Project Cost Forecasting: How to Make it More Accurate

According to a study by PMI, 35% of projects fail due to their budgets. And, what's more, research shows that 91.5% of ‘big projects’ (those that exceed US $1 billion in costs) go over budget, schedule, or both. While your budget estimates how much you can spend on a project, your cost forecast predicts what you will actually spend. This takes your project’s progress, changes, developments, and more into consideration. So, for project leaders, budget size may not be as important as your project cost forecasting accuracy.

If your cost forecast is incorrect, it can lead to delays, overspending, challenges in decision-making, and misallocation of resources. However, factors such as data gaps, human error, economic uncertainty, and market volatility can impact cost forecasting accuracy. So, how can project leaders and project management offices (PMOs) ensure they deliver the most accurate cost-forecasting data? The right technology can make all the difference.

In this article, we’ll explore technology’s crucial role in accurate project cost forecasting and the features and capabilities organizations should look for to level up their cost forecasting.

Enhancing Cost Forecasting Accuracy Technology

PMOs must look for several key features and capabilities when using technology to improve project cost forecasting accuracy. We recommend these 6 key functionalities when implementing this kind of software:

• Predictive analytics
• Greater visibility of cost components
• Automated work breakdown structures (WBS)
• Access to data
• Scenario analysis
• Easier communication

Let’s take a look at why these 6 areas are important for project leaders.

1. Predictive Analytics for Project Cost Forecasting

Let’s start with predictive analytics — the linchpin of cost forecasting accuracy. Predictive analytics is the process of collecting and analyzing past data, trends, risk factors, and more to identify patterns and make predictions. Traditionally, relying on manual mathematics, machine learning (ML), artificial intelligence (AI), and big data now power up predictive analytics. These AI-enabled predictive analytics can analyze greater volumes of rich data faster, identify more patterns from that data, and continue to learn.

When we apply this to cost forecasting, the benefits are immense. Project leaders can better understand costs, make more informed decisions about resource allocation, identify cost-saving opportunities, and more. Remember that your data quality plays a crucial role here—from completeness and consistency to trustworthiness and volume, there are several data quality checks you must undertake to ensure accuracy.

2. Increasing Visibility of All Cost Components

Businesses often store financial data in several places, whether in spreadsheets, local files, customer relationship management (CRM) systems, or accounting software. This can be hard to manage and keep on top of. On the other hand, a holistic PPM tool keeps all your data in one space, creating a single source of truth (SSOT) that ensures your data's accuracy, reliability, and timeliness. The right tool will also provide a project dashboard that makes it easy to access all the data you need in the same place at the same time. This way, project leaders can track cost component data (like finance and budget). They can also track other influencing factors like activity, resources, delivery, and milestones to ensure accurate and up-to-date cost forecasting.

3. Creating Accurate Work Breakdown Structures (WBS)

Using generative AI, PPM tools can automatically create and schedule your project work breakdown structure (WBS) in seconds. With a comprehensive WBS, PMOs can:

• Avoid hidden costs from missed steps or tasks.
• Utilize helpful cost estimates based on historical project data.
• Benefit from real-time, automated updates to the project plan when faced with delays, impact on the budget, or scope changes.

By eliminating any potential hidden extras, your budgets and timelines are more likely to stay on track, boosting your project’s overall return on investment (ROI).

4. Access to Historical Project Data and External Data to Inform Forecasts

Intelligent PPM tools can analyze past projects and external data, like market trends, supplier information, and regulatory changes, to help project leaders make more informed decisions. While this is valuable in all aspects of project and portfolio management, it can be instrumental in improving the accuracy of project cost forecasting. By interrogating large volumes of relevant data from multiple sources, the technology delivers accurate and timely predictions that better reflect historic project outcomes while considering external factors.

Let’s say, for example, you're overseeing the development of a new electric vehicle model. Historical project data can provide insights into production timelines, raw materials costs, and prior challenges in scaling manufacturing. However, integrating external data—such as fluctuating battery material costs, emerging regulations for EV emissions, and regional adoption rates of EV infrastructure allows for a more dynamic approach. By combining both types of data, project leaders can create cost forecasts that adapt to real-world developments, helping ensure the project stays on budget and aligns with market demand.

5. Scenario Analysis

‘What if’ scenario planning technology enables PMOs to simulate various scenarios — based on factors that directly impact costs. This could include budget cuts, price increases, and delays due to supplier or vendor dependencies, external factors, or client/stakeholder changes. Understanding how different scenarios will impact your project helps project leaders better manage risks, resources, timelines, and budgets — and ensure cost forecasting remains accurate and up-to-date. Whether the price of raw materials increases, you experience an unplanned technology failure, or new regulations incur additional costs — nothing is unexpected if you’ve already considered it. You can implement preventative measures, quickly pivot to contingency plans, avoid hidden additional costs, and get back on track in no time.

6. Easier Stakeholder Communication and Approvals

Intelligent PPM technology can transform complicated approval processes and ease stakeholder communications while ensuring the most accurate data is at the heart of the project. This eases communication and improves stakeholder visibility.

Accurate project cost forecasting helps PMOs build more reliable business cases. An SSOT improves stakeholders’ trust in data. Understanding trends and external influences helps business and finance decision-makers better manage costs, deadlines, resources, and expectations. With real-time updates and access to features and capabilities that promote accuracy, boost agility, and analyze multiple outcomes, stakeholders can make important decisions faster and with ease.

Discover how Stora Enso used PPM technology to elevate visibility and gain control over the company’s project portfolio.

PPM Technology for Project Cost Forecasting

Accurate project forecasting is not a ‘one off’ task. Instead, project managers and PMOs must work together, continuously monitoring and updating forecasts and working with the right tools to achieve success.

By implementing PPM technology, like Planisware, to boost project cost forecasting accuracy, PMOs will benefit from:

• High-quality data and a single source of truth (SSOT). With all data stored in one place and access to valuable external data, intelligent PPM tools can update cost forecasts with relevant and historical data to boost accuracy, improve project outcomes, and make smarter, data-driven decisions.
• Improved risk management. Access to large volumes of data in real-time and ‘what-if’ scenario planning capabilities enables project leaders to identify and manage risks earlier in the project. This allows greater visibility, more reliable budgets, and accurate timelines.
• Real-time updates and automation. From automated WBS creation to AI-enabled predictive analytics, PPM technology that harnesses ML and AI can ensure projects run smoothly, reducing hidden or missed costs caused by human error.