Ways 3pls Leverage Predictive Analytics To Prevent Service Failures

As shipping demand rises and capacity tightens across the freight industry, transportation becomes more difficult and expensive.

Fremont, CA: A service failure occurs when a customer does not receive a delivery on time or at all. With short turnaround times and a plethora of external elements, there are several possibilities for things to go wrong when carrying cargo, such as bad weather, technical troubles, workforce shortages, and dock capacity limits.

In order to preserve customer happiness, costs, and productivity, many shippers and consignees are implementing "must arrive by" dates, which impose harsh penalties if deadlines are not reached. In today's transportation climate, on-time performance is critical, and shippers must have a toolset that prepares them for success. Manual planning, which worked in the past, will not suffice in today's environment. The quantity of real-time data from many sources required to improve operations and move merchandise fast necessitates modern, AI-enabled technologies. Here comes predictive analytics.

  • It provides information on market trends

With transportation costs at an all-time high, it's critical to monitor market activity and identify patterns in real-time. Predictive analytics collects and analyses data from hundreds of sources to identify market trends and identifies cost-cutting opportunities.

  • Allows for a more flexible workforce and volume planning

Predictive analytics provides crucial insight into the supply chain's workflow and timetable, allowing shippers to undertake precise labor and volume planning daily. For example, assume a shipper receives several purchase orders into its warehouse management system (WMS) on a Monday that must be picked and delivered by Wednesday. As that data enters the WMS, the shipper may utilize predictive analytics to determine if it intends to staff up or down in the coming days. The same is true for dock scheduling and capacity planning.

  • Equipment failure is predicted and prepared for.

Trucks break down or require repair all the time, but without powerful analytics, determining the real-time health of each truck in a fleet is practically difficult. Vehicle failures can get avoided with the use of equipment failure analytics. Shippers can forecast the lifespan of a component and arrange proactive maintenance by gathering data on how recently maintenance has been performed on a truck (such as the latest brake or tire replacement) and the condition of parts when they come off the vehicle. In a nutshell, predictive analytics transforms past service records and part examinations into prescriptive, actionable data.

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