Motivated by the agricultural industries, this paper studies the economic and environmental implications of biomass commercialization; that is, converting organic waste into a saleable product from the perspective of a processor that uses a commodity input to produce both a commodity output and biomass. We characterize the economic value of biomass commercialization and examine how input and output spot price uncertainties affect this value. Using a model calibration, we find that lower input spot price variability or higher output spot price variability or correlation between the two spot prices increases this value for a typical palm oil mill. To measure the environmental impact, we use total expected carbon emissions resulting from profit-maximizing decisions and characterize the change in total expected emissions after commercialization. Our analysis reveals that, although higher biomass demand or biomass price always increases the value of biomass commercialization, these changes are not necessarily environmentally beneficial as they may increase the emissions associated with biomass commercialization. We also characterize conditions under which biomass commercialization is environmentally beneficial or harmful; that is, it leads to a reduction or an increase in the total expected emissions, respectively. In comparison with the existing understanding which does not take into account optimization of operational decisions, our analysis highlights two types of misconceptions (and characterizes the specific conditions under which they appear): (i) we would mistakenly think that biomass commercialization is environmentally beneficial when it is not, and (ii) we would mistakenly think that biomass commercialization is environmentally harmful when it is not. This paper was accepted by Victor Martínez-de-Albéniz, operations management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4518 .

This section studies the economic implications of waste-to-energy conversion; that is, converting organic waste into a saleable product to be used as a feedstock in another firm (e.g., biomass boiler) to generate energy (e.g., heat, electricity). We focus on the context of an agricultural processor that uses a commodity input to produce both a commodity output and organic waste. By making a comparison with a benchmark case in which waste goes to landfill, we characterize the economic value of waste-to-energy conversion. Using a model calibration based on palm oil industry in Malaysia, we examine how this economic value is impacted by (i) input and output spot price uncertainties and (ii) organic waste price’s dependence on the output spot price.

This chapter develops a theoretical basis for understanding the trade-offs facing a farmer for allocating his farmland among several crops over multiple growing seasons. Specifically, we focus on the farmland allocation among two cash crops (corn and soybeans) and letting the farmland lay fallow to rejuvenate the soil and increase the revenue for the crop grown on this farmland in the subsequent seasons. In each growing period, the farmer chooses the allocation in the presence of revenue uncertainty for each cash crop, and crop rotation benefits across periods, where revenue is stochastically larger and farming cost is lower when a cash crop is grown on a rotated farmland (where the same crop was not grown in the previous period). We solve for the optimal dynamic allocation policy. show that growing corn in the entire farmland provides the best performance.

This paper examines crop planning decisions in sustainable agriculture-that is, how to allocate farmland among multiple crops in each growing season when the crops have rotation benefits across growing seasons. We consider a farmer who periodically allocates the farmland between two crops in the presence of revenue uncertainty where revenue is stochastically larger and farming cost is lower when a crop is grown on rotated farmland (where the other crop was grown in the previous season). We characterize the optimal dynamic farmland allocation policy and perform sensitivity analysis to investigate how revenue uncertainty of each crop affects the farmer's optimal allocation decision and profitability. Using a calibration based on a farmer growing corn and soybeans in Iowa, we show that growing only one crop over the entire planning horizon, as employed in industrial agriculture, leads to a considerable profit loss-that is, crop planning based on principles of sustainable agriculture has substantial value. We propose a simple heuristic allocation policy, which we characterize in closed form. Using our model calibration, we show that (i) the proposed policy not only outperforms the commonly suggested heuristic policies in the literature, but also provides a near-optimal performance and (ii) compared with the optimal policy, the proposed policy has a higher allocation of crops to rotated farmland, and thus, it is potentially more environmentally friendly.

