How FMCG companies can prepare for the red season spike

However, over-production is not an ideal solution either, as it can leave manufacturers sitting with unsold stock that costs money to store. Data is the key to successfully navigating not only the red season, but also other periods of high demand, including holidays and major sporting events.

Here are five critical areas where effectively analysing available information can help manufacturers to align production with demand.

1. Matching manufacturing targets to rate of sale

Manufacturers need to make use of the historical data available from their customer groups and retail chains to understand the rate of sale by region and by store. This can help to ensure that stock held in store, as well as within the distribution channel as a whole, is matched to demand and the expected rate of sale over the festive season.

Using the past three to five years’ worth of historical data offers a view of past sales, and analytics can be applied on top of this to map trends going forward.

2. Analysing trends, events and even weather patterns to determine demand

Historical data can be analysed to predict future behaviour and trends. This data can also then be overlaid with event trends, for example, the key sale days within the red season, as well as any external events such as a significant sporting event or special holiday that could add extra demand.

In addition, other external factors such as weather trends can influence the rate of sale of certain products, so this data can also be harnessed to more accurately predict demand.

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3. Ensuring your products are live on the shelves at all times

Stock on shelves is the lifeblood of the FMCG industry – if your products are not readily available to purchase, you are losing money. It is therefore essential to ensure your merchandisers are empowered with technology to assist them ensure stock on shelves maps to the designed planogram for optimal sales results.

Intelligent solutions can assist merchandisers to more easily identify when shelves are empty and when in-store stock is running low to ensure an order can be placed timeously.

4. Making sure your promotions are actually generating profit

The red season is all about promotions and specials, but it is sometimes difficult to ascertain whether promotions are simply driving volume or if they are actually enhancing profitability. Empirical data from historical data sets can be utilised to conduct volume versus profit analyses so that promotions can be relooked and improved as necessary.

Promotions also need to be mapped to strategy, and it is vital for manufacturers to have visibility into whether or not promotional pricing and displays are aligned. Analysis of accurate available data can help to ensure you are delivering against targets.

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5. Predicting what your customers might buy together

The more granular the data that can be analysed, the greater the level of intelligence that can be gained. Visibility into the data generated by retailer loyalty programmes, for example, can provide manufacturers with detailed information on what products are being sold when and where and in what basket of goods.

This not only assists with production planning, it can also help manufacturers to understand what combinations of products their end consumers are purchasing. This will enable them to offer promotions that are more likely to entice the consumer, as they are based on real insight and understanding.

Data is an invaluable asset to manufacturers, particularly big data and especially when new models of streaming analytics are applied. With accurate data available for effective analytics, manufacturers are empowered to more precisely predict demand and gear their production toward this.

This is essential in catering toward not only to the red season but also other days and special events throughout the year that are potentially just as profitable. Moving toward a Just-in-Time (JIT) model of production should be the ultimate goal to help manufacturers maximise profitability all year round.