MATHEMATICAL-MODELS FOR DEVELOPING A FLEXIBLE WORKFORCE

As manufacturers seek ways to stay competitive in world matters, many are discovering that the most important resource they have is their em ployees. Consequently, at least in the United States, a renewed emphas is is being placed on employee development. For production environment s adopting newer forms of manufacturing organizations, this has often resulted in more training and/or cross-training for employees. Decisio ns of whom to train and how much training should be done are often mad e in a qualitative fashion by human resource or personnel managers. Qu antitative approaches have also been used but primarily when the focus has been on long term strategic staffing levels, or on short term sta ffing and cross-training levels to optimize specific operational perfo rmance measures. The problem of planning for cross-training to meet th e requirements of a medium range production horizon in a manufacturing environment has not been addressed with quantitative models. The obje ctive of this research was to develop formal models and optimal soluti on approaches for various worker training scenarios. The models were i ntended to assist managers in deciding optimum tactical plans for trai ning/retraining a workforce according to the skills required by a fore casted production schedule for a definite planning horizon in a manufa cturing plant. Four models were developed with objectives of (1) minim izing the total cost of training, (2) maximizing the flexibility of th e workforce, (3) minimizing the total time required for training, and (4) optimizing the trade-off between minimizing the total cost of trai ning and maximizing the flexibility of the workforce. Constraints in e ach of the models were developed with respect to production hours avai lable, production requirements (from the master schedule), and budget. The paper discusses the reasoning behind the attributes used in the m odels as well as the formulation themselves. Significant effort is spe nt on discussing the applicability of the models, with attention being focused on the relative advantages, disadvantages, data requirements, and suitability of each model. Computational considerations are also presented.