Making Essential Choices with Scant Information

Business is becoming increasingly projectised or project oriented, and global spend on projects is now many billions of dollars annually. Developing new technological products, building new capital assets, or carrying out unique large-scale enterprises all require major projects to be undertaken. The place of projects has become increasingly important in the life of corporations and, indeed, of nations.

A project is a restricted undertaking assigned to produce a specified output within an agreed time limit and budget. It is undertaken in order to solve a problem or respond to certain needs in society. Projects are considered unique in the sense that even when the objectives and outputs are the same they may differ in varying degrees, since the context in which they operate and the uncertainty to which they are exposed differ from project to project. Projects are temporary in the sense that they have a definite beginning and end. They may involve a single person or many thousands of people. They may require less than a hundred hours to complete or several million hours. Projects may involve a single unit of one organisation or may cross organisational boundaries.

This chapter, if it succeeds, will open wider two doors in management theory and practice: one, to show concretely how corporate and business strategy implementation can be effected via project management, something rarely addressed in the academic literature; two, to show how project management, as a discipline, can contribute to strategic management. In doing, so it should clarify the confusion which has crept into perceptions of what programme and project management are. It will illustrate the importance of work done in the project “front-end”. It will stress why value and benefits, and other measures of effectiveness may be more significant than the traditional project management efficiency measures of budget, schedule and scope attainment. And finally, it will show the importance of leadership in achieving this.

This chapter discusses the contribution of scenario planning to project conceptualisation and definition. In essence, the methodology helps to improve judgements made under conditions of scant information. The focus is on the area of appreciative judgement (Vickers, 1995), based on making sense of diverse and often contradictory signals against a background of significant uncertainty. A general discussion of the topic is followed by case studies illustrating many of the points made.

This chapter discusses needs analyses in connection with front-end planning of large-scale public works projects. Relevant methods for needs analyses include normative, market-oriented and interest group based approaches. The analyses may be carried out within more or less objectivistic versus interpretative perspectives, reflecting different interpretations of the notion of need. Based on a review of different methods of analysis and experience of deficient and misleading needs analyses in large public investment projects, the chapter provides advice regarding the extent and demarcation of needs analyses, what documentation is needed, appropriate scheduling and organizational responsibility for the analyses.

Before starting significant work on a project or programme, its goals and objectives need to be made explicit and assessed to ensure that, if accomplished; they will further the sponsoring organization’s chosen corporate strategy and contribute to its overall goals. Unfortunately, this is not always done and can result in significant underperformance compared to expectations. Challenges are created by the different nature of the tasks facing both the organization and the project, by the variety and differing worldviews of those involved and by the multiplicity of roles necessary to achieve front- end alignment. These challenges can be overcome through judicious use of a well-founded business case and by the skilful execution of a well-formulated engagement programme.

According to a research report published in the 1990s, one of the areas where new and original research could provide the most practical benefit is the front-end of projects: “better understanding is needed of the ‘soft’ methodologies and their relevance and credibility”. This chapter focuses on one of these “soft” methodologies, namely, soft systems methodology (SSM) and how it can be applied at the front-end. The chapter will demonstrate the relevance of SSM, together with its strong credibility, having been developed in real situations for more than thirty years. A real example of SSM in action is presented and, linked to this, a brief introduction to the approach. It is not intended to be a definitive guide to using SSM at the front-end: it merely presents one experience of using the approach in order to highlight its relevance and credibility and to illustrate the kind of role that SSM can play at this crucial stage.

Major projects generally have the following characteristics:

People’s judgements are not perfect. Even experts make mistakes. Interestingly, mistakes in judgement and decision-making are not random. Professionals deviate systematically from normative standards. Per definition, systematic biases result in reduced decision quality, and accordingly, may have serious consequences. However, the fact that judgement biases are systematic also gives hope. If the biases and their causes can be identified, it should be possible to correct them.

