The Effect of Incentive Schemes and Organizational Arrangements on the New Product Development Process

Natter, Martin; Mild, Andreas; Feurstein, Markus; Dorffner, Georg; Taudes, Alfred

doi:10.1287/mnsc.47.8.1029.10228

Cited by 45 publications

(37 citation statements)

References 26 publications

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“…In the sequential organizational structure, we can compare with team based organization and incorporating method such as house of quality. Based on the key findings of the firm's organizational structure and agents' incentive system, we show that the house of quality is less affected by the incentive scheme and this becomes an important factor for new product success (Natter et al 2001).…”

Section: Methodsmentioning

confidence: 98%

Product development strategy with quality function deployment approach: A case study in automotive battery

Darmawan¹,

Purba²,

Rezeki³

et al. 2017

10.5267/j.msl

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Customer satisfaction is one of the main factors in determining the competitiveness of every industry. Along with the technological advances, it will impact on the increasingly intense competition in the business of providing great opportunities to the consumer to find a quality product at competitive rates. The purpose of this study is to develop the quality of automotive battery products that meet consumer needs by using Quality Function Deployment (QFD) method. The application is then analyzed and its results produced a proposal for product development according to the weight and priority development on product attributes that are considered important by customers. There are two main priorities that are most desired by customers, among others for improving the quality of products maintenance free battery in automotive battery industry with quality function deployment according to consumers. Consumers need a car battery with a good durability and great performance, low price, and environment friendly features, which can be achieved by using absorbent glass mat and expanded machine technology. Based on relative weight in House of Quality, Absorbent Glass Mat receives the highest percentage of technical priority that is equal to 31% whereas technology expanded gets the second highest percentage of technical priority that is equal to 19%. It means that both technologies are more important to develop this product. Therefore, the maintenance free battery products are expected to be attractive for consumers and extensive marketing.

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Section: Methodsmentioning

confidence: 98%

Product development strategy with quality function deployment approach: A case study in automotive battery

Darmawan¹,

Purba²,

Rezeki³

et al. 2017

10.5267/j.msl

View full text Add to dashboard Cite

show abstract

“…Using a more abstract model, Natter et al (2001) represent product development organizations using two agents (one called marketing, one called production) that can learn but have limited knowledge and computational ability. The model uses neural networks to model each agent's learning and a life cycle model to predict the organization's profitability over time.…”

Section: Models Of Product Development Organizationsmentioning

confidence: 99%

Viewing Product Development as a Decision Production System

Herrmann

Schmidt

2002

Volume 4: 14th International Conference on Design Theory and Methodology, Integrated Systems Design, and Engineering Design And

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ISR develops, applies and teaches advanced methodologies of design and analysis to AbstractProduct development includes many different types of decision-making by engineers and managers. Design decisions determine the product form and specify the manufacturing processes to be used. Development decisions, however, control the progress of the development process by defining which activities should happen, their sequence, and who should perform them. This paper introduces the concept of a decision production system to describe a product development organization as a system of decision-makers who use and create information to develop a product. This perspective does not advocate any particular type of product development process. Instead, it looks at the organization in which the product development process exists and considers the decision-makers as a manufacturing system that can be viewed separately from the organization structure.A new perspective is needed to reconcile product development practice and design theory. This paper argues that viewing product development as a decision production system provides a perspective to understand the costs and benefits associated with different forms of product development processes. The paper describes some of the benefits that this perspective and decision production system models would bring to product development organizations and to the design research community. Comprehensive models are needed to improve communication about the nature of product development and to understand the impact that changing product development processes will have on the organization's overall performance and profitability.

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“…Carley (1995) addresses the linkage between individual learning on the basis of cognitive models of agents and models of organizational learning based on structural models as a major research question for computational and mathematical organizational theory. Natter et al (2001) study new product development processes for different organizational structures and incentive schemes. They use a computational model of distinct artificial agents which are based on connectionist models (neural networks) to simulate the distribution of knowledge across different departments and persons.…”

Section: Introductionmentioning

confidence: 99%

An agent-based investigation into the new product development capability

Mild

Taudes

2006

Comput Math Organiz Theor

Self Cite

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The capability to bring products to market which comply with quality, cost and development time goals is vital to the survival of firms in a competitve environment. New product development comprises knowledge creation and search and can be organized in different ways. In this paper, we study the performance of several alternative organizational models for new product development using a model of distributed, self-adapting (learning) agents. The agents (a marketing and a production agent) are modelled via neural networks. The artificial new product development process analyzed starts with learning on the basis of an initial set of production and marketing data about possible products and their evaluation. Subsequently, in each step of the process, the agents search for a better product with their current models of the environment and, then, refine their representations based on additional prototypes generated (new learning data). Within this framework, we investigate the influence of different types of new product search methods and generating prototypes/learning according to the performance of individual agents and the organization as a whole. In particular, sequential, team-based Trial & Error and House of Quality guided search are combined with prototype sampling methods of different intensity and breadth; also, the complexity of the agents (number of hidden units) is varied. It turns out that both the knowledge base and the search procedure have a significant impact on the agents' generalization ability and success in new product development.Keywords New product development . Knowledge management . Agent-based model A. Mild ( ) . A. Taudes

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The Effect of Incentive Schemes and Organizational Arrangements on the New Product Development Process

Cited by 45 publications

References 26 publications

Product development strategy with quality function deployment approach: A case study in automotive battery

Product development strategy with quality function deployment approach: A case study in automotive battery

Viewing Product Development as a Decision Production System

An agent-based investigation into the new product development capability

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