A theoretical framework for increasing line worker mobility in discrete product manufacturing through skills and training

There is a need among US manufacturing industries for a worker training process plan (including information on what to train the worker in given the changes in product process, and system level technologies, and how best to deliver such training at minimal cost to industry). Also, possession of transferable skills is commonly expected to provide the worker flexibility and mobility (mobility also being a function of other factors such as geographical location, age of the worker, etc.). Mobility is an important asset for the worker, especially in the present day scenario of massive layoffs in big corporations—a worker who is laid off must be able to find work, and find work that pays wages comparable to the previously held job. Generating a training plan for the entire US discrete product manufacturing industry is hence a worthy but a challenging task due to the seemingly different products, processes, and system technologies in different manufacturing industries. The objective of this dissertation is to examine the issues involved in generating such a training plan, and suggest a framework for generating such a plan. The framework is based upon the following premises: (a) that all discrete product manufacturing is composed of some form of material processing, some form of assembly, some form of inspection, and some form of maintenance and repair, and further, that these activities are common to any and all discrete product manufacturing, a fact that can be exploited in generating training plans that provide maximum mobility to the worker while at the same time reducing the training costs for the entire discrete product manufacturing industry; (b) that the fundamental manufacturing activities are becoming, and will in the future become, information intensive due to advances in information technology—any training plan should account for this factor. The special feature of the framework is its multidisciplinary nature—it calls for an integration of knowledge and methods from four vast and diverse disciplines of study: manufacturing engineering, cognitive engineering, industrial engineering, and labor economics.