We built a workforce planning platform for a supply chain operator that models growth scenarios from ERP and HR data to plan hiring years ahead. Hiring costs fell 22%, and forecasts are 94% accurate.
Client: Omni DiaryIndustry: HR TechService: AI & Automation
Overview
Client
Omni Diary
Supply chain operator running 14 hubs across several regions
Engagement snapshot
The challenge
Workforce plans were based on fixed annual budgets and the assumption that growth would be steady. Regional hubs were either short of staff at peak times or carrying 15% extra labour costs after badly timed bulk hiring.
Rushed hiring pushed recruitment costs up, staff shortages led to SLA penalties, and mid-level managers left because they could not see a path to their next role.
Short staffing during peak logistics surges led to service-level agreement (SLA) penalties and lost customers.
Emergency, last-minute hiring pushed recruitment costs 30% above the industry benchmark.
With no clear, data-based paths to new roles, turnover was high among mid-level engineering and operations managers.
The solution
We brought separate data from the ERP, the HR information system (HRIS) and regional performance logs together into one anonymised source.
Monte Carlo simulations model 50,000+ expansion scenarios, taking account of market swings and regional labour laws.
Every change to the roadmap is saved with the data that triggered it, giving the board a clear record when it approves headcount.
Key features
AI simulates more than 50,000 expansion scenarios and produces region-by-region hiring plans.
Business impact
94% forecast accuracy, up from 72%
Recommendations are explained, plan changes are logged, and capacity is monitored against logistics volumes.
Business impact
Zero peak capacity gaps
The engine creates recruitment tasks for each region and adjusts them for local competition for staff and changes in cost per hire.
Business impact
Zero manual forecasts
Each recommendation shows the data behind it, which helped the board approve multi-million-dollar expansion budgets.
Business impact
100% audit compliance
Headcount needs are linked live to logistics volumes, so the operator can add staff quickly when supply chain demand surges.
Business impact
22% talent acquisition savings
Before and after
Before
Back-filling
Recruitment started only after someone resigned or a hub ran out of capacity.
After
Forecast-led hiring
Hiring starts 6 months ahead, based on planned expansion.
Before
Fixed spending caps
Spending limits that stayed the same however day-to-day needs changed.
After
Budgets tied to return
Hiring budgets move in line with projected revenue per employee.
Before
Manual requests
Career moves depended on spreadsheets and on individual managers pushing for their people.
After
Skills mapping
Automatic skills mapping finds internal candidates to train up before the company looks outside.
Results
22% TA Spend Reduction
Hiring earlier and promoting from within meant less use of expensive third-party recruitment agencies.
Before: UnoptimizedAfter: 22% Lower
22% Accuracy Lift
Accuracy rose by 22 points, from 72% for manual HR projections to 94% for the back-tested models, across all 14 hubs.
Before: 72%After: 94%
Zero Capacity Gaps
Staffing kept pace with seasonal volume spikes, so the operator paid no SLA penalties for capacity shortfalls.
Testimonial
“Hiring forecasts used to be 72% accurate. Across our 14 hubs they now reach 94%, based on simulations of more than 50,000 expansion scenarios. Hiring costs are down 22%, and we had zero peak capacity gaps.”
Laura AlvarezVP of Workforce Planning, Omni DiaryKey results
Trust and control
Talent data is anonymised in every region to meet GDPR and local labour privacy rules.
SecureEach hiring task records the model version and the weight given to each factor, so the board can see how it was reached.
Verifiable dataThe cloud set-up runs 50,000+ scenario simulations without slowing down.
Ready to growThe client owns 100% of the simulation models and planning systems once the project is complete.
100% ownedFaster delivery
These modules saved about 5.5 weeks of setup, which went into connecting the client's ERP and HR systems and modelling its regional labour laws.
Gave every employee an anonymised profile that links their ERP, HRIS and performance records.
Provided the tested Monte Carlo base that runs the 50,000+ expansion scenarios.
Shows headcount against forecast demand for each of the 14 hubs.
Linked hiring budgets to projected revenue per employee, so spending followed real needs.
The platform combines ERP, HR and regional performance data in one anonymised source. Monte Carlo simulations then model 50,000+ expansion scenarios, allowing for market swings and regional labour laws. The output is a 60-month hiring roadmap that starts recruitment 6 months before each hub needs the extra staff.
Talent acquisition costs fell by 22%, largely because the client relied less on third-party recruitment agencies. Forecast accuracy rose from 72% to 94% across 14 hubs. There were no capacity gaps during seasonal peaks, so the operator avoided SLA penalties for short staffing.
Every roadmap change is saved with the data that triggered it, and each hiring task records the model version and the weight given to each factor. This gave the board a clear audit trail when approving multi-million-dollar expansion budgets. Talent data is anonymised throughout to meet GDPR.
If your sites swing between short staffing and costly bulk hiring, we can help you model demand and plan headcount from the data you already hold.