Valuing information technology infrastructures: a growth options approach

Decisions to invest in information technology (IT) infrastructure are often made based on an assessment of its immediate value to the organization. However, an important source of value comes from the fact that such technologies have the potential to be leveraged in the development of future applications. From a real options perspective, IT infrastructure investments create growth options that can be exercised if and when an organization decides to develop systems to provide new or enhanced IT capabilities. We present an analytical model based on real options that shows the process by which this potential is converted into business value, and discuss middleware as an example technology in this context. We derive managerial implications for the evaluation of IT infrastructure investments, and the main findings are: (1) the flexibility provided by IT infrastructure investment is more valuable when uncertainty is higher; (2) the cost advantage that IT infrastructure investment brings about is amplified by demand volatility for IT-supported products and services; (3) in duopoly competition, the value of IT infrastructure flexibility increases with the level of product or service substitutability; and (4) when demand volatility is high, inter-firm competition has a lower impact on the value of IT infrastructure.     

A real options approach for evaluation and justification of a hospital information system

Nowadays healthcare organizations globally recognize the importance of investing in information technologies to improve the quality of care delivery and reduce costs. The key drivers of healthcare sector such as continuously improving healthcare standards and insurance systems have introduced new requirements for hospitals, which in return provided a solid ground for decision-makers to consider implementing hospital information systems that are customized and improved versions of enterprise resource planning (ERP) systems designed according to the needs of the healthcare sector. The conventional discounted cash flow methods ignore the value of managerial and strategic flexibility inherent in these investments, which is crucial for justification of the investment decision. This study introduces a real options-based methodology which overcomes the limitations of traditional valuation methods and enables decision-makers to value an ERP system investment incorporating multiple options. The option valuation model developed in this study extends the binomial lattice framework to model a hospital information system (HIS) investment opportunity with compound options. The potential application of the proposed model is illustrated through evaluation of a real-world HIS investment.     

Valuation of expansion flexibility in flexible manufacturing system investments using sequential exchange options

In this paper, a valuation approach that modifies traditional discounted cash flow (DCF) methodology is presented to incorporate the option premium of expansion flexibility while evaluating a flexible manufacturing system investment. Expansion flexibility allows for changing the production capacity in response to deviations in demand, while disregarding the assumption that management makes an irrevocable decision based on its future market expectations. Expansion flexibility provides a key strategic advantage by avoiding the large financial commitment at the initial investment stage and enabling investment in a phased manner according to the changes in market conditions. The aim of this paper is to present a thorough valuation methodology that accounts for both the benefits of keeping the option to expand alive and the loss of market share to competing firms if the expansion investment were delayed. In this paper, the value of expansion flexibility is computed using sequential exchange options. The proposed method employs an analytic approximation scheme for valuing American exchange options on dividend-paying assets. A numerical example demonstrates the application of the valuation framework. The options approach that incorporates the expansion flexibility option into the analysis results in a higher value than the standard DCF approach, which ignores the value of the option to expand. We also perform sensitivity analyses to see whether the expansion option increases in value in cases of high uncertainty where management can respond flexibly to new market information, and where the investment analysis ignoring flexibility yields a marginally positive net present value. The results of the proposed valuation framework are presented in comparison with a previous model that ignores the opportunity cost of delaying expansion investment.     

The valuation of RFID investment using fuzzy real option

Net present value (NPV) and return on investment (ROI) are commonly used to evaluate investment in new technologies. Sometimes, however, measuring the value of investment in new IT becomes very difficult due to its wide scope of application coupled with embedded options in its adoption. Therefore, comprehensive but easily understandable methodologies are needed to solve the complicated problems resulting from the complexity of new technologies. This paper employs a real option analysis to evaluate RFID adoption in the supply chain. Real options analysis should be a better way to evaluate a disruptive technology like RFID. However, the pure (probabilistic) real option rule characterizes the present value of expected cash flows and the expected costs by a single number, which is not realistic in many cases. To solve the problem, this paper considers the real option rule in a more realistic setting, namely, when the present values of expected cash flows and expected costs are estimated by trapezoidal fuzzy numbers. Specifically, it drew out their means and variance and presented a method of calculating fuzzy real options through numerical value examples of RFID investment assuming the current value of expected cash flow and investment costs using trapezoid fuzzy number fuzzy real options. Since advanced information technology such as RFID has very high risk and options such as change, extension, delay and withdrawal, etc., investment valuation using the real options technique should be done, and in the process, in a more realistic and practical approach, the fuzzy real options model presented in this study is judged to be useful.     

