Project Investment Decision Research Articles

What does return on investment (ROI) mean to the pharmaceutical/biotechnology industry?

Return on investment (ROI) calculations are common tools used by pharmaceutical and biotechnology companies in evaluating product returns, guiding decisions on further product development or terminating programs prior to company losses. ROI calculations compare the estimated profits from a product to the investments necessary to get the product developed, approved and marketed. Risk factors are incorporated into the ROI, which consider multiple factors including competitor products and market demand. ROI calculations enables a company to set and apply metrics for the evaluation of projects that can be applied consistently, across a portfolio, and goes beyond simple profitability assessments. When coupled with additional financial tools and strategic evaluations such as strengths, weaknesses, opportunities and threats (SWOT) analysis, ROI can provide invaluable and comparable insight into the value of potential projects and products. Here, we present a framework for executing ROI calculations to enable investment or termination decisions for research and development projects. This is augmented with insight on how to apply the framework to academic, government and NGO evaluations where ‘profits’ are not always measured in purely economic terms. Implementing ROI in the evaluation process facilitates go/no go decisions and the comparison of projects using a simple, consistent financial approach.

Towards improved port capacity investment decisions under uncertainty: a real options approach

ABSTRACTPort activity plays an important role in facilitating international trade. Sufficient capacity is indispensable for a port to attract flows to a region and retain them. The capacity decision is the result of a trade-off between investment and waiting costs. Traditional methods to value expansion projects do not deal adequately with managerial flexibility in the face of uncertainty from different sources in the complex port environment. In this paper, real options (RO) models are identified as an alternative method to making project valuations and investment decisions, as they attribute the correct value to managerial flexibility under uncertainty. In order to be able to build and use such RO models for port capacity investment decisions, the sources and implications of uncertainty in the port and the different RO model specifications need to be understood. To this end, both the literature about uncertainty in the port context and the literature about real options models are reviewed in order to provide researchers who want to build their own decision-making models, with the necessary knowledge of both fields. The review makes clear that the complex interactions in and competition between the logistics chains and their actors coming together in ports have significant impacts on port capacity. Uncertainty is also caused by uncertain international trade flows and changes in legislation following new technologies and environmental impacts. An analysis of the components of some general RO models shows how the options of flexible output, investment size and timing are valued by RO models in a setting with demand uncertainty. Moreover, the review presents researchers with insights in how to deal with cooperative and competitive interactions in the chain, time to build, cyclical markets and legislation changes. It also shows how to value the expansion and the phased investment options. The new insights resulting from this review are subsequently combined in a framework that serves as a guideline to build RO models for port capacity investments. Finally, an exemplifying application of the framework is used to build an actual port capacity investment decision model.

A TODIM-Based Investment Decision Framework for Commercial Distributed PV Projects under the Energy Performance Contracting (EPC) Business Model: A Case in East-Central China

Distributed photovoltaic (DPV) projects have been rapidly proposed in China due to policy promotion, and investment decisions immensely decide the success of DPV projects. This paper aims to propose an investment decision framework for DPV projects under the energy performance contracting (EPC) business model which is currently vigorously promoted in China, thereby improving the efficiency and accuracy of decision making. Firstly, the distinctive criteria system for DPV project investment decision is established, including natural, market, technical, policy, competitive and economic factors. Secondly, the weights of criteria are determined by integrating subjective and objective weights to obtain more accurate weights. Then, the TODIM (an acronym in Portuguese of interactive and multicriteria decision making) approach is utilized to rank the alternative DPV projects, taking into account investors’ psychological behavior. Finally, a case study in central and eastern China is carried out to illustrate the rationality and feasibility of the proposed framework. The results show that the Project A4 located in Nanchang City is the optimal project, and the rank of alternatives is sensitive to the recession coefficient. This paper provides insightful information for the DPV investors with different risk preferences to evaluate the investment performance of EPC projects and select the most appropriate one under uncertain environment.

