Cash flow at risk valuation of mining project using Monte Carlo simulations with stochastic processes calibrated on historical data

Mining projects are subject to multiple sources of market uncertainties such as metal price, exchange rates, and their volatilities. Assessing a mining project's exposure to market risk usually requires Monte Carlo simulations to capture a range of probable outcomes. The probability of a major loss is extracted from the probability density function of simulated prices at a given time into the future. This article proposes an approach to calibrate the stochastic process to be used in Monte Carlo simulations. The simulations are then used for measuring the cash flow at risk of a mining project. To assess the performance of the proposed approach, a case study is conducted on a mining project. The results show that the calibration approach is robust and apt at fitting various stochastic processes to historical observations.     

Oil and Gas Exploration Valuation and the Value of Waiting

The timing flexibility of investments in oil and gas assets can potentially add value. In this article, we examine the value of waiting in exploration projects and propose a real option–based valuation method using least-squares Monte Carlo simulation. We show that the dynamics of the oil and gas prices have a large impact on the value of the option to wait, especially for projects with long lead times and durations. The uncertainty in the forward price curve is modeled using a two-factor stochastic price process. The article also presents the valuation method in the form of MATLAB functions and routines that can be used as an efficient test and analysis platform using the industry-standard input formats.     

倒边盖挖逆作法基坑支护结构可靠性分析

为了解决采用标准Monte Carlo法计算复杂基坑工程上常见小概率失效,导致计算效率低的问题,以南京市湖南路地下商业街工程为工程背景,首先,将随机响应面法与基坑工程三维模型相结合,求解极限功能函数的响应面方程,并用标准Monte Carlo法计算失效概率和可靠指标,探讨采用倒边盖挖逆作法作为基坑支护结构施工方法的可行性;其次,基于该响应面方程,以土体的弹性模量为随机变量参数,采用马尔可夫链蒙特卡罗子集模拟法(MCMC子集模拟法)计算基坑支护结构的失效概率,并与标准Monte Carlo法结果进行对比分析。结果表明：当支护结构最大侧移控制指标为25 mm时,计算得到的可靠指标均大于4.6,即采用倒边盖挖逆作法施工过程中基坑是安全的;10万次和50万次标准Monte Carlo法计算得到的失效概率均为零,说明对于标准Monte Carlo法,在计算小概率失效问题时10万与50万的样本量是不足的;而MCMC子集模拟法用2.98万个样本计算出的结果与标准Monte Carlo法采用100万个样本计算的结果相对误差仅为1.7%,表明MCMC子集模拟法对于小概率失效问题求解的优势。所提算法在一定...     

Prioritizing Project Selection

We consider capital investments under uncertainty. A typical approach to this problem, when the problem parameters are assumed known, is via a multi-knapsack model. This model takes as input annual budgets as well as the cost streams and profit—i.e., net present value (NPV)—of each project. Its output is a portfolio of projects with the highest total NPV, observing yearly budget constraints. We argue that such a portfolio fails to hedge against uncertainties in the budgets, the cost streams, and the profits. As an alternative, we propose a model that forms an optimal priority list of projects, incorporating multiple scenarios for these input parameters. We apply our approach to two sets of example projects from the South Texas Project Nuclear Operating Company.     

Statistical dependence in risk analysis for project networks using Monte Carlo methods

Monte Carlo simulation of project networks is increasingly used by engineering firms to analyze schedule/cost risk for bidding purposes. However, one serious methodological flaw of most Monte Carlo simulations is the assumption of statistical independence of activity durations in the network. In this paper, a method is proposed to model and quantify positive dependence between uncertainty distributions of activities. This method inherits the theoretically sound foundations of the rank correlation method, but provides a less cumbersome method to elicit dependency information from project engineers. Details of the methodology are described along with an example of project risk analysis in a manufacturing domain (shipbuilding).     

