Sales Forecasting Tips For F&B Suppliers and Chains

Tan Siew Ann
Tan Siew Ann


What are F&B managers doing wrong with their sales forecasting? And how can they optimize their predictions for maximum efficiency?

Quick Links
Why is sales forecasting important in the F&B industry?
Common sales forecasting mistakes among F&B suppliers and chains
Using only historical data to conduct sales forecasts
Manually and infrequently forecasting sales 
Doing only "top-down" forecasts 
Being unable to interpret forecast results 
Improving the sales forecasting process
Data analytics for smart businesses

Sales forecasting is an important tool for food and beverage (F&B) suppliers and chains. As demand in this industry is heavily influenced by ever-evolving consumer tastes and preferences, sales forecasting helps them manage supply chains while continuing to meet consumer demand. 

However, the usefulness of the forecasting exercise may be hampered if the forecasts are conducted improperly, or done using outdated methods.

In this article, we look at the importance of sales forecasting for the F&B industry, common mistakes when forecasting sales, and how F&B managers can improve their sales forecasting processes.

Why is sales forecasting important in the f&b industry?

With food being one of the basic physiological needs of human beings, the F&B industry undoubtedly plays a crucial role in feeding the hungry mouths of society. However, getting food ingredients from the supplier to the restaurant, and finally onto the diner’s plate, is no piece of cake. 

The F&B industry faces many unique challenges such as:

  • Long raw material lead-times: Whether they are fresh or processed ingredients, raw materials often take time to be acquired, with bulk orders needing to be placed far in advance. These long-lead times often complicate inventory management, possibly leading to “empty shelves” at retailers if done incorrectly;

  • Volatile commodity pricing: Prices of food commodities are subject to much volatility, whether due to the effects of climate change on growing conditions or currency fluctuations. Such price uncertainty makes it difficult for F&B players to consistently estimate their costs and make appropriate decisions. If the price of an ingredient for a certain product suddenly skyrockets, typical responses would be operating on thinner margins or altering the quality of the end-product, much to the detriment of the customer;

  • Safety and quality requirements: For health and safety reasons, food products are subject to strict quality control requirements, driving up operating and regulatory compliance costs. The failure to detect food spoilage or contamination can result in illness and would require complicated food recalls;

  • Expiration dates: Food products have limited shelf life, which necessitates the eventual replenishment of inventory even if it is unused. This is especially so for perishables like fresh fruit and vegetables. Food spoilage is not just a wasted opportunity, it also has huge environmental consequences. 

  • High demand uncertainty: Most seasonality in the F&B industry is demand-driven, with consumer tastes and preferences being influenced by factors such as the weather, seasonal holidays and even regulatory restrictions. 

  • Immense competition: As consumer tastes are constantly changing, the F&B industry needs to be extremely agile or able to adapt to the latest trends. Only the most dynamic organisations can make it to the top.

All these considerations affect the viability of players in the F&B industry, where being able to deliver the right products at the right time, and in the right portions, is critical. 

This is where sales forecasting comes into the picture: by using past and present sales data to predict future sales, F&B suppliers and chains will be able to pre-empt and respond to potential consumer demand by maximising working capital, keeping up with changing tastes and consumer trends, reducing food waste, and improving coverage and service levels.

Common sales forecasting mistakes among f&b suppliers and chains 

While sales forecasting isn’t a new concept in the F&B industry, it’s not always done properly. Some common mistakes include:

Using only historical data to conduct sales forecasts 

At the most basic level, sales forecasting involves using historical data to make future sales projections. But some F&B suppliers may make the mistake of thinking that this is the only thing that needs to be done when conducting sales forecasts.

Such an approach fails to take into account the fact that rapid changes in consumer demand may not always be captured in past data. Instead, more sophisticated sales forecasting techniques that do not just rely on historical data, need to be adopted to ensure forecast accuracy.

Manually and infrequently forecasting sales

Despite advancements in sales forecasting technologies, some F&B value-chains are still relying on the outdated technique of manually keying data into spreadsheets to run calculations.

As a result, their sales forecasting process is slower, more tedious, and comes with a higher risk of inaccuracy. Plus, it is more difficult to conduct proper handovers in case the data needs to be passed down in the event the previous manager or sales forecaster leaves. 

Meanwhile, F&B suppliers who find it a chore to forecast sales — especially because their calculations are done manually — may put it off. Instead, they may choose to conduct their sales forecasting at more infrequent intervals such as quarterly, and incorporate data from larger time periods.

While this may reduce the frequency of sales forecasting to a more manageable level, such “convenience” comes at the expense of potentially deriving forecast results that are less reactive to changes in consumer demand.

Doing only "top-down" forecasts 

When conducting sales forecasts, F&B suppliers and chains might adopt a “top-down” approach. This involves starting with industry-level data and assessing the extent of the market that the business’ respective products might be able to capture in the future.

The “top-down” approach to forecasting sales can have certain usefulness if industry data is widely available. However, actual sales data plays a smaller role in this approach, which may lower the accuracy of the forecasts compared to a “bottom-up” approach (where projections are made for each product and aggregated for the whole business) or a “middle-out” one (a combination of both “top-down” and “bottom-up” approaches).

Being unable to interpret forecast results

Even after having conducted a sales forecast, organisations may not have the requisite knowledge or skills to interpret the forecast results and their potential business implications. A forecast conducted under such conditions will have limited value to the business in question, no matter how accurate the forecast may be.

improving the sales forecasting process

The invention of spreadsheet software was a boon to sales forecasting as calculations could be computed automatically instead of by hand, and on pen and paper. But technology has since improved even further, and the emergence of data analytics solutions looks set to take sales forecasting to a whole new level.

A number of forecasting tools like GMDH, Streamline, and R-Project are available online and free for individual users. These tools are designed to aid with pipeline management, demand forecasting, inventory planning, and optimisation. 

Meanwhile, data analytics solutions like DataVLT are built with the necessary infrastructure to both receive and store data sets for sales forecasting purposes, saving users the trouble of manually feeding such data into the software. 

DataVLT even offers the option of using blockchain for data distribution, security and lowering the risks of data manipulation. The stored data is processed using artificial intelligence and machine learning, and can then be visualised in a variety of user-friendly formats to facilitate the uncovering of trends and insights.

The result is that organisations spend less time and effort gathering and inputting data for more frequent sales forecast exercises, while generating in-depth predictions a lot quicker.

Data analytics for smart businesses 

Sales forecasting helps address the unique challenges of the F&B industry, but only if it is done right. 

Enter data analytics, whose technological capabilities allow for smarter, more efficient sales forecasting and with much less hassle. It’s time to keep those spreadsheets and adopt more sophisticated methods to move with the times.

Hopefully this article provided much food for thought. If you are an F&B supplier or chain struggling with sales forecasting, we can help you harness the power of data analytics to conduct better sales forecasts — and ultimately improve your bottom line. Get in touch with us today to learn more.




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