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.