Supply Adjustments to Demand Shocks in the Commercial Real Estate Market

This paper contributes a theoretical investigation of real estate supply adjustments in the commercial real estate market. Simple theoretical linkages between the goods market (the demand side) and the space market (the supply side) are developed and then used to explain the optimal supply decisions of space producers. Propositions relating to how space production decisions are made under conditions of demand certainty, demand uncertainty and free entry are derived from optimization models. Under demand certainty, the adjustment of space supply is shown to be affected by whether an exogenous shock is perceived as mild or disastrous. Under demand uncertainty, construction based on pent-up demand is shown to be suboptimal.     

Entry,imperfect competition,and futures market for the input

We analyze firms' entry, production and hedging decisions under imperfect competition. We consider an oligopoly industry producing a homogeneous output in which risk-averse firms face an entry cost upon entering the industry, and then compete in Cournot with one another. Each firm faces uncertainty in the input cost when making production decision, and has access to the futures market to hedge the random cost. We provide two sets of results. First, under general assumptions about risk preferences, demand, and uncertainty, we characterize the unique equilibrium. In contrast to previous results in the literature (without entry), both production and output price depend on uncertainty and risk aversion. Specifically, when entry is endogenized and the futures price is not actuarially fair, access to the futures market does not lead to separation. Second, to study the effect of access to the futures market on entry and production, we restrict attention to constant absolute risk aversion (CARA) preferences, a linear demand, and a normal distribution for the spot price. In general, the effect of access to the futures market on the number of firms and production is ambiguous.     

Risk averse retail pricing with robust demand forecasting

Good demand estimates are the key to effective pricing decision-making. However, they are subject to a high degree of uncertainty due to various factors that are unpredictable or difficult to model, thus making pricing decisions risky. This research provides a simple proposal for a robust optimization methodology that incorporates both demand uncertainty and the decision maker's degree of risk aversion. Uncertainty is explicitly considered for two coefficients of a linear demand function, price expressions are derived, and a criterion is proposed for defining the degree of risk aversion. The resulting model is also applied to an exponential demand case to better reflect a more realistic retail setting.     

RELATIVE RISK CHARACTERISTICS OF ROLLING HORIZON HEDGING HEURISTICS FOR CAPACITY EXPANSION

This paper applies measures of risk to capacity expansion decisions made under uncertainty. Eight different decision making rules are constructed by varying both the frequency of the forecast updates and the hedge against uncertainty in a rolling horizon heuristic procedure. Using demand, capacity, and cost data from the utility division of a manufacturing company, the risk characteristics of each decision making rule are evaluated by simulation. The results indicate that annual forecast revisions hedged by ninety percent prediction limits are preferred over decision rules with less frequent forecast revisions or fixed-width hedges.     

Optimal pricing and quality choice of a monopolist under Knightian uncertainty

This paper analyzes a simple vertical product differentiation model with demand uncertainty and derives a risk neutral monopolist's optimal market entry timing, her optimal pricing and optimal quality choice by incorporating Knightian uncertainty, irreversibility, and flexibility in quality-enhancing investment into a continuous-time stochastic model. It is shown that an increase in Knightian uncertainty induces decreases in the optimal price, the optimal quality, and the value of undertaking the quality-enhancing investment by the monopolist. The social optimal entry timing, pricing and quality are also analyzed.     

Investment under Demand Uncertainty,Ex-Ante Pricing,and Oligopoly

This paper considers the capacity choice of duopolists who set price ex-ante under demand uncertainty with risk-neutrality. The duopolists compete for market shares on the basis of availability of supply, rather than by price competition. Collusive pricing coexists with Cournot–Nash capacity choice. A formal model is presented, where the market share of each firm may deviate from the certainty share due to rationing. With shares reflecting different costs, capacity utilisation for the lower cost firm is expected to be substantially lower. The implications for the price-cost margin and capacity formation are also explored.     

