...manufacturer commit quality improvement philosophy and strategy that fosters continuous process learning and improvement.

When considering the relationship between learning and process improvement, it is useful to view organizational learning as being either autonomous or induced (Levy, 1965). Autonomous learning is associated with learning by doing and captures the efficiency gained through repetitive implementation of tasks and experience. Induced learning is generated by conscious managerial or engineering actions that improve the efficiency of the system through changes in the technology, the underlying processes, and physical or human capital. Some specific examples of such actions are engineering design changes, and personnel training programs (Adler and Clark, 1991).

Our interest was motivated by our involvement in improvement activities with several large companies. One was a major pharmaceutical corporation faced with the problem of capacity planning. It was found that process learning in this organization's manufacturing operations resulted in an annual increase in capacity of 15%. A second was a major consumer electronics manufacturer who undertook induced learning investments via six sigma type quality training using project execution teams at one of its manufacturing plants in Latin America that produced an average of 3000 units per day. These induced investments in quality training yielded "mature quality levels" in 2 weeks from product start-up (defined as at least 90% first-pass yield), resulting in increased productivity and reduced process costs. This was a 94% improvement in the time to achieve process maturity vis-a-vis the time to do so when improvements were based on autonomous learning, which was 9 months. Moreover, savings in manufacturing costs were grea ter than 11 million dollars over the 2-week period.

In developing its induced investment strategy, the consumer electronics manufacturer had to determine an appropriate sequence of investments in each of the three major stages of its manufacturing process: (i) Automatic Component Insertion (ACI); (ii) Manual Assembly (MA); and (iii) Soldering (S). The induced learning program was launched by holding a plant-wide 3-day active learning workshop that focused on statistical quality concepts and analysis tools. This was followed by pre-defined, targeted team-based projects to make specific improvements in each of the three major stages of the manufacturing process. For example, at ACI, defects such as incorrect lead length and epoxy contamination were identified, and, corrective actions determined and implemented. At MA, reversing parts was determined to be a major contributor to poor quality and corrected. At S, too much or too little solder was identified as a major quality issue, and also corrected.

One of the challenges faced by both the above pharmaceutical and consumer electronics companies was determining what should be the optimal sequence of investments in autonomous and induced learning for process improvement and concomitant capacity increase to improve quality, reduce costs, and reduce cycle times, resulting in a capacity increase. Motivated, in part, by this decision problem, we investigate the dynamic behavior of manufacturing quality costs as a function of variance reducing investments which are realistically associated with multiple quality characteristics. In this context, the main goal is to select the most promising/beneficial areas for focusing quality improvement efforts, given the options available in each time period. Based on contemporary learning and quality cost theories, we develop a dynamic model of relating investment and learning analytical formulation that provides answers to questions such as: What is the optimal investment in learning path to minimize expected quality costs? Is there an easy-to-prescribe optimal investment policy that is robust under fairly general conditions? How are quality improvement decisions influenced by autonomous and induced types of learning? The multi-period model to be presented relies on the fundamental notion that improvements in quality are realized through gradual and continuous decreases in process variation over time.

1.1. Relationship to the learning literature

The link between the learning curve and quality improvement activities has been explored quite extensively. Fine (1986) developed a quality-based learning model in which the quality level, represented by economic conformance to tolerances, was a management-controlled decision variable. By dynamically changing the economic conformance level, management controls the cumulative production of conforming output, which determines the unit cost of production. Thus, a model based on quality-weighted volume replaces the well-known volume-based learning curve. Higher quality levels lead to higher percentages of conforming output as well as faster rates of reduction in unit production cost. Fine and Porteus (1989) studied a different quality improvement model that included only induced learning with stochastic rewards. Kini (1994) incorporated the influences of both good and defective items on the learning rate. Zangwill and Kantor (1998) described how various forms of the learning curve such as power and exponential fu nctions can be treated in a unified manner. Moskowitz et al. (1997) and Plante (2000) formulated single-period models to determine target levels for quality improvement in the presence of induced learning.

