Complexity and Control: Forecasting, Planning, and Budgeting in Complex Firms

Complexity and Control: Forecasting, Planning, and Budgeting in Complex Firms

DOI: 10.4018/978-1-5225-3987-2.ch005


The aim of this chapter is to illustrate forecasting, planning, and budgeting as managerial activities involving decisions on a deliberate set of future actions aimed at pursuing strategic objectives. The chapter starts by emphasizing the importance of complexity in managerial decision-making and its implications on predicting future. The discussion then moves to forecasting, highlighting forecasting process, main methods, goals, and the selection of the techniques. Next, the chapter focuses on planning, depicting the traditional approach to strategic planning cycle, its role in firms, main limitations, and alternative frameworks developed to support strategic decisions under uncertainty. Finally, budgeting is considered, describing the steps involved in the preparation of the master budget, main criticisms, and discussing the use of budgets in uncertain contexts.
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Forecasting, planning and budgeting are consolidated managerial practices concerned with an intentional and deliberate set of future actions aimed at formulating the decisions about the firm’s strategic directions. When decisions are complex in nature, the decision-making process concerns the choice between two or more alternative courses of action without having complete and precise information about the variables used in the analysis of the problem and their cause and effect relationships.

The key assumption in strategy and management literature is that accurate forecasts of future actions will lead to competitive success and superior firm’s performance. Therefore, the rigorous analysis of internal structure and external competitive environment and the precise construction of scenarios of the future are essential for enabling conscientious projections into the desired future. Given that one of the great challenges for firms in the current socio-economic context is making decisions with confidence under the complexities posed by hostile environments, increased competition, globalization, high rate of technological innovation and regulatory changes, scholars have elaborated a variety of methods and approaches that enable a rational decision-making process. In a similar vein, normative decision theory emphasizes the role of models, algorithms and analytical tools in simulating decision outcomes and leading decision makers to choices that fit with the principles of rationality. It is important to specify that the adoption of methods and tools for complexity reduction doesn’t assure that the final decision will be optimal but only that it will be the outcome of a rational process. An effective approach to rational decision making is developed around three sequential phases: a) formulation of the objectives of the decision; b) identification of the most suitable alternatives; c) evaluation of alternatives with respect to objectives by forecasting the outcomes of each alternative. The ultimate goal of this process is the generation of new knowledge, because the decision-making is not equal to processing information and getting the right answer, but leads the decision-maker to a more complete awareness of the multi-faceted nature of the decision problem by enabling him to observe the problem from a variety of different angles. Thus, methods and approaches assume a central role in the structuration of the decision process by shaping available information and modelling it in a way that should increase the level of understanding of the problem and the sense-making of the relevant uncertainties that impact on decision outcomes. In sum, the emphasis on these methods doesn’t imply that framing complexity to support decision-making is more important than the decision-maker. Values, knowledge, past experiences, interests, risk aversion are the key components of the mental models that decision-makers activate for giving sense to the problem from its subjective view. Consequently, different perceptions may lead to similar conclusions, and similar perceptions may lead to different conclusions, depending upon the ability to manage and overcome the mental impediments to construct opportunities that are more cognitively distant from the predominant ways of thinking in the industry. In complex settings, effective perceptual filtering helps decision-makers to distinguish relevant from irrelevant information and to minimize the errors that occur when focusing attention on unimportant stimuli, avoiding the risk of leading organizational efforts towards the pursuit of objectives that would look much less attainable if analyzed with objective accuracy. Therefore, in the field of decision analysis, the normative approach, which is consistent with the axioms of rationality, tends to coexist with the descriptive approach, which draws theoretical insights from social, behavioral and cognitive research areas. The focus of the descriptive approach is to explain how executives make judgments in the context in which decisions occur and recognize that cognition is not a mere function of mental ability, because sense-making is enabled by the context in which it takes place. Analyzing human cognition in the context or action in which it is exercised means focusing attention on the interactive web of actors and artefacts among which cognition is distributed. Thus, a cognitive task like making decisions under complexity is best understood as a materially and socially distributed activity that is performed through the active engagement of material and cultural artefacts and the interaction with other actors/decision makers.

Building on both normative and descriptive approaches on decision-making, the objectives of this chapter are:

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