green product development and proposed specific risk response measures.