The learning curve depicts a decrease in production cost per unit as cumulative volume increases. However, a learning curve does not realize even if the history of production translates into an increase of cumulative volume. It is major premises for cost reduction to change technology in expectation of increasing scale of production. For example, new machinery and equipment are installed in preparation for commercial manufacturing, or product designs are modified for mass production. In other words, the first step to realize the learning curve is the management decision to mass-produce without falling victim to ceiling psychology. When building a prototype, you would use the unique building method suitable for it. If you were producing 10 units, you would naturally use a manufacturing method suited for 10 units. Similarly, for 100 units you would use a method for 100 units. For manufacturing 10,000 units, a mass production methodology for efficiently producing 10,000 units is best suited. Only forecasting of gargantuan cumulative volume from the very beginning enables the factory to adopt appropriate methods and technology for mass production. The learning curve could emerge only if the management has an unwavering conviction on a gigantic scale perspective. As a major premise for technological options, management's scale perspective is the secret of the learning curve.
How does the introduction of information technology (IT) influence production systems? This paper reveals two important changes by introducing information technology (IT) into a production system in the case of the Japanese anime industry. In particular, it focuses on the production system and the relationship between outsourcing and in-house production. Given this objective, this research explores each process and technology of the production system. The processes that are focused in this research are the art production process, painting process and the process that follows. In addition, it examines three-dimensional computer graphics and digital sakuga (drawing) in the technology. The results of the paper elucidate the following. First, companies in Japan cannot attain successful introduction of IT until IT achieve low cost or/and high quality. Second, the process of choosing in-house production or outsourcing depends upon the following factors. Realizing the integration of multiple processes or enhancing the flexibility of the production process leads to the transition from outsourcing to in-house production. On the other hand, realizing lower cost by the delivery of digitalized data through storage media or the Internet results in the transition from in-house production to outsourcing.
Rumelt (1974), one of the basic researches related to diversification strategy, reveals that Dominant-Constrained (DC) and Related-Constrained (RC) strategies make possible a higher level of corporate performance. However, Rumelt (1982) clarifies that these strategies are not necessarily the ideal diversification strategies, because the industry effect have an influence on the performance of companies in these research. This paper re-consolidates and re-examines the data in Rumelt (1974) to reveal that companies in the particular industry tend to adopt the particular strategy. For example, many of the companies that adopted an RC strategy (Related-Constrained firms), a strategy that was viewed as leading to high performance, were in the drug, chemical, and machinery (except electrical) industries.
This paper reviews product development research with focus on performance indicators. The following are the four primary indicators of product development performance: (1) degree of success, (2) survival, (3) product competitiveness, and (4) development productivity and development lead-time. From 1960 to 1980, when the field of product development research was established, most studies adopted indicator (1). In contrast, after 1990, indicator (4) became mainstream. The trigger for this transition was the comparative study of international product development projects in the automobile industry by Harvard University's Kim Clark and Takahiro Fujimoto (Clark & Fujimoto, 1991). They proposed the groundbreaking empirical research method of statistically analyzing organizational structure, process, and strategies that influence the three product development performance indicators: development productivity, development lead-time, and total product quality.
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