Data-Driven Logistics: The Growing Use of Predictive Analytics
July 9, 2018
Logistics companies depend on accuracy, timeliness and the efficient transportation of parts to meet distribution and customer requirements. The challenge is in the constant balancing act between managing inventory levels and meeting customer demand. What if there was a way to make predictions about future events – to know more precisely what the demand is at any given moment?
Predictive analytics is changing the game for distributors and logistics operators that are using historical and real-time patterns to make such predictions and, as a result, decrease costs, create greater reliability and improve customer satisfaction.
Connecting the Dots
Tapping big data, predictive analytics uses algorithms to track historical data to anticipate demand, avoid risks and adjust schedules—all of which better enable logistics managers to create proactive solutions. Through predictive analytics, logistics managers can anticipate future behavior by finding patterns, spotting trends and zeroing in on customer preferences.
For example, the logistics industry is already producing data streams through the use of sensors on delivery trucks; beacons, which are devices built on the latest Bluetooth® standards to broadcast their presence to other nearby devices, such as computers and smartphones; the “internet of things” (IoT); and radar devices. If a shipment is going to be late, a carrier can make immediate adjustments to prevent bottlenecks further down the supply chain.
Some logistics companies are using advanced predictive analytics to understand the likelihood of a shipment shifting in status from being on time to arriving late by using simulation models. By examining the probability of a status change, they can know the predicted date and time for specific shipments in specific locations.
The Future is Now
According to the 2018 22nd Annual Third-Party Logistics (3PL) Study, which examines leading trends for shippers and 3PLs in the logistics industry, there is a much higher expectation among shippers that seek enhanced analytics to drive more effective supply chain decisions. The study reveals that 70% of respondents chose “improving logistics optimization” as the most important use of big data for the logistics industry.
If you are not currently employing predictive analytics in your logistics or supply chain management strategy, consider the following advantages:
Predictive analytics will continue to grow in importance as more advanced tools become available to drive strategic planning. Logistics companies can achieve greater efficiencies and gain a competitive edge by using these tools to prevent financial losses associated with inaccurate stocking and the mismanagement of deliveries and schedules.
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