The management of Army R & D projects

This study of the accuracy of cost and duration estimates for British defence R & D projects is based on an analysis of the records of 29 Army projects, initiated since 1960. The results are generally similar to American defence studies with which they are compared. The reasons for schedule slippages and cost escalations are examined and it is suggested that performance could be improved by more thorough project feasibility and definition studies and by a stronger project management organization.     

The accuracy of project cost and duration estimates in industrial R&D

Abstract . This study of the accuracy of cost and duration estimates, both initial and intermediate, for industrial R&D projects, is based mainly on analysis of the records of 475 projects in four varying research organizations. Mean ratios of actual to estimated cost ranging from 0.97 to 1.51 are obtained, and mean ratios of actual to estimated duration from 1.39 to 3.04. The results are generally similar to those of other British and American studies, with which they are compared.
There is no evidence that the information gained as projects progress enables their future cost and duration to be estimated more accurately; at best, the accuracy of such estimates remains constant. No effect of project size on estimate accuracy is found; effects of project length on accuracy, and of time (i.e. increasing experience) on accuracy are found only in one organization each. The pattern of expenditure over time is examined, and found on average to be not far from linear, though with wide individual project variations.
When the individual project ratios are reduced by constant factors representing optimistic or pessimistic bias (derived from the mean ratios, and assumed to be characteristic of the firm), and then subjected to a log transformation to make their distribution more symmetrical, the remaining variation, which is a measure of the inaccuracy of estimation for the individual projects, is closely similar in the four organizations, and not greatly different in the organizations covered by other studies when the data are similarly treated.     

R and D tactics: applications of RPD decision analysis

Abstract: The application of RPD decision analysis to R & D tactics is discussed and the role of the technique as an aid to self-consistent decision making is enaphasized. RPD decision analysis is used to define limits to its own cost effectiveness.
Two example problems are analysed in detail. Simple priority ratings are defined for two special cases: the sequence of independent tests all of which must succeed, and the sequence of independent problem solving attempts, any of which may succeed. The compromise between project duration and project cost is analysed in terms of time dependence of the value of success.
The discrimination study is defined as an investigation which aims to provide information for an improved assessment of project success probability.
It is shown that such a study is never worth more than one quarter of the total expected benefits of the project.     

The Debiasing of Forecasts in Research and Development

Abstract . A possible way to improve the accuracy of forecasts is to estimate (and subsequently remove) any systematic bias by reference to the bias found in similar forecasts in the past. This paper discusses the applicability to industrial R & D of a method proposed by Summers for debiasing estimates of the costs of military projects by means of a regression equation derived from past experience, and gives the results of applying a similar method to estimates of costs, sales, product prices, and return factors for ten industrial development projects.     

How well did the new Sumitomo Electric project ranking method predict performance?

The development and implementation of a new R&D project scoring method (NS-method) within Sumitomo Electric Industries was introduced in a previous report. This paper evaluates the NS-method's effectiveness by comparing the information generated by application to 146 projects in 1997 with the project achievements in 2000. The test results revealed that the NS-method's evaluation scores were correlated to the performance indicators such as the gross profit and R&D productivity. It also showed that, when projects are classified into three groups (i.e., Top, Middle, Bottom) according to the forecast data generated by the NS-method, the projects in the Top category had higher actual productivity ratios and success rates than those in the Bottom category.     

Firm Density and Industry R & D Intensity: Theory and Evidence

By deriving a formal model of industry R & D that identifies factors influencing industry R & D intensity, this paper first suggests firm density, defined as the inverse of average firm sales or simply the number of firms divided by industry sales, as a measure of market structure that is appropriate in explaining industry R & D intensity. The model shows that the cost structure of R & D, consumer preference over quality and price, the appropriability of R & D, firm density, and the average level of firm R & D intensity jointly determine industry R & D intensity. In particular, firm density has a positive relationship with industry R & D intensity, implying that firms in higher firm-density industries feel fiercer competitive pressure and thus engage more intensively in R & D. An empirical analysis of panel data on industry R & D activities of Korean manufacturing industries during the period 1991–1996 provides supportive evidence for the predictions of the model including the positive relationship between firm density and industry R & D intensity. The theoretical model and the empirical results are also consistent with the recent survey of U.S. corporate R & D activities by the U.S. Department of Commerce and the National Science Foundation (1999).     

RESPRO-an interactive procedure for planning newproduct chemical research

The model has been developed specifically for the great uncertainties obtaining in new-product chemical researchparticularly in the pharmaceutical industry-but it is hoped that it will prove flexible enough to be used in other circumstances. The program is in Fortran and could be run on most computer systems.
Procedures for selecting projects and reviewing their progress are becoming increasingly important aspects of R & D management. Modelling a procedure mathematically can prove advantageous, especially if such a model is capable of computational analysis, for then the policy alternatives can easily be explored and compared. This paper describes one such computer based procedure. In quantifying the value of any effort allocation, the program automatically takes account of the fact that estimates of the likelihood that a project will result in a new discovery change as work continues on the project. It also computes a marginal profitability index for each project. Comparison of these indices suggests ways in which effort might profitably be re-allocated among projects.     

