Building “First Expire, First Out” models to predict food losses at retail due to cold chain disruption in the last mile

Current less-than-truckload (LTL) shipping practices allow for temperature abuse (TA) in the last segment (last mile) of food supply chain. When this TA is combined with “First In, First Out” product rotation methods, it could lead to spoilage and waste; therefore, data-based decision models are needed aid retail managers. An experiment was designed using pallets (4 layers/pallet × 5 boxes/layer) commercially produced boneless chicken breast filet trays. The were exposed 24 h simulated LTL (cyclic 2 at 4°C, then 23 ± 2°C). Filet temperatures recorded all 20 boxes dataloggers thermocouple wires. Additionally, microbiological sampling filets [aerobic plate counts (APC) psychrotrophic (PSY)] conducted before (0 TA) after (24 select pallet compared control (maintained 4°C). After TA, a shelf-life by storing from predetermined 4°C until (7 log CFU/ml). Temperature data augmented Monte Carlo simulations (MC) build making two methods; (1) risk each box on reaching bacterial “danger zone” (>4°C) determined; (2) risk-of-loss (shelf-life < 4 days; minimum required prevent waste) determined. results indicated that top bottom layers reached faster than comprising middle while perimeter layer centrally located boxes. Shelf-life indicate reduced 2.25 1.5 days APC PSY, respectively. first MC method showed average 94.96%, 43.20%, 27.20%, 75.12% 1–4, second exposure >4°C 8 43.8%. findings decreases heterogenous manner according location pallet. Therefore, predictive make objective decisions so Expire, can be implemented reduce wastes due during mile.