Technology Investment Decisions Research Articles

Total Cost Ownership for Information Technology Investment Decisions (IT)

Total Cost Ownership for Information Technology Investment Decisions (IT)

Life-cycle modeling framework for generating energy and greenhouse gas emissions inventory of emerging technologies in the chemical industry

Assessing the life-cycle energy and environmental impacts of emerging technologies is critical to promote sustainable chemical production because such assessment can provide policy makers with useful insights for future investment and technology deployment; it also provides manufacturers and researchers with quantitative understandings of technology potential, possible bottlenecks, and future Research, Development, and Deployment directions. However, this is a challenging task for emerging technologies due to the lack of inventory data. In this paper, a general and flexible modeling framework was developed to generate the inventory data for new technologies in the chemical industry from a life cycle perspective. The modeling framework is applied to an emerging technology in the U.S. ethylene industry, ethane oxidative dehydrogenation, for demonstration. Broadly, the work described in this paper provides a decision-support method that can be further used by researchers, environment/energy analysts, and policy makers to evaluate the net benefits of innovative technologies, guide early-stage technology development and investment decisions, and conduct strategic planning for meeting energy and emissions reduction goals of the U.S. chemical industry.

Profit-Driven Corporate Social Responsibility as a Bayesian Real Option in Green Computing

The idea that socially responsible investments can be viewed in terms of real options is relatively new. We expand on this notion by demonstrating how real option theory, within a Bayesian decision-making framework, can be used by managers to help when making green technology investment decisions. The Bayesian decision framework provides a more flexible approach to investment decision making because it adjusts for new information. Responding to a call for multidisciplinary and multifaceted research in environmental sustainability, this paper integrates ethics, finance, and information technology by viewing investments in environmentally friendly technology as a profit-driven CSR real option. Our model provides managers with the analytic tool needed to make the business case for CSR initiatives providing an opportunity for firms to create economic, social, and ecological solutions that benefit all stakeholders. The practical applicability of our model is demonstrated in an illustrative case scenario based on real data.

Modeling the Convergence of Collaborative Systems of Systems: A Quantitative Case Study

A system of systems SoSs provides functionality beyond that offered by its constituent systems. The functionality emerges over time, and there is a need to predict and affect the system's convergence toward the desired functionality. The convergence of a collaborative SoS is dependent on political, economic, societal, and technological PEST factors. In this paper, the authors propose a model for predicting and analyzing the convergence of SoSs. We use the United States smart grid, a collaborative SoS, to demonstrate the power of the developed model. The United States smart grid's convergence depends on factors such as energy policies, roadmaps, grants, cost recovery, consumer support, and sufficiency of technological solutions. We constructed a dynamic Bayesian network to predict the convergence and then assess the influence of each of the PEST factors. The output of the convergence model can be used to predict and communicate technical progress to stakeholders and to analyze and optimize the investment of resources to affect the PEST factors. Understanding the convergence of SoSs is important for making sound business and technology investment decisions. This approach of predicting and analyzing convergence should be extended to other SoSs.

SMART-Invest: a stochastic, dynamic planning for optimizing investments in wind, solar, and storage in the presence of fossil fuels. The case of the PJM electricity market

In this paper, we present a stochastic dynamic planning model called SMART-Invest, which is capable of optimizing investment decisions in different electricity generation technologies. SMART-Invest consists of two layers: an optimization outer layer and an operational core layer. The operational model captures hourly variations of wind and solar over an entire year, with detailed modeling of day-ahead commitments, forecast uncertainties and ramping constraints. The outer layer requires optimizing an unknown, non-convex, non-smooth, and expensive-to-evaluate function. We present a stochastic search algorithm with an adaptive stepsize rule that can find the optimal investment decisions quickly and reliably. By properly capturing the marginal cost of investments in wind, solar and storage, we feel that SMART-Invest produces a more realistic picture of an optimal mix of wind, solar and storage, resulting in a tool that can provide more accurate guidance for policy makers.