Evaluation of two alternative carbon capture and storage technologies: A stochastic model

In this paper we evaluate two alternative CCS technologies at a coal-fired power plant from an investor's point of view. The first technology uses CO₂ for enhanced oil recovery (EOR) paired with storage in deep saline formations (DSF) and the second merely stores CO₂ in DSF. The paper updates and improves on an earlier publication by Tzimas et al. (2005). For projects of this type there are many sources of risk, three of which stand out: the price of electricity, the price of oil and the price of carbon allowances. In this paper we develop a general stochastic model that can be adapted to other projects such as enhanced gas recovery (EGR) or industrial plants that use CO₂ for either EOR or EGR with CCS. The model is calibrated with UK data and applied to help understand the conditions that generate the incentives needed for early investments in these technologies. Additionally, we analyse the risks of these investments. Investments with EOR and secondary DSF storage can only be profitable (NPV > 0) when there is a high long-term equilibrium price for oil of more than $56.38/barrel. When the investment decision can be made at any time, i.e. there is an option value, then the trigger value for optimal investment is significantly higher.     

一种新型机器人三维力柔性触觉传感器的设计

基于柔性力敏导电橡胶材料,设计了一种能测量三维力的新型机器人柔性触觉传感器。研究了力敏导电橡胶材料的压阻效应,阐述了触觉传感器的设计思想,分别进行了触觉传感器单元设计和阵列结构设计和研究。获得了计算三维力的数学模型,并通过实验进行了三维力的验证。结果表明,设计的机器人三维力柔性触觉传感器具有设计简单,造价低廉,柔顺性好等优点,而且布置成阵列结构可用于医疗、体育、机器人等领域中检测三维力信息。     

An Approach for Integrating Valuable Flexibility During Conceptual Design of Networks

Energy and industrial networks such as pipeline-based carbon capture and storage infrastructures and (bio)gas infrastructures are designed and developed in the presence of major uncertainties. Conventional design methods are based on deterministic forecasts of most likely scenarios and produce networks that are optimal under those scenarios. However, future design requirements and operational environments are uncertain and networks designed based on deterministic forecasts provide sub-optimal performance. This study introduces a method based on the flexible design approach and the concept of real options to deal with uncertainties during conceptual design of networks. The proposed method uses a graph theoretical network model and Monte Carlo simulations to explore candidate designs, and identify and integrate flexibility enablers to pro-actively deal with uncertainties. Applying the method on a hypothetical network, it is found that integrating flexibility enablers (real options) such as redundant capacity and length can help to enhance the long term performance of networks. When compared to deterministic rigid designs, the flexible design enables cost effective expansions as uncertainty unfolds in the future.     

An R&D Investment Game under Uncertainty in Real Option Analysis

One of the problems of using the financial options methodology to analyse investment decisions is that strategic considerations become extremely important. So, the theory of real option games combines two successful theories, namely real options and game theory. The investment opportunity and the value of flexibility can be valued as a real option while the competition can be analyzed with game theory. In our model we develop an interaction between two firms that invest in R&D. The firm that invests first, defined as the Leader, acquires a first mover advantage that we assume as a higher share of market. But, several R&D investments present positive externalities and so, the option exercise by the Leader generates an “Information-Revelation”, that benefits the Follower.     

Activity-based justification of IT investments

Ever since investments in IT moved from operational to decision support, academics and practitioners have been looking for ways to justify the costs. Traditional approaches, such as NPV, have been shown inadequate in capturing qualitative and quantitative benefits, and techniques developed to address the issue have proved limited in their ability to link investment justification with impact assessment. Recent literature has called for the use of business processes to assess the impact of IT and the same processes can be used to justify investments in IT. The activity-based costing (ABC) approaches in the field of accounting were developed to relate investments to product profitability by allocating the burden to those that receive the most benefit. Thus, this paper uses the ABC approach to relate investments to activities, so that it can be used to justify IT investment. The paper provides a model to determine when the approach, referred to as activity-based justification, is most appropriate and its value is illustrated using a case study.     