Capacity flexibility allocation in an outsourced supply chain with reservation

We consider a contract manufacturer that serves a limited number of outsourcers (customers) on a single capacitated production line. The outsourcers have different levels of demand uncertainty and the contract manufacturer faces the question how to allocate the contractual capacity flexibility in an optimal way. The contractual capacity flexibility is a contract parameter that sets the amount of demand the contract manufacturer is obliged to accept from the outsourcers. We develop a hierarchical model that consists of two decision levels. At the tactical level, the contract manufacturer allocates the capacity flexibility to the different outsourcers by maximizing the expected profit. Offering more flexibility to the more uncertain outsourcer generates higher expected revenue, but also increases the expected penalty costs. The allocated capacity flexibilities (determined at the tactical level) are input parameters to the lower decision level, where the operational planning decisions are made and actual demands are observed. We perform a numerical study by solving the two-level hierarchical planning problem iteratively. We first solve the higher level problem, which has been formulated as an integer program, and then perform a simulation study, where we solve a mathematical programming model in a rolling horizon setting to measure the operational performance of the system. The simulation results reveal that when the acceptance decision is made (given the allocated capacity flexibility decision), priority is given to the less uncertain outsourcer, whereas when the orders are placed, priority is given to the most uncertain outsourcer. Our insights are helpful for contract manufacturers when having contract negotiations with the outsourcers. Moreover, we show that hierarchical integration and anticipation are required, especially for cases with high penalty cost and tight capacities.     

Optimal Pricing and Order-Up-To S Inventory Policy with Expected Utility of the Present Value Criterion

The article determines pricing and order-up-to level S inventory decisions over an infinite planning horizon from the point of view of a risk-averse decision maker. The demand is assumed to be stochastic but influenced by the selling price which is a decision variable. Shortages are allowed and backordered partially. We calculate the present value of the cash flow over the entire planning horizon and incorporate the notion of risk aversion into the model using a concave utility function. We numerically demonstrate the model and investigate the impact of different model-parameters on the optimal decisions. It is observed that the optimal selling price for a risk-averse decision maker is not less than the optimal selling price of a risk-neutral decision maker while the optimal order level for the risk-averse decision maker is always less than that of the risk-neutral decision maker.     

Capacity reservation under spot market price uncertainty

Capacity reservation contracts and spot markets are two alternative purchasing practices. We focus on the cost-effective management of the combined use of these two procurement sources. Due to the variability of the spot market prices and demand uncertainty, the flexibility of combined sourcing can be advantageous. Spot market purchasing is a benefit in case of low spot market prices or insufficient reserved capacity, and the capacity reservation contract is an operational risk hedging for high spot market price incidents. The structure of the optimal combined purchasing policy is complex. In this paper we consider a simple and easy-to-implement capacity reservation—base stock policy and compare it to single sourcing options. We examine the joint effect of demand and spot market price uncertainty. Our analysis shows that in the case of large spot market price variability the combined sourcing is superior over spot market sourcing even in the case of low average spot price. The combined sourcing is also superior over long-term sourcing even in the case of high average spot price if there is large spot market price variability. Analytical and simulation results are presented to show the effect of the different price, cost, and uncertainty parameters on the optimal capacity reservation—base stock policy and on the expected percentage gain over single sourcing.     