Impact of efficiency, investment, and competition on low carbon manufacturing

Low carbon economy has become a top agenda for many countries following the agreement in the Paris meeting on climate change. In this article, we take price and emission sensitive demand into account and incorporate competition between the two rival manufacturers in the demand function. This research takes more proactive actions incorporating carbon emissions in the strategic and operational decisions, which complements the existing literature on low carbon manufacturing, in which the carbon emissions attribute is often used as a constraint, or only the single manufacturer's demand is considered. Based on game theory, the pricing and carbon emissions reduction decisions are investigated. Our study contributes to the existing literature on low carbon manufacturing by specifically examining the impact of production efficiency, carbon emissions reduction efficiency, and market power structure on achieving low carbon manufacturing. Through the systematic analysis of optimal pricing and green technology investment decisions to improve the economic and environmental performance under different market power structures, our findings provide valuable managerial implications, which will help many manufacturing firms to make important strategic and operational decisions regarding low carbon manufacturing.

A Technology Selection and Operation (TSO) optimisation model for distributed energy systems: Mathematical formulation and case study

This paper presents a model which simultaneously optimises the selection and operation of technologies for distributed energy systems in buildings. The Technology Selection and Operation (TSO) model enables a new approach for the optimal selection and operation of energy system technologies that encompasses whole life costing, carbon emissions as well as real-time energy prices and demands; thus, providing a more comprehensive result than current methods. Utilizing historic metered energy demands, projected energy prices and a portfolio of available technologies, the mathematical model simultaneously solves for an optimal technology selection and operational strategy for a determined building based on a preferred objective: minimizing cost and/or minimizing carbon emissions. The TSO is a comprehensive and novel techno-economic model, capable of providing decision makers an optimal selection from a portfolio of available energy technologies. The current portfolio of available technologies is composed of various combined heat and power (CHP) and organic Rankine cycle (ORC) units. The TSO model framework is data-driven and therefore presents a high level of flexibility with respect to time granularity, period of analysis and the technology portfolio. A case study depicts the capabilities of the model; optimisation results under different temporal arrangements and technology options are showcased. Overall, the TSO model provides meaningful insights that allow stakeholders to make technology investment decisions with greater assurance.

Do deployment policies pick technologies by (not) picking applications?—A simulation of investment decisions in technologies with multiple applications

The role of deployment policies that aim to foster technological change has grown considerably, especially in the fields of energy and climate. However, recent research has shown that the adoption of deployment policies carries the potential of locking in the technology that is most cost-effective at the point of policy introduction, but may be inefficient in the long term. The present paper contributes to the emerging literature on the role of deployment policies in creating path-dependency and eventually technology lock-in. While previous studies focused on the relationship between lock-in and the technology-specificity of deployment policies, this paper introduces a new factor: the existence of multiple applications for a technology. We argue that this factor is highly relevant for technological lock-in and should be considered by policy makers. To support our argument, we simulate the competition among four stationary battery technologies across energy system applications in an investment simulation model. This simulation shows that the degree of competition among technologies differs strongly across applications, which corresponds with a highly varying lock-in probability. Hence, selecting applications in deployment policies very likely corresponds to selecting technologies. We discuss the implications of these results for both policy makers and for the academic debate on deployment policies and technological lock-in as well as on technology assessment and governance more generally. Based on the notion that policies can have different technology-specificity levels, we develop the idea of the application-specificity of policies and provide examples of currently enacted deployment policies that vary in terms of their technology and application specificity.

