Fazel Ansari

Textual meta-analysis of maintenance management’s knowledge assets

Maintenance management activities include identifying, creating and storing knowledge assets. The knowledge assets are either documented/codified or undocumented/non-codified. In order to utilise the accumulated knowledge assets in the operational, tactical and strategic layers, documented knowledge should be meta-analysed. Meta-analysis is to discover the strength of the relationship between certain variables by means of automatic or semi-automatic algorithms. Meta-analysis has three methods: (1) statistical; (2) mathematical; and (3) textual. In this paper, the textual meta-analysis of maintenance managements knowledge assets is examined. In particular, the objective is to develop a virtual application for finding the relationship between three classes of text entities such as machines, practitioners and maintenance operations by means of technical implementation of the concept for the imitation of the mental ability of word association (CIMAWA). In addition, the virtual application is designed to be integrated as an add-on in the framework of computerised maintenance management information system (CMMIS).

Using Word Association to Detect Multitopic Structures in Text Documents

A new method for detecting multitopic structures in text documents, called Associative Gravity, is based on a text-mining method entitled CIMAWA, which imitates the human ability of word association. Specifically, Associative Gravity utilizes word association to detect different topics in a text. The authors named it Associative Gravity because of its resemblance to the physical law of gravitation, that is, mass and attraction. The mass corresponds to the importance of words in a text and the attraction to the asymmetrical associative word space. The innovative characteristic of the described topic detection method is supplied with asymmetrical associative word space provided by CIMAWA. A comparative case study proves the capability of Associative Gravity to separate different topics at very high accuracy.

Towards analytical evaluation of professional competences in Human Resource Management

Managers of enterprises concern with a major challenge for optimal management of human resources based on availability of domain experts and highly qualified personnel. The process of allocating right people to the right positions in a right time is a key to success. To achieve this goal, managers need to deploy evaluation tools integrated with the gap analysis method. This paper presents the concept and implementation details of an in-house developed software tool for competence evaluation of domain specific competencies and selection of professionals. A generic mathematical representation of competences in this project makes the software tool applied in a wide variety of organizations. A standard competence model has been first defined in this project with 5 main competence categories and related sub-categories including over 70 competence questionnaires in different managerial and employee levels. Test and evaluation of the software have been carried out by initializing the lab data of over 50 candidates with student groups involved in the project at the institute of Knowledge Based Systems and Knowledge Management, University of Siegen. The paper reflects the conception and the outcomes of the implementation of the software tool. The ultimate objective of this interdisciplinary project is to fill the gap in the selection process by means of an efficient and practical competency evaluation tool. The generic software tool is aimed to be used as a component in research and industrial projects of the institute.

Design and realization of competence profiling tool for effective selection of professionals in maintenance management

Enterprises and industrial companies strive to improve their functional performance by identification of core competencies in order to utilize human resources and optimise the knowledge integration processes of the company. In addition, maintenance operations are one of the most important sections in industrial companies which consist of key personnel and also explicit and implicit knowledge resources that have direct effects on product quality and return on investment. In the context of implicit knowledge resources, the principal objective is firstly to identify knowledge holders who are mainly domain experts (e.g. Chief Maintenance Officer-CMO) and maintenance practitioners (e.g. engineers, technicians, etc.), and secondly to measure their domain expertise. This paper presents the concept and implementation results of an interdisciplinary research which aims at improving the knowledge measuring of maintenance practitioners. In this way, the companies are enabled to deduce rate of human failures in maintenance operations by allocating the right professionals in the right positions using competency profiling of employees. The implementation results in developing a competence profiling tool as an add-on for Computerized Maintenance Management Information Systems (CMMIS), which is previously developed in the Institute of Knowledge Based Systems and Knowledge Management (KBS&KM).

Problem-solving approaches in maintenance cost management: a literature review

Purpose The purpose of this paper is to investigate the use of problem-solving approaches in maintenance cost management (MCM). In particular, the paper aims to examine characteristics of MCM models and to identify patterns for classification of problem-solving approaches. Design/methodology/approach This paper reflects an extensive and detailed literature survey of 68 (quantitative or qualitative) cost models within the scope of MCM published in the period from 1969 to 2013. The reviewed papers have been critically examined and classified based on implementing a morphological analysis which employs eight criteria and associated expressions. In addition, the survey identified two main perspectives of problem solving: first, synoptic/incremental and second, heuristics/meta-heuristics. Findings The literature survey revealed the patterns for classification of the MCM models, especially the characteristics of the models for problem-solving in association with the type of modeling, focus of purpose, extent and scope of application, and reaction and dynamics of parameters. Majority of the surveyed approaches is mathematical, respectively, synoptic. Incremental approaches are much less and only few are combined (i.e. synoptic and incremental). A set of features is identified for proper classification, selection, and coexistence of the two approaches. Research limitations/implications This paper provides a basis for further study of heuristic and meta-heuristic approaches to problem-solving. Especially the coexistence of heuristic, synoptic, and incremental approaches needs to be further investigated. Practical implications The detected dominance of synoptic approaches in literature – especially in the case of specific application areas – contrasts to some extent to the needs of maintenance managers in practice. Hence the findings of this paper particularly address the need for further investigation on combining problem-solving approaches for improving planning, monitoring, and controlling phases of MCM. Continuous improvement of MCM, especially problem-solving and decision-making activities, is tailored to the use of maintenance knowledge assets. In particular, maintenance management systems and processes are knowledge driven. Thus, combining problem-solving approaches with knowledge management methods is of interest, especially for continuous learning from past experiences in MCM. Originality/value This paper provides a unique study of 68 problem-solving approaches in MCM, based on a morphological analysis. Hence suitable criteria and their expressions are provided. The paper reveals the opportunities for further interdisciplinary research in the maintenance cost life cycle.

