kheoos|cast

Probabilistic Forecast

kheoos|cast

Probabilistic Forecast

kheoos|cast

The management of maintenance parts in the industry is an area that is attracting more and more attention from decision makers. It is now necessary to reduce inventory levels and obsolescence, without increasing the risk of shortage and therefore of operating losses. kheoos has identified major sources of reduction in the stock levels and offers manufacturers of all sizes new tools and innovative uses.

 

kheoos|cast is a set of forecasting aid tools, ranging from traditional replenishment rules, to the probabilistic prediction, based on artificial intelligence, for the most complex models.

The right forecast for the proper stock level

The rigour of demand forecasting (or consumption) is key to getting the right stock level; neither too low, at the risk of causing shortages, nor too high, at the risk of mobilizing capital and increasing obsolescence. As part of the kheoos community, the prediction can be made at a member level, but also for a group of members who share virtually a fraction of their stock.

 

The right forecast for the proper stock level

The rigour of demand forecasting (or consumption) is key to getting the right stock level; neither too low, at the risk of causing shortages, nor too high, at the risk of mobilizing capital and increasing obsolescence. As part of the kheoos community, the prediction can be made at a member level, but also for a group of members who share virtually a fraction of their stock.

 

Artificial intelligence learns from the contextualized history of your uses

Demand forecasting is an exercise that is still often practiced more or less manually on spreadsheets, while today's technologies and computational capabilities are available at reasonable cost. Data are also present in abundance in management systems, but most often under-exploited. The "machine learning" allows learning from a dataset, in order to determine probabilities of future demand.

 

Artificial intelligence learns from the contextualized history of your uses

Demand forecasting is an exercise that is still often practiced more or less manually on spreadsheets, while today's technologies and computational capabilities are available at reasonable cost. Data are also present in abundance in management systems, but most often under-exploited. The "machine learning" allows learning from a dataset, in order to determine probabilities of future demand.

 

For supporting operational actions

The algorithm then proposes, depending on the constraints defined, operational actions like purchasing parts, selling dormant inventory, etc. The inventory manager can then review the options and make informed decisions.

 

For supporting operational actions

The algorithm then proposes, depending on the constraints defined, operational actions like purchasing parts, selling dormant inventory, etc. The inventory manager can then review the options and make informed decisions.

 

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Our partners

EDF Pulse-Open Innovation-finalist Pulse contest-kheoos STMicroelectronics-logo-f@ST.lab-kheoos Michelin kheoos dormant maintenance parts circular economy reuse réemploi Schneider Electric-Business Innovation-kheoos Photowatt-EDF ENR PWT-kheoos STMicroelectronics-f@st.lab-open innovation-kheoos Investissement Avenir Ademe BPI Innovation concours i-nov inov 2020 kheoos lauréat ademe kheoos economie circulaire La French Tech-BPI-financing innovation-kheoos-startup BPI-Banque Publique d'investissement-France-financing innovation-bourse frenchtech Linksium-satt-grenoble-innovation-network financing laboratories-intellectual property-kheoos-startup Challenge Linksium OutOfLabs-kheoos awarded-startup-2018 Université Grenoble Alpes UGA kheoos Réseau Initiative Pays Voironnais-kheoos-award-financing-innovation Région Rhône Alpes-Grenoble-Isère-kheoos-lauréat

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