Application of knowledge management in production management

Knowledge Management (KM) addresses the critical issues of organizational adoption, survival, and competence in the face of an increasingly changing environment. Knowledge management embodies organizational processes that seek a synergistic combination of the data and information processing capabilities of information and communication technologies (ICT), and the creative and innovative capacity of human beings. Knowledge is rapidly becoming the most important asset of virtually all organizations. Manufacturing is no exception. The ability to manage and exploit knowledge will be the main source of competitive advantage for the manufacturing industry of the future. In that role, knowledge management will improve production management and avoid or minimize losses and weakness that usually come from poor performance as well as increase the competitive: level of the company and its ability to survive in the global marketplace. In this article, we are concerned with the improvement of production management theory, in the manufacturing context, through the application of some core principles. The best production practices worldwide have a common core. The cores principles investigated are the reduction of cycle time, reduction of variability, increase in transparency, and build of continuous improvement into the process. The fundamental rationale underlying these principles is the concept of flow, where production is seen as composed of waiting, transporting, inspecting, and transformation (processing) activities. According to this concept, transformation activities are the only ones that actually add value. Hence, all other activities should be reduced or eliminated from the flow while increasing the efficiency of transformation activities. We d evelop a knowledge management perspective in production management appropriate for the manufacturing industry. It is anticipated to serve as a foundation for wider applications of knowledge management in other sectors of the manufacturing industry. We also argue for more than simply gathering data to be utilized and managed in the form of tacit and explicit knowledge.