The purpose of this study is to provide the priority of the front-loading factors in the design stage of the automotive parts development process in order to efficiently and effectively respond to the demands of the car maker (customer). Front-loading is defined as a strategy in order to improve development performance by shifting the identification and solving of design problems to earlier phases of a product development process. Two approaches of the front-loading are project-to-project knowledge transfer and rapid problem solving. For the study, a survey was conducted on the R&D department in the automobile parts company and analyzed by AHP (Analytic Hierarchy Process) method. The result of the survey shows the cost savings is the highest weight in terms of front-loading effect and in terms of front-loading factors, it gives priorities as “the problems of past project” first, “Design Review” second, “CAE (Computer Aided Engineering)” third, “FMEA (Failure Mode and Effects Analysis)” fourth, “benchmarking” and SR (Sourcing of Requirements). The results of the study will be helpful to provide practical value for improving product design of component development.

The Korean fisheries industry is a traditional business, the majority of which are small and medium-sized enterprises (SMEs). It has played an important role in the South Korean economies in the past several decades, but it currently faces the limitations of growth potential and profitability due to declining workforce, aging populations, deteriorating fishery environments, climate changes, and rapid changes in the global industrial ecosystem. Many studies have suggested solutions for the fisheries industry in macro perspective, but there are rarely any studies taking the strategic approaches for the problem. If it is possible for governments to support the companies that are likely to increase their value-added selectively, it will break through the current situation more effectively. This paper introduces a study on the selection method utilizing data envelopment analysis (DEA) to find SMEs with potentials to increase profits and growth. We suggest selecting SMEs with high management efficiency and ability to utilize intangible assets as the target companies. We also suggest policy objectives for SMEs in the domestic fisheries industry based on the results of DEA analysis and propose a data-based method for the policy decisions.