Techniques for Capital Expenditure Analysis

Time and cost outcomes of large and complex projects are forecast poorly across all sectors. Over recent years, Monte Carlo (MC) simulation has increasingly been adopted to forecast project time and cost outcomes more realistically. It is recognised that the simultaneous analysis of time and cost impacts makes sense as a modelling objective, due to the well-known relationship of time and money in projects. But most MC practitioners advocate the use of Schedule Risk Analysis (SRA) feeding into Cost Risk Analysis (CRA) because they believe it is too hard to perform Integrated Cost & Schedule Risk Analysis (IRA) realistically. This chapter elaborates an IRA methodology that produces realistic forecasts without relying on questionable assumptions and enables identification and ranking of all sources of cost uncertainty for risk optimisation as part of the process. It also describes an extension of IRA methodology to include assessment of the assets produced by the project as well as the project itself, thus enabling the analysis of business risks as well as project risks.

Preface. 1 Introduction. 1.1 The Importance of Risk and Uncertainty Assessments. 1.2 The Need to Develop a Proper Risk Analysis Framework. Bibliographic Notes. 2 Common Thinking about Risk and Risk Analysis. 2.1 Accident Risk. 2.1.1 Accident Statistics. 2.1.2 Risk Analysis. 2.1.3 Reliability Analysis. 2.2 Economic Risk. 2.2.1 General Definitions of Economic Risk in Business and Project Management. 2.2.2 A Cost Risk Analysis. 2.2.3 Finance and Portfolio Theory. 2.2.4 Treatment of Risk in Project Discounted Cash Flow Analysis. 2.3 Discussion and Conclusions. 2.3.1 The Classical Approach. 2.3.2 The Bayesian Paradigm. 2.3.3 Economic Risk and Rational Decision-Making. 2.3.4 Other Perspectives and Applications. 2.3.5 Conclusions. Bibliographic Notes. 3 How to Think about Risk and Risk Analysis. 3.1 Basic Ideas and Principles. 3.1.1 Background Information. 3.1.2 Models and Simplifications in Probability Considerations. 3.1.3 Observable Quantities. 3.2 Economic Risk. 3.2.1 A Simple Cost Risk Example. 3.2.2 Production Risk. 3.2.3 Business and Project Management. 3.2.4 Investing Money in a Stock Market. 3.2.5 Discounted Cash Flow Analysis. 3.3 Accident Risk. Bibliographic Notes. 4 How to Assess Uncertainties and Specify Probabilities. 4.1 What Is a Good Probability Assignment? 4.1.1 Criteria for Evaluating Probabilities. 4.1.2 Heuristics and Biases. 4.1.3 Evaluation of the Assessors. 4.1.4 Standardization and Consensus. 4.2 Modelling. 4.2.1 Examples of Models. 4.2.2 Discussion. 4.3 Assessing Uncertainty of Y. 4.3.1 Assignments Based on Classical Statistical Methods. 4.3.2 Analyst Judgements Using All Sources of Information. 4.3.3 Formal Expert Elicitation. 4.3.4 Bayesian Analysis. 4.4 Uncertainty Assessments of a Vector X. 4.4.1 Cost Risk. 4.4.2 Production Risk. 4.4.3 Reliability Analysis. 4.5 Discussion and Conclusions. Bibliographic Notes. 5 How to Use Risk Analysis to Support Decision-Making. 5.1 What Is a Good Decision? 5.1.1 Features of a Decision-Making Model. 5.1.2 Decision-Support Tools. 5.1.3 Discussion. 5.2 Some Examples. 5.2.1 Accident Risk. 5.2.2 Scrap in Place or Complete Removal of Plant. 5.2.3 Production System. 5.2.4 Reliability Target. 5.2.5 Health Risk. 5.2.6 Warranties. 5.2.7 Offshore Development Project. 5.2.8 Risk Assessment: National Sector. 5.2.9 Multi-Attribute Utility Example. 5.3 Risk Problem Classification Schemes. 5.3.1 A Scheme Based on Potential Consequences and Uncertainties. 5.3.2 A Scheme Based on Closeness to Hazard and Level of Authority. Bibliographic Notes. 6 Summary and Conclusions. Appendix A: Basic Theory of Probability and Statistics. A.1 Probability Theory. A.1.1 Types of Probabilities. A.1.2 Probability Rules. A.1.3 Random Quantities (Random Variables). A.1.4 Some Common Discrete Probability Distributions (Models). A.1.5 Some Common Continuous Distributions (Models). A.1.6 Some Remarks on Probability Models and Their Parameters. A.1.7 Random Processes. A.2 Classical Statistical Inference. A.2.1 Non-Parametric Estimation. A.2.2 Estimation of Distribution Parameters. A.2.3 Testing Hypotheses. A.2.4 Regression. A.3 Bayesian Inference. A.3.1 Statistical (Bayesian) Decision Analysis. Bibliographic Notes. Appendix B: Terminology. Bibliography. Index.

