EXPERIENCE AND LEARNING CURVES



Experience And Learning Curves 585
Photo by: Marzky Ragsac Jr.

Experience and learning curve models are developed from the basic premise that individuals and organizations acquire knowledge by doing work. By gaining experience through repetition, organizations and individuals develop relatively permanent changes in behavior or learning. As additional transactions occur in a service, or more products are produced by a manufacturer, the per-unit cost often decreases at a decreasing rate. This phenomenon follows an exponential curve. The organization thus gains competitive advantage by converting this cost reduction into productivity gains. This learning competitive advantage is known as the experience curve, the learning curve, or the progress curve.

It is common for the terms experience curve and learning curve to be used interchangeably. They do, however, have different meanings. According to definitions by Hall and Starr, the experience curve is an analytical tool designed to quantify the rate at which experience of accumulated output, to date, affects total lifetime costs. Melnyk defined the learning curve as an analytical tool designed to quantify the rate at which cumulative experience of labor hours or cost allows an organization to reduce the amount of resources it must expend to accomplish a task. Experience curve is broader than learning curve with respect to the costs covered, the range of output during which the reductions in costs take place, and the causes of reduction.

The idea of "learning by doing" is intuitive. We often experience this effect when we take up a new sport or start to keyboard. Our skill levels increase rapidly with practice, up to a point, and then progress at a slower rate. Eventually, our golf score levels off around some value and our keystrokes per minute (without errors) levels off as well.

Organizational learning is complex in that we learn at many levels simultaneously. In organizations, procedures, norms, rules, and forms store knowledge. March states that managers of competitive organizations often find themselves in situations where relative position with regard to a competitor matters. This possible competitive advantage through enhanced learning is the essence of the study of experience and learning curves.

The analytical use of the concept for business purposes first surfaced in 1936 during airplane construction, when Wright observed that as the quantity of manufactured units doubled, the number of direct labor hours needed to produce each individual unit decreased at a uniform rate. The variation of labor cost with production quantity is illustrated by the following formula:
F = log F /log N
where F equals a factor of cost variation proportional to the quantity N. The reciprocal of F represents a direct percent variation of cost versus quantity.

This insight shows that experience-based learning is closely correlated with cumulative output, extending beyond changes in design and tooling. Wright found empirical evidence that as unit volume increases there are predictable corresponding reductions in cost. These data become central concepts for strategic and operational planning. There has been much discussion on the role of learning in business organizations. A seminal work in learning theory is the 1963 A Behavioral Theory of the Firm by Cyert and March. These authors viewed firms as adaptively-rational systems. This means that the firm learns from its experience. In its basic form, an adaptive system selects preferred states for use in the future. With experience, management uses decision variables that lead to goals and shuns those that do not lead to goals.

The learning curve model was expanded by Adler and Clark into a learning process model. A key conceptual difference from the prior model is that "a significant part of the effect of experience on productivity (captured in the learning curve model) might be due to the influence of identifiable managerial actions". The authors present two orders of learning. First-order learning refers to the classic learning curve model where productivity is an exponential function of experience. Second-order learning denotes that which is driven by changes in technology or human capital that lead to goal attainment.

FUNDAMENTALS OF EXPERIENCE AND LEARNING CURVES

Following a strategy of increasing market share, the experience curve focuses on cost leadership. Management attempts to increase market share while simultaneously reducing costs. This is a detriment to market entry as the firm can lower its price, which may further increase its market share and place added pressure on potential competitors, as found in a study by Lieberman. Learning through experience becomes an important component of the increased market share strategy.

Quality learning is enhanced through the shared experience at the worker and organizational levels. Quality increases as the firm moves further along the experience curve, thus increasing productivity and efficiency. As the individual employees and organization become more efficient, there should be a corresponding increase in productivity. More output for less input effectively increases capacity; taken together with the increased efficiency and productivity, this should lead to a reduction in unit cost. The business is investing in a cost-leadership posture based on the assumption that price is a basis for competition. If the firm is able to produce quality units and reduce market price, there is the opportunity for increased market share (the business strategy). Increased market share via reduced price may lead to the global goal of improving profits.

