Using Location Intelligence to Ease the Supply Chain Crisis

The global supply chain crisis is being exacerbated by factors such as pandemic demand disruption, driver shortages, post-Brexit checks, and fuel shortages, resulting in empty shelves and disrupted retailer operations. To mitigate this crisis, Forbes suggests that supply chain workers should prioritize anti-fragility, sustainability, access, and equity over productivity and profit goals. Spatial Data Science can help identify CPG demand hotspots, optimize Modern Distribution Management, Fleet Management, and Route Optimization, reducing costs and improving visibility. Accurate demand modeling using spatial models can predict and test future demand scenarios, while routing engines use heuristics to find the shortest and most efficient paths for deliveries. The gig economy has also impacted last-mile logistics, enabling delivery drivers to quickly download apps and scan multiple items in one go. By leveraging Spatial Data Science, supply chain firms can optimize pick-up and drop-off site selection, predict demand using new data streams, and visualize existing fleets to improve efficiency and reduce costs.

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