Probabilistic assessment of fleet-level noise impacts of projected technology improvements

Demand projections for civil aviation have forecast increases in operations in future decades. Increases in demand are beneficial to the growth and advancement of the aviation industry, but also come with the threat of significant increase in environmental impacts. In response, the industry is focusing on programs to develop technologies for reductions in fuel burn, NOx emissions, and noise. While aircraft-level impacts are an obvious metric of success, it is difficult to make informed robust technology investment decisions with respect to noise without understanding the fleet-level impacts. Fleet-level predictions of noise for technology explorations are especially complicated because it is computationally expensive, highly combinatorial, and airport-specific. Recently, rapid automated airport noise models have been developed, which can be simulated using Design of Experiments (DOE). The results of these simulations are used to generate surrogate models for airport noise contour area, which can be summed to yield a fleet-level impact. These models make use of simplifying assumptions to provide estimates of airport-level noise that are substantially cheaper to compute. They can be used to perform parametric trade-off analyses in conjunction with the equivalency assumption. Equivalency asserts that environmental impacts of a technology infused aircraft can be represented by scaled operations of the baseline aircraft in the same class. This simple assumption allows for the modeling of technology and market penetration factors under the same units: operations. This research uses surrogate models in conjunction with the equivalency assumption to examine two potential technology scenarios in a target forecast year, simulating technology and market performance factors to identify vehicle classes that could have the greatest impact in reducing contour area. Results show that technology and market performance of future notional Small Single Aisle and Large Single Aisle vehicle aircraft have the highest positive correlations with potential reductions in contour area.

The role of decentralized generation and storage technologies in future energy systems planning for a rural agglomeration in Switzerland

This study presents a framework to quantitatively evaluate decentralized generation and storage technology (DGST) performance and policy impacts in a rural setting. The role of DGSTs in the future energy systems planning of a rural agglomeration in Switzerland is examined using a cost optimization modeling approach. Heat and electricity demand for major sectors are considered. Scenarios introduce DGSTs in a stepwise manner to measure incremental impacts on future capacity planning compared to a baseline scenario. Sub-scenarios also examine the impacts of carbon mitigation policies, and a sensitivity analysis is carried out for key energy carriers and conversion technologies. DGSTs enable a significant reduction in electricity grid usage for the community considered. Small hydro with a storage reservoir and photovoltaics enable the community to become largely self-sufficient with over 80% reductions in grid imports by 2050 compared to the baseline scenario. Storage enables maximum usage of the available hydro potential which also leads to network upgrade deferrals and a significant increase in photovoltaic installations. Investment decisions in small hydro are robust against cost variations, while heating technology investment decisions are sensitive to oil and grid electricity prices. Carbon pricing policies are found to be effective in mitigating local fossil fuel emissions.

Optimal strategies for manufacturer with strategic customer behavior under carbon emissions-sensitive random demand

Purpose – The purpose of this paper is to investigate the manufacturer’s production, pricing and green technology investment decision problem when strategic customer behavior and carbon emissions-sensitive random demand is taken into consideration and discuss the impact of carbon emissions-sensitive demand on the manufacturer’s operation strategies, total carbon emissions and maximum expected profit. Design/methodology/approach – The authors formulate a model to introduce carbon emissions-sensitive demand into the newsvendor framework with strategic customer behavior. The authors characterize the rational expectations equilibrium to derive the optimal solutions to the manufacturer. The authors analyze the effects of carbon emissions-sensitive demand on the manufacturer’s optimal strategies, total carbon emissions and maximum expected profit by comparative analysis. Findings – The authors obtain the manufacturer’s optimal production, pricing and green technology investment strategies under rational expectations equilibrium in scenario of price-sensitive demand and that of carbon emissions-sensitive demand, respectively. The authors find that as customer demand changes from price-sensitive demand to carbon emissions-sensitive demand, the manufacturer’s optimal prices are the same but optimal production quantity, optimal unit carbon emissions and maximum expected profit go down. Though the total emissions decrease, the carbon emissions reduction would not increase as the demand is more carbon emissions-sensitive. Whether it increases or decreases depends on the model parameters. Originality/value – Carbon emissions-sensitive demand and strategic customer behavior are considered simultaneously in an integrated model. The result can guide the manufacturer decision-making. The proposed model are hoped to shed light to the future works in the field of sustainable supply chain management.

