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

International Journal of Production Economics

Volumn 182, Issue , 2016, Pages 26-37

  Davis, Lauren B ^a   Jiang, Steven X ^a   Morgan, Shona D ^b   Nuamah, Isaac A ^a   Terry, Jessica R ^a  

^a North Carolina Agricultural and Technical State University   (United States)

^b NORTH CAROLINA A AND T STATE UNIVERSITY   (United States)

Author keywords

Donations; Food bank operations; Food insecurity; Forecasting; Hunger relief

Indexed keywords

FORECASTING; LIFESAVING EQUIPMENT; SOCIETIES AND INSTITUTIONS; SUPPLY CHAINS;

DONATIONS; FOOD BANKS; FOOD INSECURITY; FORECAST ACCURACY; HUNGER RELIEF; OPERATIONAL EFFICIENCIES; RELIEF ORGANIZATIONS; SUPPLY CHAIN STRUCTURES;

UNCERTAINTY ANALYSIS;

EID: 84982078849     PISSN: 09255273     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijpe.2016.07.020     Document Type: Article

References (54)

1. Adivar, B., Atan, T., Sevil Ofla, B., rten, T., Improving social welfare chain using optimal planning model. Supply Chain Manag.: Int. J. 15:4 (2010), 290–305.
2. Bartholdi, J.J. III, Platzman, L.K., Collins, R.L., Warden, W.H. III, A minimal technology routing system for meals on wheels. Interfaces 13:3 (1983), 1–8.
3. Beamon, B.M., Balcik, B., Performance measurement in humanitarian relief chains. Int. J. Public Sect. Manag. 21:1 (2008), 4–25.
4. Beliën, J., Forcé, H., Supply chain management of blood products: a literature review. Eur. J. Oper. Res. 217:1 (2012), 1–16.
5. Berenguer, G., Modeling approaches and metrics to evaluate nonprofit operations. Zobel, C., Altay, N., Haselkorn, M., (eds.) Advances in Managing Humanitarian Operations, 2016, Springer International Publishing, New York, NY, 9–31.
6. Campbell, E., Hudson, H., Webb, K., Crawford, P.B., Food preferences of users of the emergency food system. J. Hunger Environ. Nutr. 6:2 (2011), 179–187.
7. Campbell, C.C., Food insecurity: a nutritional outcome or a predictor variable?. J. Nutr. 121:3 (1991), 408–415.
8. Campbell, B., 2010. Food supply dangerously low at second harvest food bank, 〈 http://myfox8.com/2012/08/13/food-supply-dangerously-low-at-second-harvest-food-bank/〉, accessed 2016-06-01.
9. Chen, R.J., Bloomfield, P., Cubbage, F.W., Comparing forecasting models in tourism. J. Hosp. Tour. Res. 32:1 (2008), 3–21.
10. Coleman-Jensen, A., Gregory, C., Singh, A. Household food security in the United States in 2013. USDA-ERS Economic Research Report (173).
11. Complex/manmade hazards: famine, food insecurity, 〈 http://www.ifrc.org/en/what-we-do/disaster-management/about-disasters/definition-of-hazard/famine-food-insecurity〉, accessed 2015-08-04.
12. Davis, L.B., Sengul, I., Ivy, J.S., Brock, L.G., Miles, L., Scheduling food bank collections and deliveries to ensure food safety and improve access. Socio-Econ. Plan. Sci. 48:3 (2014), 175–188.
13. Enders, W., Applied Econometric Time Series. 2004, Wiley, New York.
14. Food Bank of Central and Eastern North Carolina. Hunger matters: 2014 impact report, 〈 http://www.foodbankcenc.org/site/DocServer/2013-2014_Food_Bank_Impact_Report.pdf?docID=2103〉, accessed 2015-08-04 (2014).
15. Gardner, J., Everette, S., Box-Jenkins vs multiple regression: some adventures in forecasting the demand for blood tests. Interfaces 9:4 (1979), 49–54 URL 〈 http://www.jstor.org/stable/25059770〉.
