Authors : Julius Albert Tommy Hai; Ebenezer Tetteh; Matthew Oman-Amoako

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/3xmv3zk3

DOI : https://doi.org/10.38124/ijisrt/26sep495

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Perishable-goods logistics faces intertwined uncertainty in demand, supply, product condition, temperature, and disruption, while limited shelf life narrows the time available for response. This critical integrative review examines how AI enabled forecasting relates to supply-chain resilience and how predictive capability can be represented within resilience measurement. Thirty English-language scholarly studies published between 2020 and 2026 contributed to the final synthesis across predictive applications, operational use, disruption response, resilience mechanisms, measurement approaches, and reported outcomes. The evidence shows that AI and machine-learning methods can predict demand, product quality and marketability, temperature, and disruption-related conditions, but machine-learning performance varies by forecasting task, data, horizon, and operating context and does not consistently exceed conventional methods. Evidence for predictive performance and operational use is stronger than evidence linking prediction to observed resilience outcomes. The review develops an evidence-grounded, conditional prediction-to-resilience pathway in which predictive capability produces actionable information that may be converted through available response capacity into operational intervention, changes in system behavior during disruption, and an observed resilience outcome. The analysis also distinguishes predictive measures from response-capacity indicators and realized resilience measures, showing why forecast accuracy should not be treated as resilience itself. The review concludes that predictive capability is an enabling information capability within a broader resilience system. Stronger evidence of a prediction-to-resilience relationship would require studies that observe implemented interventions during defined disruptions and measure resulting changes in continuity, loss, performance decline, or recovery.

Keywords : Artificial Intelligence; Perishable Goods Logistics; Supply Chain Resilience; Predictive Analytics; Resilience Measurement.

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