{"id":21700,"date":"2024-04-02T12:34:52","date_gmt":"2024-04-02T10:34:52","guid":{"rendered":"https:\/\/www.tradecloud1.com\/?p=21700"},"modified":"2024-04-02T12:50:54","modified_gmt":"2024-04-02T10:50:54","slug":"ai-risk-indicator-product-developments-insights","status":"publish","type":"post","link":"https:\/\/tradecloud1.com\/nl\/ai-risk-indicator-product-developments-insights\/","title":{"rendered":"AI Risk Indicator – Product developments insights"},"content":{"rendered":"

Product development insights: AI-based Supply Chain Risk Indicator<\/span><\/h1>\n

In the complex world of supply chain management, unexpected challenges can often arise, leading to costly delays and logistical nightmares. To provide insights into this complex world, Tradecloud will focus on AI tools for Risk analysis & Purchase automation, as mentioned in the previous blog post.<\/span><\/p>\n

To build customer confidence in AI-driven purchase decisions, our first step is implementing an AI risk indicator that conducts a proper risk analysis. This initial version predicts potential supply chain\u00a0 issues and enables companies to proactively address issues before they escalate. It achieves this by predicting exceptions for individual order lines based on historical data within the Tradecloud One platform.<\/span><\/p>\n

The indicator will assist purchasers in estimating the risk of disruptions within their supply chain. However, the purchaser must still interpret whether this exception will indeed lead to a disruption, considering factors such as whether the order line contains a critical component or just a regular stock item with plenty of safety stock available.<\/span><\/p>\n

Once the first version of the AI risk indicator goes live, user feedback will be collected to further enhance the indicator’s sophistication. Additional supply chain related data, such as MRP, stock level, and sales orders & forecast data for ERP systems, can be synced to the Tradecloud data lake. By leveraging vast troves of data, the AI indicator can accurately assess the probability of unforeseen expectations and indicate the severity of disruptions, empowering companies to stay one step ahead in their decision-making processes.<\/span><\/p>\n

Furthermore, additional general supply chain data can be incorporated to enhance the indicator’s accuracy. This data includes traffic information, port congestions, and weather forecasts. Moreover, non-supply chain related data, such as political events, new legislations, and natural disasters, can also be integrated to provide a comprehensive risk assessment.<\/span><\/p>\n

Additionally, proactive alerts can be sent to both purchasers and suppliers. These will alert our customers when the risk indicator expects an exception that may lead to a disruption, fostering a stronger relationship between both buyer and supplier.<\/span><\/p>\n

The Proof of Concept<\/span><\/h1>\n

As described in the previous blogposts, AI will only succeed if it\u2019s used for the right use case, based on the right data.<\/span>
\n<\/span>Although we had a vision, you never know for sure if it will work out the way you planned to.<\/span><\/p>\n

As a result, we first set out to create a Proof of Concept in 2020. Together with Tilburg University and Delft University of Technology, we set out an Intership, focussed on implementing a Machine Learning or AI algorithm by ourselves, based on the data we had. At that time, Tradecloud One was only just launched and the amount of data we had available was very limited. Although it was rapidly growing, this could pose a real challenge for the internship.<\/span><\/p>\n

We were lucky enough to find Arbnor Rama, an ambitious student who was eager to take on this challenge. Supervised by our Tech Lead, who graduated in AI several years earlier, Arbnor set out to investigate how to add value to the Tradecloud One platform by using AI\/ML.<\/span><\/p>\n

The following Machine Learning Algorithms were investigated:<\/span><\/p>\n