Information acquisition and optimal project management

This paper provides a rationale for why an organization often generates a bias in favor of a new project even after learning that its profitability will be certainly below more conventional ones. We analyze a principal–agent model with two alternative projects, one of which is to be chosen by the principal. In our model, the profitability of a project is determined by the cost of implementation. All parties are familiar with one of the projects (the known project), and thus the implementation cost of this project is common knowledge. Information on the other project (the new project), however, must be acquired by the agent. We find that the new project may be chosen in the optimal contract even when it turns out to be more costly to implement than the known project, if acquiring information is costly enough and the realized implementation cost of the new project is below a particular level. We also discuss distortion in the new project's output schedule when it is selected.     

Controlling R & D projects by networks

Abstract . This paper discusses the use of network analysis in the management of a large scale engineering project carried out within the R & D department of an engineering company. It describes the use of precedence diagrams and concludes that these are simpler to use than the more commonly encountered PERT nomenclature. This method has now been extended to cover other projects in the R & D department and it is concluded that their successful introduction has been encouraged by the interest and support of top management.     

When Research Meets Development: Antecedents and Implications of Transfer Speed

This paper focuses on the organization of new product development in large, R&D‐intensive firms. In these firms, research and development activities are often separated. Research is conducted in dedicated research projects at specialized research labs. Once research results are achieved by research projects, they are transferred to business units for further development and commercialization. We investigate the speed whereby research projects transfer their first research results to business units (hereafter: transfer speed). In particular, we analyze the antecedents and performance implications of transfer speed. Based on data of 503 research projects from a European R&D intensive manufacturing firm, our results suggest that a fast transfer speed (as measured by the time it takes for a research project to develop and transfer its first research result to business units) is associated with a better research performance (as measured by the total number of transfers the research project generates). Moreover, we find that different types of external R&D partners—science‐based and market‐based partners—play distinct roles in speeding up project first research transfers. While market‐based partnerships (i.e., customers and suppliers) generally contribute to a faster transfer of first research results, science‐based partnerships (i.e., universities and research institutions) only speed up first research transfers of technologically very complex projects. Our results also show that early patent filings by research projects accelerate first research transfers.     

R & D and the company's requirements

Numerous models have been put forward in the literature for the selection and evaluation of projects, many of which are based on optimizing the return on investment by the company in R & D projects. This is a noble aim but in practice the financial data available are often of dubious accuracy and so many other factors are involved in actually adopting a portfolio of selected projects that project selection often becomes a theoretical exercise. It is suggested that the selection of projects is in essence the adoption of a strategy which is within the policy and long-term aims of the company and also within the policy and scientific development of the R & D part of the company. Such a strategy is usually sub-optimum for both the company and the R & D division, as it is a compromise between a desire for high pay-back investments by the company and the maintenance of a scientifically balanced R & D involving some long-term, unknown return, investments. A method is currently being experimented with by Unilever Research which illustrates where conflicts arise between the policy of the main company and that of research, and assists management in arriving at a strategy which most closely fulfils the needs of both policies. Both the concept of the method and its practical use are described in this paper.     

The use and development of a research project planning and progressing system

This paper discusses the use and development of a research project planning and progressing system based on the system described by Baker & Smith (1967). It is an attempt to highlight some of the practical problems involved in setting up and developing such a system at Colworth/Welwyn, the largest Unilever research laboratory in the United Kingdom. The problem at Col worth/Welwyn, as at Port Sunlight, was to improve communications at all levels of management, and, in particular, to direct senior management's attention to those areas where it was most needed.
The system described here is only a project control system, and does not formally cover other aspects of research management such as project selection and result implementation. A project control system was chosen as the starting point because it was regarded as the most important, and the easiest to implement throughout the whole laboratory.
This paper was first presented at a conference to the R & D Study Group of the Operational Research Society, February 1970.     

Estimate accuracy and causes of delay in an engineering research laboratory

Some of the main results of a study of the accuracy of cost and duration estimates in this laboratory have already been published. This short article adds some further information about the accuracy of duration estimates for stages of projects (as opposed to whole projects), and about the reasons for upward revisions of duration estimates.     

Qualitative aspects of the evaluation and control of research and development projects

Decisions in research and development are usually based on a variety of data inputs. Some of these will be concerned with the returns offered by projects in money terms. Others will relate to more qualitative aspects of research work such as, perhaps, the establishment of a base in a new area of technology or the achievement of a stronger market position. This paper describes and illustrates how checklists may be used to assemble qualitative data for purposes of decision. These were formulated in an industrial R & D laboratory and the illustrative examples in the text make reference to real projects. The checklists described are an integral part of a comprehensive system of project evaluation and control in R & D which includes an analysis of the financial implications using net present value or discounted cash flow rate of return of the project risk, analysis and sensitivity tests. The detail of this part of the scheme is conventional and is therefore omitted from the paper.     

