Growing Role of Predictive Analytics in Logistics and Supply Chain

Linda James, Logistics Tech Outlook | Thursday, March 04, 2021

By using predictive tools to produce supply and demand forecasts, businesses can proactively make the best operational decisions.

FREMONT, CA: The advent of predictive analytics has helped logistics and supply chain firms satisfy the rising demands. The logistics industry has described predictive analytics as having the most considerable effect on the supply chain this decade. This shift towards anticipatory logistics is also generally agreed by business decision-makers: one survey showed that 93 percent of shippers and 98 percent of third-party logistics companies agree that data-driven decision-making is vital to supply chain operations, and 71 percent agree that big data increases efficiency per year.

Following are a wide range of predictive analytics use cases in logistics:

Improved Supply Chain Visibility

In this modern age, shippers and manufacturers have fully revised their visibility of the shipment's life cycle. Research has demonstrated just how predictive analytics generate additional insight in the supply chain—helping 3PLs prevent late shipments by tracking devices; improving the visibility of shipping status and location; preventing late or off-scheduled shipment costs; and generating new market opportunities by fulfilling visibility criteria.

top logistics analytics solution companies

Forecast

Today, anyone can schedule weeks or even months in advance to organize inventory and shipments based on consumer demand and purchasing behavior, thereby meaning less waste and more on-time deliveries. By using predictive tools to produce supply and demand forecasts, businesses can proactively make the best operational decisions. This strategy can also facilitate the rebalancing of assets through any logistic network at a minimum cost.

See Also: Top Logistics Tech Solution Companies

Transportation Management Systems (TMS)

Logistics service providers are increasingly dependent on transport management systems to track and handle shipments and lead times. Through predictive analytics, several TMS can now foresee potential disruptions before they emerge and help logistics companies manage their activities proactively rather than reactively. Predictive analytics will also provide new insight to seasonal purchasing trends and predictions to help suppliers make more educated choices.

Unanticipated Circumstances

Organizations can help plan for short-term behavioral changes impacting the supply chain and distribution, like news, weather, disruptions, and product promotion. Businesses can better adjust shipments, and inventory, in response to real, time-sensitive shifts in routes or inventory using predictive analytics models to identify unpredictable conditions.

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