Use of a cost leadership strategy based on the experience curve implies several assumptions, according to Amit:

  1. Price is a basis for competition.
  2. If per unit cost is reduced, price may be reduced, which may lead to increased market share.
  3. As cumulative output increases, the firm's average cost is reduced. Therefore, for any production rate, there is a reduction in the per-unit cost.
  4. If market share is increased, profits will increase.
  5. Another critical assumption of the experience curve, noted by Lieberman, is that learning can be kept within the organization. Where industry-wide dissemination of process technology is rapid, the benefits of organizational learning through the experience curve may be short-lived. The cost benefits, therefore, may not lead to increased market share even though industry costs are declining because all participants are learning at approximately the same rate.

LEARNING CURVE FORMULATION

The formula for the learning curve model is commonly shown either as a margin-cost model or as a direct-labor-hour model. The direct-labor-hours formula is more useful, as hourly compensation typically changes over time and there may be inflation considerations as well. However, both derivations will be presented here for clarity. Also, direct-labor hours may be easily converted into costs if necessary, according to Yelle. By convention, we refer to experience curves by the complement of the improvement rate. For example, a 90 percent learning curve indicates a 10 percent decrease in per-unit (or mean) time or cost, with each doubling of productive output. Experience and learning curves normally apply only to cost of direct labor hours.

MARGINAL COST MODEL.

The cumulative-average learning curve formulation is:
Y cx = ax -b
where Y cx = average cost of the first x units,
a = the first unit cost,
x = the cumulative unit number output,
and
b = the learning elasticity, which defines the slope of the learning curve.
This learning curve model indicates that as the quantity of units produced doubles, the average cost per unit decreases at a uniform rate.

DIRECT LABOR HOURS MODEL.

The direct labor hour model of the learning curve is:
Y = KX n
where Y = the number of direct labor hours required to produce the X th unit,
K = the number of direct labor hours required to produce the first unit,
X = the cumulative unit number,
n = log ϕ/log 2,
ϕ = the learning rate, and
1 - ϕ = the progress ratio.

These empirical models have been shown to fit many production situations very well. One criticism is that many other undocumented variables may be behind the benefits attributed to the experience curve. There are intermingling variables that also may account for cost reductions. Some of these variables might be economies of scale, product design decisions, tooling and equipment selections, methods analysis and layout, improved organizational and individual skills training, more effective production scheduling and control procedures, and improved motivation of employees. All of these variables play a role in decreasing cost and increasing capacity.

APPLICATIONS AND USES

There are three general areas for the application and use of experience curves; strategic, internal, and external to the organization. Strategic uses include determining volume-cost changes, estimating new product start-up costs, and pricing of new products. Internal applications include developing labor standards, scheduling, budgeting, and make-or-buy decisions. External uses are supplier scheduling, cash flow budgeting, and estimating purchase costs. The usefulness of experience and learning curves depends on a number of factors; the frequency of product innovation, the amount of direct labor versus machine-paced output, and the amount of advanced planning of methods and tooling. All lead to a predictable rate of reduction in throughput time.

Knowledge on the practical application of experience curves and learning curves has increased greatly since 1936. Interest was renewed in the early 1990s with the publication of The Fifth Discipline by Peter Senge. Senge melded theories on mental models, the systems approach, and learning curves in a way that made sense for executives.

These curves offer potential competitive advantage to managers who can capitalize on the cost reductions they offer. The experience and learning curves rely, however, on keeping the knowledge gained within their organization. Given rapid communication, high manager and engineer turnover, and skills in reverse engineering, this is harder to accomplish with each passing year. However, Hatch and Dyer found that in the case of the semiconductor manufacturing industry, in particular, skills acquired in one firm are not necessarily effectively transferable to another firm since knowledge is specific to the original work environment. Therefore, even if the employee is hired away, there is limited threat to the original firm.