A Robust Optimization Approach to Capital Rationing and Capital Budgeting

We study the pure capital rationing and the horizon capital budgeting problems using a robust optimization framework. The models and the methodology we propose take into account the uncertainty of the input data. The uncertainty of the cash flows is modeled as a range of values that is allowed for each uncertain data. Unlike stochastic models, this approach does not make assumptions on the probability distribution of uncertain data. Moreover, this approach is highly tractable, easy to implement, and provides insights into portfolio selection problems. An attractive point of the model is that the decision maker can set the value of the parameters that control the robustness of the optimal solution, in order to balance the trade-off between protection level and performance. We illustrate our models with examples that show promising results. We also provide new duality and KKT optimality conditions.     

An option approach to the new product development process: a case study at Philips Electronics

The paper considers the product development process as a series of (real) options with reducing uncertainty over time. Criteria are developed to decide on speeding up or delaying the development process. The paper demonstrates how, in the R&D phase, any particular project may be assigned within a 2 × 2 matrix of uncertainty versus R&D option value. A similar matrix can be established for the product launch phase. The matrices support portfolio management throughout the different phases of development and enable management to decide on an appropriate point at which to abandon individual projects. The approach originates from applying real options insights into the product development process at Philips Electronics. The paper is illustrated with some actual R&D projects.     

Cost analysis of material handling systems in open pit mining: Case study on an iron ore prefeasibility study

Selection of the optimal material handling system is one of the most significant decisions to be made in mineral industries. Rapid economic changes and technological improvements make cost analysis a complicated process. On the other hand, current low commodity prices have put a greater emphasis on cost reduction and process optimization to ensure viability of mining projects. In this article, two material handling systems, a semimobile in-pit crusher and conveyor systems (IPCC) and traditional truck and shovel systems (TS), are compared through the cost analysis of an iron ore prefeasibility study. Furthermore, robustness of the design parameters is evaluated through a sensitivity analysis to determine the relative importance of project parameters. Finally, risks associated with uncertain design parameters affecting cost analysis are assessed through Monte Carlo simulation. The results indicated that IPCC is more cost effective than TS.     

Impact of rainfall pattern on cereal market and food security in Sudan: Stochastic approach and CGE model

The paper aims at analysing the impact of the likely change in rainfall on food availability and access to food in Sudan. The empirical investigation is based on an integrated approach consisting of a stochastic method and CGE model. The former, related to the Monte Carlo analysis, provides the likely changes in rainfall patterns and their probability of occurrence based on historical data. These results are at the basis of the scenarios simulated in a standard CGE model augmented with a stochastic component. Achievements underline the negative impact on the two dimensions of food security taken into consideration, mainly due to a reduction in cereal supply, a marked cereal inflation pressure and income contraction; the greater negative effect on the poorest households; and a deterioration of the economic performance of the country. In this context, the paper stresses a strong interconnection among climate change and variability, poverty and food insecurity and thus the need for an integrated policy-making approach.     

An extension to a DEA support system used for assessing R&D projects

This paper describes an extension to the data envelopment analysis (DEA) support system that has been used for the assessment, rating, and ranking of diverse portfolios of research and development (R&D) projects at Lucent Technologies. The approach is illustrated through its application to a large portfolio of R&D projects considered by Lucent's Advanced Technologies Group. The method proceeds by first stratifying the portfolio into comparably efficient groups of projects through the construction of a series of efficient DEA frontiers, and then by lexicographically ranking each project within these groups relative to DEA-based contextual attractiveness measures calculated from the different partitions. The advantages to this approach are provided not only from the perspective of the specific project rankings that are produced but also from the broader managerial insights that can be derived from any resulting differences between officially sanctioned, quantitative decision-making procedures, and the quality of the decisions that have actually been made by managers.     

