Higher forecasting accuracy will improve your operational efficiency, resource utilization, and project outcomes. Here’s how to ace it every time.
Forecasting isn’t an exact science.
It was 1987 when weatherman Michael Fish said the immortal words ‘There is no hurricane on the way’. Hours later, the UK was battered by unprecedented storms and destruction.
It’s the same in project forecasting. No really…
Whether you’re a project manager or a resource manager, accurate forecasting helps you determine optimal staffing, schedule, and budget.
However, project forecasting is both essential and notoriously difficult. Dynamic project environments are fraught with scope creep and unpredictability.
Plus, according to research by the Resource Management Institute, less than half of organizations provide adequate guidance on how to forecast effectively – and even fewer provide the right tools to do it.
But don’t despair. In this article, you’ll find proven techniques to assess and improve the accuracy of your forecasting – so you can spot storms brewing and stop them before your projects get blown off-course.
In project management, forecasting is the process of predicting a project’s needs – such as staff, schedule, and spending. The aim is to understand and gauge project requirements, so you can allocate budget and resources effectively, and ensure timely completion.
Beyond project forecasting, businesses need to forecast at an organizational level too – predicting future demand for capacity, resources and skills. At this level, forecasting aims to align the workforce with strategic direction and demand.
Accurate forecasting – and project resourcing – relies on:
Forecast accuracy is important because it supports positive project outcomes and profitability, as well as strategic workforce and resource capacity planning.
For project and resource managers, precise forecasts ensure optimal resource allocation, prevent under- or over-utilization, and enhance overall operational effectiveness. Whereas for senior leaders, accurate forecasting reduces risks associated with skills gaps, talent shortages, and intelligence blind spots.
Here are six things accurate forecasting helps you do:
Accurately forecasting resource demand, capacity, and scheduling can boost operational efficiency – meaning you deliver your services with minimal wastage.
Equipped with precise predictions about forthcoming workload, when it’s happening, and who’s available, you can align staffing levels with production schedules. This ensures there are enough people to deliver the work well and you can meet customer demand consistently – and that they’re optimally utilized, with no overspending on superfluous staff.
With accurate forecasts, you can allocate the right resources to the right projects at the right time. This supports positive project outcomes by matching appropriate talent to tasks, as well as balancing expertise and expense.
It also reduces underutilization – where idle resources burn through money – and overutilization – which can lead to burnout and decreased efficiency.
”Having clarity and understanding what people are actually working on, what skills they have to make those projects function and deliver at the timelines expected is really, really important, because if not, you're really just sort of guessing what you'll need.
Lyssa Parisella - VP Customer Success, Runn
Accurate financial forecasts support effective budgeting. They make sure you know exactly what a project will cost, so you can build a solid profit margin into your quotes.
Additionally, these forecasts provide a benchmark for monitoring project expenses as you go along, allowing you to identify and correct overspending before you go over budget. This proactive approach helps maintain project profitability.
With precise forecasts, you can quote projects with confidence – knowing your costs and profit margins are accurately calculated, and you have the skills and talent to deliver customer success.
This lets you seize new opportunities, safe in the knowledge they’ll contribute positively to your bottom line and business reputation, leading to business growth and fewer lost sales.
Accurate forecasts equip senior leaders with the data-driven insights they need to take timely strategic action. This supports proactive decision-making around hiring, procurement, capacity planning, expansion, project prioritization, and more.
This forward-thinking approach reduces the costs associated with reactive recruitment and last-minute resourcing. This proactive workforce planning means your business runs smoother and more strategically.
More accurate forecasts mitigate risks associated with project management – such as underestimating workload or overcommitting resources. By accurately anticipating and forecasting demand, PMs can plan more effective schedules, allocate resources more effectively, and implement contingency plans.
This proactive approach helps you spot any icebergs ahead and plot your course around them. This means your projects are more likely to be delivered on time, on budget, and to client expectations.
It should be clear now that the benefits of forecasting accuracy are the same as the benefits of forecasting full stop.