Hatch and Dyer conclude that to truly maintain an advantage over the competition, firms must employ effective human resource selection, training, and deployment processes that facilitate learning by doing. Those firms that meet this challenge may enjoy the only truly sustainable advantage—the ability to learn (and improve) faster than competitors. As manufacturing and service product lives become shorter, management must be keenly on top of experience and learning curves to continue to enjoy the advantages.

SEE ALSO: Knowledge Management ; Organizational Learning

James P. Gilbert

Revised by Monica C. Turner

FURTHER READING:

Abernathy, William J., and Kenneth Wayne. "The Limits of the Learning Curve." Harvard Business Review 52, no. 5 (1974): 109–119.

Adler, Paul S., and Kim B. Clark. "Behind the Learning Curve: A Sketch of the Learning Process." Management Science 37, no. 3 (1991): 267–281.

Amit, Raphael. "Cost Leadership Strategy and Experience Curves." Strategic Management Journal 7, no. 3 (1986): 281–292.

Cyert, Richard M., and James G. March. A Behavioral Theory of the Firm. Englewood Cliffs, NJ: Prentice-Hall, Inc., 1963.

Demeester, Lieven L., and Me Fontainebleu Qi. "Managing Learning Resources for Consecutive Product Generations." International Journal of Production Economics 95, no. 2 (2005): 265–283.

Hall, G., and S. Howell. "The Experience Curve from the Economist's Perspective." Strategic Management Journal 6, no. 3 (1985): 197–212.

Hatch, Nile W., and Jeffrey H. Dyer. "Human Capital and Learning as a Source of Sustainable Competitive Advantage." Strategic Management Journal 25, no. 12 (2004): 1155–1178.

Heizer, Jay, and Barry Render. Operations Management. 5th ed. Upper Saddle River, NJ: Prentice Hall, 1999.

Jaber, M. Y., and A. L. Guiffrida. "Learning Curves for Processes Generating Defects Requiring Reworks." European Journal of Operational Research 159, no. 3 (2004): 663–672.

Junginger, M., A. Faaij, and W. C. Turkenburg. "Global Experience Curves for Wind Farms." Energy Policy 33, no. 2 (2005): 133–150.

Lieberman, Marvin B. "The Learning Curve, Technology Barriers to Entry, and Competitive Survival in the Chemical Processing Industries." Strategic Management Journal 10, no. 5 (1989): 431–447.

Linton, Jonathan D., and Steven T. Walsh. "Integrating Innovation and Learning Curve Theory: An Enabler for Moving Nanotechnologies and Other Emerging Process Technologies into Production." Research and Development Management 34, no. 5 (2004): 517–526.

March, James G. "Exploration and Exploitation in Organizational Learning." Organizational Science 2, no. 1 (1991): 71–87.

Melnyk, Steven A., and David R. Denzler. Operations Management: A Value-Driven Approach. Chicago: Richard D. Irwin, 1996.

Senge, Peter M. The Fifth Discipline: The Art and Practice of the Learning Organization. New York: Doubleday Currency, 1990.

Smunt, Timothy L., and Charles A. Watts. "Improving Operations Planning with Learning Curves: Overcoming the Pitfalls of 'Messy' Shop Floor Data." Journal of Operations Management 21, no. 1 (2003): 93–107.

Starr, Martin K. Operations Management: A Systems Approach. Danvers, MA: Boyd & Fraser, 1996.

Teplitz, Charles J. The Learning Curve Deskbook: A Reference Guide to Theory, Calculations, and Applications. NY: Quorum Books, 1991.

Wright, T. P. "Factors Affecting the Cost of Airplanes." Journal of the Aeronautical Sciences 3, no. 4 (1936): 122–128.

Yelle, Louis E. "The Learning Curve: Historical Review and Comprehensive Survey." Decision Sciences 10, no. 2 (1979): 302–328.



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