Schedule risk and cost risk analyses use statistical techniques to predict a level of confidence in meeting a program’s completion date and cost. This paper describes how to interpret key Schedule Risk Analysis (SRA) and Cost Risk Analysis (CRA) results to determine the likelihood that the program completion dates are met, how much management reserve (MR) is needed to provide the acceptable certainty of completion by a specific date, and the risks and activities that are most likely to delay or add cost to a project. The combined process is often called a Schedule/Cost Uncertainty and Risk Analysis or “SCURA.”

Tax returns, including corporate returns, are generally confidential and not disclosed to the public. However, in certain circumstances, Internal Revenue Code (IRC) Section 6103(e)(1)(D)(iii) provides that corporate shareholders who meet a 1 percent ownership criterion can request from the Internal Revenue Service (IRS) a copy of the corporate tax return. In this paper, we discuss the legislative history of IRC Section 6103 as it relates to tax return disclosure in general (for individual and corporate returns) and its precursors that provide for disclosure of corporate tax returns to shareholders who own more than 1 percent of the capital stock. We then provide examples of the valuable proprietary information that is included in corporate tax returns. Next, we provide a history and discussion of the insider trading laws and argue that the information content of a corporate tax return is such that it provides material nonpublic information that is not readily available in annual reports or other public documents filed with the Securities and Exchange Commission (SEC). While 1 percent shareholders do not meet the classical definition of an “insider,” we argue that they are similar to other “outsiders” who have been found liable for violating insider trading rules. We conclude by arguing that the actions of a 1 percent shareholder who requests and receives the corporate return and who subsequently makes purchases and/or sales of corporate stock should constitute illegal insider trading.

As an application of probabilistic fracture mechanics (PFM), a risk–benefit analysis was performed for the purpose of optimizing maintenance activities of steam generator (SG) tubes used in pressurized water reactors (PWRs). The probabilities of the SG tube leakage and rupture are defined as risks in this study. A model was made modifying pc-PRAISE (Piping Reliability Analysis Including Seismic Events) to evaluate the risks during 60 year operations due to stress corrosion cracking (SCC) of the tubes under various maintenance strategies for SG tubes. In the risk analysis, parameters such as inspection accuracy, inspection interval, sampling inspection and crack propagation law were selected for sensitivity analysis. Based on the risk analysis, a risk–benefit analysis was conducted when implementing two maintenance strategies taking both costs and revenues for 60 year operations into account. In the risk–benefit analysis, the expected cost of leakage or rupture was calculated by multiplying ‘probability of leakage or rupture’ by ‘expected loss of leakage or rupture accident’. To justify whether it is worthwhile implementing the maintenance strategies or not, the net present value (NPV) was calculated as an index, which is one of the most fundamental financial indices for decision-making based on the discounted cash flow (DCF) method. The results demonstrated that in the risk analysis, the risks are influenced significantly by the crack propagation law, accuracy of inspection and sampling inspection. In the risk–benefit analysis, it was suggested that investment to improve inspection accuracy would reduce the total costs of 60 year operations significantly and increase the NPV. Although the analysis was mainly conducted for SG tubes made of Inconel 600 mill anneal (MA) material, the analysis was also carried out for Inconel 690 thermal treatment (TT) material, making assumptions on its crack initiation and crack propagation law. In addition, the effect of introducing maintenance criteria, namely, operation with a crack justified by certain criteria, on NPV was evaluated.

