What is demand forecasting in CPG?

We all want to know what the future is going to bring. But for consumer-packaged goods (CPG) manufacturers, preparing for the future can be an existential challenge. That’s why demand forecasting—predicting customer demand for products based on existing trends and data—is a critical tool for CPG firms. It allows them to anticipate market demand and adjust their production accordingly.

Companies need to ensure that they manufacture enough product, but not too much; and that they ship stock to the right place, at the right time. Meanwhile, account managers and marketers will have planned their strategies with the demand forecast in mind.

All of this feeds into the ultimate goal of ensuring satisfaction both for retail partners and for the end customer.

Get this wrong, and the future of specific products can be cast into doubt or carefully nurtured relationships with retail partners seriously undermined.

A data-driven approach

Demand forecasting is clearly critical for CPG companies. So, how are you supposed to do this?

Some might rely on a hunch or gut feel, vaguely based on past experience. Others might sketch out a number of scenarios, and hope that the best case is the one that comes true. Others still will have developed bespoke modeling tools. These may work up to a point, but are clunky and don’t capture all the data they could.

None of these methods are good enough. To really succeed in making customers, partners, and your own stakeholders happy, you need a different approach. You’ll need to actively assess future demand, considering all the relevant internal and external factors, to develop a well-grounded demand forecast. And you need to be able to update your forecast as new information comes to light.

The challenges CPG firms face in demand forecasting

But none of this is easy. CPG supply chains are incredibly complex, stretching from the manufacturer, through distributors, wholesalers, and to retailers. Each can have different objectives. Each sees the world through a slightly different lens. And each generates and relies on different data.

Even a relatively small consumer-packaged goods player can have a wide range of products when you take into account different sizes and packaging. Larger players can have hundreds or even thousands of different SKUs.

All these categories are subject to seasonality and trends, to a greater or lesser degree. Sometimes the impact is relatively straightforward—sunscreen and ice cream in summer, cold medicines and soup in winter. Others might be much harder to unpick.

Moreover, external events can play havoc with even the most reasonable assumptions. Weather can be unpredictable. A sporting event or state occasion can boost certain product categories temporarily. A natural disaster or geopolitical unrest can cause shortages or price rises on key commodities.

Combining information from various sources

This all means there can be a dizzying range of information and data that demand forecasters could potentially draw on. But this may be in different formats, spread across multiple silos, right through the supply chain. And some crucial data might come in the form of third-party or syndicated data, direct from EPOS systems.

It can be a technical challenge just to pull all this information altogether. Companies then face the problem of generating reliable insights and forecasts from this mass of data. And, even then, different teams might need very different insights given their respective objectives.

Common demand forecasting mistakes to avoid

No wonder CPG manufacturers and their retail partners can struggle to work out what is happening right now. Never mind what they should prepare for a year, or even a quarter from now.

In the face of these challenges, we can see some common demand forecasting mistakes repeated time and time again by CPG teams.

Historical data

One of these is an over-reliance on historical data when planning. What has happened in the past is, of course, the starting point for forecasting the future. But historical data doesn’t account for changes that have happened since. Whether that’s new products, changing consumer behavior, or the myriad of external factors that can affect the market.

Seasonality and trends

Likewise, seasonality and trends can have a big impact on demand. Forecasters must be alert to how these factors might have influenced previous data—unexpectedly cold weather the previous June, a once every four years sporting event—as well as how they might shape the year ahead. Simply looking at last year’s factory data doesn’t give you insight into all these nuances and can fatally undermine forecasts.

Impact of promotions

It’s also easy to forget the positive impact of promotions and marketing. Was that earlier spike in demand for ice cream due to unseasonably warm weather? Or was it down to an unusually effective promotion? What does your marketing team have in the pipeline over the next year, and how might it affect demand?

Forecasters can also fail to deliver the right level of detail, depending on who they’re producing forecasts for—and when they are needed. As we’ve seen, different teams will need very different levels of information. And that has time implications. An “overall” demand forecast targeted at factory planners might take less time to generate. But it can leave sales teams and marketers in the dark on the granular data that should be underpinning their planning and negotiations with retail partners.

‘Shadow’ forecasts

Often, different teams will simply evolve their own forecasting tools targeted at their specific needs. Typically, this will mean a series of spreadsheets into which they’ll cut and paste whatever data they can obtain. But this laborious, manual work, often leads to mistakes. It can result in different teams developing very different views of the future—and the present. At the same time, the “official” forecast might exist within a legacy system, increasing the risk of errors if data is copied back. Additionally, this practice can lead to the creation of multiple, unofficial ‘shadow’ forecasts, further complicating the forecasting process.”

This situation highlights the wider issue of communication within the organization and throughout the supply chain. It’s crucial for all relevant stakeholders to collaborate effectively, not only to create accurate demand forecasts but also to take appropriate actions based on those forecasts.

So how can CPG firms gain clarity and reliability when they’re forecasting future demand?

How can a demand forecasting platform benefit you?

As we’ve seen, different teams within the CPG organization and in the supply chain, have different needs when it comes to developing demand forecasts. At the same time, they are trying to grasp the same reality, albeit from different angles.

This can be a starting point in considering why CPG companies can benefit by using a demand forecasting platform.

The right tool will help you pull together disparate sources of data. This will give you an overview of the whole supply chain, including retailer data, and third-party and syndicated data.

All interested parties will see the complete picture, even if they are viewing it from different perspectives. And because you can automate these workflows, you also reduce the potential for errors. And you no longer waste time pulling together data sources or cutting and pasting information into spreadsheets.

Likewise, processes such as product switching can be automated, reducing confusion. This not only enables to optimize inventory in the short term, it also produces better forecasts in the long term.