Forecasting is crucial – but actually getting it right is the real key to success.
However, therein lies the rub. Because forecasting is notoriously hard to get right…
Forecasting is most accurate when you combine a range of quantitative and qualitative techniques to arrive at a well-considered prediction. Forecasting methods that blend statistical analysis with insights from subject matter experts tend to be the most accurate. Data will get you so far. But sometimes you just need to ask an expert too.
Here are four techniques commonly used to arrive at accurate project forecasts.
This method looks at historical data to spot patterns and trends over time. Project managers use it to forecast future outcomes based on past performance.
For example, if the past three similar projects have taken 12 weeks and cost $30,000, it suggests a reasonable estimate for the duration and budget of a new project with similar characteristics.
Techniques in time series analysis range from very simple methods – like finding the average of historical data and analogous estimating – to more complex methodologies like Auto Regressive Integrated Moving Average.
It's best for projects with regular patterns, routine tasks, and consistent resource needs.
This is a more complex technique that runs through thousands of different scenarios to work out which is most likely to happen. Project managers can then forecast based on the most likely project scenario.
Using appropriate software, you input ranges of possible values (like project duration or costs) and simulate different scenarios. The software then estimates the likelihood of various project outcomes and risks.
For example, imagine you’re forecasting the cost and duration of a construction project. Monte Carlo simulation could look at the impact of weather, resource availability, supply chains, etc. One scenario might see good weather and fast progress. Another might see heavy rain and disruptions. It then amalgamates these into a single predicted outcome, that you can use as the basis for your forecast.
It's great for complex projects with many variables and unknown factors.
While we’re talking about scenarios, it’s worth mentioning scenario planning. While not strictly a forecasting methodology, it can help you ensure you’ve considered all of the what-ifs that could impact your project.
Scenario planning involves creating different scenarios for resource allocation, project timelines, and other variables. Like ‘What if we used more junior resources and upskilled them on the job?’ Or ‘What if we throw more resources at this and try to finish it quicker?’
By analyzing multiple scenarios and combinations of variables, project managers can assess the impact of different approaches on project outcomes, like cost, timeframe, and quality.
Regression analysis examines how one or more factors (like resource allocation or external influences) interact to affect project outcomes.
For example, in our construction project above, regression analysis reveals a correlation between project duration and labor costs. By analyzing historical data, the project manager notices every additional week in duration increases labor costs by 5%. This insight helps forecast budgets and resources more accurately.
Regression could be:
If in doubt, ask an expert. This method relies on insights from people who’ve been there, done that, and know what to expect.
Project managers consult subject matter experts, stakeholders, and team members to get qualitative assessments of future project outcomes. How long does this sort of project usually take? Are there unexpected costs?
These expert insights complement quantitative methods by providing insights into trends, market conditions, or technical challenges that data alone might miss.
So, now you know how to forecast, how do you know if you’re doing it right?
Given the importance of precise project forecasting, you should regularly assess the accuracy of your forecasts. Otherwise, you could be making forecast errors that undermine your outcomes…
Measuring forecast accuracy involves comparing project predictions against actual outcomes.
If there’s a significant difference between predicted and actual outcomes, you may need to refine your forecasting methods. Here are some key techniques for assessing forecasting accuracy.
Forecast accuracy formulas let you evaluate the precision of your project forecasts. They provide quantitative measures to assess how closely projected values align with actual outcomes. Two commonly used formulas for this purpose are Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE).
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Mean Absolute Percentage Error calculates the difference between forecast and actual values as a percentage. For example, you forecasted project costs to be $10,000. But they actually came to $12,000. The percentage forecast error would be ($12,000 - $10,000) / $12,000 = 16.67%
The percentage error value helps compare projects side-by-side because it is a standardized measure across different projects and datasets. For example, if you have one project forecasting in monetary value and another in sales units, you can still compare them using MAPE.
