Knowledge extraction for production management

A procedure and underlying algorithm for extracting knowledge from production and inventory databases to support engineering management activities is described. The process searches for, detects and isolates behaviour patterns inherent in the data. It relates these patterns to production irregularities, suggests connections with specific causes and helps propose possible corrective or preventive actions. The approach is based on a four-phase procedure: (1) the decision-maker focuses on the subject or difficulty at issue, represented by a target concept; (2) the KEDB algorithm, based on a machine learning approach, processes the relevant database and provides knowledge characterizing and classifying the target concept; (3) the output is interpreted in Pareto fashion as a series of possible circumstances explaining the target concept behaviour; and (4) based on these causes, the decision-maker decides on possible corrective actions to improve the situation, or preventive actions to forestall unfavourable conditions. A case study based on an actual quality control database is detailed.     

Knowledge management by example

Knowledge has many components such as data, constraints, queries, transactions, and derivation rules. Data is the only component that can be managed effectively in large quantities. All other components are in their infancy in terms of tools and techniques for efficient storage and retrieval, implementation and execution, and user specification and design. One approach to manage all components of knowledge in large quantities is to reduce them all to data. Many components of knowledge can be expressed in terms of examples, and examples are data. As such, all these components can be stored and retrieved efficiently in large quantities, their execution reduces to data comparison and can be done in parallel, and they can be specified, designed, and modified by end users since examples are more intuitive and easy to manipulate than general procedures.     

Knowledge management at Infosys

Knowledge management (KM) is important to software engineering, because organizations need more than just human capital to successfully distribute and use their employees' knowledge. Knowledge might be inexpensive, but effectively using and managing it is not. This article reviews KM practices at Infosys, highlighting KM's importance and benefits     

Knowledge management in decision support systems

This paper discusses a framework for knowledge management in a DSS. We assume decision making is based mainly on numerical data processing. Thus, we abstract data and knowledge as relations, and decision models as relators. Based on these two constructs, our framework allows a user to compose and experiment decision models interactively; it also provides decision information nonprocedurally through a knowledge processor.     

Safety management in production

The last 20 years have seen the development of broad agreement on the necessary functions to be fulfilled by a safety management system and how they relate to each other in a coherent whole. The article describes these functions and reviews subjects that still need study, notably organizational culture, conflict, management in nonbureaucratic organizations, safety auditing, and safety in the learning organization. It is organized under four headings: what we know, what we believe but have no proof of, what we may be mistaken about, and what we face as challenges and unresolved issues for the coming years. © 2003 Wiley Periodicals, Inc. Hum Factors Man 13: 185–201, 2003.     

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 develop 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. © 2005 Wiley Periodicals, Inc. Hum Factors Man 15: 249–257, 2005.     

Knowledge management in construction companies in the UK

Knowledge management is important in the construction industry, but there is a dramatic gap between rhetoric and reality, highlighting mistaken expectations of technology. We report on a case study of a major construction company. The UK construction industry, with scarce academic qualifications, and limited use of IT, depends on knowledge sharing, and, crucially, on tacit knowledge. Economic crisis presents particular problems, and recent trends in work organization have far-reaching implications. The industry depends on human knowledge, with limited systems support. A shared concern for health and safety provides the surest guarantee of sustainability of both knowledge and the company.     

Knowledge representation and processing in economics and management

Various techniques for computer-based knowledge representation and processing are widely used in management and economics. Other techniques such as rules and demons have arisen in the artificial intelligence field. These too can be useful in managerial and economics settings. A major issue is how to effectively employ multiple traditional and artificial intelligence techniques when working on a problem. In this paper, we examine the various knowledge management techniques with respect to their applicability to handling distinct types of knowledge. An object-oriented framework is presented as a basis for the unified and coordinated treatment of multiple knowledge management techniques in a single environment. Using this framework, two approaches are identified for delivering these techniques to a knowledge worker: skeletal environments and furnished environments.     

中小型软件企业知识管理的研究

越来越多的我国中小型软件企业认识到员工的知识是企业最有价值的资产,而且在企业中实施知识管理是十分必要的。首先概括了这些企业在知识管理方面的现状,提出了一个适用于中小型软件企业的知识管理体系,说明了知识管理与CMM／CMMI的关系,并给出了知识管理与软件过程管理的结合模型,介绍了一个软件企业知识管理系统的模型框架,分享了实施知识管理的一些经验。     

Knowledge management perspective on e-learning effectiveness

The synergies, functional effectiveness and integration of KM within an e-learning environment have attracted little interest for serious research, despite the overarching importance of knowledge acquisition by students for fostering their innovation and creativity. Learners often fail to reach their desired learning objects due to the failure of indexing methods to provide them with a ubiquitous learning grid. The aim of this paper is to discuss how knowledge management can be used effectively in e-learning, and how it can provide a learning grid to enable the learner to identify the right learning objects in an environment which is based on the learner’s context and personal preferences.     

Knowledge management for consumer-focused product design

As the automotive industry adopts a consumer focus in its product development strategy, it offers broader product ranges, shorter model lifetimes and the ability to process orders in arbitrary lot sizes. This offers the ability to conduct early product design and development trade-off analysis among these competing objectives. A distributed knowledge-based system, which analyzes, verifies, stores, and retrieves process definitions, is needed to manage the complexity of workflows. The use of information technologies and networking capabilities is essential in the dissemination of product knowledge in order to integrate the decision-making process among heterogeneous and distributed partners/units. This paper offers insights into a knowledge management approach that enables implementing a consumer-focused product design philosophy by integrating capabilities for intelligent information support and group decision-making utilizing a common enterprise network model and knowledge interface through shared ontologies. An automotive supply chain case study is utilized in illustrating the proposed approach.     

Modelling production management systems

This review of the IFIP working conference on “Modelling Production Management Systems”, held in Copenhagen, August 29–31, 1984, attempts to show the threads in the actual development of the science of production control. The major aim of flexibility can only be achieved by simplification and decentralization of the control structure. This requires the introduction of adapted models for production control, organization analysis and information systems development. Simulation is a promising tool, but the introduction is retarded because of problems concerning the validation of simulation models and the integration of simulation models with production databases.