How Predictive Analytics Cycle Counting reduces low-value warehouse work by up to 30% and optimizes productivity, throughput, and growth activities.

Although standard cycle counting is a reliable way to check and maintain inventory accuracy, it does have several drawbacks. These include substantial workforce effort, delays in identifying and resolving errors, and fast-moving inventory not being checked often enough.

GEODIS has improved on standard cycle counting and created a new model for counting, checking, reconciling, and correcting inventory: Predictive Analytics Cycle Counting (PACC). PACC uses a mathematical model to predict cycle count locations that have a high probability of mismatch in a warehouse, based on historical cycle counting data and other contributing factors.

The benefits of PACC include enhanced root cause analysis and remediation to prevent inventory errors, redeployment of labor to revenue-generating activities, and a more engaged workforce that benefits you.

PACC delivers excellent results compared to traditional cycle counting:

  • Up to 30% reduction in low-value labor hours previously spent on cycle counting.
  • An increase in the units per hour processed through GEODIS facilities.
  • An increase in productivity performance.
  • A reduction in per-unit costs for picking and packing.

Download our white paper today and learn how predictive analytics cycle counting can save you time, money, and effort in the warehouse.

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