The benefits of modern solutions

A truly powerful CPG demand forecasting solution will apply AI and Machine Learning to the data. That way, it will deliver better insights into historical data and produce more accurate and actionable forecasts about the future.

Modern solutions offer a ‘single source of truth’ due to their integrated nature, ensuring consistency across the organization. The automated workflows they enable mean that any changes made by one team can be immediately reflected across the entire organization. That might be the impact of new data or information from factories or retail partners. Or it could be third-party information, such as consumer trends data or weather events, that can dramatically change forecasts.

You can utilize these updated forecasts to enhance the efficiency of other groups within the organization. This could involve optimizing factory output or inventory levels, or adjusting promotional activities to align with the latest projections.

This approach leads to cost reductions by minimizing waste and optimizing inventory levels. Simultaneously, it provides a competitive edge as it allows for more targeted marketing and promotional efforts. This results in enhanced collaboration within your organization and with retail partners, and, ultimately, increased customer satisfaction.

What AI means for demand forecasting in CPG?

Historically, forecasting was as much art as science. Even if demand forecasting professionals could pull together both historical and real time data, they might not have had adequate tools, or the time, to properly analyze it.

AI and machine learning have completely changed this, with the best systems employing industrial-strength algorithms. These are tuned to the needs of CPG vendors and their retail partners.

Such systems are capable of processing a wide range of data, from historical records and structured ex-factory data to real-time inputs like weather forecasts. They can even analyze unstructured data, including news articles and social media posts, to provide comprehensive insights.

This enables forecasting professionals to generate precise, detailed forecasts tailored to the diverse requirements of their colleagues. Overall figures for the manufacturing side, highly granular for the trade promotion team, down to individual SKUs.

Automation streamlines the process, reducing the likelihood of errors associated with manual entry. It ensures that any adjustments a demand planner makes are instantly updated across the organization. Consequently, teams like sales and marketing can observe the effects in real-time, enabling them to react swiftly to any changes.

The outcome is that forecasting teams, along with their peers in manufacturing or marketing, can adopt an exception-based approach. They can concentrate their expertise on addressing anomalies, unforeseen events, and the most complex challenges.

The future of demand forecasting in CPG

As we’ve seen, AI and machine learning are transforming the possibilities of demand forecasting.

But producing a forecast is not a one-off exercise. Data is changing and evolving all the time. This is the case for third-party or syndicated information but also for the data emerging directly from CPG vendors’ supply chains. This can lead to constantly optimized forecasting—for those teams that have the appropriate tooling.

Other technology trends will contribute to changes in demand forecasting. The same increased interconnectivity and automation that makes it easier to pool and analyze data, should also make sharing insights automatically much easier.

Moreover, you can tailor these insights to the specific needs of different teams. Whether that is a focus on supply chains, granularity to help marketeers plan better campaigns for different retailers, or highlighting financial metrics for the CFO’s office.

Better visualization tools will make it easier for teams to extract meaningful conclusions from complex datasets and focus on the aspects most relevant to their roles.

As each team models the data and refines their forecasts, the effects can be replicated instantly across the organization, ensuring they are all pulling together in the same direction.

Improving sustainability

Society’s increased focus on sustainability makes this more important than ever. Ever more refined forecasting, and better integrated systems, will mean manufacturers can address inefficiencies that result in wasted goods or sub-optimal distribution. Producers will be in a better position to address sustainability issues, both within their own business and in addressing consumer concerns.

This will reshape how teams work with each other, and how manufacturers work with retailers and ultimately, end customers.

Visualfabriq Demand Forecast Master

Visualfabriq’s Demand Forecast Master platform enables you to easily pull together historical, ex-factory and syndicated data.

This provides the fuel for its advanced AI models to automatically generate statistical baselines, discern trends, and make informed, accurate forecasts of future demand.

You can push these to your organization’s demand planning and supply chain planning teams with confidence. Because the statistical baselines are created bottom up, your teams can get forecasts for each plan account or product. And this takes into account planned promotions, likely risks, and potential opportunities.

This will be even easier with Visualfabriq’s Trade Spend Master and Trade Promotion Master tools. Each team can dive down to the right level of detail they need, comparing forecasts and actuals. They can model or optimize plans, while being confident they are all dealing with a single source of truth.

And with Visualfabriq’s Revenue Planning Platform, you can monetize demand forecasts to a full year value plan, showing how volume plans contribute to business goals.

You can never eliminate the unexpected completely. But with Demand Forecast Master, it’s safe to say you will have more time focusing on the exceptions and outliers. Consequently, meetings will generate more answers than questions.

What next?

Demand forecasting is always complex. Simply producing a well-grounded forecast is a challenge. But information changes all the time, meaning forecasts can potentially be out of date almost as soon as they are drafted.

Legacy systems can make it hard or impossible to pull together accurate or up-to-the-minute data in the first place. Distributing forecasts may be difficult and updating or optimizing forecasts an impossibility.

This lack of connectivity and agility means a firm might default to a lowest common denominator forecast. Such an approach lacks the specificity that specialist teams require to perform their roles effectively. It leads to inefficiency, or even different teams working up their own visions of what the future might be.

But your future needn’t look like that. With the right demand forecasting platform, you can pull together both historical and up to-the-minute data, constantly. Together with cutting edge AI, this can give you accurate and dynamic forecasts which you can tailor to the needs of your individual teams.

This will save them time and means they can focus on the exceptions they can bring most value to. Changes and optimizations will be reflected instantly, meaning all your teams are working with the most up to date information.

Your entire organization will be able to work on a single version of the truth. Even if it hasn’t actually happened yet.

To see how your organization can benefit from an integrated, AI-powered demand forecasting platform head here. Today.