A marginal analysis guided technology evaluation and selection

Making decisions on strategic investments, such as early stage manufacturing technology (MT), is a complicated task. Early stage technologies are usually costly, and surrounded by uncertainty. The potential benefits are often hard to quantify prior to implementation. Thus, how could managers make good decisions in a high-risk, technically complex business when the information they need to make those decisions comes largely from the project champions who are competing against one another for resources? Traditionally, in this problem domain, decisions are made based upon gut-feeling and past experience, sometimes with the support of some multi-criteria decision-support tools. The criteria evaluation process is very subjective and relies heavily on managers’ experience, knowledge, as well as intuition. Thus, the evaluation approach is often not effectively carried out as there is lack of visibility and traceability in the decision making process. The impact of this scenario is that managers are not confident that resources are being optimised and applied to a mixed portfolio of projects to maximise benefits. This paper proposes a marginal analysis directed branch and bound approach for evaluating and selecting early stage manufacturing technology (MT) projects. A case study is used to demonstrate the application of the proposed approach. Implications of the proposed approach to practitioners and academia are discussed and future research outlined.     

Managing the Sources of Uncertainty: Matching Process and Context in Software Development

There is increasing interest in the literature about the notion of a contingent approach to product development process design. This interest stems from the realization that different types of projects carried out in different environments are likely to require quite different development processes if they are to be successful. Stated more formally, a contingent view implies that the performance impact of different development practices is likely to be mediated by the context in which those practices operate. This article provides evidence to support such a view. Our work examines whether projects in which the development process matches the context achieve superior performance. We focus on two sources of uncertainty that generate challenges for project teams: platform uncertainty, reflecting the uncertainty generated by the amount of new design work that must be undertaken in a project; and market uncertainty, reflecting the uncertainty faced in determining customer requirements for the product under development. We develop hypotheses for how these sources of uncertainty are likely to influence the relationships between a number of specific development practices and performance. We then test these hypotheses using data from a sample of 29 Internet software development projects. Our results provide evidence to support a contingent view of development process design. We show that in projects facing greater uncertainty, investments in architectural design, early technical feedback, and early market feedback have a stronger association with performance. The latter relationships are influenced by the specific sources from which this uncertainty stems: platform uncertainty mediating the impact of early technical feedback and market uncertainty mediating the impact of early market feedback. Our results also indicate that while greater uncertainty is associated with making later changes to a product's design, this practice is not associated with performance. Our findings suggest that managers carefully must evaluate both the levels and sources of uncertainty facing a project before designing the most appropriate process for its execution. In particular, they should explore the use of specific development practices based upon their usefulness in resolving the specific types of uncertainty faced. Importantly, these decisions must be made at the start of a project, with purposeful investments to create a process that best matches the context. Reacting to uncertainty ex‐post, without such investments in place, is unlikely to prove a successful strategy.     

New Product Portfolio Management: Practices and Performance

Effective portfolio management is vital to successful product innovation. Portfolio management is about making strategic choices—which markets, products, and technologies our business will invest in. It is about resource allocation—how you will spend your scarce engineering, R&D, and marketing resources. It focuses on project selection—on which new product or development projects you choose from the many opportunities you face. And it deals with balance—having the right balance between numbers of projects you do and the resources or capabilities you have available. In this article, the authors reveal the findings of their extensive study of portfolio management in industry. This study, the first of its kind, reports the portfolio management practices and performance of 205 U.S. companies. Its overall objective was to gain insights into what portfolio methods companies use, whether they are satisfied with them, the performance results they achieve with the different approaches, and suggestions for others who are considering implementing portfolio management. The research first assesses management's satisfaction with portfolio methods they employ and notes that some firms face major problems in portfolio management. Next, businesses are grouped or clustered into four groups according to management's view of portfolio management: Cowboys, Crossroads, Duds, and Benchmark businesses. The research first assesses management's satisfaction with portfolio methods they employ and notes that some firms face major problems in portfolio management. Next, businesses are grouped or clustered into four groups according to management's view of portfolio management: Cowboys, Crossroads, Duds, and Benchmark businesses. Various performance metrics are used to gauge the performance of the business's portfolio. The results reveal major differences between the best and the worst. Benchmark businesses are the top performers. Their new product portfolios consistently score the best in terms of performance—high-value projects, aligned with the business's strategy, the right balance of projects, and the right number of projects. The authors take a closer look at these benchmark businesses to determine what distinguishes their projects from the rest. Benchmark businesses employ a much more formal, explicit method to managing their portfolio of projects. They rely on clear, well-defined portfolio procedures, they consistently apply their portfolio method to all projects, and management buys into the approach. The relative popularity of various portfolio methods—from financial methods to strategic approaches, bubble diagrams, and scoring approaches—are investigated. Not surprisingly, financial approaches are the most popular and dominate the portfolio decision. But what is surprising is the dubious results achieved via financial approaches. Again, benchmark businesses stand out from the rest: they place less emphasis on financial approaches and more on strategic methods, and they tend to use multiple methods more so than the rest. Strategic methods, along with scoring approaches, yield the best portfolios; financial methods yield poorer portfolio results. The authors provide a number of recommendations and suggestions for anyone setting out to implement portfolio management in their business.     