This study explores the implementation of an integrated capital budgeting visual mapping framework comprised of both Discounted Cash Flow (DCF) and Real Options Analysis (ROA) techniques. Physical asset investment decisions are based largely on rigid discounted cash flow tools which provide untimely and incomplete decisional criteria. While literature outlines the widespread use of traditional DCF techniques, it nevertheless reveals extensive limitations, including its static inflexibility and slow-to-evolve framework. ROA is a more recent valuation tool based on stock option theory. It brings into account added value found in the flexibility of managerial decision-making and uncertain conditions. This study implements a combined DCF and ROA capital budgeting tool within a Physical Asset Management (PAM) environment. The validity of the framework is realised through an industry-relevant case study presented by a South African mining company.

Accounting innovation beyond the enterprise: Problematizing investment decisions and programming economic growth in the U.K. in the 1960s

Nowadays, the urban population is steadily increasing worldwide and, as a result, global construction output is expected to grow to more than 16 trillion EUR by 2030. This rapid urbanization has created a strong need for the successful selection and delivery of urban development projects to meet the challenges related to the provision of sustainable and resilient infrastructure, together with affordable residence solutions. Meanwhile, the dominance of the traditional capital budgeting discounted cash flow (DCF) technique has long been questioned for its inability to be effectively applied to the complex, uncertain, and turbulent current environment. The main cause of this stems from its deficiencies in recognizing and incorporating the value of managerial interventions through strategic decisions to delay, expand, or even abandon an investment. A real options analysis (ROA) is proposed in this paper as a dynamic “wait and see” alternative to the static “now or never” DCF methodology, which is based entirely on a positive net present value (NPV) output. Thus, the aim of the research is to explore whether the practical application of ROA for the assessment of the financial viability of urban development capital investment projects can be improved from the obtained managerial flexibility in the decision-making process. Spreadsheet-based mathematical models are developed for the analysis and implementation of both the Black–Scholes formula and the binomial lattice method. The results are discussed and compared with a classic DCF analysis. The main advantages of using ROA, i.e., determining alternative paths of urban development and providing a practical and flexible means to adapt to changing external conditions, are highlighted through the application of a common type of real option to an actual new multistorey office building project. Based on the DCF model and its negative NPV, the investment under study is not viable. However, when simply considering the delay strategic option, the project turns out to be highly valuable. For comparison reasons, future work is recommended on alternative types of real options, like the compound staging option, and towards the use of alternative ROA tools, like the Monte Carlo Simulation technique, non-recombining binomial lattices, and the dividend-based version of the Black–Scholes model.

Summary Discounted cash flow methods for evaluating investment opportunities enjoy wide use in the petroleum industry. The two most popular measures are net present value and internal rate of return. While these two measures are generally suitable as accept/reject criteria, they are not so useful in capital rationing situations, where a ranking of projects is required. Decades ago authorities suggested that the net present value divided by the initial outlay is a better measure for ranking projects. This kind of measure becomes more difficult to apply correctly when ail the capital costs in a project are not incurred up front, but are spread over a number of years. It is common practice to discount these future capital outlays back to the present and include them in investment (the divisor in the index). Simple examples demonstrate, however, that some obviously erroneous rankings can result from this procedure. By using an alternative definition of project investment, based only on the project net cash flows and the discount rate, we can avoid these pitfalls and obtain a better profitability measure for ranking projects.

Cost-benefit analysis on an animal health program involves considerable data collection and analysis. The analysis step involves expressing data in the form of cash flows, and then performing discounted cash flow (DCF) analysis. The output of DCF analysis consists of one of more of the various economic performance criteria - such as net present value, internal rate of return and benefit-to-cost ratios - which assist decision makers in judging whether to proceed with a project. This discussion paper examines the ‘hands-on’ side of cost-benefit analysis, viz. using a computer to carry out the analysis. Here, cash flow data are entered into electronic spreadsheet, and the various spreadsheet financial functions and presentation features are utilized to summarise estimated project performance.

The objective of this work is to describe a method of assessing the flexibility of a manufacturing system, in an uncertain market environment, under lifecycle considerations. The flexibility of a manufacturing system is determined by statistical analysis of the discounted cash flow (DCF) estimates of the manufacturing system's lifecycle cost, over a time horizon and for a large variety of market scenarios. The system's lifecycle cost is calculated at a minimum level with the help of a specially developed algorithm. The method is used in a test case for investing in a production system, using real-life data derived from the automotive industry.

