A neural network approach for predicting manufacturing performance using knowledge management metrics

This paper aims to devise a model for predicting the knowledge management (KM) effect on manufacturing performance via neural network (NN). This is the first empirical study that applies NN to forecast manufacturing performance using 48 KM metrics which cover knowledge resources, KM processes, and KM factors. The training, validation, and testing of the NN model were based on 580 usable data points of KM and manufacturing performance collected from manufacturing companies in Malaysia. The research findings reveal that the NN model serves as a reliable yet simple tool to predict the manufacturing performance of a company by considering various essential KM metrics. The network prediction is in good correlation with the actual data. Lastly, the prediction model will be useful for practitioners to determine future KM strategies and targets to improve manufacturing performance

The Preventive Maintenance Practices and Performance among Manufacturing Organizations in Malaysia; the Moderating Role of Technological Capabilities

The importance of maintenance has become the main focus in the manufacturing environment. New technologies and advancements in the manufacturing industry have driven many companies to implement reliable maintenance program in order to avoid stoppages and disruptions of equipment from occurring in their daily operations. The purpose of this study was to examine the preventive maintenance (PM) practices and performance among manufacturing organizations in Malaysia, and the technological capabilities (TC) used as a moderator in the relationship between preventive maintenance practices and performance among Malaysian manufacturing organizations. The correlations between three components of PM, namely time-based maintenance (TBM), condition-based maintenance (CBM) and predictive maintenance (PdM),:and manufacturing performance dimensions (cost; quality; flexibility and delivery) were evaluated and validated by employing Smart-PLS statistical tools. 600 questionnaires were circulated to various manufacturing organizations in all regions of Malaysia. However, only 155 questionnaires were returned and were usable for analysis. Correlation analysis was carried out and the results show that there is a positive relationship among PM practices. In general, PM practices, for instance predictive maintenance show positive and significant correlations among this sample of Malaysian manufacturing organizations. Meanwhile, the hypothesis result indicates that the performance of the manufacturing organizations is only influenced by PdM and the TBM and CBM practices, fail to positively influence manufacturing performance. In addition, the moderation analysis indicates that the TC not positively moderate TBM, CBM and PdM toward manufacturing performance. The overall results suggest that PdM practices can be identified as one of the best strategies to face stiff competitive environments in enhancing the effectiveness of quality improvement among Malaysian manufacturing organizations

Analysing approaches to specify automated manufacturing systems

Automating manufacturing systems can achieve competitive advantage leading to growth in profits through efficiency gains and other advantages including safety of workers and quality of produced products. However, without accurate specification there is no guarantee of realising return on investment. Automated systems are becoming more complex as the need for customisability and variability of products increases and can only be satisfied through flexibility of production processes. To aid companies in specifying automation and mitigate the risks of project failure an approach is needed that guides users choices. The aim of the research was to investigate approaches to specify automated manufacturing systems to provide a basis for a methodology that would aid practitioners in this difficult task. The objectives were in two phases. Firstly to categorise and criticise conclusions of other researchers resulting in identification of themes and criteria for an approach. Secondly to experiment empirically with promising approaches in a company producing of automated manufacturing systems (AMS) and compare the results of the experiments with those found in literature and provide a ranking of themes and criteria to aid future researchers in designing new approaches to specify AMS. The methodology used was literature review followed by mini case studies in a host company to test theory. The results from literature and the experiments were classified into four themes quantitative modelling and simulation (QM&S), database decision aids (DDA), flowcharts and consultancy. These were compared using analytical hierarchy process (AHP) against the identified criteria; rapid application, usability by managers, considering costs and benefits other than financial ones, reducing required resources, being applicable to engineer to order products and usable at the early stage of planning. The results were the strengths and weaknesses of each theme defined by the identified criteria and showed that none of the themes fulfilled all of the criteria for an approach to specify AMS. For this reason a hybrid approach was proposed beginning with a flowchart group session to make an outline plan, followed by a database decision aid to provide options and guidance in creating a detailed plan. Finally, an optional simulation stage could test the planned system for suitability. It is hoped that the comparison of approaches will aid future researchers in the creation of new approaches to assist engineers in specifying automated manufacturing systems in a rapidly changing world