Managing Operations of a Hog Farm Facing Volatile Markets: Inventory and Selling Strategies

Panos Kouvelis (), Ye Liu (), Yunzhe Qiu () and Danko Turcic ()
Additional contact information
Panos Kouvelis: Olin Business School, Washington University in St. Louis, St. Louis, Missouri 63130
Ye Liu: Olin Business School, Washington University in St. Louis, St. Louis, Missouri 63130
Yunzhe Qiu: Department of Information Management, Peking University, Beijing 100871, The People’s Republic of China
Danko Turcic: A. Gary Anderson Graduate School of Management, University of California, Riverside, California 92507

Manufacturing & Service Operations Management, 2023, vol. 25, issue 5, 1711-1729

Abstract: Problem definition: We study a dynamic finishing-stage planning problem of a pork producer who at the beginning of each week gets to see how many market-ready hogs she has available for sale and the current market prices. Then, she must decide which hogs to sell to a meatpacker and on the open market and which hogs to hold until the following week. The farmer is contracted to deliver a fixed quantity of hogs to the meatpacker each week priced according to a contractually predetermined market index. If the farmer underdelivers to the meatpacker, she pays a contractually predetermined unit penalty also linked to a market index. Biosecurity protocols prevent the farmer from buying hogs on the open market and selling them to the meatpacker. The farmer can, however, use the open market to sell hogs for prevailing market prices. Methodology/Results : We treat the problem as a dynamic, multiitem, nonstationary inventory problem with multiple sources of uncertainty. The optimal policy is a threshold policy with multiple price-dependent thresholds. The computational complexity required to evaluate the thresholds is the biggest impediment to using the optimal policy as a decision-support tool. So, we utilize an approximate dynamic programming approach that exploits the optimal policy structure and produces a sharp heuristic that is easy to implement. Managerial implications : Numerical experiments calibrated to a pork producer’s data reveal that the optimal policy with the heuristically estimated thresholds substantially improves the existing practice (around 25% on average). The success of the proposed model is attributed to recognizing the value of holding underweight hogs and effectively hedging supply uncertainty and future prices—an insight missed in the planning actions of the current practice.

Keywords: dynamic programming; incentives and contracting; inventory theory and control; operations strategy; risk management (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/msom.2023.1216 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:25:y:2023:i:5:p:1711-1729

Access Statistics for this article

More articles in Manufacturing & Service Operations Management from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().