Improving Manufacturing Performance Through Process Change and Knowledge Creation

A model is introduced to guide a profit maximizing firm in its quest to enhance performance through process change. The key benefit sought from process change is a long term increase in effective capacity. However, realizing success from process change is not trivial. First, while process change may increase effective capacity in the long run, the disruptions during implementation typically reduce short term capacity. Second, competitive forces such as decreasing revenue streams and shrinking product life cycles complicate the implementation of process change. Third, while knowledge may enhance the ultimate benefits derived from process change, the correct timing and means of knowledge creation are difficult to discern. Lastly, a variety of trade-offs must be evaluated when selecting the particular process change to pursue. For example, choices range from hardware and software replacements to modification of manufacturing procedures. The model introduced here explicitly considers both the short term loss due to disruption and the long term gain in effective capacity associated with the process change. In addition, investments in the accumulation of knowledge are investigated for their potential to enhance process change effectiveness. Knowledge is generated from investment in preparation and training (learning-before-doing) and as a by-product of process change (learning-by-doing). Analysis of the model provides managerial recommendations for several key decisions relating to process change implementation including: (i) the selection of an appropriate process change alternative, (ii) the rate and timing for investment in process change, and (iii) the rate and timing for investment in preparation and training. New results are reported reflecting the important relationship between process change and knowledge. For example, we show that under certain conditions, a firm should optimally delay investment in process change until sufficient accumulation of knowledge is achieved. More generally, we identify conditions whereby investment in process change occurs at an increasing rate over time. This result is particularly important since it demonstrates a limitation of the existing literature where process change always occurs at a decreasing rate.process change, knowledge management, optimal control theory

A Distributed Parameter Cohort Personnel Planning Model That Uses Cross-Sectional Data

The two types of mathematical manpower planning models that appear in the literature involve either longitudinal or cross-sectional formulations. Despite the high degree of realism achieved, the use of longitudinal models is limited because the implementation requires the knowledge of a large amount of historical personnel data that is often unavailable. The value of cross-sectional models requiring a minimal amount of data is diminished due to (1) the difficulty in transferring cross-sectional results into cohort information, and (2) an assumption implicit in the structure of these models stating that the movement of an individual from one grade in the organization to another is independent of that person's organizational age. In this paper, we present a cohort (longitudinal) personnel planning model solved using distributed parameter optimal control theory that requires only cross-sectional data. We derive the optimal hiring, promotion, separation and retirement policies of an organization as functions of time and a person's organizational age and grade. In response to changing goal levels of manpower, we observe changes in the optimal policies and their subsequent effect on the career paths of cohort groups over time.cohort personnel model, personnel planning, optimal control, distributed parameter control