Sequential price and quantity decisions under supply and demand risks

Firms need to deal with not only risks from stochastic demand but also risks from supply side. The supply side risk may be due to parts/service outsourcing, third party logistics, or random yield in production processes. In this paper, we study how firms sequentially make price and quantity decisions under these two risks. The first question we try to answer is how these two risks affect the decisions and profits of the firm. We find that increased supply risk usually causes increased quantity/stocking decision, however, there exists a threshold level of supply risk above which the firm reduces quantity/stocking amount as supply risk increases. This observation may be used in a supply chain setting, where reduction of the supply risk can cause higher delivered quantity and improve supply chain performance. This observation also provides support and insights on prioritizing the risk reduction efforts from marketing and operations to achieve better coordination. At the same time, reduction of the risks help not only firms but also consumers as the optimal price decreases. To further improve decision making process under both uncertainties, we study the impact from information revelation and postponement of decisions. We compare results from different sequential decision making cases. As illustrated in the paper, firms gain competing advantage when decision postponement is available and this advantage becomes further significant as the risks increase. Our numerical examples also indicate that price postponement strategy is usually preferred but the relative profit difference between price postponement and quantity postponement become smaller as consumers become more sensitive to the price.     

MACHINE TOOL PRODUCTION IN DEVELOPING COUNTRIES by M. M. Huq and C. C. Prendergast,Scottish Academic Press,Edinburgh, Scotland, 1983, xlll + 132 pp.

Engineering economics courses and textbooks have typically acknowledged the existence of the role of risk in economic decision making, but often in a peripheral way. The approach has been to suggest the existence of risk and uncertainty in engineering decisions, and to quantify that risk. The world of financial decision making includes many more powerful tools and techniques for risk management, rather than just risk measurement. The techniques of risk management include diversification, hedging, insuring, the capital asset pricing model (CAPM), futures contracts, options, and other investments. These portfolio tools are useful to engineering decision making, and the engineering economics community would be well served to include these topics in engineering economics undergraduate education.     

Multiproduct Price Regulation Under Asymmetric Information

We discuss the regulation of a multiproduct monopolist when the firm has private information about cost or demand conditions. The regulator offers the firm a set of prices from which to choose. When there is private information only about costs, the firm should always have a degree of discretion over its pricing policy. When uncertainty concerns demand, whether discretion is desirable depends on how demand elasticities vary with the scale of demands. If a positive demand shock is associated with a reduction in the market elasticity, discretion is good for overall welfare; otherwise it is not.     

Competition under capacitated dynamic lot-sizing with capacity acquisition

Lot-sizing and capacity planning are important supply chain decisions, and competition and cooperation affect the performance of these decisions. In this paper, we look into the dynamic lot-sizing and resource competition problem of an industry consisting of multiple firms. A capacity competition model combining the complexity of time-varying demand with cost functions and economies of scale arising from dynamic lot-sizing costs is developed. Each firm can replenish inventory at the beginning of each period in a finite planning horizon. Fixed as well as variable production costs incur for each production setup, along with inventory carrying costs. The individual production lots of each firm are limited by a constant capacity restriction, which is purchased up front for the planning horizon. The capacity can be purchased from a spot market, and the capacity acquisition cost fluctuates with the total capacity demand of all the competing firms. We solve the competition model and establish the existence of a capacity equilibrium over the firms and the associated optimal dynamic lot-sizing plan for each firm under mild conditions.     

基于CVaR的第三方回收闭环供应链的优化与协调

利用条件风险值理论研究了第三方回收闭环供应链的优化与协调问题。在随机需求与收益共享———费用共担契约下,建立了由单个风险规避零售商、单个风险规避制造商和单个风险中性第三方回收商组成的三阶闭环供应链的条件风险值模型和基于条件风险值的最优订购与定价决策模型。在对模型进行分析的基础上,揭示了制造商和零售商的风险规避水平对最优订购量、最优定价、条件风险值及闭环供应链协调性的影响。最后通过一个算例验证了研究结论。     

Equilibrium Real Options Exercise Strategies with Multiple Players: The Case of Real Estate Markets

This article derives a closed-form solution for an equilibrium real options exercise model with stochastic revenues and costs for monopoly, duopoly, oligopoly and competitive markets. Our model also allows one option holder to have a greater production capacity than others. Under a monopolistic environment we find that the optimal option exercise strategy in real estate markets is dramatically opposite to that in a financial (warrant) market, indicating the importance of paying attention to the institutional details of the underlying market when analyzing option exercise strategies. Our model can be generalized to the pricing of convertible securities and capital investment decisions involving both stochastic revenues and costs under different types of market structures.     

