PurposeThe paper aims to investigate the waste in a job shop environment and proposes an assessment method aimed at helping companies to identify root causes of waste.Design/methodology/approachThe seven wastes (overproducing; processing; inventory; transporting; producing defects; time waiting; and motion waste) and their relationships were explored. A waste matrix was developed to quantify in a percentage form the relationships among wastes and represents a probability that a certain type of waste will affect others or be affected by others. An assessment questionnaire was employed to allocate the source of waste and differentiate between the levels of waste. The waste matrix and the assessment questionnaire were incorporated in the assessment method to rank the existing waste in a job shop.FindingsThe developed model serves as guidelines for simplifying the search of waste problems and identifies opportunities for waste elimination. A case study was conducted to validate the model; and the results of the assessment and the real situation concur.Research limitations/implicationsThis paper has investigated a method to allocate waste, quantify it and discuss the relationships among wastes without quantifying the potential savings. Further research should be done in order to investigate the level of reduction in effort and time as a result of implementing the method.Practical implicationsThe approach provides a method by which managers can identify the sources of waste, differentiate between the levels of waste and rank their significance.Originality/valueThe simplicity of the matrix and the comprehensiveness of the questionnaire contribute to the achievement of accurate results in identifying the root causes of waste. The new model provides an insight into on where to concentrate effort by weighing the contributions of the different waste types.

PurposeThe purpose of this research is to examine the impact on organizational performance of the application of management and human resource practices, and to attempt to outline key elements and assess development of the learning organization (LO) concept in Jordan.Design/methodology/approachThe tool described in this article assesses relationships between LO practices and financial and operational performance measures. The empirical research aims at deconstructing the LO formation through the development and validation of a conceptual model. A total of 41 companies belonging to large industrial sectors in Jordan participated in a survey by responding to a research questionnaire.FindingsThe outcomes of the study indicate that the LO concept can be explored in Jordanian industry using eight constructs. These constructs were found to be strongly correlated. In general, this study identifies basic steps in the process of transformation into a learning organization in Jordan.Originality/valueThe study identifies the sequence of stages in the process of transformation into a learning organization in Jordan, which might be regarded as a developing country. The study culminated in a novel measurement instrument to evaluate learning organizations. Application of the tool facilitated LO constructs to be analyzed.

PurposeThe purpose of this paper is to present the essence of the Jordan Quality Award (JoQA) that has been developed and implemented in Jordan. The award characteristics, framework, examination criteria, objectives, benefits and comparative assessment are described. The JoQA is benchmarked with two international quality awards: Malcolm Baldrige National Quality Award and European Quality Award.Design/methodology/approachIn order to investigate the experiences of companies and gain feedback on the award's benefits, achievements, problems, and criteria weights, a questionnaire was developed. A sample of 49 companies which had applied for the award was selected to test a set of hypotheses regarding the award's objectives, benefits, problems, and criteria weights, and to determine areas of weaknesses and potential improvements.FindingsThe testing of the hypotheses shows that the objectives of the award, externally, and internally viewed benefit were achieved. However, various implementation problems exist. Based on the findings, a recommended change is proposed for the weights of the award criteria.Research limitations/implicationsThe study is based on a relatively small number of companies who had participated on one occasion in the award's process. Although the findings confirm the theoretical framework, more empirical work is needed to better understand the award's impact over a longer time span. Further research should also identify if and how the award influences the participating companies in managerial, technical and financial aspects.Originality/valueThe paper is unique insofar as it is the first to explore the experiences of users of the JoQA. It contributes to a better understanding of such awards' impact on organizations in developing countries.

Rotational molding is a method for manufacturing hollow plastic parts. In the work reported here, adaptive fuzzy logic techniques have been used to relate the machine oven temperature to other manipulated parameters of the process. The objective is to design a reliable control system for the rotational molding process. An adaptive fuzzy network was developed to correlate changes in oven temperature to changes in the opening of the control valve on the fuel system. The network parameters were optimized using real-valued genetic algorithms. This network gave good results when its performance was compared with experimental data from a commercial rotational molding machine. The

The fuzzy regression has been found effective in modeling the relationship between the dependent variable and independent variables when a high degree of fuzziness is involved and only a few data sets are available for model building. This research, therefore, proposes an approach for optimizing multiple responses in the Taguchi method using fuzzy regression and desirability function. The statistical regression is formulated for the signal to noise (S/N) ratios of each response replicate. Then, the optimal factor levels for each replicate are utilized in building fuzzy regression model. The desirability function, pay-off matrix, and the deviation function are finally used for formulating the optimization models for the lower, mean, and upper limits. Two case studies investigated in previous literature are employed for illustration; where in both case studies the proposed approach efficiently optimized processes performance.