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MSE calculates the difference between forecast and actual values and then squares it. For example, if you forecasted a project duration of 8 weeks but it actually took 14 weeks, the squared error would be (14 - 8)2 = 36
MSE is particularly useful when you want to find and address the biggest forecast errors, as it penalizes larger deviations more than smaller ones.
EVM is both a forecasting method and a way of monitoring forecast accuracy. It uses information on a project’s scope, schedule, and cost to plot an intended course and keep things on track.
During the project, PMs can see whether they’re meeting schedule and cost forecasts – and correct things if not.
EVM uses two measures of progress:
By monitoring SPI and CPI, project managers can predict when the project will finish and what it will cost if current trends continue.
Forecast bias looks for consistent forecast errors. It helps identify common forecasting mistakes so you can account for them in future predictions.
Say, for example, your financial forecasts are consistently wrong. If you always underestimate, you have a negative bias. If you always overestimate, you have a positive bias. This suggests a systematic issue with your costing process.
Knowing this is valuable, as it pinpoints somewhere to review your forecasting process, and improves the overall accuracy and reliability of predictions.
To identify forecast bias, you conduct calculations such as MAPE or MSE and see if you spot trends towards overestimation or underestimation in specific areas – such as schedule, budget, or resource requirements.
Just like weather forecasting – where cold fronts, jet streams, and atmospheric pressure combine to create climate surprises – variables in project-based business make life unpredictable too.
However, there are proactive steps you can take to make your forecasts as robust as possible – within the context of a wholly unpredictable environment.
Data management, analytics, skills training, and scenario planning are luckily all within your control, so they’re a great place to start improving your forecasting abilities.
Much like eating ice cream for breakfast and then crashing before noon, if you put garbage in, you get garbage out. You need high-quality data to make high-quality forecasts.
Here are seven steps to improve your forecasting through better data management.
Use project management software to consolidate data from various sources into a single platform (such as project performance, time tracking, financial metrics, resource costs, etc)
Develop standardized procedures for gathering and inputting data across all projects and teams. This ensures consistency and completeness of information.
Encourage team members to input data in real-time or at frequent intervals to keep information current.
Use automated tools to flag inconsistencies or errors in data entry, and ensure data quality. Regularly review and clean data to maintain its accuracy and relevance.
Present data in visual formats to make it easier to spot trends, patterns, and potential errors.
Spot patterns that people might miss by integrating advanced analytics and predictive AI into your forecasting toolkit.
Nobody loves data entry but it’s critically important. Educate staff on the importance of accurate data entry and proper use of data management tools.
Although it might seem like a snooze, effective data management significantly improves the availability of data for accurate forecasting. And that leads to better resource allocation, more realistic timelines, and improved project outcomes. Which can mean higher productivity and profitability, less risk, lower costs, and happier clients.
Learn more: Forecasting Best Practices for Resource Management (Webinar 🎥) ➡️
If you’re sold on the benefits of better data management and more accurate forecasting, we’d love to show you how Runn can help.
Dedicated project and resource scheduling platforms like Runn play a crucial role in improving forecast accuracy. By centralizing data, automating calculations, and improving transparency, Runn helps you make better decisions, faster.
Runn is your single source of truth for all project and resource data – containing everything you need to know on resource utilization, project timelines, expenditure, etc. – to manage current projects and forecast future ones.
See resource allocations across all projects in real-time, so you know who’s available for upcoming projects, helping you plan a realistic resource schedule and forecast budget and timelines accurately
Plan projects accurately even if you don’t have specific resources in mind. The placeholder function lets you assign tasks and budget as you plan your project… while you work on recruitment or resource requests.
Get reports on resource utilization, capacity, and availability to support strategic capacity planning and inform timely decision-making around hiring, training, and taking on new projects.
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Create and assess different scenarios for resource allocation and project timelines, modeling various what-if scenarios to understand the impact of different combinations on your project forecasts.
Easily monitoring EVM with side-by-side resource and budget monitoring, helping you ensure resource schedules are within budget limits. See data clearly with data visualization, personal dashboards, and detailed reports.
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