Purpose-To survive high-level management needs business intelligent information to efficiently manage corporate operations and support its decision making. Knowledge management (KM) is recognized as one of the most critical factors for obtaining organizational competitive advantage. A variety of factors determines significant success ingredients for successful implementation of KM in any organization. The primary challenge in KM initiation is how to integrate the above factors with organizational and personnel constraints and capabilities. This paper aims to develop a priority framework based on multi-criteria decision making (MCDM) to help organizations build awareness of the critical influential factors affecting successful implementation of KM. Design/methodology/approach-To identify critical influential factors, the authors studied and reviewed relevant literature from numerous fields of study associated with the essential issues of KM projects implementation. These cover the factors that affect a KM implementation based on comprehensive analysis of KM literature from numerous research studies. Research methodology used in this study is based on a combination of other methodologies such as action research, group discussion, documentary study and questionnaire research. For this purpose a group of experienced managers were selected and discussion sessions were held to set objectives and road map the study. Finally group analysis hierarchy process (GAHP) was used to analyse questionnaires and prioritize influential factors. Findings-The conceptual framework presents a roadmap for success of KM programs in the organizations. The paper identifies eight major aspects, 44 influential factors and a conceptual framework to assisting managers to design and implement a KM system in their organizations. The results show top management, executive management and culture have great impact on success of KM implementation among main aspects. The conceptual framework presents guidelines for success of KM implementation in organizations. Practical implications-The result of this study not only validates theory with reality, but it also provides a reference for the academic as well as the business world. It is hoped that the factors proposed in this study help organizations to manage knowledge activities effectively and implement knowledge projects smoothly in order to maximize benefits from KM projects and returns from knowledge assets. Originality/value-This study is the first to provide an integrated perspective of critical success factors in KM implementation in Khorasan Science and Technology Park (KSTP). It gives valuable guidelines for top managers and leaders to accomplish KM projects effectively.
Good layout plan leads to in improve machine utilization, part demand quality, efficient setup time, less work-in-process inventory and material handling cost. Cellular Manufacturing (CM) is an application of GTCM is the combination of job shop and/or flow shop. Facility Layout Problem (FLP) for CMS includes both inter-cell layout and intra-cell layout. A bi-level mixed-integer non-linear programming continuous model has been formulated to fully define the problem and the relationship between intra-cell and inter-cell layout design. Facilities are assumed unequal size; operation sequences, part demands, overlap elimination, aisle are considered. The problem is NP-hard; hence, a simulated annealing meta-heuristic employing a novel constructive radial-based heuristic for initialization have been designed and implemented. For the first time, a novel heuristic algorithm has been designed to allocate and displace facilities in radial direction. In order to improve the search efficiency of the developed SA algorithm, the cell size used in the initialization heuristic algorithm is assumed twice as that of the original size of the cells. A real case study from the metal cutting inserts industry has been used. Results demonstrate the superiority of the developed SA algorithm against rival comparable meta-heuristics and algorithms from the literature.
Cellular Manufacturing Systems (CMSs) play an important role in today's small-to mid-size production enterprises. The facility layout problem for such manufacturing system is a group-one that includes both intercellular and intracellular relationships. A novel continuous formulation has been developed for the problem to model manufacturing shops with vertical and horizontal aisles and to eliminate any possible overlap between machine tools as well that between cells. The overall approach adopted is a bi-level one; initially an upper-level leader facility layout problem is being solved for each cell at a time; then, a lower-level follower facility layout problem (FLP) is being solved after at the shop level determining the overall layout of the shop, where the position of the different cells are to be determined. A case study from the local machining industry has been utilized to verify the model.
A good layout plan results in improvements in machine utilization, setup time, and reduction in work-in-process inventory and material handling cost. Facility layout problem (FLP) for CMS includes both intercellular- and intracellular-layout. Most of the literature takes a discrete approach and rarely considers operations sequence and part demand. In this paper, a novel bi-level heuristic and mixed-integer non-linear programming continuous model for the layout design of cellular manufacturing are developed. Machine tools and manufacturing cells layout are determined sequentially by solving a leader and follower problem, respectively. Facilities are assumed unequal sizes. Both overlap elimination and aisle constraint modeling have been considered. The model is nonlinear; problem is NP-hard. Hence, only small instances of the problem can be solved using the exact linearized model. The developed heuristic is used to solve large instances of the problem. A real case study from the metal cutting inserts industry, where multiple families of inserts have been formed, each with its distinguished master plan, is presented.
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