As part of Weapon Design Cost Report (WDCR), LLNL will be performing site Schedule Risk and Cost Risk Analysis. This analysis uses statistical techniques to predict a level of confidence in meeting the program’s completion date. This document includes the process and steps to conduct the Schedule Risk and Cost Risk Analysis to determine the likelihood that the program completion date are met, how much management reserve is needed to provide the acceptable certainty of completion by a specific date, risks most likely to delay the project, and the paths or activities that are most likely to delay the program. The analysis focuses on uncertainty and key risks and how they affect the schedule’s activity durations and established a management reserve.

Abstract:This research delves into the intricate domain of Cost of Illness (COI) studies, also known as Burden of Disease (BOD), with a specific focus on depressive disorders. Employing a bottom-up approach, the study meticulously scrutinizes the economic implications of depression at various levels, emphasizing direct and indirect costs. The analysis integrates a detailed exploration of diverse discounting methods, including Net Present Value (NPV) and Discounted Cash Flow (DCF), with a particular emphasis on the Indian context to comprehend unique socio-economic factors.Methodologically, the study systematically reviews scientific literature over the past decade, utilizing databases such as Web of Science and Google Scholar. The inclusion criteria prioritize patient-based studies related to the cost of illness associated with depression. The research adopts a comprehensive approach, considering various methodologies and discounting techniques to enhance the understanding of economic implications.The comparative analysis of the total cost of illness across diverse countries unravels significant variations, highlighting the intricate nature of healthcare expenditures and economic structures. Specific breakdowns for each country consider factors suchas currency exchange rates, inflation, and Purchasing Power Parity (PPP) adjustments, shedding light on the complexities involved.The study across five countries highlights significant variations in depression-related costs. Direct medical costs have a mean of $42,668 (SD=16,940) with a high t-statistic of 27.83 (p-value 0.001), emphasizing substantial differences. Direct non-medicalcosts average $27,171 (SD=13,220) with a t-statistic of 14.07 (p-value 0.001), indicating a notable economic burden beyond medical expenses. Indirect costs related to depression stand at a mean of $63,816 (SD=25,230), with a t-statistic of 35.30 (p-value 0.001), highlighting significant impacts on productivity and societal costs. The total cost of illness, encompassing both direct and indirect costs, averages $113,590 (SD=43,990), with a t-statistic of 49.49 (p-value 0.001), reflecting the extensive economic implications of depression across these diverse nations.In conclusion, this comprehensive analysis contributes depth to our understanding of the economic impact of depressive disorders, providing valuable insights for policymakers, healthcare providers, and researchers. The research underscores the multifacetednature of the economic burden of depression, emphasizing variations across countries and advocating for comprehensive, context-specific approaches in assessing and addressing global implications. Acknowledging challenges with discounting methods, the paper identifies opportunities for refining methodologies and improving economic evaluations in the future, emphasizing the importance of nuanced insights for effective policymaking and healthcare strategies on a global scale.

Over the past two decades, risk management and risk analysis have emerged throughout the business community in the USA as prominent planning and development strategies used to mitigate risk of failure and to ensure a high return on investment for business endeavours (financial and otherwise). They are generic tools that can be applied to any business regardless of the sector (i.e. government, university, and private) and have been used by the Federal Government in the form of institutional practices aimed at maximizing the probability of success in business activities. One US Federal agency that incorporates risk management and analysis techniques into business and/or engineering activities is the National Aeronautics and Space Administration. The present work is a discussion on mission, spacecraft and instrument design (as well as technology development), and the role of risk management, analysis, and mitigation as fundamental tools in the design process.

This study presents a comprehensive framework for Project Risk Management (PRM), tailored specifically for Engineering, Procurement, and Construction (EPC) projects. Addressing gaps in traditional risk assessment methodologies, the proposed approach integrates advanced techniques for risk identification, analysis, and evaluation based on risk characteristics. A three-stage framework is proposed utilizing the Delphi method for risk identification and contextualization of risks, the risk analysis stage employs the Fuzzy Level-Based Weight Assessment (F-LBWA) method to achieve fuzzy weights for risk characteristics which the risks will be evaluated by. The final evaluation stage uses the Fuzzy Combined Compromise Solution (F-CoCoSo) method to rank risks, categorizing them as threats, opportunities, or hybrids. A case study of an EPC project demonstrates the framework’s practical application, highlighting construction-phase risks as the most critical threats (negative risks) while also emphasizing opportunities (positive risks) which can be exploited. By incorporating fuzzy logic and innovative Multi-Criteria Decision-Making (MCDM) methods, the framework provides a flexible and robust tool for modern PRM.