Most learning models in the literature have commonly considered only autonomous learning, and explored optimal production policies that minimize production costs (Mazzola and McCardle, 1997). However, several recent papers have addressed both autonomous and induced learning simultaneously For example, Li and Rajagopalan (1998) differentiated between the "productivity knowledge" and "quality knowledge" gains resulting from learning efforts by building a model in which both autonomous and induced learning activities influence the changes in the accumulated levels of productivity and quality knowledge. Lapre et al. (2000) proposed a learning curve for the waste rate of a manufacturing process, which includes both autonomous and induced learning.

There have also been recent empirical studies on autonomous and induced learning. Ittner (1996) investigated the relationship between the expenditures on quality improvement activities and the costs associated with product defects. Mukherjee et al. (1998) proposed the following two main dimensions for the knowledge gained via quality improvement projects: (i) operational learning which refers to the acquisition of "know-how"; and, (ii) conceptual learning which is defined as the acquisition of "know-why". By analyzing the quality improvement projects undertaken by a steel wire manufacturer, they attempted to assess the impact of these learning dimensions on the waste rate of the production process. Empirical research by Li and Rajagopalan (1997) concluded that quality improvement activities led to identifying inefficiencies in the production process, and such a knowledge gain resulted in increased productivity. Analyzing data pertaining to 12 manufacturing plants and consistent with our proposed modeling appr oach, Ittner et al. (2001) find that production quality was influenced by both autonomous and induced learning.

1.2. Rationale of our modeling approach

Building on the literature, we simultaneously; (i) consider multiple learning curves in a manufacturing environment; (ii) use the variability of quality characteristics as a performance metric (cf. Zangwill and Kantor, 1998); and (iii) quantify the quality-related costs based on the combined effect of these metrics. More specifically, we employ Taguchi's "quality loss function" to estimate quality-related costs (Taguchi and Clausing, 1990). For this model, the ideal state of a quality characteristic that maximizes user satisfaction is called the target value (Kackar, 1985), and, all deviations from this target value incur some cost. Taguchi's loss function thus implies that quality costs are incurred whenever the quality (performance) characteristic is not on its target, even if the product conforms to specifications. The traditional quality cost theory dating back to Juran (1951), on the other hand, does not consider it as a costly outcome when the performance of a product falls in the interval between the l ower and upper specification limits. As compared to the traditional approach, Taguchi's loss function places emphasis on reducing the variability of the performance characteristic as the key element of modern quality management practice. Since the performance characteristics are usually modeled as random variables, the overall quality level of production is inversely related to performance variation around the target value. Thus higher variation implies higher manufacturing costs. Consequently, reducing variability in the performance characteristics will simultaneously reduce both quality costs to the users, and production costs to the manufacturer with concomitant reductions in scrap and rework costs, and improvements in productivity, cycle time, service call rates, etc. (Kackar, 1985).

Consistent with Taguchi's loss function approach and unlike that of Fine (1986), we relate the reductions in quality costs to decreases in process output variation overtime. Process variation reduction is a continuous effort that is influenced by the accumulation of process knowledge (MacKay and Steiner, 1997). We draw from the learning literature to specify this relationship between the level of process variation at a specific point in time and the level of "learning" achieved up to that point. Rather than relating the cumulative production volume...

NOTE: All illustrations and photos have been removed from this article.



More articles from IIE Transactions
An error rate comparison of classification methods with continuous exp..., June 01, 2003
Bootstrap Methods: A Practitioner's Guide. (Book Reviews).(Book Review..., June 01, 2003
Categorical Data Analysis, Second Edition. (Book Reviews).(Book Review..., June 01, 2003
Box on Quality and Discovery with Design, Control, and Robustness. (Bo..., June 01, 2003
Reliability of Computer Systems and Networks Fault Tolerance, Analysis..., June 01, 2003

Looking for additional articles?
Search our database of over 3 million articles.

Looking for more in-depth information on this industry?
Search our complete database of Industry & Market reports by text, subject, publication name or publication date.

About Goliath
Whether you're looking for sales prospects, competitive information, company analysis or best practices in managing your organization, Goliath can help you meet your business needs.

Our extensive business information databases empower business professionals with both the breadth and depth of credible, authoritative information they need to support their business goals. Whether it be strategic planning, sales prospecting, company research or defining management best practices - Goliath is your leading source for accurate information.