Exploring Portfolio Decision‐Making Processes*

Portfolio management is the set of activities that allows a firm to select, develop, and commercialize a pipeline of new products aligned with the firm's strategy that will enable it to continue to grow profitably over the long term. To appropriately manage the firm's new product portfolio, decisions must be made about which projects to fund, to what levels, at what point in time. Previous research has investigated portfolio management decisions as individually discrete decisions. Significant streams of research have investigated both project selection and project termination decisions. This research project shows, however, that portfolio decision making may be better understood if it is considered as an integrated system of processes that considers these decisions simultaneously, along with other decisions such as those to continue a project with reduced funding. Using in‐depth data from four diverse case studies, we use a grounded theory approach to develop a general model of how firms make new product portfolio decisions. According to the findings from these cases, effective portfolio decision‐making processes produce a portfolio mindset, focus effort on the right projects, and allow agile decision making across the portfolio's set of projects. Effective portfolio decision making is the result of the interaction between three types of decision‐making processes that managers use in making decisions: evidence‐, power‐, and opinion‐based. Being able to use each of these types of processes to make decisions depends upon having the data inputs that they require. Three domain‐based decision input‐generating processes (i.e., cross‐functional collaboration, practices of critical thinking, and practices of market immersion) are associated with making evidence‐based portfolio decisions. In addition, organizational politics produces the inputs that are associated with power‐based portfolio decision making, while managerial intuition is associated with opinion‐based portfolio decision making. Firm cultural factors, including trust, collective ambition, and leadership style, are associated with how these evidence‐, power‐ and opinion‐based processes are combined into an overall portfolio decision making process, and whether the firm's processes are more rational and objectively made, or more politically and intuitively made. The article presents propositions for how the decision‐making processes interact in their associations with decision‐making effectiveness.     

基于战略Partnering模式的工程项目组合管理研究

工程项目组合管理模式的选择对项目组合综合效益有显著影响,将战略Partnering模式应用于工程项目组合管理中,有助于提高工程项目组合的管理水平及收益。通过对战略Partnering模式概念和特点的分析,设计了战略Partnering模式的组织结构,提出了战略Partnering模式的工作流程,详述了基于战略Partnering模式的工程项目组合管理机制应包括信任机制、沟通机制、协作机制、利益机制与调节机制。     

Refining the search for project success factors: a multivariate, typological approach

Although the causes for project success and failure have been the subject of many studies, no conclusive evidence or common agreement has been achieved so far. One criticism involves the universalistic approach used often in project management studies, according to which all projects are assumed to be similar. A second problem is the issue of subjectiveness, and sometimes weakly defined success measures; yet another concern is the limited number of managerial variables examined by previous research. In the present study we use a project-specific typological approach, a multidimensional criteria for assessing project success, and a multivariate statistical analysis method. According to our typology projects were classified according to their technological uncertainty at project initiation and their system scope which is their location on a hierarchical ladder of systems and subsystems. For each of the 127 projects in our study that were executed in Israel, we recorded 360 managerial variables and 13 success measures. The use of a very detailed data and multivariate methods such as canonical correlation and eigenvector analysis enables us to account for all the interactions between managerial and success variables and to address a handful of perspectives, often left unanalyzed by previous research. Assessing the variants of managerial variables and their impact on project success for various types of projects, serves also a step toward the establishment of a typological theory of projects. Although some success factors are common to all projects, our study identified project-specific lists of factors, indicating for example, that high-uncertainty projects must be managed differently than low-uncertainty projects, and high-scope projects differently than low-scope projects.     

