Capacity Investment Decisions Research Articles

Incorporating flexibility requirements into long-term energy system models – A case study on high levels of renewable electricity penetration in Ireland

Efforts to meet climate change mitigation and energy security targets spur investments in variable renewable energy sources. Their implications for the operation of power plants are frequently investigated drawing on unit commitment and dispatch models. However, the temporal granularity and operational detail these models consider is commonly omitted in the broader family of long-term energy system models. To compensate this short-coming, these two types of tools have sometimes been ‘soft-linked’ and harmonised for limited simulation years. This paper assesses an alternative approach. We examine an extended version of an open source energy system model (OSeMOSYS), which is able to capture operating reserve and related investment requirements within a single tool. The implications of these model extensions are quantified through comparison with an Irish case study. That case study examined the effects of linking a long-term energy system model (TIMES) with a unit commitment and dispatch model (PLEXOS). It analysed the year 2020 in detail, applying a yearly temporal resolution that is over 700 times higher than in OSeMOSYS. Without increasing temporal resolution (and computational burden) we show that results of the enhanced OSeMOSYS model converge to results of TIMES and PLEXOS: Investment mismatches decrease from 21.4% to 5.0%. The OSeMOSYS analysis was then extended to 2050 to assess the implications of short-term variability on future capacity investment decisions. When variability was ignored, power system investments in 2050 were found to be 14.3% lower. This might imply that energy policies derived from such long-term models – of which there are many – may underestimate the costs of introducing variable renewables and thus meeting climate change or energy security targets.

Sustainable Toll Pricing and Capacity Investment in a Congested Road Network: A Goal Programming Approach

AbstractThis paper addresses the road toll pricing and capacity investment problem in a congested road network in a multicriteria decision-making framework. A goal programming approach is used in which the following four major goals are considered: (1) cost recovery, (2) service level, (3) environmental, and (4) equity. The multiobjective road toll pricing and capacity investment problem is formulated as a bilevel goal programming model. The upper level of the model aims to minimize the deviations from stated goals for a given priority ranking of the goals, while the lower level is the road users’ route choice equilibrium problem. A simulated annealing-based solution algorithm is developed to solve the proposed model. Numerical results show that the priority structure of the goals can significantly affect the road toll pricing and capacity investment decisions. The simulated annealing-based solution algorithm outperforms the sensitivity analysis-based solution algorithm in terms of solution quality. The p...

Accounting Performance and Capacity Investment Decisions: Evidence from California Hospitals

ABSTRACTCapacity decisions involve trade‐offs between the cost of capacity and the opportunity costs of lost sales. Accounting researchers posit that accounting performance provides sufficient information about these trade‐offs and thus can be used to formulate simple rules to assist capacity decisions. Empirical research has not examined the role of accounting information in capacity investment decisions at the department level in a multiproduct firm in the presence of social costs. Empirical analyses using department‐level data from California hospitals for the period 1998–2005 show that hospitals are more likely to make capacity investments in departments with high accounting performance. However, in the presence of demand variability, the association between accounting performance and capacity investment is attenuated because of the resulting increase in noise in accounting performance measures. Thus, the weight on accounting performance as a decision tool for capital investments reduces when there is demand variability. Another factor that reduces the weight on accounting performance is capacity utilization. Higher capacity utilization can lead to turning away or rerouting of patients to other hospitals and negatively impacts reputation and quality of care, which increases the hospital's social costs. Hence, hospitals do not require high accounting performance before investing in a department with high capacity utilization. This empirical evidence of the role of accounting performance in capacity investment decisions fills a gap in the capacity investment literature and furthers our understanding of the interactions between accounting performance and the operational determinants of firms’ capacity investment behavior.

