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How to bring demand forecasters and sales teams together in CPG 

Understanding demand forecasting

In any consumer-packaged goods (CPG) company, sales teams and demand forecasters ultimately have the same aim – growing the business and making consumers happy. But in practice, it can seem as if their objectives are somewhat at odds. 

CPG demand forecasting looks months ahead. They want to ensure that the company produces the optimal amount of stock and that it hits retail channels at the right time. They’re looking to ensure that growth is steady and predictable, and that there are no unpleasant surprises for their counterparts in manufacturing, logistics, finance or indeed, top level management. 

Sales teams are focused on developing the company’s sales as well as their relationship with retailers. If anything, they have an interest in overstocking in comparison to the demand plan. A good example is that if a promotion takes off, and the stock isn’t on hand to fulfill demand, the result is unhappy consumers and retailers. Thus, sales teams will always want to secure stock cover.  

This tension might all be rather predictable. But if a CPG manufacturer can find a way to bring sales and demand forecasters closer together, it will harvest benefits right down its supply chain. 

Common methods of forecasting 

In theory, demand forecasters have a wide range of tools and techniques at their disposal. They have internal historical data to draw on, of course. They can use moving averages to plot a forecast over the course of a year, exponential smoothing for more short terms analysis, and regression analysis to tie together demand and other variables. 

They can also turn to market research, both qualitative and quantitative, to get a grip on long-term consumer trends. Collaborative planning, with suppliers, distributors, and retailers, can uncover unexpected insights. And they can tap judgemental sources, such as market experts and futurists. These are all particularly important when it comes to planning around new launches, where there is scant historical data to hand. 

And increasingly, forecasters are adopting predictive analytics, using machine learning and AI to quickly process large amounts of data. 

But each of these methods has its drawbacks. Simply pulling together historical data can be a challenge, particularly if it is spread across multiple silos or sources. Teams may have developed their own bespoke tools for analysis, which might be adequate for top-level forecasts, but fail to offer real depth. Processes which have a high degree of manual input carry the potential for simple, but often catastrophic, mistakes. And this all takes time. 

This means that what works for demand forecast teams, might not work for the sales teams working hand in glove with retailers. Sales professionals are closer to the market, often highly attuned to specific patterns or shifts in demand. These may be due to regional factors or seasonal patterns that might not always be captured by traditional demand forecast tooling. And such insights may not be passed back to the forecasting professionals. 

Factors affecting demand 

Multiple factors can affect demand. Seasonality will always be an obvious factor. Sales of some products will ebb and flow through the year – ice creams and charcoal in summer, soup and hot drinks in the winter months. 

But these patterns can be profoundly affected by the unexpected. A surprise price promotion or product launch by a rival, or an advertising campaign that goes viral can all have an impact that couldn’t have been factored in six months earlier. 

At a broader level, unexpectedly cold weather in summer can stymie expectations for sunscreen sales. A national team’s progress in a sports tournament can boost sales of certain items – only for these to plummet if the squad is knocked out early. 

Other factors can be even more wide-ranging. Natural disasters, war, or pandemic can all affect customer demand, as well as supply costs. And economic disruption can hit the pockets of vast numbers of consumers. 

Such events are not going to be reflected in historical data but can send ripples, or even shock waves, right through the organization’s supply chain. 

How demand forecasting software can benefit you  

It might seem then that demand forecasters and sales teams are trying to negotiate very different problems. The former are looking to generate predictable long-term forecasts months, or a year or more in advance, the latter are working day to day, week to week, with retailers to deepen relationships and boost sales. 

But sales teams still need reliable baseline forecasts from their demand colleagues, both for planning their own activities, and for analyzing the effects of their promotions, and demonstrating them to their retail partners. And forecasting teams need the close-to-the-market insights sales teams can generate. 

This is why an integrated demand forecasting software platform can bridge that gap and bring both teams closer together. 

If they choose the right platform, demand professionals benefit from automated integration of multiple data sources, both internal and external, historic, and contemporary. Data cleansing becomes an integral part of the process. Data analyzed using the most appropriate tools, including cutting-edge AI, is producing more accurate forecasts, which are also more in-depth. 

Moreover, with a company-wide system those forecasts can be shared, interrogated, and analyzed more easily. And with all departments – sales, marketing, manufacturing, procurement – working to the same forecast, marketing events, promotional programs, and more can be better planned and truly in sync. 

Monitoring and adjusting forecasts 

And this is not just a one-off benefit. 

As we’ve seen, life doesn’t turn out quite as planned. But modern demand forecasting software can continue to absorb new information. This can include consumption and sales data, so that forecasts can be recalibrated continuously, over the course of a quarter, a year, or longer. 

At the same time, ongoing statistical analysis and error identification can detect new patterns and areas for improvement. Forecast accuracy can be plotted on an ongoing basis. 

When data integration is automated, and reliable analytics and AI take an increasing role, demand professionals can focus on outliers, special cases and anomalies – alerting sales teams to take prompt action when necessary. 

This means sales teams can gauge the impact of their activity while it is in progress and change tack as necessary. These insights can be shared internally. But they can also be shared externally, so sales teams can demonstrate the success of their efforts to retailers, and have a solid starting point for developing new, more innovative, future plans. 

Key takeaway

While it can seem that sales teams and demand forecasters are pulling in different directions, that’s largely a question of timing and perspective.  

By adopting integrated demand forecasting software, both groups benefit. Demand forecasters gain more data, more accurate tooling, and more time to focus on outliers. Sales teams get more robust, more dynamic forecasts. And they get the ability to share insights with their colleagues, and with their partners in retail. The result is more predictability. In the best possible way. 

To see how your sales team can benefit from more accurate, insightful forecasting right now, book a demo with Visualfabriq today