How Can Logistics Become More Resilient to Crises?

The logistics sector is the beating heart of global trade, yet it remains vulnerable to all kinds of crises: wars, recessions, or extreme weather events can halt supply chains. A recent study combined expert interviews with the Analytic Hierarchy Process (AHP), a method that weighs options against multiple criteria, to pinpoint which strategies truly work.

Key risks identified include supply chain disruptions, market volatility, and infrastructure problems. The most effective response? Diversifying transport routes, so if one path closes, alternatives stay open. Alongside this, companies are adopting business continuity plans that outline ready-to‑activate procedures during emergencies.

Technology takes center stage: artificial intelligence and predictive analytics allow firms to anticipate issues before they happen, optimizing routes and warehouses. Yet technology alone isn’t enough; collaboration among shippers, suppliers, and policymakers is essential, building a support network that can react swiftly.

The AHP ranking gives businesses a practical priority map: first alternative routes, then continuity planning, and finally tech investments. In an increasingly uncertain world, these moves can turn a potential disaster into a simple detour.

The Analytic Hierarchy Process (AHP) is a multi‑criteria decision‑making technique developed by Thomas Saaty in the 1970s. In the logistics context, experts defined three hierarchical levels: (1) main goal – increase resilience; (2) criteria – route diversification, operational continuity, advanced technologies, inter‑sector collaboration; (3) alternatives – specific actions for each criterion. Each pair of alternatives was compared using a judgment scale from 1 (equal importance) to 9 (extreme importance). Values were normalized to obtain relative weights, and the Consistency Ratio (CR) was checked to ensure logical coherence (CR < 0.1 is acceptable). Results showed route diversification receiving a weight of 0.42, followed by continuity planning (0.28) and AI technologies (0.20). Collaboration obtained 0.10, indicating a supportive but not primary role.

From a technical standpoint, AI applied to logistics uses supervised machine‑learning models to predict delays based on historical traffic, weather, and supplier performance data. Deep‑learning algorithms such as LSTM neural networks handle complex time series, improving forecast accuracy by up to 30% over traditional methods. Additionally, Monte Carlo simulation evaluates risk scenarios, generating probability distributions for extreme events. These methodologies, integrated into Decision Support System (DSS) platforms, give managers real‑time visibility of vulnerabilities, enabling proactive decisions and reducing response time to sudden crises.