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Unlike palm reading and crystal ball gazing, ERP forecasting relies on logic rather than guesswork. The purpose isn't to predict a single certain future, but to identify a range of possibilities.

Of course, nothing is certain, and you’ll ultimately rely on intuition and judgment for business planning. But, effective forecasting provides the crucial context that informs your intuition.

In this article, I’ll teach you how to use the data within your ERP system to uncover latent financial opportunities for your business. 

What is ERP Forecasting?

ERP forecasting is the process of leveraging past data and trends to produce informed assumptions about future conditions, providing you with a data-driven guide to help with the business planning process.

This data-driven approach provides valuable insights that can guide strategic decision-making, ultimately leading to a more successful business. This is possible because ERP systems help bring together and manage all business processes—inventory management, accounting, project management and customer relationship management—from one centralized platform.

What You’ll Need to Create Proper Forecasts

To get the most out of ERP forecasting, you'll need:

Solid Data

ERP forecasts are like weather forecasts: you rely on them to plan ahead, but they’re worthless if not backed by data. Ergo, gathering relevant data, both internal and external, is the first step towards proper forecasting. For example:

  • Inventory data: stock movement, locations and product stocks
  • Sales data: sales invoices, customer orders, and customer payments. Including reports from all sales touchpoints—point-of-sale systems, online transactions and sales from social media platforms
  • Financial data: Information about profits, revenue, and expenses
  • Production costs: production times, production process, and labor cost
  • Employee data: payroll, employee attendance and performance reports. 
  • Customer feedback reports
  • Market research reports
  • Industry trends

The Right ERP Software 

ERP systems vary greatly in scale, scope, and functionality. So, naturally, not just any ERP software would do for your forecasting needs. At the very least, you need to consider scalability, cost-effectiveness, integration requirements, and implementation models before picking a winner.

For many companies, modern ERPs offer immediate operational support without extensive customization. However, some require additional functionality beyond the considerable built-in capabilities.

Say your company uses a lot of specialized processes; you’d be better served by an extensible type of ERP software that integrates with legacy or homegrown solutions, or that allows your IT staff to write code that adds needed features.

Who’s Involved in the ERP Forecasting Process?

Short answer: each team member in your company's revenue chain who manages KPIs that feed into a forecast. The specific team members will differ depending on your industry, but in general, they will include: 

1. Sales Representatives

Sales representatives manage and close new business, renewal, and expansion opportunities. Because they are individual contributors who focus on one or more prospective opportunities at any one moment, they play an important role in finding, producing, and revising the opportunity data required for ERP forecasting. 

2. Sales Managers

Sales managers are the team members who oversee your sales reps; in most organizations, they are known as account managers or account executives. For ERP forecasting, the sales manager compiles data from all sales reps' deals to build a single team-wide forecast dataset. They also monitor pipeline updates and provide their own input on the dataset. 

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3. Sales Leaders

Sales leaders are quota-bearing sales leadership above your sales managers, including everyone up to the CRO. They play a key role in ERP forecasting by providing historical sales data, insights on market trends, and insights on customer buying behavior. This information helps create realistic forecasts that support achievable sales targets and inform strategic business decisions.

4. Revenue Operators (RevOps)

The role of a revenue operator is to deliver visibility across the entire revenue team, improve efficiency across the revenue process, drive revenue predictability, and achieve revenue growth. For forecasting, they drive the process of the forecast and take charge on rollout to the sales team. 

5. Business Analysts

Business analysts are considered subject-matter experts in their industry. These individuals are skilled at collecting, cleaning, and analyzing data. They also provide valuable industry insight for a forecast, ensuring its accuracy.

If you don’t have internal business analysts, it would be worth considering how you could use some—in a contractual agreement—to help you set up your ERP forecasts.

6. Chief Financial Officer (CFO)

The CFO serves as the steering committee for the entire ERP forecasting process. They are an escalation point for revenue operators, sales leaders, and business analysts. In addition, they investigate trends in the forecast and redirect business resources based on the overall picture. 

How to Forecast Using ERP

Here's an easy five-step process you can use to start generating ERP forecasts right now.

Step 1: Define the Problem 

This is often the most challenging part of ERP forecasting because it relies less on the ERP system itself and more on your own business knowledge. Here, you need to clearly articulate three key things: 

  1. What the forecast is for
  2. How the market you play in works
  3. Who your customers and competitors are 

These questions can help:

What are you forecasting? This is typically sales figures, production output, or inventory levels.

How will you use the forecast? Are you setting sales targets, planning resource allocation, or mitigating potential risks?

What's the context of your forecasting task? Is it short-term or long-term? Are there seasonality patterns? What are the key factors influencing your forecast?

Let's assume you're a retail store owner who needs to predict daily sales for the next quarter. Understanding seasonal trends (e.g., holiday spikes) and external factors (e.g., promotions) will be key metrics to base your forecast on.

Step 2: Gather and Prepare the Data

This step involves identifying the relevant data points within your ERP system. For instance, sales history, lead times, production capacity, costs, and inventory levels that’d feed your forecast task. 

Once you have your hit list, extract and consolidate the data from different ERP modules.

Data querying simplifies the process

Data querying simplifies the process

It entails writing and executing queries against the centralized database of your ERP system to retrieve the required data based on predetermined parameters.

If the techie language gives you pause, don’t worry—data queries are generally made in the natural computing language of the ERP system. All you have to do is ask data-specific questions—in natural language—to extract specific information or answers from the complex datasets in your ERP system.