This paper examines the interaction between operational flexibility and financial flexibility in a multi-product business unit that makes operational decisions based on financial resources provided by its parent company (or headquarters). We capture operational flexibility through investment in flexible technology and financial flexibility through higher availability of financial resources. We consider the flexible-versus-dedicated technology choice and capacity investment decisions of a two-product business unit under demand uncertainty in the presence of budget constraints. The unit operates under a capital budget for financing the capacity investment, and an operating budget, which is uncertain in the capacity investment stage, for financing the production. We investigate how financial flexibility in the capacity investment stage (as captured by the stringency of the capital budget) and financial flexibility in the production stage (as captured by the likelihood of having sufficient operating budget to fully cover the production cost) shape the optimal technology choice. We identify the critical role that the relative capacity intensity (the ratio of unit capacity cost to total unit capacity and production cost) of each technology plays. Our results have implications about how to deploy technologies with different capacity intensity profiles, which are shaped by automation level or plant location choices.

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 .

This paper studies the flexible versus dedicated technology choice and capacity investment decisions of a multiproduct firm under demand uncertainty in the presence of budget constraints. The firm operates under a capital budget for financing the capacity investment, and an operating budget, which is uncertain in the capacity investment stage, for financing the production. We investigate how the tightening of the capital budget and a lower financial flexibility in the production stage (the likelihood of having a sufficient operating budget) shape the optimal technology choice. We find that the dominant regime is one where dedicated technology should be adopted for a larger investment cost range, and thus, is the best response to the tighter capital budget, whereas flexible technology is the best response to lower financial flexibility. We identify the key roles that the capacity intensity (the ratio of unit capacity cost to total unit capacity and production cost) of each technology and the pooling value of operating budget with dedicated technology, which brings this technology closer to flexible technology in terms of the resource network’s flexibility, play in a budget-constrained environment. Managerially, our results underline that in the presence of financial constraints, firms should manage technology adoption together with plant location, which shapes capacity intensity, or product portfolio, which shapes financial flexibility. This paper was accepted by Serguei Netessine, operations management.

This paper studies the supply management of a primary input, where this input gives rise to multiple products in fixed proportions. My objective is twofold. First, I study fixed proportions technology under demand uncertainty in comparison with the flexible and dedicated technologies. I show that fixed proportions technology has a cost-pooling value over dedicated technology, which is larger than the capacity-pooling value of flexible technology over dedicated technology. I identify the critical role that demand correlation plays with the fixed proportions technology: in contrast to the capacity-pooling value, which decreases in demand correlation, the cost-pooling value increases in demand correlation. Second, focusing on the fixed proportions technology, I study supply management in the presence of contract and spot markets. I investigate how the optimal supply management strategy should respond to changing market uncertainties, and the differences in this response based on the contract type. I find that when the exercise price of the contract is high, a higher contract market dependence is the best response to the increasing demand correlation or spot price variability. However, a lower contract market dependence is the best response to the same when the exercise price is low. Managerially, these results are important because they imply that the supply management strategy adopted as a response to a change in the business environment should differ depending on the contract type. My results have implications about the new product strategy and the procurement contract choice of the processors in the agricultural industries. This paper was accepted by Yossi Aviv, operations management .

The objective of this chapter is twofold. First, to study how a farmer should dynamically allocate farmland between two crops when the crops have rotation benefits across growing seasons, i.e., when it is more profitable to grow a crop on rotated farmland (where the other crop was grown) than on non-rotated farmland (where the same crop was grown). Second, to develop a practically implementable heuristic allocation policy and examine its performance in comparison with other heuristic policies commonly suggested in the literature. While the chapter is based on our companion paper (Boyabatlı et al (Management Sci 65(5):2060–2076, 2019)), we characterize the optimal dynamic allocation policy in a more general setting where the rotation benefits carry through for two growing seasons. To propose a heuristic allocation policy we focus on a special case of our model where the rotation benefits carry through for one growing season, as in our companion paper. We propose a one-period lookahead policy, which we can characterize in closed form based on the optimal policy structure and examine its performance in a numerical study with models calibrated to data obtained from United States Department of Agriculture and extant resources. We show that our proposed one-period lookahead policy outperforms all other heuristic policies and provides a near-optimal performance.

