A model for R&D performance measurement

R&D activities are increasingly costly and risky and, as a consequence, measuring their performance and contribution to value becomes critical. This paper illustrates a formal model for measuring R&D performance, based upon a balanced and synthetic evaluation of quantitative indicators from five different perspectives of performance: financial, customer, innovation and learning, internal business, alliances, and networks. The model is built in coherence with the suggestions coming from the theory of measurement in soft systems, which gives relevant guidelines for ensuring validity, objectivity and inter-subjectivity of the model. Then, an application in a real R&D setting is described, which helps to understand the model and to enlighten its main advantages and limits.     

R&D 'programmes-competencies' matrix: analyzing R&D expertise within the firm

The author suggests that the systematic identification of a firm's technologies opens up the possibility of evaluating, protecting, optimizing, enriching and exploiting these technologies to the full. A firm's technologies fall into two parts: those embodied and exploited in current operations and those evolving from the collective competencies of its R&D staff. The author's aim is to evaluate and describe the firm's R&D competencies.
The identification of R&D competencies proceeds in three steps: tracing the background of the current and past programmes, constructing a programmes/competencies matrix, and deriving an expertise profile. The construction of the matrix is the key activity and requires much care in its execution, if competencies are to be properly defined and truly existing, and if it is to be exhaustive. The author sketches a procedure for constructing a matrix and discusses problems of implementation and draws attention to some of its limitations.
The author claims that such an inventory of expertise could improve the efficiency of the use of the R&D resource, identify a laboratory's strengths and weaknesses, direct R&D into hitherto neglected channels, assist individuals to identify and evaluate their own expertise, justify obtaining funding for building expertise in shortage areas. The method should be considered as a starting point to formulate appropriate strategies to gain access to technology.     

A basic model for evaluating R&D performance: theory and application in Italy

Nowadays the public R&D laboratories have a fundamental role in countries' development, supporting businesses as they face the technological challenges in the turbulent world scenarios. Measuring the performance of R&D organisations is crucially important to decisions about the level and direction of public funding for research and development. This research considers the public laboratories like systems and develops a mathematical model based on the measurement of R&D activities with k‐indices. The score obtained from the research laboratories evaluation (relev) methodology synthesises in single value financial, scientific and technological aspects. It is an indicator, for R&D manager and policy maker, of performance in relation to other research organisations or in a time series. The method is an instrument of strategic planning and can be used for the improvement of individual activities and the overall performance of public R&D bodies.     

New models for measuring the R&D performance and identifying the productivity of public research institutes

Research evaluation of public institutes is important for measuring the performance of the science sector and improving the cost effectiveness and efficiency of public resource allocation. The purpose of this article is to construct a means of classifying publicly-funded R&D institutions and their performance (high or low). The main findings are two research performance functions, created using discriminant analysis with direct and Wilks methods, which have been successfully applied to 200 public research institutes belonging to the Italian National Research Council. The large number of indicators used within the models creates a macro index that produce, as output, the research performance score. The data analysed is from the 2001 period. The results show that 22.5% of public labs fall into the 'high performance institute' bracket and that they have a site larger than 'low performance institutes'. The models are useful tools for decision making within the research bodies and for reducing X-inefficiency.     

Improving R&D/Marketing relations: R&D's perspective

Abstract
Successful product innovation hinges on close R&D/marketing relationships, especially in technology-based organizations. However, there is evidence that linkage problems are common and when they are not overcome failure is the usual result.
R&D's perspective on what R&D, marketing and senior management can do to improve their relationship with marketing is presented. The recommendations are based on suggestions from R&D directors involved in new product development efforts in 83 technology-based companies. Also presented is how the need for integration has changed over time and where it is going in the future.     

Towards networked R&D management: the R&D approach of Sonera Corporation as an example

In this paper the authors delineate the challenges of a dynamic environment to R&D management. The authors build on most recent ideas, such as the dynamic capability view of the firm, as strategic foundation for modern R&D management. Collaboration is emphasized as a meta-capability for innovation. These ideas are merged into a 'Networked R&D Management' approach that emphasizes internal and external collaboration networks as critical for companies operating in a dynamic business environment. The approach is illustrated with ICT industry as an example. The implementation of Networked R&D Management is reflected in the illustrative case discussion of R&D management of Sonera Corporation.     

