Resource planning looks complex on the surface, but many of the biggest planning problems start with simple mistakes. Forecasting, capacity planning, scheduling, and real-time management all sit together, and each one depends on the other. For example, a misunderstood demand signal can create the wrong forecast. It can then affect the capacity models and schedules based on it, and put pressure on intraday teams when the demand is not met. A simple mistake upstream can easily travel downstream if left unchecked and create a big impact within the functions you’re planning for.
The good news is that these mistakes also have a simple fix. In our recent webinar, Ben Heslop, Director of Resource Planning at ResQ, and founder and author of Smooth Planning, covered 6 planning mistakes in detail and what to do instead. This blog presents the key takeaways from the webinar.
1. Adjusting forecast without understanding demand
One of the most common planning mistakes is jumping straight into the forecast as soon as a number changes.
How can this mistake look? Consider you have a one-off, really busy week that you naturally try to smooth out of your forecast as a random event. Sharing an example from his early days, Ben mentioned a time when he saw 3 really busy Mondays and thought he’d spotted a pattern, and adjusted his forecast. However, he later discovered that it was due to high absence on Mondays, and when absence went back to normal, the spike was gone.
This happens more often than you’d think. Volumes rise, and the instinct is to push that change into the forecast. Volumes drop, and the instinct is to do the same. But a number on its own only shows movement and does not explain why the movement exists. Ben advises pausing to understand what drove it and what's likely to happen going forward.
The fix: Understand demand before predicting it
Before any change enters the forecast, answer the three questions:
- What happened?
- Why did it happen?
- Will it happen again?
2. Planning in isolation
Planning cannot sit apart from the rest of the business.
Customer behaviour changes for many reasons, and often the business itself drives those changes. New marketing campaigns, system changes, bill runs, product launches, and technology changes can all affect demand.
When planning teams don’t know about those changes beforehand, the forecast moves off track, and real-time teams face the pressure. Average handle time (AHT) gets longer, queues build up, abandon rates rise, and everyone starts asking why the forecast missed the mark.
The fix: Make planning cross-functional
Involve planning teams early on and make planning cross-functional.
Create a demand calendar that holds anything likely to affect volume, workload, or customer behaviour. Make it cross-functional and keep it updated with input from departments such as:
- Marketing
- Finance
- Technology
- Product teams
- Operational teams
Then review it continuously, check whether dates change, and look back after the event to compare what was signed off against what actually happened.
If an event can move customer behaviour, planning needs to know before the customer does. For contact centers to deliver a consistent customer experience and maintain a competitive advantage, it's critical to integrate WFM data. Modern WFM software is designed to support this by providing one source of truth for all your WFM data. When your forecasts, capacity, and scheduling all live in the same platform, it's easier to align with everyone.
3. Trusting historical data blindly
Historical data helps, but only when it reflects the business as it exists today.
More data does not always mean better data. A long history can smooth out patterns, hide changes, or include information that no longer fits the current operation. Customer journeys change, technology changes, contact channels change, and business structures change too.
If the business introduces chat, splits a department, moves from paper bills to digital bills, or changes a process, older data may stop being relevant. For example, your business introduced bots to handle simple contact types. You want to forecast demand based on year-on-year variants; you use the 5 years' worth of data you have. But 2 years ago, customer service and sales were one department, and it was later split into 2. The 5 years of data are not an accurate representation of where your business stands today. The only relevant data to be used in this scenario, Ben advises, is the last 2 years.
The fix: Use relevant history, not simply more history
Check:
- Whether the data definition is correct
- Whether the data still reflects current customer journeys
- Whether missing demand exists
- Whether a reporting change has affected the numbers
- Whether a clean-looking data set still tells the right story
A forecast only works when the data behind it matches the business being planned for now.
4. Overcomplicating our work
Complexity has a place in planning, but it has to earn that place.
It can be tempting to build a model that looks sophisticated. More inputs, longer formulas, more weightings, and more layers can make a forecast feel smarter. But complexity can also hide mistakes. Sharing a personal example again, Ben mentioned a previous role where he joined the largest planning function of his career. Every planner worked from their own copy of the same capacity model template, and years of additions had turned it into what he called "an absolute monstrosity": hidden sheets and formulas too long to trace.
The model consistently predicted better performance than the operation delivered. The business averaged that gap and added it back as a correction. If a department's gap was 7% on abandon rate, planners added 7%. For operations leadership and finance, that meant headcount decisions rest on a number nobody can explain.
The actuals and assumptions matched, so Ben worked through the formulas and discovered that the cause was an inaccurate shrinkage calculation. The model added a flat 10% to the staffing requirement, which left required FTE short. It was a simple mistake, hidden in an overcomplex model.
The fix: Let complexity earn its place
Start with a transparent baseline model. Having that can make this kind of error visible at its source. Modern Capacity Planning software uses the same validated calculation logic for every plan, so planners don't maintain their own copies of a template. This ensures everyone is working from the same numbers and removes unnecessary complexity.
Always validate the model you have by asking:
- Is it accurate enough?
- Does it explain the story clearly?
- Does added complexity improve the outcome?
If extra complexity does not improve the forecast or the operational decision, it does not belong.
5. Mishandling outliers
Outliers can create trouble in both directions.
It is easy to smooth them out or delete them from the data. But if the outlier signals a new pattern, removing it can hide the next arrival profile. On the other hand, some outliers should not stay in the forecast at all because they do not reflect future demand.
That means each outlier needs individual judgement. For example, when working with Excel, you can see a huge spike in contact and smooth it or just delete it from the data you’re going to use for forecasting. What if it was a pattern you will now miss?
This is where modern WFM software can help remove this mistake by spotting patterns quicker and deciding what to use and what isn't yet a pattern enough and seen regularly enough to include as a pattern going forward.
The fix: Treat exceptions deliberately
First, understand why the outlier exists. Then decide whether it belongs in future planning:
- Include it if it will continue, and you know when it will happen again
- Exclude it if it does not reflect future contact
- Adjust for it if it belongs in the learning but not in the forecast
Examples from planning practice include outages, marketing-driven demand, repeated contact from poor service conditions, and process changes that reduce contact through digital containment or self-service.
The key is simple: understand the outlier before deciding what to do with it.
6. Defending the plan
Plans go wrong sometimes, and that's normal. The real mistake is defending the plan instead of learning from it.
When a plan misses, teams can fall into one of two traps:
- Moving straight on without looking into the reason
- Explaining why the plan would have worked if conditions had been different
Neither approach helps the next planning cycle.
The fix: Learn from reality every time
Turn every result into learning.
When things go wrong, review:
- What demand actually did
- What assumptions changed
- What operational events affected performance
- What worked well
- What forecasting method supported the outcome
When things go right, review those too. Great performance also contains lessons.
Key takeaways for better workforce planning
The most effective workforce management improvements often come from simple habits:
- Understand demand before predicting it
- Make planning cross-functional
- Use relevant history, not simply more history
- Let complexity earn its place
- Treat exceptions deliberately
- Learn from reality every time