Institutional means-ends decoupling work in industrial R&D project implementation

Managing goals is a key network management function and is critical in the implementation of industrial R&D projects. In this paper, we explore the implementation of an industrial R&D project, focusing in particular upon the role of means-ends decoupling work to understand how the goals are managed. We combine several data sources in our case research to explore project implementation through an understanding of means-ends decoupling work. We collected in-depth interviews, archival records and field observations within the R&D research setting of an industrial R&D project in the period of 2015 to 2017. Our findings identify three types of means-ends decoupling work in R&D project implementation: ‘work on’ causal complexity, ‘work at’ behavioural invisibility, and ‘work with’ practice multiplicity. In addition, we uncover six dynamic micro-mechanisms that collectively influence the making and nature of means-ends decoupling work and therefore serve to allow for the fluid switching of work as the institutional conditions permit. Overall, our findings have significant implications for understanding means-ends decoupling as a highly skilled network competence for managing R&D project implementation goals.     

Budgets for research and development: an empirical study of 69 Swedish firms

Questionnaires were sent out to 124 large Swedish firms, asking how they determined budgets for research and development (R & D) and who decided about the size of these budgets. 94 firms answered and 69 of these undertook R & D.
It was found that, as a rule, firms decide upon individual projects and do not specify in advance a fixed level for the R & D budget. The decision about individual R & D projects and/or the R & D budget was most frequently made by the president of the company.
The implication of our results for various kinds of research on the economics of R & D are discussed.     

The use of ranking formulae in R & D projects

Abstract . Numerous ranking formulae have been proposed in the literature for use in the evaluation and selection of R& D projects, but their rate of adoption has been relatively slow. This situation appears to be changing in the U.K. and a considerable amount of effort is being expended to improve the methods of obtaining estimates for the variables which appear in the formulae. This paper argues that the most commonly quoted formulae do not adequately represent the practical situation and are likely to introduce bias into the system as well as tending to make estimating more difficult. A modified ranking index derived from a decision tree type of analysis is suggested as a more useful estimate of the worth of a project, but it is concluded that any such formulae will always have serious limitations because they must inevitably ignore important characteristics of the research process.     

Bias and Error in Mine Project Capital Cost Estimation

Previous analyses of small samples of mining projects have found that feasibility studies tend to underestimate the as-built capital costs of the project. Our review of 63 international mining and smelting projects confirms that as-built capital costs are, on average, 14% higher than as estimated in the bankable feasibility study. There is little attenuation over time of this bias in capital cost estimation, appearing to reflect an absence of learning on the part of the project sponsor or the consulting engineering firm. We argue that this persistence of bias is instead intentional and rational, driven by a scarcity of project financing and the need by project sponsors to inflate the project economics in a bid to secure financing. We find some empirical support for our contention. A second phase of the analysis examines estimation error. Roughly half of all projects' as-built capital costs fall outside of the expected ± 15% of the feasibility study capital cost estimate, even after allowing for intentional estimation bias. Cost overruns of 100% or more happen in roughly 1 out of 13 projects. Smaller projects have less estimation accuracy than large projects. Finally, our analysis of the cost overrun data reveals that a shifted lognormal probability distribution should be used when modeling mining project capital costs in a Monte Carlo analysis.     

The role of the forecasting process in improving forecast accuracy and operational performance

Several operations decisions are based on proper forecast of future demand. For this reason, manufacturing companies consider forecasting a crucial process for effectively guiding several activities and research has devoted particular attention to this issue. This paper investigates the impact of how forecasting is conducted on forecast accuracy and operational performances (i.e. cost and delivery performances). Attention is here paid on three factors that characterize the forecasting process: whether structured techniques are adopted, whether information from different sources is collected to elaborate forecasts, and the extent to which forecasting is used to support decision-making processes. Analyses are conducted by means of data provided by the fourth edition of the Global Manufacturing Research Group survey. Data was collected from 343 companies belonging to several manufacturing industries from six different countries. Results show that companies adopting a structured forecasting process can improve their operational performances not simply because forecast accuracy increases. This paper highlights the importance of a proper forecasting-process design, that should be coherent with how users intend to exploit forecast results and with the aim that should be achieved, that is not necessarily improving forecast accuracy.     

Principal researcher and project manager: who should drive R&D projects?

In this paper, it is analyzed the hypothesis that in R&D the principal researcher (PR) is accepted as the coordinator or project manager (PM), carrying out the search for financing and to manage contracts, resources, cost, time, scope, risk and uncertainty, communication, stakeholders and so on, in addition to internal research activities. Thus, this study tries to verify this hypothesis through a major literature review in different types of projects developed by university, but also with a look to industry and industry-university cooperation. Two case studies are also analyzed, centered in its R&D project management maturity level. It is concluded that there is an important issue in projects’ success and in the time spent by PR in management, work for which they are under trained; while at the industry there is a greater approach to project management by the proximity of the innovation projects to other industrial projects. Following these initial findings and according to the case study results, it is proposed that R&D Projects in universities would be separated into two synergistic knowledge areas: R&D Management and Project Management. It is also recommended to allocate them to two distinct roles, where they could add value to R&D through their better knowledge and skills.