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Cross-industrySolution

Warehouse Operations and Labour Optimization

Forecast warehouse workload by hour and shift, then build labour plans against it, so staffing matches the work arriving rather than the rota that was set last month.

Solution
Machine Learning
Industry
Cross-industry
Company Size

Solution Overview

industry
Cross-industry
solution
Machine Learning
technologies
Azure Machine Learning, Microsoft Fabric, Power Apps, Power BI, Dynamics 365 Supply Chain, Azure Batch

The Challenge

Warehouse labour is planned against a rota set well in advance while the actual workload (inbound receipts, order profile, returns volume) varies substantially day to day and hour to hour.

  • 1Labour planned against a rota set in advance while workload varies day to day
  • 2Overtime on peak days and idle labour on quiet ones, sometimes in the same week
  • 3Workload profile by hour unknown, so shifts are staffed uniformly against uneven demand

Our Approach

CloudGate forecasts warehouse workload by function and hour from inbound schedules, order profile and historical patterns, then generates labour plans against the forecast with skills, contracts and working time rules as constraints.

  • Workload forecasting by function and hour from inbound schedule and order profile
  • Labour planning against forecast with skills, contracts and working time as constraints
  • Agency requirement identified early enough to book at standard rather than premium rates

Typical Outcomes

By hour
WORKLOAD FORECAST, NOT DAILY AVERAGE
Planned
AGENCY BOOKED AHEAD AT STANDARD RATES
Refreshed
SLOTTING AS VELOCITY CHANGES

Typical outcomes for this solution pattern, not the results of a named client engagement.

Technologies Used

Azure Machine LearningMicrosoft FabricPower AppsPower BIDynamics 365 Supply ChainAzure Batch
Full architecture and data flow in the Data & AI Catalog

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