Project benefit management: Setting effective target benefits

Target benefits such as “reduced operational costs” are project goals that can contribute to the long-term improvement of organizational performance following project completion. Setting effective target benefits is critical because it supports project investment decisions, clear project management direction, and thereby enhanced project and organizational performance. Based on goal setting theory, we present three studies to develop and validate a scale to measure effective target benefits. The proposed scale is comprised of three dimensions - specificity (e.g., specific target values), attainability (e.g., the capacity to realize the target benefits), and comprehensiveness (e.g., reflect the views of key stakeholders). This scale can be used by senior managers to assess proposed projects' target benefits, contributing to more informed project investment decision-making and the subsequent benefit management process. Theoretically, it can also be used as an instrument to facilitate theory development in the fields of project benefit management, strategy implementation, and organizational performance.

Risk adaptation and emotion differentiation: An experimental study of dynamic decision-making

We report a study of risky decision-making in a dynamic risk environment, looking in particular at idea that variations in risk preferences over time are subject to both risk adaptation and ability to differentiate negative emotions. In a between-group experiment, 175 participants completed 20 binary project investment decisions under three objective probabilities of success conditions (20%, 50%, and 80%). The results showed that participants’ risk-taking increased at Time 2 only when risky projects have a medium-to-high chance of success (50% or 80%) at Time 1. We also found that participants who could differentiate their negative emotions under the condition with high favorability of risk-taking (80%), achieved higher returns, suggesting that negative emotion differentiation provides emotional information to capture the pattern of decision trials in the environment more favorable to risk-taking success. We conclude with a discussion of the theoretical and practical contributions of our findings for individuals and firms.

Modeling managerial behavior in real options valuation for project-based environments

Project valuation, as a decision-making tool for initiating investments in projects, should be able to value project flexibilities and incorporate reasonable risk preferences of relevant decision makers. Real options valuation methods are the available approaches for valuing project flexibilities, whereas they have shortcomings in considering managers’ reasonable risk preferences in project decisions. Therefore, researchers have suggested approximating the perspective on risk of real options methods and practitioners in project management. This study proposes a fair real options valuation for project-based environments by a behavioral economic approach, which adopts binomial lattice method, Monte-Carlo simulation, and cumulative prospect theory. The results show that behavioral factors such as ‘risk attitude’ and ‘loss aversion’ should be accepted in project investment decisions while limited to an acceptable amount depending on the project conditions (e.g. uniqueness of decision-making scenarios). This research contributes to the project management domain by enhancing project investment decisions that include project flexibilities.

Pythagorean Fuzzy LINMAP Method Based on the Entropy Theory for Railway Project Investment Decision Making

The uncertainty and complexity of the decision-making environment and the subjectivity of the decision makers will lead to the inevitable errors of the decision-making data. A poor decision will be produced with those errors, whereas the linear programming technique for multidimensional analysis of preference (LINMAP) method can adjust such errors through constructing an optimal programming model based on the consistency of the decision-making information, and it has been applied widely in multiple attribute group decision making (MAGDM). Moreover, Pythagorean fuzzy information is useful to simulate the ambiguous and uncertain decision-making environment. Therefore, the LINMAP method under the Pythagorean fuzzy circumstance will be proposed in this paper to solve MAGDM problems. To measure the fuzziness and uncertainty of Pythagorean fuzzy set (PFS) and interval-valued PFS, Pythagorean fuzzy entropy (PFE) and interval-valued PFE (IVPFE) grounded on the similarity and hesitancy parts have been defined, respectively. Then, Pythagorean fuzzy LINMAP (PF LINMAP) methods are constructed on the basis of the PFE and IVPFE correspondingly. Under the given preference relations, the maximum consistency and the amount of knowledge can be realized by the proposed methods. After investigating the relevant indicator system, the decision-making problem concerning railway project investment has been solved through the proposed PF LINMAP method with PFE. Finally, the practicability and effectiveness of the PF LINMAP method has been verified via the comparative analysis with the existing methods.