Imagine two managers who want to develop a real estate project. At some point during the planning phase it becomes crucial to predict the future sales prices of houses in a certain area. The first project leader, Mr O, approaches this task by searching exhaustively for all available pieces of information that he knows will influence the selling price, such as property tax, lot size, total living space, age of the house, number of bathrooms and so on. Based on his past experience, he weighs all that information according to its importance and then integrates it to predict the selling price of each house, using some statistical software. The second manager, Mr F, makes a fast decision, relying on a simple strategy based on just one single piece of information that he regards as most important, such as total living space. Which of these two managers will make a more accurate forecast? Many people, researchers and lay persons alike, suppose that the outcome of a decision can be improved by (a) an exhaustive search for information, and the integration of many pieces of information, (b) having more time to think, and calculate possible outcomes, or (c) having more computational power and the use of complex forecasting software or decision tools. This seems to indicate that Mr O will make the better decision. However, this chapter demonstrates that less can sometimes be more, and that a strategy relying on very few pieces of information, and quickly deriving a decision based on a simple algorithm, may well outperform more sophisticated, supposedly rational decision strategies.

This chapter reviews extant research on the quality of expert judgement of probability and risk. Both types of judgement are of great relevance to the concerns of this book, because expert judgement under uncertainty is a key component in the making of decisions with scant information.

Major investment projects are essential for companies and governments to develop new products and services to maintain the growth of the company or the viability of the government agency. Early in an investment’s life cycle, the identification and evaluation of risk is a critical project management task that will have a direct impact on the future success of the project. In fact, the key to successful project management of high technology projects is the identification, evaluation and management of risks. Unfortunately, decisions have to be made with little information in the front-end decision-making phase of major investment projects. This chapter focuses on the process of developing and assuring the quality of the risk evaluation in the early-stage concept development for major projects. Several qualitative and quantitative risk analysis techniques are surveyed. A complex investment project, a large data centre design and location decision is used to illustrate some of the risk techniques.

Decision theory provides a model for rational decision-making under uncertainty. The model involves

This chapter compares and contrasts three methods for handling quantitative decision analysis when information is limited: Bayesian Robustness, Bayes Linear and Minimum Information. The way they utilize partial model specification from experts and their potential use as an exploratory tool is considered. The possibility of carrying out sensitivity analysis with these methods is explored and the recommendation made that this type of analysis is useful in extending the small world scope of such decision analyses to include various potential control mechanisms. The discussion is illustrated by simple examples.

This book discusses the quandary every project faces: having to make a multitude of decisions, from when the earliest ideas appear, very often without much information on which to base these decisions. Several strategies can be pursued to increase the likelihood of making the right decisions, including obtaining more and/or better information, postponing decisions until information is available, involving more and better qualified people in the decision-making, etc. Another approach is to make the most out of the information available, and for this purpose there are many different tools and techniques at the project’s disposal that have proven worth in clarifying issues or aiding the decision process. The purpose of this chapter is to present and discuss a selection of such analysis tools and techniques, always in the light of application in a project setting.

Provision of quantitative forecasts of cost is unavoidable, even at the very earliest stages of a project. Commonly such forecasts are excessively optimistic. However, this is not inevitable. Accurate forecasts of this kind are possible, provided suitable techniques are employed. Of particular importance is the fact that the forecaster concentrates upon total costs and avoids being drawn into excessive detail. To descend prematurely into detail is to base forecasts upon what is not yet known and can only be conjectured.

What is information? One common definition is that it is the result of gathering, processing and organizing data in a way that adds to the knowledge of the receiver.1 What is interesting here is that it implies that obtaining information requires some effort, i.e. that costs are involved. Obviously, some information is worth the cost and some is not, but how can one decide? Also, how should the knowledge provided by new information be used in the assessment of various project concepts?

All large projects can be considered complex. They have the characteristics identified by Williams (1999) as sources of complexity, i.e. structural complexity, in that they are composed of many interrelated components, subsystems and technologies and also uncertainty. The sources of uncertainty include the technology, the political and social environment and the market, with many interactive effects among them. The types of projects discussed here have additional characteristics that further increase their complexity and the difficulty of managing them.

Profitability depends on how efficiently scarce resources are utilised. The relevant easure of efficiency depends on the economic objective. In a private company, the objective is to maximise its contribution to the income and wealth of the owners in the short or long term. The eco- nomic profit in each period is given by the maximum distribution to the owners, without reducing the firm’s market value. The value concept is forward-looking, in the sense that the market value of the company is the present value of the future cash flow that it is expected to generate for the owners. A positive present value means that the enterprise is yield- ing an average rate of the return on invested capital that is at least as large as the owners could have achieved by reinvesting the financial cap- ital in the external financial market, assuming this to be the alternative investment opportunity. Hence, profitability is defined in relation to the external rate of return opportunity for the capital required to operate the company.