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Browsing by Author "Digalwar, Abhijeet Keshaorao"

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    Development and Validation of Performance Measures for World Class Manufacturing
    (BITS Pilani, 2006) Digalwar, Abhijeet Keshaorao
    Global competition, rapidly changing technologies and shorter product life cycles have contributed in making the current manufacturing environment extremely competitive and uncertain. Customers are demanding a greater variety of high quality, low cost goods and services. Organizations must consequently develop new methods and perspectives to meet these market needs in a timely and cost effective manner. Traditional manufacturing approaches are no longer sufficient competitive weapons by themselves. Creating a world class organization is one response to deal with such challenges. An organization, which is world class and possesses a set of different strategic options, can respond effectively to dynamic and volatile environments. In world class manufacturing, the focus is on continuous improvement. Performance measurements should therefore activate continuous improvement. As organizations adopt world class manufacturing (WCM), they need new method of performance measurement to check the continuous improvement. The aim of present study is to develop and validate a comprehensive set of performance measures and their items/variables for world class manufacturing. newlineUsing a thorough synthesis of WCM literature and discussion held with practitioners, sixteen performance measures top management commitment, knowledge management, employee training, innovation and technology, employee empowerment, environmental health and safety, supplier management, production planning and control, quality, flexibility, speed, cost, customer involvement, customer satisfaction, customer services and company growth of world class manufacturing with their items/variables have been developed. newlinexiii newlineA survey instrument is developed to collect the data by using snowball sampling technique from four sectors of Indian manufacturing industry automobile, electronics, engineering and process. Descriptive analysis of the data suggests that the targeted sample was appropriate and the various items/variables developed for the performance measures.

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