Managing project investments irreversibility by accounting relations

Analytical techniques usually employed in making project selection decisions are of strictly financial origin and, traditionally, tend to consider projects as separate entities from undertaking organizations. This fact underestimates potential negative (and pervasive) outcomes considering that binding constraints affect the whole organization in further additional developments. This paper proposes a methodological algorithm to analyze, model and quantify irreversibility aspects to integrate and support traditional financial techniques. The goal is pursued by considering widespread well-known accounting indexes, and assuming reversibility rate as time needed to return to the “optimal original state” (as defined in accounting literature) prior project investment decisions. An illustrative case is proposed to explain how the methodology can be applied since the pre-feasibility step of the management of project framework. As calculations show, such a reversibility rate can be usefully implemented to improve effectiveness of planning processes within project cost management knowledge area.

Portfolio selection model of oil projects under uncertain environment

This paper discusses the oil project optimal portfolio selection under uncertain environment where cash flows of the projects are mostly determined by experts’ estimations due to the lack of historical investment data. The oil project investment is usually distinguished by its high input, high risk and highly fluctuating ROI sensitive to the economic, political and technology uncertainties. Besides, in most of the cases, it is quite difficult to find reliable referential historical data for a specific project. All these peculiarities make actual oil project investment decision under high uncertainties. In this paper, we use normal uncertain variables to describe the cash flows and estimate the uncertainty distribution of the cash flows by experts’ experimental data. Then, under the constraint of controlling for bankruptcy, we give uncertain programming models to construct portfolios that maximize the expected returns and minimize the sine cross-entropy of the actual return from a prior return. Finally, we provide some numerical examples that fit different risk preference assumptions to further illustrate the feasibility and effectiveness of the models.

Considering economic and geological uncertainty in the simulation of realistic investment decisions for CO2-EOR projects in the North Sea

The use of anthropogenic CO2 for enhancing oil recovery from mature oil fields in the North Sea has several potential benefits, and a number of assessments have been conducted. It remains, however, difficult to realistically simulate the economic circumstances and decisions, while including the economic uncertainties that surround the relevant markets and policies, and the geological and technological uncertainties that are inherent to dealing with reservoirs and novel technologies in a challenging environment. A new method is proposed here introducing a unique combination of innovations, that include true limited foresight, project flexibility, and the consideration of realistic investment risk. The value of project is here expressed as the Net Present Value (NPV). These elements are combined in the PSS IV simulator. This is a techno-economic simulator for CO2-enhanced oil recovery (CO2-EOR), which applies limited foresight and Real Options Analysis to make realistic investment decisions on projects with significant uncertainties and thus risk. Consecutive project decisions are taken based on a decision tree. Multiple oil fields can be approached as a single cluster project, which can provide a lower investment hurdle. In a first test case for PSS IV, the Claymore and Scott oil fields are assessed, and it is shown that economic simulations where EOR projects are regarded as a sum of the individual field assessments will undervalue projects. Simulation results show that results are in a realistic range compared to published numbers, with individual project values for the Claymore field on average of 15.8€/barrel (bbl; standard deviation SD=8.3) and for the Scott field of 14.3€/bbl (SD=8.6). Due to the inclusion of uncertainties and the application of limited foresight, results range from −6€/bbl (loss) to over 30€/bbl. In a cluster configuration 5€/bbl of additional value is created.

How economics can further the success of ecological restoration.

Restoration scientists and practitioners have recently begun to include economic and social aspects in the design and investment decisions for restoration projects. With few exceptions, ecological restoration studies that include economics focus solely on evaluating costs of restoration projects. However, economic principles, tools, and instruments can be applied to a range of other factors that affect project success. We considered the relevance of applying economics to address 4 key challenges of ecological restoration: assessing social and economic benefits, estimating overall costs, project prioritization and selection, and long-term financing of restoration programs. We found it is uncommon to consider all types of benefits (such as nonmarket values) and costs (such as transaction costs) in restoration programs. Total benefit of a restoration project can be estimated using market prices and various nonmarket valuation techniques. Total cost of a project can be estimated using methods based on property or land-sale prices, such as hedonic pricing method and organizational surveys. Securing continuous (or long-term) funding is also vital to accomplishing restoration goals and can be achieved by establishing synergy with existing programs, public-private partnerships, and financing through taxation.