On Capital Investment

We deal with the problem of making capital investments in machines for manufacturing a product. Opportunities for investment occur over time, every such option consists of a capital cost for a new machine and a resulting productivity gain, i.e., a lower production cost for one unit of product. The goal is that of minimizing the total production costs and capital costs when future demand for the product being produced and investment opportunities are unknown. This can be viewed as a generalization of the ski-rental problem and related to the mortgage problem [3]. If all possible capital investments obey the rule that lower production costs require higher capital investments, then we present an algorithm with constant competitive ratio. If new opportunities may be strictly superior to previous ones (in terms of both capital cost and production cost), then we give an algorithm which is O (min{1+log C , 1+log log P , 1+log M }) competitive, where C is the ratio between the highest and the lowest capital costs, P is the ratio between the highest and the lowest production costs, and M is the number of investment opportunities. We also present a lower bound on the competitive ratio of any on-line algorithm for this case, which is Ω (min{log C , log log P / log log log P , log M / log log M }). This shows that the competitive ratio of our algorithm is tight (up to constant factors) as a function of C , and not far from the best achievable as a function of P and M . Received February 6, 1997; revised November 17, 1997.     

An efficient algorithm for stochastic capacity portfolio planning problems

This study investigates capacity portfolio planning problems under demand, price, and yield uncertainties. We model this capacity portfolio planning problem as a Markov decision process. In this research, we consider two types of capacity: dedicated and flexible capacity. Among these capacity types, flexible capacity costs higher but provides flexibility for producing different products. To maximize expected profit, decision makers have to choose the optimal capacity level and expansion timing for both capacity types. Since large stochastic optimization problems are intractable, a new heuristic search algorithm (HSA) is developed to reduce computational complexity. Compare to other algorithms in literature, HSA reduces computational time by at least 30% in large capacity optimization problems. In addition, HSA yields optimal solution in all numerical examples that we have examined.     

Flexible platform component design under uncertainty

Incorporating flexibility into product platforms allows manufacturers to respond to changing market needs with a minimal increase in product family complexity and investment cost. To successfully design a flexible product platform, proper design of flexible platform components is critical. These components can be described as “cousin” parts as they are neither completely unique nor completely common among variants. In this paper, a multidisciplinary process for designing flexible product platform components is introduced, assuming the platform component is decided a priori. The design process starts with identification of uncertainties and generation of multiple design alternatives for embedding flexibility into the component. Design alternatives are then optimized for minimum cost, while satisfying the component performance requirements. The flexible designs are then evaluated for economic profitability under identified uncertainty, using Monte Carlo simulation. At the end, the most profitable flexible component design is selected. The proposed design process is demonstrated through a case study, in which different flexible designs are generated and optimized for an automotive floor pan, an essential element of most vehicle product platforms. Results suggest that the way in which the flexibility is incorporated in the component, production volume trends, and the degree of built-in flexibility are important factors to consider when designing flexible product platforms. D. Chang was retired from General Motors R & D     

Valuation of R&;D Sequential Exchange Options Using Monte Carlo Approach

This article describes a methodology for evaluating R&D investment projects using Monte Carlo method. R&D projects generally involve multiple phases with or without overlapping. R&D investments are made often in a phased manner, with the commencement of subsequent phase being dependent on the successful completion of the preceding phase. This is known as sequential investment. Moreover, each stage creates an opportunity (option) for subsequent investment. Therefore, R&D projects can be considered as ‘Compound Options’ in which investments present uncertainty both in the gross project value and in their costs. It is possible to use exchange options to value the R&D investment opportunities. In this paper, we propose to evaluate the European and American Real Compound exchange options through Monte Carlo simulations. We also provide a set of numerical experiments to provide evidence for the accuracy of the proposed methodology.      

Economic aspects of environmental investment in plant facilities

This paper discusses what kind of investment in environmental equipment is desirable in enterprises such as the petrochemical companies in Yokkaichi, which are mainly based on large production facilities. In Yokkaichi's case, this kind of investment was undertaken after harmful atmospheric pollution had already become a serious problem, but the present research concludes that it would normally be more effective, in terms of equipment and damage costs, to include environmental investments from the starting stages of production.     

A cost/benefit model for investments in inventory and preventive maintenance in an imperfect production system

In this research, a cost/benefit model is developed for supporting investment strategies about inventory and preventive maintenance in an imperfect production system. The effect of such investments on the return is expressed as a function of measurable variables. Using this model, the decision maker can decide whether investments in inventory and preventive maintenance are necessary and how much to invest. This investment model is developed for an imperfect production system with imperfect product quality and supplied quantity. Investments in inventory and preventive maintenance increase service level for the customer and reduce the proportion of defective products, and hence affect stockout and backlog of supplied products and the delivery time to the customer. This model includes in its scope investment in inventory and preventive maintenance, manufacturing cost, inventory cost, backlog cost, stockout cost, and delay cost. This model can be used to evaluate the effects of investments on the financial cost/benefit and other relevant critical performance measures. This model can be solved by an iterative process using the Sequential Quadratic Programming Method. The optimal investment in inventory with respect to the service level and the optimal investment in preventive maintenance with respect to the proportion of defective items can be obtained first, and then other relevant costs can also be obtained.     