Metaheuristic-based simulation optimization approach to network revenue management with an improved self-adjusting bid price function

Making accurate accept/reject decisions on dynamically arriving customer requests for different combinations of resources is a challenging task under uncertainty of competitors' pricing strategies. Because customer demand may be affected by a competitor's pricing action, changes in customer interarrival times should also be considered in capacity control procedures. In this article, a simulation model is developed for a bid price–based capacity control problem of an airline network revenue management system by considering the uncertain nature of booking cancellations and competitors' pricing strategy. An improved bid price function is proposed by considering competitors' different pricing scenarios that occur with different probabilities and their effects on the customers' demands. The classical deterministic linear program (DLP) is reformulated to determine the initial base bid prices that are utilized as control parameters in the proposed self-adjusting bid price function. Furthermore, a simulation optimization approach is applied in order to determine the appropriate values of the coefficients in the bid price function. Different evolutionary computation techniques such as differential evolution (DE), particle swarm optimization (PSO), and seeker optimization algorithm (SOA), are utilized to determine these coefficients along with comparisons. The computational experiments show that promising results can be obtained by making use of the proposed metaheuristic-based simulation optimization approach.     

互补性资产研究综述

本文在归纳不同产业领域中互补性资产的影响和作用基础上,对互补性资产的衡量指标的研究进行了评价分析,认为互补性资产的研究应结合相关领域,研究方法和衡量标准应该符合各产业的实际情况。本文结合建立自主创新型国家、提科研能力的目标,提出从多学科、多角度综合集成的视角探究互补性资产的影响,以及影响的机制和途径。     

Market structure,capacity expansion,and pricing: A model applied to the Japanese iron and steel industry

The main objective of this paper is to study empirically the simultaneous and recursive relations between short-run pricing, capacity decision, and fluctuations in the elements of market structure. For this purpose, a time-series industry model was constructed and applied to the Japanese iron and steel industry for 1957–1975. The statistical results imply that over time industry market structure — especially seller concentration — might change endogenously in the system. The model also indicated that seller concentration as an element of market structure was a significant determinant of short-run domestic and export prices and also had a significant effect on the capacity decisions. These findings support the proposition that market structure affects market conduct at any moment in time while it is some degree shaped by past market conduct or behavior and structure.     

Integration of financial statement analysis in the optimal design of supply chain networks under demand uncertainty

Models that aim to optimize the design of supply chain networks have become a mainstream in the supply chain literature. This paper aims to fill a gap in the literature by introducing a mathematical model that integrates financial considerations with supply chain design decisions under demand uncertainty. The proposed Mixed-Integer Linear Programming (MILP) problem enchases financial statement analysis through financial ratios and demand uncertainty through scenario analysis. The applicability of the model is illustrated by using a case study along with a sensitivity analysis on financial parameters expressing the business environment. The model could be used as an effective and convenient strategic decision tool by supply chain managers.     

Endogenous capacities and price competition: The role of demand uncertainty

We analyze the role of demand uncertainty in markets of fixed size, in which firms take long-run capacity decisions prior to competing in prices. We characterize the set of subgame perfect Nash equilibria under various assumptions regarding the nature and timing of demand uncertainty. In order to prove equilibrium existence, we identify a sufficient condition for the capacity choice game to be submodular. This condition resembles the standard downward-sloping marginal revenue condition used in Cournot games. A robust conclusion of the analysis is that equilibrium capacity choices are asymmetric, even when firms are ex-ante identical. Concerning the equivalence between the capacity-price game and the Cournot game, we find that with inelastic demands, the equilibria of the former belong to the equilibrium set of the latter. However, as compared to the Cournot game, the capacity-price game leads to lower prices and generates price dispersion.