This chapter provides insights on the optimal procurement decisions of a commodity processing firm that sources a primary input from a quantity flexibility contract (which is characterized by a unit reservation price and a unit exercise price), to produce two outputs in fixed proportions. The firm faces uncertainties in input spot price and output demands. Our objective is twofold. First, in a single-contract setting, we investigate the role of demand correlation in the presence of fixed proportions technology and show that the firm benefits from a higher demand correlation. Second, we investigate the firm’s optimal contract selection strategy between the two available quantity flexibility contracts. Focusing on deterministic output demands, we characterize a contract index in closed form that determines the optimal contract choice. We find that a higher expected spot price always increases the reliance on the contract with the lower exercise price. However, a higher spot price variability does the same only when the expected spot price is low and the difference between the exercise prices of two contracts is sufficiently high. Otherwise, a higher spot price variability increases the reliance on the contract with the lower reservation price.

This paper analyzes the impact of endogenous credit terms under capital market imperfections in a capacity investment setting. We model a monopolist firm that decides on its technology choice (flexible versus dedicated) and capacity level under demand uncertainty. Differing from the majority of the stochastic capacity investment literature, we assume that the firm is budget constrained and can relax its budget constraint by borrowing from a creditor. The creditor offers technology-specific loan contracts to the firm, after which the firm makes its technology choice and subsequent decisions. Capital market imperfections impose financing frictions on the firm. Our analysis contributes to the capacity investment literature by extending the theory of stochastic capacity investment and flexible versus dedicated technology choice to understand the impact of capital market imperfections, and by analyzing the impact of demand uncertainty (variability and correlation) on the operational decisions and the performance of the firm under different capital market conditions. We demonstrate that the endogenous nature of credit terms in imperfect capital markets may modify or reverse conclusions concerning capacity investment and technology choice obtained under the perfect market assumption and we explain why. The theory developed in this paper suggests some rules of thumb for the strategic management of the capacity and technology choice in imperfect capital markets. This paper was accepted by John Birge, focused issue editor.

This paper analyzes the optimal procurement, processing, and production decisions of a meat-processing company (hereafter, a “packer”) in a beef supply chain. The packer processes fed cattle to produce two beef products, program (premium) boxed beef and commodity boxed beef, in fixed proportions, but with downward substitution of the premium product for the commodity product. The packer can source input (fed cattle) from a contract market, where long-term contracts are signed in advance of the required delivery time, and from a spot market on the spot day. Contract prices are taken to be of a general window form, linear in the spot price but capped by upper and lower limits on realized contract price. Our analysis provides managerial insights on the interaction of window contract terms with processing options. We show that the packer benefits from a low correlation between the spot price and product market uncertainties, and this is independent of the form of the window contract. Although the expected revenues from processing increase in spot price variability, the overall impact on profitability depends on the parameters of the window contract. Using a calibration based on the report by the GIPSA (Grain Inspection, Packers and Stockyards Administration. 2007. GIPSA livestock and meat marketing study, vol. 3: Fed cattle and beef industries. Report, U.S. Department of Agriculture, Washington, DC), this paper elucidates for the first time the value of long-term contracting as a complement to spot sourcing in the beef supply chain. Our comparative statics results provide some rules of thumb for the packer for the strategic management of the procurement portfolio. In particular, we show that higher variability (higher spot price variability, product market variability, and correlation) increases the profits of the packer, but decreases the reliance on the contract market relative to the spot market. This paper was accepted by Yossi Aviv, operations management.

In this paper we employ intraday transaction prices of liquid E-mini S&P 500 index futures options to form 10-minutes ahead risk-neutral skewness forecasts and show profitable options trading strategy net of transaction costs. We do not find profitable trading based on 10-minutes ahead risk-neutral volatility and only very marginal cases of profitable trading using kurtosis forecasts. The skewness profitability anomaly may be an indication of informational market inefficiency in intraday S&P 500 futures options markets, which is contrary to findings using longer-span daily and weekly moments. Our results lend credence to the persistence of intraday trading activities in the markets. JEL classifications G13; G17; G23