A methodology of constructing a decision path for IT investment

Information Technology (IT) may be used for organizational efficiency, but should also be flexible to adapt to the rapidly changing competitive business environment. In competitive business circumstances, management continually asks: (1) How flexible must the firm be in investing in IT in order to meet unknown business needs in the future? At the same time, how efficient must the firm be in order to meet current business needs?; (2) How well must the firm align its business strategy with IT investment in order for it to support its strategic goals?; (3) how to construct a decision path for IT investments with respect to flexibility, efficiency and alignment between business strategy and IT investments?Although many researchers have struggled to answer these questions, they generally provide no means for incorporating these factors into the IT investment decision process.This paper suggests a method that identifies the degree of flexibility required (α-value), and accounts for and incorporates the α-value in making IT investments. The proposed method is based on a product development method called Quality Function Deployment (QFD). It will be applied to a real case of the “H-company” in Korea to validate and evaluate the proposed methodology.     

The economic value of flexible automation

Flexible Manufacturing Systems (FMS) have the potential to substantially improve the productivity of mid-volume, mid-variety production. The introduction of these systems into industry must be done on the basis of cost justification. Such justification has historically been possible through the use of easily measurable costs such as labor, inventory reduction, and reduced scrap. In the case of flexible automation, the value of flexibility is difficult if not impossible to measure. This paper attempts to provide a measure of the value of flexibility by comparing the costs of a typical fixed automation approach, with those of a transfer line (TL) in the same management environment. It is concluded that there is economic value to flexibility under many circumstances.     

A fuzzy long-term investment planning model for a GenCo in a hybrid electricity market considering climate change impacts

We study the long-term generation capacity investment problem of an independent power generation company (GenCo) that functions in an environment where GenCos perform business with both bilateral contracts (BC) and transactions in the day-ahead market (DAM). A fuzzy mixed integer linear programming model with a fuzzy objective and fuzzy constraints is developed to incorporate the impacts of imprecision/uncertainty in the economic environment on the calculation of the optimal value of the GenCo’s objective function. In formulating the fuzzy objective function we also include the potential impacts of climate change on the energy output of hydroelectric power plants. In addition to formulating and solving the capacity planning/investment problem, we also performed scenario-based (sensitivity) analysis to explore how investment decisions of the GenCos change when fuzziness (tolerance) in the maximum energy output of hydroelectric units and/or drought expectation increases. The proposed model is novel and investigates the effects of factors like drought expectations of climate changes, hydroelectric power plant investments, and other power generation technology investment options.     

Human Capital valuation and return of investment on corporate education

This paper presents the Attitude, Skills, Knowledge, and Experience–Knowledge Value Added (ASKE-KVA) methodology developed from the designed Individual Technical Competence (ITC) of a value chain to assess changes in the Human Capital of a company. It is based on the Knowledge Value Added (KVA) method, which proposes the use of a proxy variable for measuring the flow of knowledge used in a key Process. This variable creates a relationship between the company’s financial results and the resources used in each of the business processes. The KVA method uses an indicator that measures the result of knowledge per unit (Kμ), which transforms costs and investments in the same unit. The ASKE-KVA methodology expands the previous concept, using fuzzy logic to measure the flow of knowledge associated with each ITC and, therefore, making it possible to obtain the return on investment of a particular business process.     

Simulation–optimization mechanism for expansion strategy using real option theory

A right expansion strategy can bring a company more market shares and profits, and hence increase shareholders’ equities. However, limited financial resources and various uncertainties require business practitioners to achieve their goals while controlling the risks incurred at an acceptable level. Therefore, justification of expansion investments is an important and complex topic in industry. The traditional investment analysis tools such as net present value (NPV) often tend to undervalue investment decisions. We formulate the expansion investments using real options, and develop a financial model to assess the option value. Monte Carlo simulation is considered a good way to estimate the value of the option. This valuation gives decision makers a way to choose the appropriate expansion strategy based on an integrated view of the market dynamics, but optimization is still a difficult problem to resolve. This paper presents a model of optimization under uncertainty combining system simulation with GA-based optimization to resolve the expansion problem. An industry case is used to demonstrate the application of real options to value expansion investment by using simulation–optimization. This approach also provides some new insights for the real options theory.