Demand Uncertainty and Excess Supply in Commodity Contracting

Strategic and economic implications of forward contracting in the presence of capacity expansion

Executive Summary. This study analyzes the lead-lag relationship between the spot and forward returns on direct real estate investments. Based on the forward price index (for which the term to maturity is zero) and the ex-post spot price index of residential property in Hong Kong, changes in information flow between the spot and forward markets are tested to see how they affect the lead-lag relationship. The findings suggest that (1) during periods of low-volume ratios (i.e., the forward market is relatively less active than the spot market), the spot return Granger causes the returns of forward contracts; and (2) during periods of higher-volume ratios, there are feedback relationships between the two markets.

Multinational firms will engage in operational hedging only when both exchange rate uncertainty and demand uncertainty are present. Operational hedging is less important for managing short-term exposures, since demand uncertainty is lower in the short term. Operational hedging is also less important for commodity-based firms, which face price but not quantity uncertainty. For firms with plants in both a domestic and foreign location, the foreign currency cash flow generally will not be independent of the exchange rate. Consequently the optimal financial hedging policy cannot be implemented with forward contracts alone but can be implemented using foreign currency call and put options, and forward contracts.

Using risk-management tools on B2Bs: An exploratory investigation

This paper presents a stochastic decision making framework of the retailer for the power procurement in the deregulated electricity market taking into account the risk induced by the volatile market prices. Unlike conventional related literature, the paper incorporates the three types of trades, which are a forward contract, a spot market, and a real-time market for adjusting the imbalance, in the transaction model with rational responses of the consumers to the selling prices offered by the retailer. For the risk hedge using the forward contract, it is quite significant to reflect the risk attitude of the retailer on decision making in market transactions. In the literature, the weighting method is often used to formulate the retailer's decision making for the power procurement. In the weighting method, however, it is difficult to determine both the weighting coefficients and the confidential level associated with the risk so as to reflect the risk attitude of the retailer adequately, particularly in the bi-level programming problem. In this paper, in order to naturally model the retailer's decision making, we employ the fractile model in which the target variable is maximized subject to the probability that the profit is not less than the target variable exceeds a given assured level given by the retailer according to the risk attitude. Through the computational experiments, we demonstrate the retailer's behavior with satisfactory level for the profit and risk in the deregulated electricity market.

Commodity trading is as old as human civilization and one of the earliest economic pursuits of mankind. Over the centuries, commodity trading has evolved from the barter system to spot markets to derivatives markets. In barter system, goods were exchanged between two parties with matching and opposite needs (for example, bags of wheat were exchanged for cattle). Over a period of time, commodities brought from distant places were exchanged for gold and silver. With the introduction of Money as a medium of exchange, there was a paradigm shift in commodity trading with the value of commodity being expressed in monetary terms and trading in commodities was conducted mainly through the medium of currency. Commodity spot markets evolved in many places and the counterparties met at these common places where goods were brought for immediate sale and delivery at the market price decided by the demand and supply forces. In commodity spot markets, traders sell goods such as rice for immediate delivery against cash. At some stage, counterparties started entering into agreements to deliver commodities (e.g.: wheat) at a specified time in future at a price agreed today. These forward contracts, more often than not, were not honored by either of the contracting parties due to price changes and market conditions. A seller pulled out of the contract if the spot price was more profitable for him than the contracted price. A buyer also backed out from executing the contract on maturity if he was able to get the commodity at a cheaper price from the spot market. Futures emerged as an alternative financial product to address

The paper presents an investigation of market power potential in a deregulated hydro and thermal based electricity market through a two-stage stochastic Cournot model. The optimal hydro and forward strategies are examined when generation companies take into account the commitment effect of these decisions, i.e. the influence on the future play of opponents. The generation capacity of each company is regarded as given. In the first stage, firms decide on forward contracting, hydro and thermal generation (including nuclear) for the current spot market. In the second stage, they learn about water inflows to reservoirs before deciding on generation for the second stage spot market. Using cost and capacity data and historical inflow series from the Scandinavian market, large pure hydro producers are found to have little or no market power on the seasonal level, and that the largest producer, Vattenfall, has some incentive to withdraw capacity from the market. The simulated price in an oligopoly case is less than 7% higher than under perfect competition, thus the market is expected to be reasonably competitive. Still, competition authorities should monitor and possibly prevent market concentration to increase.

