Maintenance Knowledge Management with Fusion of CMMS and CM

Abstract- Maintenance can be considered as an information, knowledge processing and management system. The management of knowledge resources in maintenance is a relatively new issue compared to Computerized Maintenance Management Systems (CMMS) and Condition Monitoring (CM) approaches and systems. Information Communication technologies (ICT) systems including CMMS, CM and enterprise administrative systems amongst others are effective in supplying data and in some cases information. In order to be effective the availability of high-quality knowledge, skills and expertise are needed for effective analysis and decision-making based on the supplied information and data. Information and data are not by themselves enough, knowledge, experience and skills are the key factors when maximizing the usability of the collected data and information. Thus, effective knowledge management (KM) is growing in importance, especially in advanced processes and management of advanced and expensive assets. Therefore efforts to successfully integrate maintenance knowledge management processes with accurate information from CMMSs and CM systems will be vital due to the increasing complexities of the overall systems. Low maintenance effectiveness costs money and resources since normal and stable production cannot be upheld and maintained over time, lowered maintenance effectiveness can have a substantial impact on the organizations ability to obtain stable flows of income and control costs in the overall process. Ineffective maintenance is often dependent on faulty decisions, mistakes due to lack of experience and lack of functional systems for effective information exchange [10]. Thus, access to knowledge, experience and skills resources in combination with functional collaboration structures can be regarded as vital components for a high maintenance effectiveness solution. Maintenance effectiveness depends in part on the quality, timeliness, accuracy and completeness of information related to machine degradation state, based on which decisions are made. Maintenance effectiveness, to a large extent, also depends on the quality of the knowledge of the managers and maintenance operators and the effectiveness of the internal & external collaborative environments. With emergence of intelligent sensors to measure and monitor the health state of the component and gradual implementation of ICT) in organizations, the conceptualization and implementation of E-Maintenance is turning into a reality. Unfortunately, even though knowledge management aspects are important in maintenance, the integration of KM aspects has still to find its place in E-Maintenance and in the overall information flows of larger-scale maintenance solutions. Nowadays, two main systems are implemented in most maintenance departments: Firstly, Computer Maintenance Management Systems (CMMS), the core of traditional maintenance record-keeping practices that often facilitate the usage of textual descriptions of faults and actions performed on an asset. Secondly, condition monitoring systems (CMS). Recently developed (CMS) are capable of directly monitoring asset components parameters; however, attempts to link observed CMMS events to CM sensor measurements have been limited in their approach and scalability. In this article we present one approach for addressing this challenge. We argue that understanding the requirements and constraints in conjunction - from maintenance, knowledge management and ICT perspectives - is necessary. We identify the issues that need be addressed for achieving successful integration of such disparate data types and processes (also integrating knowledge management into the “data types” and processes)

Coevolution of Firm Capabilities and Industry Competition

This paper proposes that rival firms not only search for new capabilities within their organization, but also for those that rest in their competitive environment. An integrated analysis of these search processes at both firm and industry levels of analysis shows how their interaction makes industries and firms coevolve over time. To contribute to an enhanced understanding of the concept of coevolution, a dynamic and integrative framework crossing meso and micro levels of analysis is constructed. This framework is applied to a longitudinal study of the music industry with a time-span of 120 years. The first part, a historical study, covers the period 1877 - 1990. The second part, a multiple-case study, covers the period 1990 - 1997. We conclude that search behavior drives coevolution through competitive dynamics among new entrants and incumbent firms and manifests itself in the simultaneous emergence of new business models and new organizational forms.coevolution;competitive regime;longitudinal research;multilevel research;music industry

Indicators for Public Mental Health: A Scoping Review

Background: To monitor population mental health, the identification of relevant indicators is pivotal. This scoping review provides a comprehensive overview of current indicators representing the various fields of public mental health core topics. It was conducted as a first step to build up a Mental Health Surveillance for Germany. Methods: We conducted a systematic MEDLINE search via PubMed. This search was supplemented by an extensive examination of the websites of relevant national as well as international institutions in the context of public mental health and an additional internet search via Google. To structure the data, an expert-based focus group identified superordinate topics most relevant to public mental health to which the identified indicators could be assigned to. Finally, the indicator set was screened for duplicates and appropriate content to arrive at a final set. Results: Within the various search strategies, we identified 13.811 records. Of these records, a total of 365 records were processed for indicator extraction. The extracted indicators were then assigned to 14 topics most relevant to public mental health as identified by the expert-based focus group. After the exclusion of duplicates and those indicators not meeting criteria of specificity and target group, the final set consisted of 192 indicators. Conclusion: The presented indicator set provides guidance in the field of current concepts in public mental health monitoring. As a comprehensive compilation, it may serve as basis for future surveillance efforts, which can be adjusted and condensed depending on the particular monitoring focus. Our work provides insights into established indicators included in former surveillance work as well as recent, not yet included indicators reflecting current developments in the field. Since our compilation mainly concludes indicators related to mental health in adults, it should be complemented with indicators specific to children and adolescents. Furthermore, our review revealed that indicators on mental health promotion and prevention are underrepresented in current literature of public mental health and should hence be focused on within future research and surveillance.Peer Reviewe

Measuring capacity building in communities: a review of the literature

<p>Abstract</p> <p>Background</p> <p>Although communities have long been exhorted to make efforts to enhance their own health, such approaches have often floundered and resulted in little or no health benefits when the capacity of the community has not been adequately strengthened. Thus being able to assess the capacity building process is paramount in facilitating action in communities for social and health improvement. The current review aims to i) identify all domains used in systematically documented frameworks developed by other authors to assess community capacity building; and ii) to identify the dimensions and attributes of each of the domains as ascribed by these authors and reassemble them into a comprehensive compilation.</p> <p>Methods</p> <p>Relevant published articles were identified through systematic electronic searches of selected databases and the examination of the bibliographies of retrieved articles. Studies assessing capacity building or community development or community participation were selected and assessed for methodological quality, and quality in relation to the development and application of domains which were identified as constituents of community capacity building. Data extraction and analysis were undertaken using a realist synthesis approach.</p> <p>Results</p> <p>Eighteen articles met the criteria for this review. The various domains to assess community capacity building were identified and reassembled into nine comprehensive domains: "learning opportunities and skills development", "resource mobilization", "partnership/linkages/networking", "leadership", "participatory decision-making", "assets-based approach", "sense of community", "communication", and "development pathway". Six sub-domains were also identified: "shared vision and clear goals", "community needs assessment", "process and outcome monitoring", "sustainability", "commitment to action" and "dissemination".</p> <p>Conclusions</p> <p>The set of domains compiled in this review serve as a foundation for community-based work by those in the field seeking to support and nurture the development of competent communities. Further research is required to examine the robustness of capacity domains over time and to examine capacity development in association with health or other social outcomes.</p

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable