Many researchers have speculated about factors responsible for the productivity gains observed in organizations with increasing experience. For example, Joskow and Rozanski (1979) discussed the following factors as contributors to the produc- tivity gains observed with increasing experience: routinization of tasks, more efficient production control, improved equipment design, and improved routing and material handling. Thus, these researchers emphasize changes in the task and tech- nology as contributors to productivity gains associated with experience. Hayes and Wheelwright (1984) listed a broader set of factors as facilitators of organizational learning. According to Hayes and Wheelwright (1984), organizational learning curves are due to individual learning, better selection and training, improved meth- ods, enhanced equipment and technology, more appropriate division of labor and specialization, improved product design, substitution of capital for labor, incentives, and leadership. Similarly, Porter (1979) noted that with more experience, firms learn to make methods more productive, to design layout and workflow more efficiently, to coax more production out of machinery, to develop specialized new processes and product design modifications that improve manufacturability, and to institute better management control. Skilton and Dooley (2002) distinguished between operating knowledge, held by direct production workers, and structuring knowledge, held by managers. Structuring knowledge describes how to structure or organize operations most effectively while operating knowledge describes how to perform most effec- tively within an established structure.
In our interviews with managers at manufacturing plants about their views of the most important determinants of organizational learning curves, our respondents emphasized the following (1) increased proficiency of individual workers; (2) improvements in the organization’s technology, tooling, and layout; (3) improve- ments in its structure, organization, and methods of coordination; (4) and better understanding of who in the organization is good at what (Argote, 1993). This last factor is similar to the concept of transactive memory, which Wegner (1986) devel- oped to describe the knowledge of who knows what that develops between individu- als in close relationships. Better understanding of each individual’s skills enables the organization to assign tasks more appropriately so as to take better advantage of each individual’s unique capabilities. Knowledge of each member’s special expertise is also beneficial because members of the organization know whom to go to for help or advice about specific issues.
These myriad factors believed to affect learning can be classified into three general categories: improvements in the performance of individual employees, including direct production workers, managers, and technical support staff; improve- ments in the organization’s structure and routines; and improvements in the organi- zation’s technology. Examples of improvements in each of these categories will now be discussed. These examples are drawn from our field studies of learning in manufacturing and service organizations.
1. Increased Individual Proficiency
Most discussions of factors responsible for organizational learning curves cite learning by individual workers as a key factor (e.g., see Hayes & Wheelwright, 1984; Yelle, 1979). A long stream of research in psychology has documented that individual performance improves as individuals acquire more experience with a task (Graham & Gagne, 1940; Thorndike, 1898; Thurstone, 1919). Reviews of the large body of research on individual learning can be found in Anzai and Simon (1979), Newell and Rosenbloom (1981), and Mazur and Hastie (1978).
Our interest is in individuals working in organized settings. What qualifies as examples of improvements in individual performance that occur in ongoing groups and organizations as individuals gain experience in production? Many examples of individuals becoming more skilled at their particular tasks can be found in our study of fast-food franchises. For example, pizza makers typically became more proficient at hand-tossing pizza dough and transforming it into a pizza shell as they acquired experience. Much of the knowledge about how to hand-toss pizza was tacit and therefore difficult to articulate to others (Nonaka, 1991; Polanyi, 1966). This knowl- edge remained primarily embedded in the individual workers who had acquired experience with the pizza-tossing task.
We also observed improvements in the performance of individual workers in manufacturing plants. At one plant we studied, a second shift was introduced almost 2 years after the plant had been in operation with one shift. Workers on the new shift worked side by side workers on the first shift to learn their jobs. Workers on the new shift were gradually “weaned” from their experienced counterparts until the new employees were working independently on the second shift. Through observing workers on the first shift and gaining experience with the task, workers on the new shift learned their individual jobs and became very proficient at them.
2. Modifications in Technology
Modifications in technology are another major contributor to the productivity gains observed in organizations with increasing experience. By technology, we mean equipment, including hardware and software (cf. Amber & Amber, 1962; Barley, 1986; Blau, Falbe, McKinley, & Tracy, 1976) used in production. An example of modification in technology that derived from experience in production can be found in the paint shop at one of the truck assembly plants we studied. The plant experi- enced problems in its new highly automated paint shop. When light-colored prod- ucts followed dark-colored ones, vestiges of the dark color remained on the subsequent, light-colored product. This was clearly unacceptable. Plant managers and engineers tried various approaches to remedy the problem. The most effective solution that was developed involved dedicating particular paint booths to particular dark colors. Thus, only products of the same dark color would be processed through each booth. If any residue paint remained in the system, it would not be harmful because all the products going through the booth were the same color. While dedi- cating a paint booth to a particular color resulted in some loss in flexibility for the system, the lost flexibility was more than offset by the improved product quality and the reduced waste. This manufacturing example illustrates how knowledge acquired via learning by doing can lead to modifications in an organization’s technology. Knowledge was embedded in the “software” and the “hardware” of the paint shop that enabled the organization to produce a higher quality, less costly product.
We also observed several examples of improvements in technology in our study of fast-food franchises (Argote & Darr, 2000). Technology in the context of these pizza stores includes the equipment, such as ovens, and tools used to make pizzas, as well as the physical layout of the stores. The “cheese spreader” is an example of an innovation developed through production experience that became embedded in the organization’s technology. Achieving an even distribution of cheese across a pizza is a desired goal. Too much cheese decreases profit margins, whereas too little cheese decreases customer satisfaction. A manager at one of the stores we studied decided that spreading cheese by hand was not the best method. The manager believed that the problem was analogous to spreading fertilizer on a lawn and that some type of “spreader” was needed. The manager experimented with various configurations of plastic dishes and metal screens to develop a tool that would help pizza makers use a consistent amount of cheese and achieve an even distribution of the topping. The final version of the “cheese spreader” tool was a plastic cone with holes that sat on feet several inches above the pizza. A pizza maker would pour grated cheese into the cone and the cheese would fall in a consistent pattern over the pizza. This example illustrates—in a very different organizational context—how knowledge acquired via experience can be embedded in an organization’s technology.
3. Elaborations in Structure and Routines
Elaborations in structure and routines made as organizations gain experience in production also contribute to organizational learning. One such elaboration we saw at a manufacturing plant involved changing the structure of the industrial engineering group. A decision was made to deploy the industrial engineering group that had previously been centralized in one area of the plant to various areas on the plant floor so that the engineers could be more responsive to production problems. Thus, the industrial engineers were shifted from a functional-type organization where they were centralized in one area to a product-type organization where they were decen- tralized to various areas on the plant floor. The decentralized organization enabled the engineers to respond more quickly to issues on the plant floor. In this example, knowledge about how to be more responsive was embedded in the manufacturing plant’s structure.
Another example of knowledge embedded in routines occurred in a manufacturing plant we studied. The particular routine involved preparing the products (trucks) for painting: painting two-tone trucks was challenging because workers had to mask the areas of the truck that were not to be painted a particular color by taping large sheets of protective paper over the appropriate areas. As experience was gained with the task, a better method for placing the protective paper was discovered. Initially, workers masked the area of the truck that was not to be painted a particular color to protect those areas and then painted the rest of the truck the desired color (e.g., white). They then reversed the masking by placing protective paper over the area that had already been painted the desired color (e.g., white) and painted the remain- der of the truck the second color (e.g., red). This process required two stages of carefully masking the truck with protective paper. A new method of masking was discovered that required only one round of masking. All of the truck was painted one background color (e.g., white). The parts of the truck that were to remain the background color were then masked and the truck was painted the second color (e.g., red). The new process saved considerable time because the trucks had to be masked with protective paper only once. The new method, which required fewer labor hours and less material to achieve the desired two-tone paint job, ultimately became embedded in a routine that all workers used.
We also observed knowledge embedded in an organization’s routines in our study of fast-food franchises (Argote & Darr, 2000). When deep-dish pizza was intro- duced at the pizza stores, all stores experienced a persistent problem with the new product. The usual method of distributing pepperoni on pizzas was to distribute it evenly over the pizza before the pizza was cooked. Although this method worked for regular pizzas, it did not work well for deep-dish ones. When pepperoni was distributed evenly on deep-dish pizzas, the pieces of pepperoni would all move into the center in one “clump” as the pizza cooked and the cheese flowed. Various meth- ods of dealing with the problem were implemented. The most successful one involved distributing the pepperoni on the pizza before it was cooked in a pattern that resembled spokes on a wheel. As the pizza was cooked, the flow of the cheese distributed the pepperoni pieces (more or less) evenly over the pizza. Thus, knowl- edge about how to distribute pepperoni evenly became embedded in a routine. This routine proved to be very effective at achieving an even distribution of pepperoni. The routine is now used by virtually every store in the corporation.
Source: Argote Linda (2013), Organizational Learning: Creating, Retaining and Transferring Knowledge, Springer; 2nd ed. 2013 edition.