Organizational learning and knowledge depreciation in supplier quality improvement

Weijia Wang, Purdue University

Abstract

Organizational learning has become a key competitive capability for firms today. In particular, learning has been identified as a driving force of continuous quality improvement. At the same time, knowledge depreciation with its deleterious effects, also begins to draw more attention among scholars and practitioners. The focus of this research is to explore the dimensions of organizational learning and knowledge depreciation and to examine their impact on cost effective strategies regarding supplier process/quality improvement. This research primarily solves two important problems within a supply chain system: 1) a manufacturer's optimal distribution of quality improvement targets to suppliers; 2) a supplier's optimal resource allocation strategy to simultaneously meet demand and quality cost goal specified by its manufacturer. Three models are presented consecutively in this dissertation. The first two models are both system cost models to assist a manufacturer in assessing the minimum cost allocations of quality improvement targets to suppliers. The first model accounts for the effects of both autonomous learning and induced learning on quality improvement via variance reductions of supplier processes. This model further accounts for the effects of planned and unplanned disruptions in supplier production processes. It is found that while resulting in loss of autonomous learning, such gaps in production provide an opportunity for induced learning, thereby alleviating disruptions' negative effect on quality improvement. Moreover, the uncertainty in realized induced learning rate as well as the uncertainty in realized level of process disruptions are also considered. A numerical example is used to demonstrate the implementation of this model and to assess the sensitivity of the optimal target allocations to several model parameters. Building upon the first model, the second model incorporates knowledge depreciation, which is often concurrent process disruptions. In this model, knowledge depreciation is differentiated along the two dimensions of learning. In autonomous learning, forgetting results from under-capacity production, which encompasses scenarios with dropped production rates, including disruption. Similarly, in induced learning, knowledge decay results from under-capacity induced learning activities when continuous knowledge integration fails over time. To develop this model, a comprehensive quality cost progress function is first constructed, which accounts for effects of both learning and knowledge depreciation and is able to produce patterns similar to those observed in empirical data. A numerical example of internal supplier processes is used to demonstrate this model and to assess the sensitivity of the optimal solutions to different model parameters. The results suggest a significant interaction between the magnitude of under-capacity production and knowledge depreciation. Greater knowledge depreciation intensifies the adverse effect of under-capacity production, but again induced learning is found to have a counteracting effect. The third model is to assist suppliers to strategically allocate limited resources in both production and investment in quality improvement activities. This continuous optimal control model maximizes the supplier's profit over a finite planning horizon with constraints of simultaneously meeting demand and achieving a quality improvement goal. The model takes into account the effects of both learning and knowledge depreciation, which has not been done in any past research. In general, optimal paths of production and induced learning activities over the planning horizon can be obtained numerically. A numerical example is used to assess the sensitivity of optimal paths to several model parameters. The results suggest that demand and quality cost goal, which the manufacturer stipulates, are critical to determine supplier's optimal strategy. Generally the optimal strategy commences with induced learning and then switches to production. Induced learning activities sometimes are resumed to accelerate quality cost reduction, when production (autonomous learning) alone is insufficient to attain the quality cost goal. And the presence of knowledge depreciation causes substantial losses of knowledge, thereby impeding continuous quality improvement and resulting in different resource allocation strategies.

Degree

Ph.D.

Advisors

Plante, Purdue University.

Subject Area

Management

COinS