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Flowlity’s Intelligent Materials Management Solution combines the latest machine learning, ensemble learning, and deep learning algorithms to help you achieve further inventory reduction and reduce the risk of shortages while keeping the same or improved level of service.
Using AI, our solution helps you optimize inventories, manage unforeseen disruptions, facilitate supplier communication, and enhance supply and inventory strategies. It senses risks of demand surges or supplier delays and adjusts inventory buffers accordingly.
Streamline and optimize your supply chain
Flowlity, the leading Intelligent Management solution, facilitates continous improvement in materials replenishment by:
• Challenging the forecasts out of the MRP/ERP. The solution warns you when MRP is really unreliable and proposes smarter and more accurate forecasts
• Calculating and warning of any supplier delays so that you can proactively anticipate and adjust your orders
• Providing intelligent alerts whenever the risk of a shortage arises
• Providing hints and advice in defining your inventory strategy
Built as a decision support!
Our results can be easily understood by users. Flowlity was not built to be a black box but to be a solution to help users make better decisions.
Is highly
scalable
Takes into account significant relationships between products during the learning process
Calculates the probability of an event happening and its forecast value
Delivers intelligent safety-stock recommendations
Provides simple forecasts for products with little historical data
Provides daily to yearly forecasts
Uses probabilistic forecasting
In contrast to the traditional MRP approach, Flowlity’s Intelligent Materials Management Solution (IMMS) identifies specific decoupling points within the supply chain and independently forecasts consumption and supply delays for each of these points. Additionally, Flowlity incorporates a consumption forecast while also gathering dependent requirements from the MRP system.
Flowlity’s forecast is enhanced with MRP data to capture trends that are not visible in the historical data. It uses a probabilistic approach to compute stock buffers and fight the bullwhip effect by:
• Computing several forecasting scenarios with different parameters and possibilities
• Computing the probability of each of these scenarios
• Selecting the scenarios with the highest probability
• Displaying the “confidence interval” being the synthesis of other less-probable scenarios
Sales forecasts on
finished goods
MRP requirements
calculation
Components
consumption
MRP
Probabilistic forecast using machine learning
• Taking into account MRP trends
• Analyzing correlation between products
Dependant requirements
on components
Forecast at component level & confidence interval
to cover for demand uncertainly
Volatile and very sensitive to change of upstream or downstream
More resilient signal (not connected to sales forecast)
& directly linked with the stock buffer
Sales forecasts on
finished goods
MRP
Dependant requirements
on components
Volatile and very sensitive to change of upstream or downstream
MRP requirements
calculation
Components
consumption
Probabilistic forecast using machine learning
• Taking into account MRP trends
• Analyzing correlation between products
Forecast at component level & confidence interval
to cover for demand uncertainly
More resilient signal (not connected to sales forecast) & directly linked with the stock buffer