To procure the electric energy to be sold to its clients, a retailer has to face two major difficulties. While buying electric energy, it must cope with uncertain pool prices or sign forward contracts at higher average prices. While selling electricity, it should handle the uncertainty of the end-user demand and the fact that customers might choose a different retailer if the selling price is not competitive enough. We propose a risk-constrained stochastic programming framework to decide which forward contracts the retailer should sign and at which price it must sell electricity so that its expected profit is maximized at a given risk level. The developed model takes into account the elastic behavior of end users with respect to the selling price offered by the retailer. A realistic case study illustrates the methodology proposed.

Supply chain management is widely accepted as a means for companies to gain competitive advantage. While product and information flows have been widely covered in the literature, relatively little attention has been paid to the management of a supply chain's finances. The objective of this dissertation, therefore, is to add to the emerging literature on financial flows. One inroad to researching financial flows in supply chains is to build on finance theory. Research in finance has analyzed financial flows from a credit point of view. Although researchers have identified more than ten motives for why firms offer and demand trade credit, a comprehensive review of this literature is missing. Following an introduction in Chapter 1, we therefore review this literature in Chapter 2 of this dissertation. We find that two explanations for the existence of trade credit receive broad support. Suppliers offer trade credit to increase product sales. Buyers demand trade credit to circumvent credit rationing. Following the review, we develop a detailed agenda for future research. We find that the relevant operations management literature builds only on a fraction of the insights from the finance literature and propose in-depth studies of capital access, transaction pooling, and control protection as promising areas for future research. Another way to researching financial flows in the context of supply chains is to leverage the methodological richness of the finance domain. In Chapter 3, we therefore investigate optimal financial flows by analyzing secondary data through multivariate regression. To test our hypotheses, we compile panel data on 3,383 groups of public United States firms from three databases. One of these databases is Revere Relationships, a relatively novel database on commercial relations. We find that our data are consistent with the causal relations and theoretical predictions of the operations management literature. Firm profitability is positively associated with payment delay. Payment delay, in turn, is positively associated with the capital cost difference between buyer and supplier and negatively associated with the price elasticity of demand and the deterioration rate of inventory. However, we do not observe any significant interactions between these factors, which raises a number of questions for future research. Another well established methodology in the finance domain is the analysis of stock market reactions to corporate events. Supply chain researchers have used this methodology to analyze the effect of corporate events such as product introduction delays, total quality management implementations, and supply chain disruptions. However, despite the fact that supply chain management is concerned with connected entities, these studies have mainly focused on single firms. In Chapter 4, we therefore study the shareholder value loss associated with disruption announcements across suppliers, customers, and rivals. Our event study finds that suppliers experience -1.24%, customers -0.07%, and rivals -0.19% abnormal stock market returns over a two-day period when firms announce supply chain disruptions. We also implement cross-sectional regression to test several hypotheses. For suppliers, dependence on the disruption announcing firm aggravates the effect. The substitutability of the disruption announcing firm, however, seems to be irrelevant in both the suppliers' and the customers' case. To facilitate the adoption of our results by practicing managers, we consolidate the previously generated insights and propose three trade credit strategies (win-win, follow, and squeeze) and a decision tree in Chapter 5. Furthermore, we highlight an innovative example of a win-win strategy (reverse factoring) and examine it through a survey of 213 finance and operations managers. On average, we find that companies that use reverse factoring benefit from a reduction in working capital of 13%. The same managers report that suppliers benefit, too, by reducing working capital by 14%. However, we find that three factors distinguish successful from less successful implementations – choosing the right banking partner, ensuring CEO sponsorship, and involving at least 60% of the supply base. We complete the dissertation with a conclusion in Chapter 6.

Production planning as a strategic decision involves the allocation of aggregate production resources for groups of aggregate products. Is performed in a manufacturing setting to provide efficient utilization of those production resources while meeting sales opportunities for the finished products. In this paper the production planning is studied under demand uncertainty. The consideration of uncertainty in manufacturing systems requires consideration not only of demand but also of alternative scenarios of variations in these demands. The aim of this study is to evaluate the robustness of a system consists of satisfying the total demand in a multi-facility plant forming a production-inventory network. To consider the demand uncertainty, a set of possible scenarios for demands is created based on the Monte Carlo simulation. The deterministic model takes the form of a Mixed Integer Linear Program (MILP) which objective is to maximize the customer demand satisfaction (CDS) level. The proposed model provides an effective tool for evaluating the exposure of a firm assets to market uncertainties. The key features of the integrated model is illustrated with a case study of fertilizers production industry.

