For the past several years, the scale of camera market has increased a little or been in steady state. However, as the technology advances, the internal structure of the camera market changes rapidly in its composition. The direction of the change is toward, but not restricted to small size, variety of function, customer fit colors and design, link with PC. Also this technology reduces the life cycle of the camera whereas makes it as a high value added product.
Quick response manufacturing focuses on the first two phases of product life cycle: the early adapter stage and the tornado stage. Because the life cycle of the camera is short, a company can take advantages of high financial and market value if it can provide new, various and good quality cameras to customers more quickly, and timely manner than other competitors do. Most of these are achieved in the early phase of the product life cycle so it is inevitable to apply quick response manufacturing in this case.
This paper suggests a method which enables the establishment of customer satisfaction strategy based on quick response manufacturing utilizing customer satisfaction index data. We will unfold this method by selecting a decision making unit which has an effective property in accomplishing the goal of the camera company and in establishing future goal of camera company based on either the comparison with competitor data or last two year's its own data applying DEA; finding potential customers which correspond to demographic features of the previous target group using such machine learning modules as SOM and C4.5; and improving quality elements which distinguish the quality-satisfaction-group from the quality-dissatisfaction-group using the same machine learning modules C4.5 and SOM.
