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Dr. René Sachse

Manager Analytics and AI

As team leader of SupplyOn's Analytics and AI department, I integrate advanced analytics technology and AI functionalities into our platform to enable data-driven decisions in the supply chain. My focus is on using innovative technologies to optimize processes and achieve sustainable efficiency gains for our customers.

Posts by Dr. René Sachse:

Industry News

Predictive delivery date: AI predicts production completion and delivery time

Production processes can be severely disrupted when it is only discovered on the scheduled delivery date that the ordered production material will not arrive on time. Since this material often travels through long and complex supply chains consisting of many planning, ordering, production, and delivery processes, bottlenecks are not uncommon in practice. The problem is exacerbated by the fact that modern manufacturing plants often have very low inventory levels, meaning that materials are delivered just prior to production.It is therefore particularly important that production and material planners are informed as early as possible of any potential delivery problems. Only then can timely action be taken, such as ordering a replacement or rescheduling production.Shortly before delivery, the goods are in transit. During this time, the carrier closely tracks and monitors the status of the shipment. However, modern transportation times can be very short, and notification of delivery delays often comes too late to take countermeasures.Insight into supplier productionThe solution is to identify discrepancies in production planning and production at the supplier long before transportation. SupplyOn automatically collects a wide range of standardized production data from the supplier. This makes it possible to track which production quantities are in the planning phase and which are actually in production. The transmitted data also makes it possible to monitor production progress and determine which specific production step the order is in. This provides visibility down to the sub-assemblies and raw materials used.The power of our production-to-supply solution is that quantities in production can be mapped directly to buyer requirements. This provides immediate visibility into which requirements are already scheduled with the supplier or are already in production. By constantly monitoring the progress of work, the AI-powered system automatically learns normal production lead times and also recognizes when current production is slower than usual.Using AI to spot variances before they become a problemA complex, continuously learning predictive model based on historical monitoring data calculates an estimated completion date, taking into account recently observed production times and current work progress. This makes it possible to estimate, either during production or before production begins, whether the manufactured goods will be ready for shipment and whether the desired delivery date can be met.The highly AI-driven system generates early warnings for various situations:The planned production quantity is too lowThe planned production start is too lateThe quantity in production is insufficient to meet the upcoming demandThe production start is too late or will be delayed to meet the delivery dateBased on the forecast of the current production time, it is also possible to determine when the production for the upcoming demand has to start in order to avoid delivery delays. The system also records the difference between the quantity produced and the quantity delivered. This can be used to determine the percentage of scrap and to calculate a recommended production quantity in addition to the recommended start of production.In addition to clear graphical and tabular displays, the predicted delivery date, recommended production start and recommended production quantity can be sent directly to the customer's ERP system. This means that the AI system's calculations are available to the customer at the line item level.Optimized planning at both ends of the supply chainBy combining demand and production data and performing intelligent calculations, the system provides valuable insights to both the purchasing and supply sides, enabling both to improve their planning processes. This increases the likelihood that production will start on time and in sufficient quantity.Production delays are automatically detected and alerts are generated, providing insight into exactly where production is stuck. This allows for timely and targeted adjustments
Predictive delivery date: AI predicts production completion and delivery time
SCM Insights

AI-based chatbot assists with CCF surveys

Sustainability and climate initiatives are increasingly important today. At the same time, companies are under increasing pressure to meet legal requirements for reporting carbon emissions. For suppliers, this means providing their customers with detailed information on their production emissions. To make this process easier, SupplyOn offers an innovative survey tool that optimally supports the user by integrating an intelligent chatbot.SupplyOn's new chatbot is based on the latest generative AI technology. It is trained to support users in completing the Corporate Carbon Footprint (CCF) survey. The bot draws on a comprehensive database of user guides, question and answer lists and other content.By interacting with the chatbot, users can pose questions directly to a digital assistant that provides immediate, expert answers. Long waits for support feedback are a thing of the past.Strengths of the chatbotOne of the chatbot's greatest strengths is its ability to explain and clarify the technical vocabulary used in the survey. Users can ask questions to better understand what exactly is meant by a particular question. The chatbot can also provide information on measurement and calculation methods for different types of emissions, as well as details on possible energy mixes.A particularly helpful feature of the chatbot is its intelligent "reasoning". The bot can independently analyze whether the emission of a described process is a direct or indirect emission, or whether it should be classified as a downstream or upstream emission. In this way, the digital assistant can recommend to the user in which field of the survey specific emissions should best be entered.Launch of a comprehensive AI-based support strategyThe CCF chatbot marks the start of a company-wide introduction of an AI-based help and support system at SupplyOn. The aim is to simplify the use of our applications and significantly reduce support times for our platform users.The integration of the chatbot into the CCF survey is an important step in making it easier for companies to comply with sustainability standards while increasing efficiency. By using AI technology, SupplyOn ensures that its customers are optimally equipped for the challenges of the future.SupplyOn's goal is to provide customers with innovative solutions that meet both the demands of the present and the challenges of the future. The new chatbot is an integral part of this strategy and underlines our commitment to sustainability and technological excellence.
AI-based chatbot assists with CCF surveys