Capacity investment decision by Monte Carlo approach in a cooperation network

This paper proposes a Monte Carlo approach to make the capacity investment decision. The environment concerns the capacity expansion issue for independent plants operating in a collaborative network. The plants that compose the network can share capacity through a negotiation protocol in order to balance under and over utilisation due to the customer demand. The plants apply the capacity expansion process following a periodic review policy. The generic plant makes the decision using a Monte Carlo approach that takes into account the information of the plant and the information of the collaboration with the plants of the network. The Monte Carlo simulator provides the information to each plant to support the capacity investment decision. The proposed approach is compared with a case characterised by information sharing among the plants proposed in literature and a case without information sharing. A simulation environment based on the JAVA package has been developed in order to test the approach in several market conditions. Several customer demand scenarios have been tested. The simulation results highlight these main findings: the robustness of the proposed approach; the reduction of the capacity investment keeping the same level of total profit performance; and the higher utilisation of the cooperation with the other plants of the network.

Capacity Investment and Product Line Decisions of a Multiproduct Leader and a Focus Strategy Entrant

ABSTRACTIn this article, I investigate the capacity investment cost conditions where a multiproduct market leader may respond to a focus strategy entrant by using different strategies such as changing the product mix, production volumes, quality levels, and/or by investing in more capacity. The products offered in the market are quality differentiated and customers are heterogeneous in their willingness to pay for quality. The capacity investment costs of the two firms (i.e., the leader and the entrant) may also be different. The classical Stackelberg model predicts that an incumbent does not change its position in response to entry. However, when heterogeneous customer base, product differentiation, and capacity costs are taken into consideration, I find that the leader with a low capacity cost may choose to expand its product line and increase its production. The leader with low capacity cost may introduce a product that it was holding back when the entrant has to bear the high‐capacity cost and cannibalization threat is relatively small. Nevertheless, the extent of production volume strategies reduces as the capacity cost increases for the leader. I also find that when the leader has the power to set the industry standards by deciding the quality levels, as a response to a high‐quality focused entrant, the leader increases both levels of quality and production of the low‐quality product. Moreover, when the capacity investment cost is high for both the entrant and the leader, I find that market prices may increase with entry.

Short-term market power assessment in a long-term dynamic modeling of capacity investment

This paper proposes a dynamic time simulation model for long-term generation capacity investment decisions in the presence of either perfect or imperfect electricity market. The model is based on system dynamics concept in which the dynamics of capacity construction is traced using the forecast of electricity price cleared in the short-term electricity market. Both the perfect and oligopolistic competitions are considered and a market power index is defined to evaluate the competition level of the electricity market. The short-term and long-term dynamic analysis are used to represent the generation firms' behavior in bidding strategy and capacity investment, respectively. Both possibilities of using forward bilateral contracts and application of wind power generation have been assessed individually in the proposed model in order to mitigate the market power. The time simulation results derived from a case study demonstrate the effectiveness of the model in illustrating the oligopoly in electricity marketplace and also the alternative decisions to attenuate it. Such a decision tool enables both the generation firms and the regulators to gain perfect insight into the oligopoly and its consequences in the long-term capacity investment in the electricity market.

Management Insights

Management Insights

Competitive Market Selection With Timing and Capacity Decisions

Firms faced with large capacity investment decision often face complex decisions: when to invest, which market to pursue, and how much to invest into those markets, all of which have strategic implications in competitive environments. We study a setting where two firms with symmetric costs are each considering capacity investments into one of two alternative projects, one targeting an emerging market and the other a mature market. Demand for the emerging market is uncertain and may expand through firms' market entry (positive diffusion effects), while demand for the mature market is known with certainty and cannot expand. Our analysis reveals that the existence of multiple investment opportunities may induce firms to delay their investment even in the absence of demand uncertainty, and that high diffusion effects coupled with low demand uncertainty can drive firms to invest early even if both firms could increase returns by delaying their investment. The analysis further uncovers a key ratio (a function of the average demand and costs of each market) that can help managers make timely decisions and focus their resources early. In an extension, we consider an alternative sequence of decisions.