Combination of real options and game-theoretic approach in investment analysis

Investments in technology create a large amount of capital investments by major companies. Assessing such investment projects is identified as critical to the efficient assignment of resources. Viewing investment projects as real options, this paper expands a method for assessing technology investment decisions in the linkage existence of uncertainty and competition. It combines the game-theoretic models of strategic market interactions with a real options approach. Several key characteristics underlie the model. First, our study shows how investment strategies rely on competitive interactions. Under the force of competition, firms hurry to exercise their options early. The resulting “hurry equilibrium” destroys the option value of waiting and involves violent investment behavior. Second, we get best investment policies and critical investment entrances. This suggests that integrating will be unavoidable in some information product markets. The model creates some new intuitions into the forces that shape market behavior as noticed in the information technology industry. It can be used to specify best investment policies for technology innovations and adoptions, multistage R&D, and investment projects in information technology.

Optimal dynamic pricing and preservation technology investment for deteriorating products with reference price effects

Marketing and consumer behavior literature has empirically demonstrated that reference prices play a critical role in customer purchase decisions. In this paper, we propose a joint dynamic pricing and preservation technology investment model for a deteriorating inventory system with time-and-price sensitive demand and reference price effects. A generalized model is presented to jointly determine the optimal selling price, preservation technology investment and replenishment strategies that maximize the retailer's total profit over a finite planning horizon. Beginning with mild assumptions, we derive theoretical results to demonstrate the existence of an optimal solution for the deteriorating inventory problem, and reveal the sensitivities of optimal pricing and preservation technology investment decisions to an initial reference price. A simple iterative algorithm is then used to solve the proposed model by employing the theoretical results. Numerical examples and sensitivity analysis are then provided to illustrate the features of the proposed model. Finally, concluding remarks are offered.

Optimal dynamic trade credit and preservation technology allocation for a deteriorating inventory model

In this paper, we examine an optimal dynamic decision-making problem for a retailer selling a single deteriorating product, the demand rate of which varies simultaneously with time and the length of credit period that is offered to the customers. The deterioration rate is time dependent and can be reduced by an investment in preservation technology. In addition, the risk of default increases with the credit period length. A generalized model is presented to determine the optimal trade credit, preservation technology investment and replenishment strategies that maximize the retailer’s total profit after the default risk occurs over a finite planning horizon. Under certain conditions, we first establish a single period deteriorating inventory model, and then provide a comparative statics analysis that characterizes the impacts of key parameters on the retailer’s optimal trade credit and preservation technology investment decisions by using the properties of the supermodular function. Dynamic programming is then used to solve the proposed model by employing the obtained theoretical results. At the end of this paper, some numerical examples and the results of a sensitivity analysis are used to illustrate the features of the proposed model; we then offer our concluding remarks.

Technology Investment Decisions to Increase Company Value

One of the main factors, influencing economic growth in today’s global business environment is the penetration and implementation of new technologies in business. Scientific research and development of new technologies alone does not ensure total technological progress, because technological innovations without implementing and utilizing them, will not grant welfare, measured by financial – economic factors. At micro – company level, the importance of technology investment is defined as adopting of new technological equipment or innovative processes. This helps to reach higher activity results, market share and increase company market value at the same time. Aiming to develop effective new technology implementation process in a company, strategic importance is on defining investment environment and company hopes related with new technologies, which reflects the choice of the company for “technology leader” or “technology follower” position. This position is important aiming to invest in risky, but perspective-looking technological innovation or slowing investment process at the current moment by collecting information about market leaders’ actions proving or disapproving the value of new technology on the market. In this situation company managers solve optimal investment timing problem for technological innovation adoption, combining it with company value maximization aims. These aims are formulated as generated cash flows, competitive advantage, activity effectiveness increase, market share enlargement finally evaluated as company value increase

Development of a two-stage advanced manufacturing technology option selection model to use in Turkish manufacturing companies

Limited capital resources force manufacturing companies in Turkey to use systematic and thorough selection models in making their investment decisions in advanced manufacturing technologies (AMTs). Various AMT selection models are available in the literature. However, the authors' literature survey and discussions with the potential buyers of AMTs showed that there is still a need for a selection model, which should provide an optimal set of AMT options and consider strategic benefits along with financial and other constraints in a multi-level model. This study aims to develop such an AMT option selection model by obtaining the contributions of the AMT options into a manufacturing company's competitive strategy in its first level, and then incorporating them into a GP model along with financial and other constraints to select an AMT option set in its second level. A real-world case study is developed to illustrate the application of the proposed model.

