Heineken to use AI to control volatile demand
Heineken is to use Blue Yonder’s machine learning to control volatility in its supply chain.
The brewer has announced that it will use the demand planning solution to help improve forecasting in order to stabilise a supply chain that has been hit by restrictions on the hospitality industry during the Covid-19 pandemic.
Heineken will implement the Blue Yonder AI in five of its biggest operating companies around the globe.
The system takes data from a range of internal and external sources to project the amount of inventory likely to be needed. It could also help the brewery to understand customer behaviour and events likely to drive demand.
Marc Bekkers, director of global supply chain planning at Heineken, said, “We have embarked on an ambitious journey to become the best-connected brewer, going from fragmented to seamless digital interaction across the whole value chain.
“The introduction of Machine Learning as part of building an integrated cross-functional planning capability across our business is a critical component of this journey. The solution will assist us in continued growth and meet the changing demands of our customers and consumers.”
Blue Yonder’s EMEA president Johan Reventberg said, “Heineken has been a long time Blue Yonder customer, so we are looking forward to expanding their supply chain footprint by helping them develop highly accurate forecasts with our AI- and ML-driven demand planning solution.
“This capability paired with Microsoft Azure will help Heineken continue to meet consumer demand while solidifying its position as a leading global beverage producer.”
Google is using AI to design faster and improved processors
Engineers at Google are now using artificial intelligence (AI) to design faster and more efficient processors, and then using its chip designs to develop the next generation of specialised computers that run the same type of AI algorithms.
Google designs its own computer chips rather than buying commercial products, this allows the company to optimise the chips to run its own software, but the process is time-consuming and expensive, usually taking two to three years to develop.
Floorplanning, a stage of chip design, involves taking the finalised circuit diagram of a new chip and arranging the components into an efficient layout for manufacturing. Although the functional design of the chip is complete at this point, the layout can have a huge impact on speed and power consumption.
Previously floorplanning has been a highly manual and time-consuming task, says Anna Goldie at Google. Teams would split larger chips into blocks and work on parts in parallel, fiddling around to find small refinements, she says.
Fast chip design
They have created a convolutional neural network system that performs the macro block placement by itself within hours to achieve an optimal layout; the standard cells are automatically placed in the gaps by other software. This ML system should be able to produce an ideal floorplan far faster than humans at the controls. The neural network gradually improves its placement skills as it gains experience, according to the AI scientists.
In their paper, the Googlers said their neural network is "capable of generalising across chips — meaning that it can learn from experience to become both better and faster at placing new chips — allowing chip designers to be assisted by artificial agents with more experience than any human could ever gain."
Generating a floorplan can take less than a second using a pre-trained neural net, and with up to a few hours of fine-tuning the network, the software can match or beat a human at floorplan design, according to the paper, depending on which metric you use.
"Our method was used to design the next generation of Google’s artificial-intelligence accelerators, and has the potential to save thousands of hours of human effort for each new generation," the Googlers wrote. "Finally, we believe that more powerful AI-designed hardware will fuel advances in AI, creating a symbiotic relationship between the two fields.