Capacity planning and performance contracting for service facilities

Market demand uncertainty and time-based competition make capacity investment and managerial incentive decisions for service facilities such as high-end diagnostic medical imaging centers, modern IT services, or contract manufacturing shops particularly challenging. These facilities compete on service quality, short queuing times and speed. Therefore, having insufficient capacity can be economically devastating for them. Given the high up-front costs involved, firms want to make sure that they neither over- nor under-invest in service capacity. These problems get exasperated by the fact that typically firms are unfamiliar with the local market conditions and do not closely observe the demand-generating efforts of the hired managers. Most prior studies of cost allocation methodologies, contract design, and service resource management tend to address these aspects of the problem separately. They ignore the interaction effects between the capacity decisions and the managerial adverse selection and moral hazard issues, which are crucial elements for successfully running services with fixed capacity, random arrivals, and stochastic service times. Our paper instead focuses on the development of an integrated-approach to the simultaneous design of efficient managerial contracts and of capacity planning for capital intensive service facilities. We derive optimal linear contracting structures under information asymmetry between the firms and management, and analyze their impact on capacity decisions, service levels, service volumes, and the allocations of costs. Surprisingly, we prove that even though a franchise (charge-back) contract induces the first-best effort from the manager, it is not always the best choice for the firms as it may lead to inferior profits for them. In fact, our results explain why a firm's eventual contract choice should be a function of its prior on the probability distribution of the local market demand. We also explain when it may be optimal (for both the firm and the manager) to charge the manager up front a fixed franchising fee that is even greater than the total costs of capacity. Our study applies to many capital-intensive and congestion-prone service systems, where the success of significant up-front capacity investments also hinges on the daily operations of those facilities run by hired managers—who typically possess specific knowledge—that gives them a significant information advantage.

Managerial Incentives for Capacity Investment Decisions

Managerial Incentives for Capacity Investment Decisions

In This Issue

In This Issue

Comparative Statics Analysis of Multiproduct Newsvendor Networks Under Responsive Pricing

We propose a novel analytic approach for the comparative statics analysis of multiproduct multiresource newsvendor networks under responsive pricing. Our approach involves exploiting the properties of the primal mathematical programming formulation and of the dual variables and linking those properties to the concept of convex orders and to properties of the underlying demand function. The use of convex orders allows us to establish our main results without restriction to a specific demand distribution. A major strength of our approach is that it is “scalable,” i.e., it applies to newsvendor networks with any number of “nonindependent” (i.e., demand or resource sharing) products and resources, without an exponential increase in effort as problem size increases. This is unlike the current approaches commonly used in the operations management literature, which typically involve a parametric analysis of the recourse problem, followed by the use of Jacobians and the implicit function theorem. Providing a rigorous framework for comparative statics analysis, which can be applied to other problems that are not amenable to traditional parametric analysis, is our main contribution. We demonstrate this approach on the optimal capacity decision problem in multiproduct newsvendor networks under responsive pricing, formulated as a two-stage stochastic programming problem with recourse: The firm determines the resource capacities ex ante, in the first stage, when demand intercepts are uncertain, and makes the pricing and production decisions ex post, in the second stage, when demand intercepts (e.g., market conditions) are fully observed. This particular problem and its variants are well studied in the operations management literature. A comparative statics analysis is integral to the study of the capacity investment decision, as it allows answers to important questions such as the following: “Does the firm acquire more or less of the different resources available as demand uncertainty increases? Does the firm benefit from an increase in demand uncertainty?” Using our proposed approach, we establish comparative statics results on how the newsvendor's expected profit and optimal capacity decision change with demand risk in multiproduct multiresource newsvendor networks. We also extend our analysis to the study of demand dependence in two-product networks.

Optimal interconnection and renewable targets for north-west Europe

We present a mixed-integer, linear programming model for determining optimal interconnection for a given level of renewable generation using a cost minimisation approach. Optimal interconnection and capacity investment decisions are determined under various targets for renewable penetration. The model is applied to a test system for eight regions in Northern Europe. It is found that considerations on the supply side dominate demand side considerations when determining optimal interconnection investment: interconnection is found to decrease generation capacity investment and total costs only when there is a target for renewable generation. Higher wind integration costs see a concentration of wind in high-wind regions with interconnection to other regions.

