Recent progress in modeling R&amp;D project-selection processes

Schmidt, Robert L.; Freeland, James R.

doi:10.1109/17.141276

Cited by 133 publications

(62 citation statements)

References 49 publications

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“…This research stream has offered a wide variety of analytical methods for choosing R&D projects to meet strategic objectives. However, the modelers involved in this research strean1 have, for a long time now, complained that their proposed approaches are not widely adopted in practice (Souder 1978, Schmidt and Freeland 1992, Cabral-Cardoso and Payne 1996, Burnett et al 1993). This has typically justified further attempts to improve (and publish) these analytical models to better meet the perceived needs of industry.…”

Section: Introductionmentioning

confidence: 99%

Selecting R&D projects at BMW: a case study of adopting mathematical programming models

Loch

Pich

Terwiesch

et al. 2001

IEEE Trans. Eng. Manage.

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Research and development (R&D) project selection is a critical interface between the product development strategy of an organization and the process of managing projects day-to-day. This article describes the project selection problem faced by an R&D group of BMW (Munich, Germany). The problem was structured as minimizing the gap between target performance of the technology to be developed and actual performance of the current technology along chosen criteria. A mathematical programming model helped this organization to increase the transparency of their selection process, which previously had been based on experience coupled with evaluation of individual projects in isolation Implementation was a success in that the predevelopment group continues to use the model to make better decisions. However, the organization did not use the model for its intended purpose: constrained optimization. The traditional explanation for this partial implementation is that the analytical model did not capture all considerations relevant to optimization (e.g., uncertainty or strategic fit), and that further model refinements are required to achieve further implementation. We offer an alternative explanation, one based on the technology transfer literature. The diffusion of the analytical model from academia to industry faced the same problems as any technology transfer: Significant tacit knowledge had to be transferred along with the codified knowledge of the analytical model. This required iterated problem solving, which required the limited time and resources of the diffusing agents (academia) as well as the adopting agents (industry). Thus, the organization adopted only those elements of the modeling method that could be transferred within the resource constraints, focusing on those elements offering the highest benefit per effort invested. Disciplines MathematicsThis journal article is available at ScholarlyCommons: http://repository.upenn.edu/oid_papers/228 Selecting R&D Projects at BMW: A Case Study of Adopting Mathematical Programming ModelsChristoph H. Loch*, Michael T. Pich*, Christian Tetwiesch**, Michael Urbschat*** September 1999Abstract R&D project selection is a critical interface between the product development strategy of an organization and the process of managing projects day-to-day. This article describes the project selection problem faced by the transmission pre-development group of BMW. The group had to choose a portfolio of projects to set the foundation for the "best powertrain 2000." This problem of project selection was structured as minimizing the gap between target perfom1ance and actual perfom1ance along chosen criteria. A mathematical progranm1ing model helped this organization to increase the transparency of their selection process, which previously had been based on experience coupled with evaluation of individual projects in isolation. The model was used to structure data collection, to apply to consistent criteria to the selection of pre-development projects, and to compare weighted project...

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Section: Introductionmentioning

confidence: 99%

Selecting R&D projects at BMW: a case study of adopting mathematical programming models

Loch

Pich

Terwiesch

et al. 2001

IEEE Trans. Eng. Manage.

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“…It is complicated and challenging work, firstly because the future success or impacts of candidate projects are hard to predict and secondly because a multi-stage multi-person decision-making process is required to obtain a reliable outcome [9,11].…”

Section: Randd Project Evaluationmentioning

confidence: 99%

“…As a result, extensive academic research has been conducted to help organizations make better decisions in R&D project selection, and a number of decision models and methods, such as analytic hierarchical process (AHP)-based modes [3], fuzzy evaluation processes [4,5], and portfolio-based methods [6,7], have been developed in the past four decades. However, current research findings indicate that many such complex models and methods are not being used, having only limited effects on decision-making for real-world project selection [8,9]. In a similar vein, Cooper et al (2001) [10] found that, among the many project selection models, financial methods have been the most frequently used at large firms, but companies using a scoring model, which is quite simple to use, together with financial analysis, generally outperform companies relying merely on the financial aspects of projects.…”

Section: Introductionmentioning

confidence: 99%

R&D Project Selection Incorporating Customer-Perceived Value and Technology Potential: The Case of the Automobile Industry

et al. 2017

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Abstract:As user-centric innovation has recently emerged as a successful way of developing new products, services, and concepts, it is worth considering the perspectives of potential technology users during R&D project selection processes. Nevertheless, little effort has been made to reflect customer-perceived value in establishing selection criteria, with the focus mainly on technological potential instead. Therefore, this study aims to develop an R&D project selection model incorporating not only technological potential but also customer-perceived value. For this purpose, a new R&D project evaluation model and process is proposed, and its feasibility is tested by potential users in a real scenario. The automobile industry is suitable for our evaluation model because it is a B2C and system-based industry where customer needs are critical to market success and a number of R&D projects are proposed every year. Finally, a supporting tool is developed to help interact with various evaluators and visualize the evaluation results, as customer involvement is recommended for accurate project evaluation from the perspective of technology users. This study is one of the earliest attempts to reflect customer-perceived value in R&D project selection, and practically, the research outputs are expected to be useful to automobile manufacturers in creating value from R&D projects.

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“…Liberatore (1987) [4] adopted an extension of the Analytic Hierarchy Process (AHP) to study on the industrial Research and Development (R&D) project selection problem, and he used the spreadsheet model to rank the projects and then adopted the cost-benefit analysis and integer programming to support the resource allocation. Schmidt and Freeland (1992) [10] analyzed and reviewed the quantitative models which are used in R&D project selection, including the introduction of R&D project selection problem, classical decision models which focused on outcomes, systems models which focused on the insights of this problem, the structural, functional and quantitative models of R&D systems and they also analyzed the future research needs in this field. Henriksen and Traynor (1999) [3] overviewed the most recent literature of R&D project selection and proposed an improved and practical scoring tool to evaluate the R&D projects which ranked the projects based on relevance, risk, reasonableness, and return criteria.…”

Section: Introductionmentioning

confidence: 99%

An Effective Method to Evaluate the Scientific Research Projects

Hou

Huang

2014

Foundations of Computing and Decision Sciences

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Abstract. The evaluation of the scientific research projects is an important procedure before the scientific research projects are approved. The BP neural network and linear neural network are adopted to evaluate the scientific research projects in this paper. The evaluation index system with 12 indexes is set up. The basic principle of the neural network is analyzed and then the BP neural network and linear neural network models are constructed and the output error function of the neural networks is introduced. The Matlab software is applied to set the parameters and calculate the neural networks. By computing a real-world example, the evaluation results of the scientific research projects are obtained and the results of the BP neural network, linear neural network and linear regression forecasting are compared. The analysis shows that the BP neural network has higher efficiency than the linear neural network and linear regression forecasting in the evaluation of the scientific research projects problem. The method proposed in this paper is an effective method to evaluate the scientific research projects.

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Recent progress in modeling R&D project-selection processes

Cited by 133 publications

References 49 publications

Selecting R&D projects at BMW: a case study of adopting mathematical programming models

Selecting R&D projects at BMW: a case study of adopting mathematical programming models

R&D Project Selection Incorporating Customer-Perceived Value and Technology Potential: The Case of the Automobile Industry

An Effective Method to Evaluate the Scientific Research Projects

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