How is AI being utilised to advance our approach to inventory optimisation?
The Slimstock Research Centre is constantly pushing the boundaries of inventory management. As excitement around AI & machine learning grows, our team of experts are actively researching how this technology can be applied to overcome the supply chain challenges business face today!
In this infographic, we explore how machine learning is being applied to specific inventory management functions to develop the next generation of supply chain tools!
Forecasting demand for new products
New products are notoriously difficult to plan for. Our team of researchers are exploring how machine learning can be utilised to remove the uncertainty and risk from new product launches. Through applying machine learning algorithms with advanced configuration, AI-based systems will cluster demand history from multiple products to identify and anticipate trends in demand. This, in turn, will enable the system to predict the volume of demand.
The result: supply chain teams will be able to build robust forecasts for new products far quicker than any existing tool available today!
“To sell or not to sell”
How can you determine whether your new product launch has been a success or not? More importantly, how can you determine whether or not a new product should be continued or killed off after the launch phase?
By utilising specialised product classifications coupled with machine learning algorithms and advanced mathematical techniques, the Slimstock Research Centre is exploring how machine learning techniques can help businesses make more proactive stocking decisions. Furthermore, our team is developing a system to identify the necessary selling price of a stocked item required to ensure the product will generate a profit.
Using similar techniques to those relied upon in fraud detection, our team is applying machine learning techniques to enable supply chain teams to identify outliers in demand history and exclude this from any analysis. Furthermore, by utilising advanced neural networks to cluster SKUs that are highly sensitive to anomalies, these products can be managed more proactively.
This development will help to detect anomalies in daily operations like customer transactions, availability and inventory status. As a result, the reliability of both processes and calculations will be drastically improved!
Minimising waste is a complex challenge! Given that waste can be caused by a broad number of factors, the Slimstock Research Centre is developing tools that helps businesses anticipate waste levels and mitigate these causes. Focusing on the optimal order quantity for items where perishability is present as well as the risk of obsolescence at the end of the lifecycle, our team is researching how AI can help supply chain teams to gain greater control over waste.
There is no question that promotions present businesses with some major headaches. However, as AI systems advance, our team of researchers are exploring how such technologies can be harnessed to optimise the decision-making process around promotions. By utilising a technique called ‘deep reinforcement learning’, the Slimstock Research Centre is actively investigating how this development can be utilised to help businesses develop more effective promotions policies.
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