Supply Chain Contracts with Capacity Investment Decision: Two-Way Penalties for Coordination

Supply chain contracting has been discussed usually under the two compliance regimes, forced and voluntary. Various contracts show different coordination characteristics in the two regimes. However, in practice, the enforcement of quantities in case of forced compliance is always an issue of concern. We propose a price compliance regime for contract where the penalties are enforceable on both parties, which are price for non compliance on quantities. In this paper, we attempt to model a contract where supplier needs to build capacity before demand is realized under the proposed price compliance regime. A need for investment in capacity can be of the form of new capacity installation or capacity enhancement or updating, and is prevalent in practice, especially in industries which witness shorter product life cycles, high rate of new product launches, and in high-tech industry. Since under-investment in capacity by supplier is a major concern for manufacturer, we model contract with supply chain capacity and cost to build the capacity are included in the model and analyze how the manufacturer can influence the capacity decision of supplier with the given contract. We analyze the impact of various penalty parameters on vendor's capacity decision, supply chain efficiency and relative allocation of supply chain profit across partners. Further, we consider a special case of uniformly distributed demand and find analytical closed form conditions for a sub-set of coordination conditions. We also consider a special case where buyer and supplier arrive at consensus on capacity related decision and capacity is verifiable by buyer.

Dynamic Interactions of TGC and Electricity Markets to Promote Wind Capacity Investment

This paper proposes a time simulation model for long-term wind capacity investment decisions in the presence of electricity market and tradable green certificate (TGC) market. Investment decisions and wind capacity development are fundamentally based on incentives gained from both these markets. In TGC market, the tradable certificates are issued to renewable generation companies for each megawatt-hour of electricity generation. Distribution companies are obligated to support fraction of their electricity consumptions from renewable sources. The dynamics of prices in both markets are simulated in a system dynamics model to trace the dynamics of wind capacity investment. Such a decision model enables both the wind generation investors and the regulators to gain perfect insight into finding possible consequences of different decisions that they should make under different policies and markets conditions particularly in the preliminary design of TGC market. The impacts of regulatory policies, trading strategies of players, and uncertainties in both markets are examined in a case study.

The capacity investment decision for make-to-order production systems with demand rate control

Abstract In this paper we study the capacity investment decision for make-to-order manufacturing firms that utilize a fixed capacity, operate in a stochastic, stationary market, and can influence their demand rate by increasing or decreasing their sales effort. We consider manufacturing situations that differ in sales contribution, in market elasticity to sales effort, work-in-process costs, and demand sensitivity to lead time. If demand is insensitive to lead time we find that for situations with a low sales contribution and high work-in-process costs (for example the manufacturing of capacity equipment that is at the end of the innovative life cycle, such as food processing machines and textile printing machines), using a dynamic demand rate policy can bring substantial improvements in profit. Moreover, when using the optimal demand rate policy, the profit is quite insensitive to the initial capacity investment. If demand is sensitive to lead time, using a dynamic demand rate policy brings substantial increases in profit in all situations considered. The profit again is quite insensitive to the initial capacity investment. Consequently, without much loss in profit, for all cases the capacity investment decision can be based on the stochastic model with stationary demand, neglecting the possibility of influencing the demand rate. However, the profit that results from this investment, and the return-on-investment, should be determined from a model that includes the optimal demand rate policy, since the stationary stochastic model can significantly underestimate the profit and could lead to the abandonment of the investment.

Capacity Management in Agricultural Commodity Processing and Application in the Palm Industry

This paper examines the capacity investment decisions of a processor that uses a commodity input to produce both a commodity output and a by-product in the context of agricultural industries. We employ a multiperiod model to study the optimal one-time processing and (output) storage capacity investment decisions—in addition to the periodic processing and inventory decisions—when both input and output spot prices as well as production yield are uncertain. We characterize the optimal decisions and perform sensitivity analysis to investigate how spot price uncertainty affects the processor’s optimal capacity and profitability. Using a calibration based on the palm industry, we study (both numerically and analytically) the performance of a variety of heuristic capacity investment policies that can be used in practice. We find that if the yield uncertainty is ignored in capacity planning, then basing those plans on the average yield is preferable to basing them (as often occurs in practice) on the maximum yield. However, planning based on the average yield performs well only when the relative (processing-to-storage) capacity investment cost is high; otherwise, it leads to a significant loss of profit. We also find that ignoring spot price uncertainty in capacity planning results in a relatively small profit loss. In contrast, ignoring by-product revenue—which constitutes a small portion of total revenues—during capacity planning substantially reduces the processor’s profit. The online appendix is available at https://doi.org/10.1287/msom.2017.0624 .