A Role for Knowledge Engineering in R&D

The author distinguishes information from knowledge: information is notes, details, evidence, etc., knowledge is general laws, theories, understanding. Information Engineering (IE) is the provision of knowledge in usable form. Artificial Intelligence (Al) is the construction of computer systems that exhibit signs of intelligence. He asserts that Al has already provided methodologies that have advanced both IE and KE. The result is that IE and KE are now in the position to act as the pencil, paper and report material for R&D.
Working knowledge of the languages is easily acquired and some useful empty shells are available, though the latter need more variety. Existing systems tend to be crude and may not address the right issues, e.g. emphasising probability rather than the risks associated with actions. Nevertheless the available tools are good enough to build useful expert systems for R&D.
The potential of KE in education is being missed. In the author's opinion all university students should be taught to use the basic tools of knowledge engineering and apply them to their own disciplines.     

R&D validation planning: a methodology to link technical validations to benefits measurement

An important activity in many R&D departments is the internal development of new process technologies and practices to assist in the marketing, design and manufacturing activities of the enterprise. An integral part of this R&D development is the planning and management of validations of potential technology projects. These validations are necessary to determine the technical, financial and organizational feasibility of the projects and to develop data for benefits measurement for further funding of selected projects. This paper describes a methodology for validation planning of new process technologies and practices. The methodology allows for the explicit linkage of a validation to the identification of its financial and strategic benefits. These often diverse measures of worth are integrated using a proven multi-attribute justification approach within the planning methodology. The methodology and the multi-attribute approach also support the comparison of dissimilar projects having different benefits. The methodology acts as an organizational planning tool integrating the needs of the diverse constituencies involved in R&D planning. It also acts as a tool to aid engineers and scientists identify and present the benefits of the proposed technology.     

Controlling new product R&D projects

New products are critical to the success of most corporations. But managing the R&D projects that produce new products has proven to be a risky and tricky business. Theorists and practitioners claim that one of the major obstacles to higher new product output is the ‘tight’ control practices found in large corporations. The conventional wisdom argues that, to correct this, firms need to find ways ‘to loosen-up.’ But is that all there is to it? This article presents five findings for effectively controlling new product R&D projects. These findings emerged at the conclusion of an empirical research investigation into the formal and informal control practices that business unit managers use to control various new product R&D situations. The article concludes with several recommendations for those managers who want to control their new product R&D projects better.     

R&D strategies and variable demand

This paper looks at some of the factors imposing on the resourcing of R&D in the face of fluctuating demand. R&D has various features that differentiate it from other aspects of corporate activity and consequently managerial strategies for dealing with R&D may vary substantially. The implications for R&D strategies and the acquisition of technology are considered under different demand conditions. Various alternative strategies are developed including contracting out, technology licensing and the maintenance of an in-house facility through labour hoarding. New and existing empirical estimates are used to support the arguments and explain corporate strategy in this area.     

OPEN ARCHITECTURE AND R&D INCENTIVES

In open systems, firms give up their property rights to technologies and permit other companies to use these technologies. We ask how an incumbent's architecture choice affects social welfare by altering R&D competition among firms. More specifically, we ask whether an incumbent, by adopting an open system, can establish its socially inefficient technology as the market standard, while an entrant is developing a more efficient technology. It is shown that an incumbent has an incentive to preempt an entrant's competing technology by choosing an open system, but that the open system might result in a premature market standard.     

Flexibility planning for managing R&D projects under risk

Incorporating managerial flexibility in an innovative R&D project is important, because managers face greater uncertainty in today's competitive and dynamic changing environment. It is essential to bring managerial flexibility into R&D project planning to decrease technical and market risks, while increasing potential market value. The objective of this paper is to develop a flexibility planning methodology based on real option analysis to improve managerial flexibility for R&D projects. The proposed methodology identifies potential risks that may occur during every R&D stage. It also recognizes a cascading option structure to resolve the identified risks, and evaluates and selects adequate options that maximize the potential value of the project. Instead of using a traditional option pricing method, a dynamic programming model that considers multidimensional product performance and market payoff is used to evaluate the R&D project value. Using the proposed methodology, managers can identify future scenarios as a function of their management actions. The proposed flexibility planning methodology can help managers improve managerial flexibility of R&D project and increase the success rate of product launch. A drug development project is used to illustrate the proposed methodology.