Investigating differences among research projects and implications for managers

As research and innovation have become central to the economy, the challenge of managing these activities has taken on greater importance. Studies have focused on the impact of organizational variables on research activities, such as work environment, human resource factors, and managerial practices. But little attention has been paid to the effect of differences among types of research projects. While the notion that differences exist among research projects is acknowledged, particularly in the research & development portfolio literature, there have been relatively few studies into the dimensions by which research projects, and needs of project team members, differ. Further, there is little recognition that these differences translate into the need for different research project management practices. The objective of this paper is to investigate differences among research projects along three dimensions, amount of funding, complexity of project teams, and research orientation. These dimensions are selected because of their central theoretical importance in the organizational literature, as well as posing a number of different challenges for research management. This study looked at 18 research projects at a national laboratory and analyzed the responses of project members to a comprehensive research environment survey conducted in 2001. The results of the analysis indicate that there are significant differences between types of projects along three dimensions and suggest ways that research performance can be improved through management intervention.     

A portfolio–evaluation framework for selecting R&D projects

This study aims to form the basis for constructing a framework for evaluating alternative portfolios of R&D projects. This study provides an extensive literature review on portfolio selection. Most of the existing studies deal with the portfolio selection problem by evaluating individual projects and then seeking ways to combine them for an R&D portfolio. However, the combination of individually good projects unnecessarily constitutes the optimal portfolio. In particular, this study discusses three portfolio effects: (1) the difference between the preference for the portfolio as a whole and the preference for the projects, (2) the interrelation among projects, (3) the size of portfolio selection problems. This study develops a three–phase framework for evaluating R&D portfolios and proposes a new taxonomy of the portfolio attributes (i.e. independent, interrelated, and synergistic). This study concludes with a discussion of future research, directed toward increasing the applicability of portfolio–selection approaches for managing R&D portfolios.     

The case for a real options approach to ex-ante cost-benefit analyses of agricultural research projects

The United States Agency for International Development (USAID), like many other development agencies and donors, increasingly emphasizes evidence-based programming. This requires assessments of project performance at all stages of implementation, comprising ex-ante impact assessment, monitoring and evaluation, and ex-post attribution of outcomes. Ex-ante impact assessment, in particular, involves performing Cost-Benefit Analysis (CBA) to determine the Expected Net Present Value (ENPV) of the project in question. Unfortunately, the traditional ENPV approach has proven inadequate for dealing with uncertainty in the timing of investments and flexibility in future decision making. This is especially relevant for Research and Development (R&D) projects which require several stages of product development and multiple rounds of testing prior to releasing final products. As a consequence, the real-options approach to CBA has increasingly been used to evaluate private sector R&D projects. This paper advocates for the adoption of the real options approach in the evaluation of public investments in agricultural research, and illustrates its practical utility with an assessment conducted by USAID to determine the economic viability of a proposed project to develop improved varieties of critical food security crops in Uganda.     

Steady state and portfolio concept in R & D management

This paper describes a new approach for closing the gap between the tools for strategic goal management and operational management in Pharmaceutical R&D. The strategic level may concern with expectation of sales, rising from new products and with total R & D budget. The operational level concerns with project selection, project and multiproject management problems, solution of project related technical problems, determination of the size of the various functions and the functional management. This new method combines the R & D project portfolio management with a steady state concept allowing the attainment of a constant number of projects under development and a continuous full use of the capacity available.