Challenge
Objective:
Increase speed, accuracy and responsiveness of quarterly planning.
- Each quarter, a team of three would dedicate more than a week’s time towards occupancy planning in the following areas:
- Demand forecasting
- Space x People allocation
- Adjustments based on upon feedback
- Tools Deployed:
- Microsoft Excel
- Autocad
Manual Planning
Historical Analysis
Historically, this client spentmore than 200 hours quarterly entirely on occupancy planning, across a 3-personteam.
Breakdown of Time Spent Quarterly (Before AI)
- Demand Forecasting: 106 Hours
- Allocations: 90 Hours
- Revisions: 48 Hours
GAPS
- Automation: Entirely manual planning.
- Speed: Slow feedback, slow results.
- Resource Allocation: competing projects.
The Results
AI-Powered Results:
- Forecasted peak demand for each group using historic attendance.
- Allocated “workpoints” to each group, fulfilling group co-location requirements.
- Created stack plans and move packs, ensuring space type requests were accommodated.
- Adapted solutions and materials in response to several rounds of stakeholder feedback, in minutes rather than days.
All With Minimal DataRequirements:
- Historic attendance from badge data
- Allocations from previous planning exercises
AI generates solutions inminutes. Even after feedback is received, once revised requirements areincluded, AI adapted, altering the recommended solution with minimum disruptionto meet new requirements in +/- 1 minute.
Automated Planning
UnifyAI’sBig Impact
Breakdown of Time Spent Quarterly (With AI)
- Demand Forecasting: 11 Hours
- Allocations: 10 Hours
- Revisions: 5 Hours
Breakdown of Time Saved Quarterly
- Demand Forecasting: 95 Hours
- Allocations: 80 Hours
- Revisions: 43 Hours
~900 Hours
Saved Annually
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