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Analysis and prediction of food donation behavior for a domestic hunger relief organization

Author

Listed:
  • Davis, Lauren B.
  • Jiang, Steven X.
  • Morgan, Shona D.
  • Nuamah, Isaac A.
  • Terry, Jessica R.

Abstract

Non-profit hunger relief organizations strive to counteract the negative effects of food insecurity by providing food and services to individuals in need. These organizations rely on the goodwill of donors, that provide cash or in-kind food donations, to help them meet their objectives. Food donations vary considerably over time in terms of their quantity and frequency, thus presenting a challenge as organizations strive to satisfy the needs of the population being served. Using data from a domestic hunger relief organization, we analyze the uncertainty associated with in-kind food donations. A comprehensive numerical study is performed to quantify the extent of uncertainty in terms of the donor, product, and supply chain structure. In addition, several predictive models are developed to estimate the quantity of in-kind donations. Based on our numerical study, we show the relationship between forecast accuracy, donation behavior, and data uncertainty. Additionally, implications on operational efficiency and service delivery are discussed.

Suggested Citation

  • Davis, Lauren B. & Jiang, Steven X. & Morgan, Shona D. & Nuamah, Isaac A. & Terry, Jessica R., 2016. "Analysis and prediction of food donation behavior for a domestic hunger relief organization," International Journal of Production Economics, Elsevier, vol. 182(C), pages 26-37.
    • Handle: RePEc:eee:proeco:v:182:y:2016:i:c:p:26-37
    DOI: 10.1016/j.ijpe.2016.07.020
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    Cited by:

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    2. Sucharitha, Rahul Srinivas & Lee, Seokcheon, 2022. "GMM clustering for in-depth food accessibility pattern exploration and prediction model of food demand behavior," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    3. Hongyong Fu & Kok Loy Teo & Yujie Li & Lei Wang, 2018. "Weather Risk–Reward Contract for Sustainable Agri-Food Supply Chain with Loss-Averse Farmer," Sustainability, MDPI, vol. 10(12), pages 1-25, December.
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    5. Mahmoudi, Monirehalsadat & Shirzad, Khadijeh & Verter, Vedat, 2022. "Decision support models for managing food aid supply chains: A systematic literature review," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    6. Gemma Berenguer & Zuo-Jun (Max) Shen, 2020. "OM Forum—Challenges and Strategies in Managing Nonprofit Operations: An Operations Management Perspective," Manufacturing & Service Operations Management, INFORMS, vol. 22(5), pages 888-905, September.
    7. Camila Pareja Yale & Hugo Tsugunobu Yoshida Yoshizaki & Luiz Paulo Fávero, 2022. "A New Zero-Inflated Negative Binomial Multilevel Model for Forecasting the Demand of Disaster Relief Supplies in the State of Sao Paulo, Brazil," Mathematics, MDPI, vol. 10(22), pages 1-11, November.
    8. Reusken, Meike & Laporte, Gilbert & Rohmer, Sonja U.K. & Cruijssen, Frans, 2024. "Vehicle routing with stochastic demand, service and waiting times — The case of food bank collection problems," European Journal of Operational Research, Elsevier, vol. 317(1), pages 111-127.
    9. Dubey, Nistha & Tanksale, Ajinkya, 2022. "A study of barriers for adoption and growth of food banks in India using hybrid DEMATEL and Analytic Network Process," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
    10. Jon M. Stauffer & Manoj Vanajakumari & Subodha Kumar & Theresa Mangapora, 2022. "Achieving equitable food security: How can food bank mobile pantries fill this humanitarian need," Production and Operations Management, Production and Operations Management Society, vol. 31(4), pages 1802-1821, April.
    11. Shubhra Paul & Lauren B. Davis, 2022. "An ensemble forecasting model for predicting contribution of food donors based on supply behavior," Annals of Operations Research, Springer, vol. 319(1), pages 1-29, December.
    12. Pérez, Eduardo & Marthak, Yash V. & Méndez Mediavilla, Francis A., 2023. "Analysis and forecast of donations at domestic hunger relief organizations impacted by natural disasters," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    13. Altay, Nezih & Narayanan, Arunachalam, 2022. "Forecasting in humanitarian operations: Literature review and research needs," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1234-1244.

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