Manual, approximate and disconnected planning undermines productivity and weakens warehouse financial performance. Decisions must simultaneously account for order volumes, capacity, productivity, workforce constraints, labour costs and service quality. Yet in many organisations, these processes still rely on scattered files and manual data entry. Teams alternate between costly overstaffing and risky understaffing, while performance gaps are identified too late. Firefighting becomes the default mode of operation.

Labour: the largest cost item and the first lever for competitiveness

70% of warehouse operating costs are labour-related (UPS Supply Chain Solutions). It is the largest cost item and the one where economic performance is determined on a daily basis. At the same time, 73% of warehouse operators struggle to recruit enough staff (Prologis, 2024). Labour shortages and cost pressures are converging.

The global warehousing market is expected to grow by around 37% between 2026 and 2030, reaching $1.73 trillion, driven by the rise of e-commerce and increasingly complex supply chains (Grand View Research). Volumes are increasing. So are expectations around speed and reliability. Managing resources at this level of complexity with scattered Excel files is no longer sustainable.

Better planning is not just about adjusting headcount. It also means assigning the right skills to the right activities at the right time. Taking certifications, versatility and productivity levels into account improves resource utilisation. It makes it possible to increase overall performance without increasing headcount.

Four structural challenges that prevent effective anticipation

Most logistics teams face the same obstacles:

• Multiple files: forecasts, planning and execution are managed in separate spreadsheets that are difficult to synchronise across sites and activities.
• Repeated manual data entry: every manual input slows teams down and increases the risk of operational errors.
• Siloed activities: receiving, order preparation and shipping are managed independently, with no consolidated view of the overall workload across teams.
• No dynamic detection: forecast changes, capacity overload alerts and productivity gaps only become visible after the fact.

The consequence is not simply inefficiency. It is a structural inability to meet customer requirements, or significant additional labour costs: teams react to peaks in activity instead of preparing for them.

Financial, operational and workforce considerations: three under-managed dimensions

This way of working generates costs that go far beyond the use of last-minute temporary labour.

From a financial perspective, capacity decisions are made without forward-looking visibility into their budgetary impact. Cost analysis takes place after the decision, never before it. It becomes impossible to compare the cost of temporary labour or overflow warehousing with the cost of smoothing order volumes. It is equally difficult to simulate the impact of a resource decision on site margins.

From an operational perspective, the lack of a multi-horizon view across days, weeks and months prevents teams from activating the right levers at the right time. Workforce versatility, smoothing inbound flows and securing external labour in advance can only be used effectively if capacity overloads are identified early enough.

From a workforce perspective, real operational constraints are rarely incorporated into the plan. Certifications such as forklift licences, mandatory rest periods, safety rules and cross-functional skills are often absent from Excel files. As a result, the plan does not reflect the capacity that is actually available. Some activities are overstaffed while others are understaffed.

The four levers of predictive workforce planning

Moving from reactive management to structured planning relies on four complementary levers.

1. Model forecast workload based on order volumes, seasonality and fluctuations in activity.
2. Integrate real operational constraints: absences, contracts, public holidays and job-specific certifications.
3. Balance internal resources and temporary labour with a consolidated view of costs and availability, while incorporating HR and contractual constraints.
4. Measure planned versus actual performance to improve the accuracy of future planning cycles.

These four levers work together. Taken individually, each delivers only limited impact.

Proof through a real-world example: a European 3PL, 40 sites and 2,000 employees planned with Anaplan

A major European third-party logistics provider was planning 2,000 employees every day across 40 warehouse sites. Receiving, order preparation and shipping were managed in separate files, across five disconnected manual processes. There was no consolidated view of workload across the network.

After deploying Anaplan:

• A single tool covers the entire network through a standardised architecture.
• Workload forecasting is automated based on customer orders.
• Daily planning is managed by activity and job role.
• Resources are managed by team and individual.
• Standard working weeks can be configured and replicated across all sites.

The enhancements identified for the next phases illustrate the progressive approach: direct integration with the WMS to eliminate manual workforce data entry, enhanced forecasting using AI/ML algorithms, and consolidated workforce budget reporting across the network.

WMS and workforce planning: two complementary roles

A WMS orchestrates real-time execution. It records inventory movements, routes orders and monitors activity throughout the day. But it does not calculate forecast workload, plan resources or anticipate capacity overloads. Nor does it provide a multi-horizon view or financial analysis of capacity decisions.

This is precisely where Anaplan comes in as a predictive planning layer, bridging the gap between the WMS and actual resource availability. Connected to the existing WMS, Anaplan retrieves operational data and creates the link between demand forecasts, available capacity and workforce planning. It also makes it possible to model the cost of planned resources by activity and site, and to simulate the financial impact of different decisions before they are made.

The two tools are complementary: one executes, the other anticipates.

The OneHive approach: a phased deployment with no organisational disruption

OneHive supports this type of implementation through a phased approach. A pilot site goes live within 2 to 3 months to validate the model and embed it into the teams’ day-to-day practices.

The solution is then rolled out across the rest of the network using the same standardised architecture, site by site, without disrupting the existing WMS.

We also train internal Model Builders to ensure the long-term sustainability of Anaplan and enable teams to extend the solution independently. The objective is to deliver an operational model quickly, ensure it is actually adopted, and then enhance it over time.

Going further

Anaplan and OneHive hosted a french webinar dedicated to this topic: Warehouses: move beyond firefighting with predictive and connected planning.

Watch the replay to see a demonstration of Anaplan applied to warehouse operations, along with feedback from a European 3PL that deployed the solution across 40 sites.
Watch the replay →