Electronic Commerce Investment Under Condition Of High Uncertainty: A Real Options Approach

Why managers in electronic commerce investment projects should pay attention to real options logic when they operate in unpredictable environment? Whether existing of real options’ reasoning in these kinds of investments led to better performance and enables firms to manage, handles uncertainty and risk effectively, and minimizes any unexpected results? Which kind of uncertainty will be reduced by using real options reasoning? In the present study, we explore how real options’ reasoning has been conceptualized in the information technology and electronic commerce investment decision processes that are subject to multiple sources of uncertainties. Finally, we show that real options reasoning does not always enhance project performance under conditions of higher uncertainty. We conclude with implications for future empirical research hope that our study stimulates future work on real options and electronic commerce investment. Keywords: uncertainty perception, e- commerce investment, real options, flexibility, project performance

Abatement technology investment and emissions trading system: a case of coal-fired power industry of Shenzhen, China

As one of China’s emissions trading system (ETS) pilots, Shenzhen established the first carbon market of China in 2013. With field data collection and benefit-cost analysis, this article assesses the abatement technology investment decisions of Shenzhen coal-fired power industry under different carbon price scenarios. The results indicate that Shenzhen ETS constitutes a main driver for the short-term technology investment of the industry, but the long-term stimulation effect appears quite limited, except for the integrated gasification combined cycle technology under high carbon price scenario. Further, the paper proposes the short-term and long-term optimal investment strategy for the industry, and relevant policy suggestions.

Technology Portfolio Planning by Weighted Graph Analysis of System Architectures

ABSTRACTMany systems undergo significant architecture‐level change throughout their lifecycles in order to adapt to new operating and funding contexts, to react to failed technology development, or to incorporate new technologies. In all cases early architecture selection and technology investment decisions will constrain the system to certain regions of the tradespace, which can limit the evolvability of the system and its robustness to exogenous changes. In this paper we present a method for charting development pathways within a tradespace of potential architectures, with a view to enabling robustness to technology portfolio realization and later architectural changes. The tradespace is first transformed into a weighted, directed graph of architecture nodes with connectivity determined by relationships between technology portfolios and functional architecture. The tradespace exploration problem is then restated as a shortest path problem through this graph. This method is applied to the tradespace of in‐space transportation architectures for missions to Mars, finding that knowledge of pathways through the tradespace can identify negative coupling between functional architectures and particular technologies, as well as identify ways to prioritize future technology investments.

International study of technology investment decisions at hospitals

Purpose – Healthcare costs have increased considerably over the past decades around the world. Major contributors to this trend are expensive medical technologies. The purpose of this paper is to use a case study approach to understand how organizational and country level factors influence hospital investment behavior. Design/methodology/approach – The paper developed a conceptual framework based upon decision theory and institutional theory from which key questions were derived. The paper conducted semi-structured group interviews with relevant stakeholders in six hospitals located in five countries (Germany, India, Thailand, South Korea, USA). Findings – The paper found that the investment decisions of the interviewed hospitals are primarily affected by the healthcare system, the socio-economic and cultural context, and the organization's mission. Most of the interviewed hospitals consider multiple criteria in their decision-making framework and share similar organizational processes. Practical implications – The paper identified an international best practice approach to investment decision making at one of the hospitals. The other hospitals, despite being leading institutions in their respective countries, do not have sufficiently advanced and objective assessment approaches and would benefit from a more data-driven and systematic decision process. Originality/value – Prior research has documented that investment decisions at hospitals are driven by organizational factors. This paper shows how, in addition, country level factors – in particular healthcare system and cultural aspects – affect hospital decision-making behavior.