Project investment decision making with fuzzy information: A literature review of methodologies based on taxonomy

Project investment decision making with fuzzy information (PIDMFI) has been investigated in a lot of literature. The aim of this paper is to discover the features, state of the art, interrelations, and research directions of existing method- ologies for PIDMFI. To do this, a literature review of the methodologies including theories, methods and models as well as applications is conducted from a taxonomic perspective of methodology. The hierarchical cluster analysis in SPSS V19 is employed to construct a methodological taxonomy framework which consists of fuzzy discounted cash flow (FDCF), fuzzy real option (FRO) and fuzzy multi-criteria decision making (FMCDM). Some hybrids of these methodologies are also shown. Additionally, a discussion is presented. Finally, a conclusion with new research directions is delineated.

Real Options and Incremental Search in Pharmaceutical R&D Project Portfolio Management

This paper investigates the role of real options reasoning in R&D project portfolio management and investment decisions of pharmaceutical firms. We analyse a unique dataset that integrates information on initiation and termination of clinical trials at the level of specific medical indications. Consistent with existing literature, we find a positive relationship between market size and firm entry in clinical trials. We also show that the option value of R&D investments, as proxied by the scope of R&D projects, affects the selection of target markets. Moreover, high‐risk research areas attract more entry, in line with the predictions of real options theory. However, we also find that more flexibility in project duration and delayed project discontinuation attract higher rates of entry. Departures from pure real options reasoning are motivated by the presence of incremental learning in pharmaceutical R&D.

The costs of uncoordinated infrastructure management in multi-reservoir river basins

Though there are surprisingly few estimates of the economic benefits of coordinated infrastructure development and operations in international river basins, there is a widespread belief that improved cooperation is beneficial for managing water scarcity and variability. Hydro-economic optimization models are commonly-used for identifying efficient allocation of water across time and space, but such models typically assume full coordination. In the real world, investment and operational decisions for specific projects are often made without full consideration of potential downstream impacts. This paper describes a tractable methodology for evaluating the economic benefits of infrastructure coordination. We demonstrate its application over a range of water availability scenarios in a catchment of the Mekong located in Lao PDR, the Nam Ngum River Basin. Results from this basin suggest that coordination improves system net benefits from irrigation and hydropower by approximately 3–12% (or US$12-53 million/yr) assuming moderate levels of flood control, and that the magnitude of coordination benefits generally increases with the level of water availability and with inflow variability. Similar analyses would be useful for developing a systematic understanding of the factors that increase the costs of non-cooperation in river basin systems worldwide, and would likely help to improve targeting of efforts to stimulate complicated negotiations over water resources.

Investment Decisions Based on the Postponed Options PROT Hydropower Project

The analysis and description of the current method used in hydropower project investment decisions and problems of the introduction of the Monte Carlo simulation method PROT hydropower project valuation. Examples of analysis results showed that: this method can better reflect the actual value of the uncertainty of hydropower projects, flexibility and projects to provide a reasonable basis for decision makers.

System Building of Supervision for Construction Engineering Quality under the Perspective of Life-Cycle

On the basis of construction engineering quality management and the whole life cycle theory as well as the related theories to value chain, this paper explores the main issues of the construction engineering quality supervision in whole life cycle from the construction project investment decision-making, preparation phase, implementation, completion acceptance and operation phase under the perspective of whole life cycle, and builds construction engineering quality supervision in whole life cycle system which gives priority to the government regulatory system with social supervision as supplement by defining the obligations of each other. The system is expected to comprehensively improve construction quality.