16. Gardner, E.S., Exponential smoothing: the state of the art. J. Forecast. 4:1 (1985), 1–28.
17. Gunes, C., van Hoeve, W.-J., Tayur, S., 2010. Vehicle routing for food rescue programs: a comparison of different approaches. In: Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, Springer, pp. 176–180.
18. Handforth, B., Hennink, M., Schwartz, M.B., A qualitative study of nutrition-based initiatives at selected food banks in the feeding america network. J. Acad. Nutr. Diet. 113:3 (2013), 411–415.
19. Hoisington, A., Manore, M.M., Raab, C., Nutritional quality of emergency foods. J. Am. Diet. Assoc. 111:4 (2011), 573–576.
20. Holguín-Veras, J., Pérez, N., Ukkusuri, S., Wachtendorf, T., Brown, B., Emergency logistics issues affecting the response to Katrina: a synthesis and preliminary suggestions for improvement. Transp. Res. Rec.: J. Transp. Res. Board 2022 (2007), 76–82.
21. Holguín-Veras, J., Jaller, M., Van Wassenhove, L.N., Pérez, N., Wachtendorf, T., Material convergence: important and understudied disaster phenomenon. Nat. Hazards Rev. 15:1 (2012), 1–12.
22. Hoyos, M.C., Morales, R.S., Akhavan-Tabatabaei, R., Or models with stochastic components in disaster operations management: a literature survey. Comput. Ind. Eng. 82 (2015), 183–197.
23. Leiras, A., De Brito, I. Jr, Queiroz Peres, E., Rejane Bertazzo, T., Tsugunobu Yoshida, H., Yoshizaki, Literature review of humanitarian logistics research: trends and challenges. J. Humanit. Logist. Supply Chain Manag. 4:1 (2014), 95–130.
24. Lien, R.W., Iravani, S.M., Smilowitz, K.R., Sequential resource allocation for nonprofit operations. Oper. Res. 62:2 (2014), 301–317.
25. Makridakis, S., Hibon, M., The m3-competition: results, conclusions and implications. Int. J. Forecast. 16:4 (2000), 451–476.
26. McLachlin, R., Larson, P.D., Khan, S., Not-for-profit supply chains in interrupted environments: the case of a faith-based humanitarian relief organisation. Manag. Res. News 32:11 (2009), 1050–1064.
27. Mohan, S., Gopalakrishnan, M., Mizzi, P., Improving the efficiency of a non-profit supply chain for the food insecure. Int. J. Prod. Econ. 143:2 (2013), 248–255.
28. Nahmias, S., Production and Operations Analysis. 2009, McGraw-Hill, New York.
29. Ohls, J., Saleem-Ismail, F., Cohen, R., Cox, B., 2002. The emergency food assistance system-findings from the provider survey. volume ii: Final report, Technical report, United States Department of Agriculture, Economic Research Service.
30. Özpolat, K., Rilling, J., Altay, N., Chavez, E., Engaging donors in smart compassion: usaid cidis greatest good donation calculator. J. Humanit. Logist. Supply Chain Manag. 5:1 (2015), 95–112.
31. Pankratz, A., Forecasting with Univariate Box-Jenkins Models. 1983, Wiley, New York.
32. Paynter, S., Berner, M., Anderson, E., When even the 'dollar value meal' costs too much: food insecurity and long term dependence on food pantry assistance. Public Adm. Q., 2011, 26–58.
33. Pereira, A., Performance of time-series methods in forecasting the demand for red blood cell transfusion. Transfusion 44:5 (2004), 739–746.
34. Phillips, C., Hoenigman, R., Higbee, B., Reed, T., Understanding the sustainability of retail food recovery. PloS One, 8(10), 2013, e75530.
