دانش ساختمان سازی به منظور بهبود عملکرد سازمانی از مدل های شبیه سازی موجودی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|9689||2011||6 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, Volume 134, Issue 1, November 2011, Pages 108–113
This paper describes the process of building knowledge to improve enterprise performance. This allows managers both to identify unknown risks and to develop solutions that mitigate these risks. One of the most critical risks that the enterprise faces involves the unidentified presence of serial-correlation components on the demand patterns. Depending upon the levels of such correlation, inventory control policies can be appreciably inaccurate. We propose to use a knowledge management portfolio that allows managers to capture and build knowledge from their complex systems. We find that the error generated from ignoring identified risk factors exponentially grows as the autocorrelation increases. We construct an enhanced simulated annealing algorithm that provides superior solutions to this type of problem.
Supply chain theory and practice suggest that an understanding of the dynamics of intricate and interrelated factors is necessary for a responsive and efficient supply chain. Successful enterprises recognize the impact these factors might exert on the organization's performance and understand the strategic value of well-designed and well-managed supply chains. Critical issues that remain uncovered may lead the firm to questionable performance. Discovering and addressing these issues become necessary to improve an enterprise's performance. To meaningfully consider these complexities, identify latent issues, and develop corrective action, it is necessary to employ a framework that allows managers to capture and transform information into usable knowledge. Knowledge Management (KM) emerges as a tool to identify risk issues and build usable knowledge that can provide substantial benefits to supply chain management (Drew, 1999). The technology-based view of KM, as its name describes, involves approaches that identifies, generates, and distributes knowledge in an organization based on the use of technology. The type of knowledge considered in this paper resides in systems external to the human individual. An example of external information sources is the identification of risk patterns when analyzing a product's demand. Information systems that keep sales records are commonly the source of the raw data from which this knowledge is developed. This class of knowledge implies exploratory formation of new knowledge as opposed to the transfer of conventional knowledge within an organization (Alavi and Leidner, 2001). While operational knowledge is related to performing operations on a daily basis, strategic knowledge is fundamental to major decisions that capitalize on critical opportunities and effectively overcome major threats (Perrott, 2006 and Perrott, 2007). Discovering knowledge at either the operational or strategic level can represent an opportunity to enhance an enterprise's performance.
نتیجه گیری انگلیسی
The process of generating knowledge for the enterprise using a knowledge portfolio approach applied to an inventory control system was described. The inventory situation assumed that the realization of the uncertain demand was consistently different than its expectation. According to the knowledge portfolio, the enterprise was experiencing ‘what we don't know, we don't know’, in which opportunities and risks might be present and can be transformed into useful knowledge. Further analysis of the probabilistic demand revealed that this demand contained positive auto-correlated components over time. In the knowledge portfolio, identifying these risks and potential negative impacts on planning and controlling the inventory system correspond to ‘what we don't know, we know’. At this point, managers were urged to address the negative effects of this issue by providing a solution that considers these complexities. Accordingly, the problem was mathematically characterized using a dynamic programming approach. Demands were described using an AR (1) equation. This information was used to select and develop a stochastic local search approach effective when exploring large decision spaces. A solution technique based on an extension of the simulated annealing was applied to approximate solutions to this inventory problem. This process is reflected in the second quadrant of the knowledge building process, ‘what we know, we don't know’. The knowledge obtained in this phase is tested and shared throughout the organization, ‘what we know, we know’. Statistical analysis and error characterization is performed to assess the robustness of the solution.