Time and Cost Schedule Dynamic–Hidden Trojan Horses

Abstract

Investment projects are not the only ones where significant complications in their implementation may occur. The fundamental question, how to specify threats hidden in time series, is one of the most important types of knowledge arising from the basic schedules’ documentation. Feasibility studies, project proposals, organizational and production procedures, research projects, and others are major resources of information. The reason why to specify threats hidden in time series is the high cost of not revealing hidden threats. An illustrative clarification of the cost is given on the current data of nuclear power plants. Wherever one works with schedules and resources, the above-mentioned issue may appear. Undeniably, valid data is discoverable ex post in accounting, documentation, or even in the documentation of the preparation and implementation, and in the analyzes of the mechanisms for non-compliance with deadlines and cost increases. For implementation (i.e., ex ante use), the majority of projects are created by expert intuitive decision-making. In terms of content, these are sources of errors from the past, lacking analytical quantitative support (suffering from the so-called evidence shortage). Production schedule time series comprise: (a) cumulative volume, (b) speeds, and (c) accelerations. More recent, in addition to statistical analysis, is the focus on the long-term memory of time series and to the application of the Hurst exponent as indicators of predictability (ex-ante). This article offers a procedure for how to reveal hidden chaotic states in the time series of a project’s output information. If it is possible to find chaotic behavior in the output information, these states must be searched for and removed in the original source model—the implementation project. Exceeding contractual terms and implementation costs leads to a threat to the economic basis—the collapse of the initial idea of the project’s economy. As an example, nuclear power plant projects are shown. The article broadens the perspective of ex ante decision-making.