35. Rancourt, M.-v., Cordeau, J.-F., Laporte, G., Watkins, B., Tactical network planning for food aid distribution in Kenya. Comput. Oper. Res. 56 (2015), 68–83.
36. Reschovsky, J.D., The emergency food relief system: an empirical study. J. Consum. Aff. 25:2 (1991), 258–277.
37. Russell, T.E., 2005. The humanitarian relief supply chain: analysis of the 2004 south east Asia earthquake and tsunami. Ph.D. thesis, Massachusetts Institute of Technology.
38. Sengul Orgut, I., Ivy, J.S., Uzsoy, R., Wilson, J.R., Modeling for equitable and effective distribution of donated food under capacity constraints. IIE Trans. 48:3 (2016), 252–266.
39. Sengul Orgut, I., Brock III, G.L., Davis, B.L., Ivy, S.J., Jiang, S., Morgan, D.S., Uzsoy, R., Hale, C., Middleton E., 2016b. Achieving Equity, Effectiveness, and Efficiency in Food Bank Operations: Strategies for Feeding America with Implications for Global Hunger Relief, In: Zobel, C., Altay, N., Haselkorn, M (Eds.), Advances in Managing Humanitarian Operations, Springer International Publishing, New York, NY, pp. 229–256.
40. Sönmez, E., Lee, D., Gmez, M.I., Fan, X., Improving food bank gleaning operations: an application in New York State. Am. J. Agric. Econ., 2015, aav069.
41. Solak, S., Scherrer, C., Ghoniem, A., The stop-and-drop problem in nonprofit food distribution networks. Ann. Oper. Res. 221:1 (2014), 407–426.
42. Syntetos, A.A., Babai, Z., Boylan, J.E., Kolassa, S., Nikolopoulos, K., Supply chain forecasting: Theory, practice, their gap and the future. Eur. J. Oper. Res. 252:1 (2016), 1–26.
43. Tabachnick, B., Fidell, L., Using Multivariate Statistics. 2007, Allyn and Bacon, Boston.
44. Tarasuk, V., Eakin, J.M., Charitable food assistance as symbolic gesture: an ethnographic study of food banks in ontario. Social. Sci. Med. 56:7 (2003), 1505–1515.
45. Tarasuk, V., Eakin, J.M., Food assistance through surplus food: Insights from an ethnographic study of food bank work. Agric. Human. Values 22:2 (2005), 177–186.
46. Tersine, R.J., Principles of Inventory and Materials Management. 1994, Prentice-Hall, Englewood Cliffs, NJ.
47. United States Department of Agriculture Food and Nutrition Service. How to get food help, 〈 http://www.fns.usda.gov/sites/default/files/ConsumerBrochure.pdf〉, accessed 2015-08-04 (2010).
48. Ülkü, M.A., Bell, K.M., Wilson, S.G., Modeling the impact of donor behavior on humanitarian aid operations. Ann. Oper. Res., 2015, 1–16.
49. Van Wassenhove, L.N., Humanitarian aid logistics: supply chain management in high gear. J. Oper. Res. Soc. 57:5 (2006), 475–489.
50. Wang, Z.-X., A genetic algorithm-based grey method for forecasting food demand after snow disasters: an empirical study. Nat. Hazards 68:2 (2013), 675–686.
51. Weatherford, L.R., Kimes, S.E., Scott, D.A., Forecasting for hotel revenue management: testing aggregation against disaggregation. Cornell Hotel Restaur. Adm. Q.(August), 2001, 53–64.
52. Wunderlich, G.S., Norwood, J.L., Food insecurity and hunger in the United States: an assessment of the measure. 2006, National Academies Press.
53. Xu, X., Qi, Y., Hua, Z., Forecasting demand of commodities after natural disasters. Expert Syst. Appl. 37:6 (2010), 4313–4317.
54. Zotteri, G., Kalchschmidt, M., A model for selecting the appropriate level of aggregation in forecasting processes. Int. J. Prod. Econ. 108:1 (2007), 74–83.