Once you've gathered the data for the forecast, clean and validate it by looking for missing values, duplicates, and anomalies. For example, if you were projecting sales for a retail chain, cleaning up the collected data would require removing anomalies (e.g., unusually high sales owing to a promotion) and dealing with null numbers (e.g., due to store closures).

Finally, label the cleansed data points to prepare them for further processing and analysis. 

Step 3: Perform a Preliminary Data Analysis

Taking a peek at the data early on can be very telling. You might immediately spot issues with data quality or usability.  This analysis can also reveal helpful patterns or trends. 

Thankfully, ERP systems come equipped with data visualization tools. These tools transform complex data sets into clear and concise maps, charts, plots, and graphs, making them much easier to understand and interpret.  So, as a natural first step, start by visualizing your data with graphs.

What do you see? 

Is seasonality important? Can you identify any repeating trends? Is there evidence that broader economic trends (periods of growth and decline) are influencing your data? Are there any data points that seem significantly different from the rest?

Now is also a good time to look for and remove any duplicate entries in your data set. In some cases, based on your understanding of the business and the data, you might be able to make reasonable assumptions to fill in small gaps. By reducing the amount of data you need to analyze, you can streamline the entire forecasting process.

Step 4: Choose a Forecasting Model

ERP software often comes loaded with various forecasting models, like moving average, exponential smoothing, seasonal models, and even more complex options like ARIMA and vector auto-regression. But with so many choices, how do you pick the best one?

Spoiler: There's no magic bullet. 

The best model for your forecast will be determined by its intended application, the strength of correlations between the forecast variable and any explanatory variables, and the availability of historical data. For example:

  • For short-term trends, a simple moving average might work well. 
  • For long-term trends, a more advanced model like neural network or prophet could be appropriate.
  • If you value interpretability and simplicity, consider using forecasting models like moving averages or linear regression.
  • Short on historical data? Qualitative approaches, such as the Delphi method, work really well. 

Step 5: Analyze and Interpret the Results

Now comes the exciting part. Once you've chosen the most suitable forecasting model for your goals, it's time to put it to the test.  Simply feed your data into the model and let it work its magic.

Author's Tip

Author's Tip

Compare your forecast to the actual data. This allows you to evaluate the accuracy of not only the model, but the entire process, and adjust each step accordingly.

For example, let’s say you have data on customer satisfaction, marketing expense, and sales. Using regression analysis, you could find out if there is a relationship between sales and marketing expenses and sales. If there is a relationship, you can then start to predict how changes in sales will be affected by marketing influences.

How To Improve the Quality of Your Forecasts

There are three main areas to focus on, for improving ERP forecasts:

Deepen Your Understanding

Look further into your ERP software, particularly its cross-analytics capabilities. The more seamlessly your ERP software integrates with third-party tools like Google Analytics, the higher the quality of your forecasts will be.

Make Sure Your Data’s Good

Ensure the data you receive from partners regarding logistics, suppliers, production, and more is accurate. Reliable data is the foundation for reliable forecasts.

Audit Your System

Make sure you have everything you need for quality forecasts. If you don’t, consider implementing additional ERP modules or upgrading your plan as your forecasting needs evolve, to gain access to advanced analytics. 

This ongoing customization streamlines the forecasting process for everyone involved.  

When To Forecast 

Your specific ERP forecasting timeline is going to be contingent on your business’s cycles and seasonality.

For example, let’s say your business has identified a trend of rising customer demand during summer. Armed with this knowledge, you’d increase production levels in summer to meet the surge in demand and maximize sales, or launch targeted marketing campaigns during these months to attract new customers and further boost revenue.

For most businesses, there are two types of forecasts to look at, which have differing timelines:

  • Regular Reviews (1-3 months): For short-term planning like cash flow and inventory management.
  • Strategic Planning (6-12+ months): For long-term planning like resource allocation or budget planning, conduct forecasts before strategic decision-making periods (e.g., annual budgeting).

ERP Forecasting Best Practices

Stay Current

Schedule regular updates and maintenance of your ERP solution. A well-maintained system prevents data discrepancies and safeguards the integrity of your information. This translates to forecasts you can rely on, as you know they’re based on clean and accurate data.

Check Your Work

Periodically compare your forecasts to actual business performance. Then, update your forecast process and models as needed based on market changes, new data, and other unforeseen circumstances.

Learn Your System Inside & Out

Make use of the training resources provided by your software provider or ERP consultant. Many vendors provide instructions on how to use their forecasting functionalities to their full potential.

Start Simple

Advanced statistical-based forecasts can seem complex to generate at first, so begin with a simpler task, such as estimating a product's historical average demand. Then, analyze how closely that simple forecast matches the actual observed demand. Work up from there to techniques that deal with complications like seasonality and trend. 

Foresight Without Fortune Telling

There is no mystical way to foretell your company's future. However, ERP forecasting is the next best thing. The reporting, BI (business intelligence), and machine learning capabilities within an ERP system turn data into actionable insights. 

So, whether you're a seasoned business or a budding startup, ERP forecasting should be a fundamental tool in your arsenal.

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Simon Litt
By Simon Litt

Simon Litt is the Editor of The CFO Club, where he shares his passion for all things money-related. Performing research, talking to experts, and calling on his own professional background, he'll be working hard to ensure that The CFO Club is an indispensable resource for anyone seeking to stay informed on the latest financial trends and topics in the world of tech.

Prior to editing this publication, Simon spent years working in, and running his own, investor relations agency, servicing public companies that wanted to reach and connect deeper with their shareholder base. Simon's experience includes constructing comprehensive budgets for IR activities, consulting CEOs & executive teams on best practices for the public markets, and facilitating compliant communications training.