The product life cycle (PLC) phenomenon has placed significant pressures on high-tech industries which rely heavily on the knowledge workforce in transferring cutting-edge technologies into products. This thesis examines systems where market changes and production technology advances happen frequently and unpredictably during the PLC, causing difficulties in predicting an appropriate demand on the knowledge workforce and in maintaining reliable performance. Knowledge workforce agility (KWA) is identified as a desirable means for addressing the difficulties, and yet previous work on KWA is incomplete.
This thesis accomplishes several critical tasks for realizing the benefits of KWA in a representative PLC environment, semiconductor manufacturing. Real options (RO) is chosen as the approach towards exploiting KWA, since RO captures the essence of KWA-options in manipulating knowledge capacity, a human asset, or a self-cultivated organizational capability for pursuing interests associated with change. Accordingly, market demand change and workforce knowledge (WK) dynamics in adoption of technology advances are formulized as underlying stochastic processes during the PLC. This thesis models KWA as capacity options in a knowledge workforce and develops a RO approach of workforce training, either initial or continuous, for generating options. To quantify the elements of KWA that impact production, the role of the knowledge workforce in production and costs in obtaining KWA are characterized mathematically. It creates necessary RO valuation methods and techniques to optimize KWA.
An analytical examination of the PLC models identifies that KWA has potential to reduce negative impacts and generate opportunities in an environment of volatile demand, and to compensate unreliable performance of knowledge workforce in adoption of technology advances. The benefits of KWA are especially important when confronting highly volatile demand, a low initial adoption level, shrinking PLCs, a growing market size, intense and frequent WK dynamics, insufficient learning capability of employees, or diminishing returns from investments in learning. The thesis further assesses RO, as an agility-driven approach, by comparing it to a chase-demand heuristic and to the Bass forecasting model under demand uncertainty. The assessment demonstrates that the KWA attained from the RO approach, termed RO-based KWA, leads to a stably higher yield, to a persistently larger net present value (NPV), and to a NPV distribution that is more robust to highly volatile demand. Subsequently, a quantitative evaluation of KWA value shows that the RO-based KWA creates a considerable profit growth, either with uncertainty in demand or in the WK dynamics. In evaluation, RO modeling and the RO valuation are identified to be useful in creation of KWA value especially in highly uncertain PLC environments. This thesis illustrates the effectiveness of the numerical methods used for solving the dynamic system problem.
This research demonstrates an approach for optimizing KWA in PLC environments using RO. It provides an innovative solution for knowledge workforce planning in rapidly changing and highly unexpected environments. The work of this thesis is representative of studying KWA using quantitative techniques, where there is a dearth of quantitative studies in the literature