Med-Assess System for Evaluating and Enhancing Nursing Job Knowledge and Performance

The European funded project Med-Assess supports assessing of work-based competences and job knowledge of nurses, indicating existing knowledge gaps, and ultimately providing recommendations for improving nursing competences. This paper presents the Med-Assess concept, and reflects the implementation results of its ontological approach for analysis and assessment of nursing job knowledge. The ontological approach matches the nursing requirements and domain specific knowledge, and provides the logic for assessment of the end-users i.e. job applicants, nurses and care-givers.

An Adaptive Model for Competences Assessment of IT Professionals

Emerging technologies such as Big Data and Cloud Computing in the field of information technology imposes further needs (requests) for professional competences in organizations and IT companies. The ultimate goal is to comply with industrial changes characterizedby adaptive solutions for fostering human-machine interactions. Here competence and job knowledge play a great role in organizations. This paper discusses the concept ofan adaptive competence profiling platform in the context of EU funded project ComProFITS. The main goal is (i) reinforcing competence analytics, and (ii) improving the quality of personnel selection and job performance in the IT sector. This project reflects the results of the research and development activities based on needs analysis with a Spanish IT company.

Dynamics of knowledge assets and change management perspectives

Change Management (CM) is the major challenge of the strategic management particularly due to dynamics of knowledge assets (i.e. Temporal knowledge transformation) and also inadequate or insufficient use of knowledge, competencies and human creativities. In this context, CM needs to utilize Knowledge Management (KM) to guarantee successful implementation of the change and also to sustain long-term organizational advantages (e.g. Time to market and Return on Investment). Such co-operation needs to comprehensively consider internal factors to organizational success as well as external (e.g. Customers). Moreover measuring and evaluating the co-operation foreground the essence of integrating feedback loop into the framework to empower CM and assure the success of KM activities. In this paper, a formerly developed model, IQMP, is customized and adapted to support CM. The IQMP provides a comprehensive framework to deploy Performance Quality Indicators (PQIs) and a knowledge based analysis of feedback. Finally the paper discusses conception and potential application of the inherited model, Dynamic Knowledge Assets Management (DKAM).

Integrating knowledge management in the context of evidence based learning: two concept models for facilitating the assessment and acquisition of job knowledge

Within the field of Human Resource Management (HRM), the role of individual knowledge has received limited research attention despite offering the promise of superior job performance and improved managerial decision-making. In part, this lack of research may be attributed to the difficulty and laboriousness inherent to the adequate and accurate modeling of job relevant knowledge, particularly since such knowledge by definition varies from job to job. Despite this caveat, there is much to be gained from a knowledge based approach to (managing) human resources. The current paper presents two ontology based concepts for modeling job relevant knowledge, namely Meta-Practitioner and Med-Assess. The former focuses on availing to a practitioner audience the evidence that has accumulated in the academic literature, whereas the latter focuses on the facilitation of personnel selection and training in the medical field through a detailed assessment of individual job knowledge and general mental ability. Ultimately both concepts are aimed at knowledge provision to job applicants and incumbents alike. Having discussed the concepts, the paper summarizes the gains that may be expected from their implementation by presenting an integrated framework. The framework focuses on integrating aspects of Knowledge Management (KM) in the context of Evidence Based Learning (EBL) for business organizations. The paper concludes by addressing the challenges that lie ahead, highlighting some of the limitations of this approach and offering suggestions for further research.

A Framework for Enriching Job Vacancies and Job Descriptions Through Bidirectional Matching

There is a huge online data about job descriptions which has been entered by job seekers and job holders that can be utilized to give insight into the current state of jobs. Employers also produce large volume of vacancy data online which can be exploited to portray the current demand of the job market. When preparing job vacancies, taking into account the information contained in job descriptions, and vice versa, the likelihood of getting the bidirectional match of a job description and a vacancy will be improved. To improve the quality of job descriptions and job vacancies, a mediating system is required that connects and supports job designers and employers, respectively. In this paper, we propose a framework of an automatic bidirectional matching system that measures the degree of semantic similarity of job descriptions provided by job-seeker, job-holder or job-designer against the vacancy provided by employer or job-agent. The system provides suggestions to improve both job descriptions and vacancies using a combination of text mining methods.