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Kuwashima, Kenichi

doi:10.7880/abas.12.31

Cited by 15 publications

(8 citation statements)

References 35 publications

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“…To maintain competitive advantage, companies must adapt their products to changes in external environments (e.g., Fukuzawa, 2015;Kuwashima, 2013). Adaptations to changes in product development include (a) the design of flexible products, and (b) the flexible design of products.…”

Section: Introductionmentioning

confidence: 99%

Management of exhaustion in continuous product development

Wei

2019

ABAS

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In case of products under continuous development, such as mobile games, (a) the development workload for bug fixing is accompanied by (b) the development workload of new content, which exhausts development teams. This paper examined a case of a company where (a) some developers suffered exhaustion while responding to bugs pointed out by users. However, many of the bugs were related to specific events and items. Thus, the company chose not to include the bug fixes in quick updates as long as they were not emergencies related to the game system. In addition, (b) initially PvE (Player versus Environment

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Section: Introductionmentioning

confidence: 99%

Management of exhaustion in continuous product development

Wei

2019

ABAS

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“…This straight line is a 50% learning curve, wherein both progress integrated into product development (Kuwashima, 2013), in which case the assumption also applies to product development. 9 Results of technological alternative search have not yet been realized, and costs are considered to be at the maximum.…”

Section: The Initial Concavity Of the Log-linear Learning Curvementioning

confidence: 99%

Jumping to Hasty Experience Curves

Takahashi

2013

ABAS

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The "experience curve" used to be treated as a rule of thumb in strategic management theory. However, it would be hasty to conclude that an approximately 80% log-linear experience curve is a rule of thumb regardless of the industry, company, or product. According to the fundamental research on learning curves, generally, the new model of a product is partly composed of old model parts and we cannot observe the progress of these parts from the beginning of the processes. Therefore, in the first product of the new model, the learning rates of old model parts cannot be identical logically, even if the parts' learning rates are identical at the beginning. In fact, empirical data repeatedly deny identical learning rates for all products. Applying search theory, we obtain an approximate log-linear learning curve and show the curve's initial concavity, that is, if we started observing the progress midstream, and not from the beginning of the process. Thus, both varied learning rate proper to each product and initial concavity of the learning curve are phenomena induced by observing the progress midstream.

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“…6 It is a type of routine (Sato, 2012) in organizational learning. In the case of assembly industries, this mainly relates to process development, but when process development is included in product development as is the case in process industries (Kuwashima, 2013), it also relates to product development. What Fujimoto (2012) explains as the strengths of Toyota relates to routines.…”

Section: Concluding Hypothesismentioning

confidence: 99%

Behind the Learning Curve

Takahashi

2013

ABAS

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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.

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Followers of Harvard Study

Cited by 15 publications

References 35 publications

Management of exhaustion in continuous product development

Management of exhaustion in continuous product development

Jumping to Hasty Experience Curves

Behind the Learning Curve

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