F&B manufacturers increasingly turn to the Cloud for supply chain visibility

By Jenny Eagle

- Last updated on GMT

Warburtons production line. Photo: Clear-Communication.
Warburtons production line. Photo: Clear-Communication.
FuturMaster (FM) has seen international growth driven by the success of its FM Cloud Services 8.0 supply chain software and plans to oversee more roll-outs across Europe, US, and APAC.

The integration of machine learning technology to calculate promotion volumes is one of the key enhancements of version 8.0. FuturMaster.

Winterbotham Darby

Its client, Winterbotham Darby​, which supplies fresh produce to supermarket chains in the UK, has launched a project integrating FuturMaster's artificial intelligence for promotion optimization. 

Michel Ramis, EVP sales and marketing, FuturMaster, said in 2018, it will continue to innovate in the area of ​​promotion optimization thanks to the launch of an end-to-end TPx solution (trade promotion management / optimisation) at the end of the year.

This will make it possible for manufacturers to define revenue uplift, the right mechanism at the right time and the promotions’ return on investment.

"FuturMaster is showing unprecedented dynamism in the first half of 2018 with a two-digit international growth driven by the success of FM Cloud Services 8.0,​” added Ramis.

We are now a key partner of the global industry leaders, supporting them in their growth strategy and operational and financial performance.​"

Other customers that have decided to migrate to FM Cloud Services include DS Smith Packaging in the UK and SnowBeer, China.

Snowbeer is one of the best-selling beers worldwide, with more than 90 breweries and 35,000 distributors.

The S&OP One project with FuturMaster is an end-to-end supply chain planning with more than 1,500 users.

It covers end-to-end planning from distributors to demand planning and its daily truck replenishment to distribution and production planning, taking into account logistics and production constraints, distribution (transportation and warehousing) and production costs.

Reduce total operating costs

From a longer term perspective, it also covers supply network optimization, the opening and closing of factories and DCs. The purpose is to maximize service level while reducing total operating costs.

According to a report by Accenture, 85% of organizations have planned to adopt digital or Artificial Intelligence (AI) technologies in their supply chains during the last year.

The value of AI is estimated to be worth $36.8bn globally by 2025 predicts US market intelligence firm Tractica.

According to Stephanie Duvault-Alexandre, consultant, FuturMaster, the obvious benefits of machines over humans are efficiency and speed. But the majority of companies it is speaking to about AI are driven by the promise of additional revenues, better margins and lower costs.

With most food manufacturers and retailers having thousands of customers and products to deal with on a daily basis, machine learning is proving much more efficient at unravelling complex data quickly and meaningfully,​” she said.

For example, retailers want to be able to cluster and identify who are their main customers - who are repeat purchasers, browsers, or so-called aliens. Or they might need to know which products are better to deliver last-minute; or which core lines should regularly be in stock​.

And with many retailers dependent on promotions for contributing between 20% and 30% of sales - particularly in grocery - machines can tell us which promotions are better​.

Machine learning is more effective at clustering promotions based on looking at similarities and many more variables than is otherwise possible using traditional, linear-based forecasting techniques. For example, a leading health food company used machine learning to analyze demand variations and shopping trends during promotions, resulting in a 30% reduction in lost sales​.”

Flavor preferences

Duvault-Alexandre said a few food processing companies have already turned to AI to better calibrate their machines to manage several products sizes and reduce waste and costs. Others have been able to identify the optimal use of raw materials - like vegetables or fruit - dependent on size and varieties.

For example, one potato processing company has turned to AI to define which potatoes will produce the least waste when cut into French fries and which ones would work best for potato crisps.

Some beverage companies have used AI to rank flavor preferences among consumers - armed with an app on their phone in front of a self-service machine, customers could opt to change the flavor of their chosen drink - as part of developing new product ranges.

One of the biggest growth areas where AI can make a significant difference is in intelligent forecasting systems​,” she added.

Previously, retailers and manufacturers were only able to predict roughly the quantities of products to order to keep shelves fully stocked using (often out-of-date) inventory levels and historical sales data (usually going back a few years, at best).

These days, AI can develop a much more accurate picture of exactly what types of products are likely to sell, by looking at multiple scenarios in real time (suppliers’ data, consumer behaviour, the weather etc.) and drawing on data from the internet. This means forecasting is no longer so much “stab in the dark” guess work​.” 

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