CAPACITY INVESTMENT, PRICING, AND PRODUCTION ALLOCATION ACROSS INTERNATIONAL MARKETS WITH EXCHANGE RATE UNCERTAINTY

In this paper, we investigate joint optimal capacity investment, pricing and production decisions for a multinational manufacturer who faces exchange rate uncertainties. We consider a manufacturer who sells its product in both domestic and foreign markets over a multiperiod season. Because of long-lead times, the capacity investment must be committed before the selling season begins. The exchange rate between the two countries fluctuates across periods and the demand in both markets are price dependent. Our model considers three scenarios: (1) early commitment to price and quantity with central sourcing, (2) postponement of prices and quantities with central sourcing, and (3) local sourcing. We derive the optimal capacity and the optimal prices for each scenario, and investigate the impact of the exchange rate parameters and the length of the selling season. We observe that while the price and production decisions in the domestic market are independent of the exchange rate under early commitment and local sourcing scenarios, the exchange rate between two countries directly impacts these decisions under the postponement setting. We identify thresholds and gain insights on capacity and production costs, exchange rate movement, and selling season length for the choice of entering a foreign market under all scenarios.

Trading strategies for wind capacity investment in a dynamic model of combined tradable green certificate and electricity markets

In this study, a time simulation model for long-term wind capacity investment decisions in the presence of an electricity market and a tradable green certificate (TGC) market is proposed. Decisions related to the investments in the wind capacity are fundamentally based on incentives gained from both of these markets. Fundamental concepts and models of these markets are described. The TGC price trend-following and value-trading strategies for both generation and distribution companies and the regulatory policies for the limitation of TGC holding time and TGC borrowing fraction are included. In this study, a new dynamic model of coupled TGC and electricity markets is considered in order to examine the performance of the hybrid trading strategies and regulatory policies in these markets. Besides, the paper analyses different strategies of the market players for trading TGCs and gives recommendations on the best combination of different strategies in order to reach the players' objectives. The decision model of this dynamic framework enables market players and regulators to determine the possible consequences of different decisions, such as adoption of different trading strategies and policies, and uncertainties in both the preliminary design and operating phases of a TGC market.

The Effect of Salvage Market on Strategic Technology Choice and Capacity Investment Decision of Firm Under Demand Uncertainty

This paper examines the effect of salvage market on strategic technology choice and capacity investment decision of two firms that compete on the amount of output they produce under demand uncertainty. A game theoretic model applies such that in the first stage firms choose their production technology between two alternatives: modular production process (flexible technology) or unified production process (inflexible technology). Then at the second stage they decide on the amount of capacity investment: flexible firm makes decision about general and specific components’ capacity and inflexible firm just about unified component (final product). One stage forward both enter the primary market in which demand is uncertain and play a duopoly Cournot game on the amount of quantity they manufacture and finally at the last stage, flexible firm will be able to sell its unsold general components in the secondary market (salvage market) with a deterministic price. Solving optimization problems of the model results in intractable equations which lead us to employ numerical studies considering a specific probability distribution to observe equilibrium behavior of competing firms. Broad range of parameters with respect to established relationships among them have been examined in order to cover all the possible economically reasonable scenarios. Findings are expressed explicitly in the form of observations where we demonstrate that with symmetric parameterization there is a unique symmetric Nash equilibrium in which both firms choose inflexible technology while applying asymmetric parameters has the potential to form two types of equilibrium when 1. Both firms choose inflexible technology or 2. Only one firm chooses flexible technology. Moreover it is shown that there is a specific unified cost threshold that could shift the equilibrium of the game. Finally we discuss on the case that there is no equilibrium and mention some managerial implications of the model.