Using unique data on Swedish households, we measure the price elasticity of electricity demand for households facing a mandatory non-linear distribution tariff, where households are charged based on their maximum consumption during a month, and where the marginal incentives are very large. We estimate the price elasticity using both 2SLS and bunching estimators, and we find that the price elasticity is smaller than what many previous studies on electricity demand have found.We show that the 2SLS estimates are not robust to changes to the set of controls or to the sample definition, while the bunching estimates suggest that the price elasticity of electricity demand is small in response to the large marginal incentives.Furthermore, we illustrate why charging households based on maximum consumption during a month leads to weak incentives in the end of the month, and discuss alternative tariff designs.

<div class="section abstract"><div class="htmlview paragraph">Shared autonomous vehicles systems (SAVS) are regarded as a promising mode of carsharing service with the potential for realization in the near future. However, the uncertainty in user demand complicates the system optimization decisions for SAVS, potentially interfering with the achievement of desired performance or objectives, and may even render decisions derived from deterministic solutions infeasible. Therefore, considering the uncertainty in demand, this study proposes a two-stage robust optimization approach to jointly optimize the fleet sizing and relocation strategies in a one-way SAVS. We use the budget polyhedral uncertainty set to describe the volatility, uncertainty, and correlation characteristics of user demand, and construct a two-stage robust optimization model to identify a compromise between the level of robustness and the economic viability of the solution. In the first stage, tactical decisions are made to determine autonomous vehicle (AV) fleet sizing and the initial vehicle distribution. In the second stage, operational decisions are made under scenarios of fluctuating user demand to optimize vehicle relocation strategies. To enhance the efficiency of model resolution, the original two-stage robust optimization model is decomposed into separable subproblems, which are transformed using duality theory and linearization. An effective solution is achieved through a precise algorithm utilizing column-and-constraint generation (C&CG). Numerical experiments are conducted on a small-scale network to validate the effectiveness of the model and algorithm. Furthermore, adjustments to the demand fluctuation scenarios are made to assess the impact of uncertain budget levels Γ on the total revenue of SAVS. This research provides AV sharing service operators with an optimal relocation scheduling strategy that balances robustness and economic efficiency.</div></div>

We investigate how different types of social information affect the demand characteristics of firms competing through service quality. We first generate behavioral hypotheses around both consumers’ learning behavior and firms’ corresponding demand characteristics: market share, demand uncertainty, and rate of convergence. We then conduct a controlled human-subject experiment in which a consumer chooses to visit one of two firms, each with unknown service quality, in a repeated interaction and is exposed to different information treatments from a social network: (1) no social information; (2) share-based social information, which details the percentage of people who visited each firm; (3) quality-based social information, which illustrates the percentage of people who received a satisfactory experience from each firm; or (4) full social information, which contains both share- and quality-based social information. A key insight from our study is that different types of social information have different effects on firms’ demand. First, promoting quality-based social information leads to a significantly higher market share, lower demand variability, and faster rate of convergence for a firm with significantly better service quality. Second, when the higher quality firm has only a marginal advantage over the other firm, promoting only share-based information leads to significantly higher market share and lower demand variability. A third important result is that providing only one type of social information can actually be more helpful to the higher quality firm than providing full social information. This paper was accepted by David Simchi-Levi, operations management.

The recent rise in public environmental awareness, concerns of national energy security, and high transportation fuel prices have all served to heighten the interest in alternative fuels. One fundamental issue influencing the economic viability of the ethanol industry is consumers’ demand-responsiveness to both gasoline and ethanol price changes. In this paper we present an alternative approach to this problem by estimating the geographic variation of price-elasticity of demand for ethanol across the study area, a departure from previous studies of ethanol demand, in which the price-elasticity of demand is identical across the space. Considering the spatial heterogeneity in household composition and demand preferences, using global estimates to explain the price-demand relationships over a large geographic area may lead to biased results. We demonstrate that the spatially weighted regression technique provides superior estimates over a global regression model. Resulting price-elasticities of demand for ethanol reveal significant geographic variation, suggesting that the use of spatially disaggregated data provides more detailed empirical results and a more thorough understanding for policy determination related to the ethanol industry. In a subsequent paper, these results will be used to simulate the effects of state-level alternative fuel policies on reducing the environmental emissions.

The role of financial slack on the relationship between demand uncertainty and operational efficiency