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Chapter - 6

Learning Curves

• Managerial Considerations in Using Learning Curves

Managers should be aware of the following factors when using and interpreting learning curves.

1. Individual learning and incentives.

Extensive research indicates a rather obvi[1]ous fact: In order to enhance worker learning, there must be adequate incentives for the worker and the organization. (It should be noted, however, that the concept of incentives may be broadened to include any of the positive or negative administrative options available to managers.)

1. Learning on new jobs versus old jobs.

The newer the job, the greater will be the improvement in labor hours and cost. Conversely, when production has been under[1]way for a long time, improvement will be less discernible. For example, for an 80 percent learning curve situation, the improvement between the first and second units will be 20 percent. However, if the product has been manufactured for 50 years, it will take another 50 years to reduce labor hours by 20 percent, assuming that yearly production volume remains the same.

1. Improvement comes from working smarter, not harder.

While incentives must be included to motivate the individual worker, most improvement in output comes from better methods and effective support systems rather than simply increased worker effort.

1. Built-in production bias through suggesting any learning rate.

If a manager expects an 80 percent improvement factor, he or she may treat this percentage as a goal rather than as an unbiased measure of actual learning. In short, it may be a "self-fulfill[1]ing prophecy." This, however, is not necessarily undesirable. What is wrong with setting a target improvement factor and then attempting to control production to achieve it?

1. Preproduction versus postproduction adjustments.

The amount of learning shown by the learning curve depends both on the initial unit(s) of output and on the learning percentage. If there is much preproduction planning, experimentation, and adjustment, the early units will be produced more rapidly than if improvements are made after the first few unitsother things being equal. In the first case, therefore, the apparent learning will be less than in the second case, even though subsequent "actual" learning may be the same in each instance.

1. Changes in indirect labor and supervision.

Learning curves represent direct labor output, but if the mix of indirect labor and supervision changes, it is likely that the productivity of direct labor will be altered. We expect, for example, that more supervisors, repairpersons, and material handlers would speed up production, whereas a reduction in their numbers would slow it down.

1. Changes in purchasing practices, methods, and organization structure.

Obvi[1]ously, significant adjustments in any of these factors will affect the production rate and, hence, the learning curve. Likewise, the institution of preventive maintenance programs, zero-defect programs, and other schemes designed to improve efficiency or product quality generally would have some impact on the learning phenomenon.

1. Contract phase-out.

Though not relevant to all contract situations, the point should be made that the learning curve may begin to turn upward as a contract nears comple[1]tion. This may result from transferring trained workers to other projects, nonreplace[1]ment of worn tooling, and reduced attention to efficiency on the part of management.

Chapter - 9

Service Processes

• An Operational Classification of Services

Service organizations are generally classified according to who the customer is, for example, individuals or other busi[1]nesses, and to the service they provide (financial services, health services, transportation services, and so on). These groupings, though useful in presenting aggregate economic data, are not particularly appropriate for OSCM purposes because they tell us little about the process. Manufac[1]turing, by contrast, has fairly evocative terms to classify production activities (such as intermittent and continuous production). When applied to a manufacturing setting, they readily convey the essence of the process. Although it is possible to describe services in these same terms, we need one additional item of information to reflect the fact that the customer is involved in the production system. That item, which we believe operationally distinguishes one ser[1]vice system from another in its production function, is the extent of customer contact in the creation of the service. Customer contact refers to the physical presence of the customer in the system, and cre[1]ation of the service refers to the work process involved in providing the service itself. Extent of contact here may be roughly defined as the percentage of time the customer must be in the system relative to the total time needed to perform the customer service. Generally speaking, the greater the percentage of contact time between the service system and the customer, the greater the degree of interaction between the two during the production process. From this conceptualization, it follows that service systems with a high degree of customer contact are more difficult to control and more difficult to rationalize than those with a low degree of customer contact. In high-contact systems, the customer can affect the time of demand, the exact nature of the service, and the quality, or perceived quality, of service because the customer is involved in the process. Exhibit 9.2 describes the implications of this distinction. Here we see that each design decision is impacted by whether the customer is present during service delivery. We also see that when work is done behind the scenes (in this case, in a bank's processing center), it is performed on customer surrogatesreports, databases, and invoices. We can thus design it according to the same principles we would use in designing a factoryto maximize the amount of items processed during the production day. There can be tremendous diversity of customer influence and, hence, system variability within high-contact service systems. For example, a bank branch offers both simple services such as cash withdrawals that take just a minute or so and complicated services such as loan application preparation that can take in excess of an hour. Moreover, these activities may range from being self-service through an ATM, to coproduction where bank personnel and the customer work as a team to develop the loan application