A numerical study for a mining project using real options valuation under commodity price uncertainty

Commodity price is an important factor for mining companies, as price volatility is a key parameter for mining project evaluation and investment decision making. The conventional discounted cash flow (DCF) methods are broadly used for mining project valuations, however, based on commodity price uncertainty and operational flexibilities, it is difficult and often inappropriate to determine mining project values through traditional DCF methods alone. In order to more accurately evaluate the economic viability of a mining project, the commodity price and its inherent volatility should be modelled appropriately and incorporated into the evaluation process. As a consequence, researchers and practitioners continue to develop and introduce real options valuation (ROV) methods for mining project evaluations under commodity price uncertainty, incorporating continuous time stochastic models. Although the concept of ROV arose a few decades ago, most of the models that have been developed to-date are generally limited to theoretical research and academia and consequently, the application of ROV methods remains poorly understood and often not used in mining project valuations. Analytical and numerical solutions derived through the application of ROV methods are rarely found in practice due to the complexity associated with solving the partial differential equations (PDE), which are dependent on several conditions and parameters. As a consequence, it may not generally be applicable to evaluate mining projects under all project-specific circumstances. Therefore, the greatest challenge to ROV modelling is in finding numerically explicit project values. This paper contributes towards the further development of known theoretical work and enhances an approach to approximating explicit numerical project values. Based on this work, it is possible to formulate more complex PDEs under additional uncertainties attached to the project and to approximate its numerical value or value ranges. To ensure the project is profitable and to reduce commodity price uncertainty, delta hedging and futures contracts have been used as options for deriving the PDE. Moreover, a new parameter for taxes has been incorporated within the PDE. This new PDE has been utilised to approximate the numerical values of a mining project considering a hypothetical gold mine as a case study. The explicit finite difference method (FDM) and MatLab software have been used and implemented to solve this PDE and to determine the numerical project values considering the available options associated with a mining project. In addition, commodity price volatility has been determined from historical data, and has again revealed price volatility as having a significant impact on mining project values.

A methodology for the management of risk related to uncertainty on the grade of the ore resources

Mining investment decisions for giant mining projects involve an enormous financial risk. Nevertheless, traditional risk evaluation methods are based on Monte Carlo simulation of cash flows and are of little value for risk management. In most mining project scenarios, the main sources of risk are those related to the evaluation of the ore body, specially the grade of the ore reserves. In this study, a new risk management methodology based on conditional simulation is developed to quantify the risk associated with uncertainty on the grade of the ore body. The methodology allows for quantifying risk for each period in the production plan and, therefore, it may be used as a powerful risk management tool to identify risk and ‘risk reduction’ objectives at the prefeasibility stages of a mining project. This model is validated by its application in a case study of a real copper ore body in Chile.

A review of the potential benefits and risks of photovoltaic hybrid mini-grid systems

Photovoltaic hybrid mini-grid systems (PVHMS) are expected to play a major role in facilitating rural electrification in the developing world, however these systems still face significant barriers to adoption. The technology occupies a middle ground of electrification options – between traditional network extension and individual home systems, possessing elements of each yet also their own distinctive characteristics. Given this, and their relatively limited application to date, such systems are the focus of a growing body of literature. This work has highlighted a range of potential benefits and risks associated with the technology. However, there still hasn't been a comprehensive review of these documented benefits and risks; an understanding of which is crucial for informed project investment and implementation decision making. This paper presents a preliminary review of the existing literature to identify claimed and demonstrated benefits and risks. The most commonly identified benefits are those that are easy to measure: reduced cost and provision of improved electrical services. Other benefits such as the social or environmental benefits are less commonly demonstrated, but are frequently claimed. The major risks identified included incorrect system sizing due to load uncertainty, challenges related to community integration, equipment compatibility issues, inappropriate business models and risks associated with geographical isolation. For all of these types of risks, associated mitigation strategies were also identified in the literature. Further research including industry surveys and additional case studies will be required to validate what has been observed in the literature to date, and identify progress as the technology matures, costs fall and stakeholders learn from these previous experiences.

Risk Decision Analysis of Commercial Real Estate

With the increasing of construction of logistics parks, it is essential for commercial real estate project to study on risk decision to avoid redundant and blind construction. Based on risk essence, the paper analyses the attitude of investment decision-makers on risk benefits. Finally, the paper improves the expected utility theory and applies the prospect theory to risk decision of commercial real estate project to provide scientific and objective basis for Project Investment Decision.