The manufacturing sector is in constant evolution: we are now witnessing the beginning of a new era, the “4th industrial revolution”, also known as “Industry 4.0”. Technological and digital progress are bringing innovations of such magnitude that executives are forced to review the very core of their industrial strategy.
Tomorrow’s factories need to be “smart” in order to cope with the challenges that lie ahead. The production means need to be more adaptive and reactive to demand. Moreover a more efficient use of resources is necessary to guarantee competitiveness and productivity.
Flowlity feets every specificities
Other discrete industries
Pharmacy and medical equipment
Optimization of components stocks of a tier 1 supplier in the automotive industry
Flowlity is now used to plan the procurement of a big name of the CAC 40, a tier 1 automotive supplier. We met two challenges with this manufacturer, minimizing the level of stock and increasing the availability of components.
When we launch the deployment of the tool at a customer we often ask the question: “Why is it so difficult to know what is the right level of stock?”
Our client’s main response was: “our customers’ fixed order horizon is 5 days while we have to commit over 2 months with our suppliers”. The consequence is that the supply plan must be made on the basis of the consumption forecast.
We also heard:
- our supplier complains of a too high safety stock level because it is on consignment, yet sometimes we go out of stock on their parts
- items we have under our own management also have a too high stock level
- our suppliers complain about the quality of our consumption forecasts
- SAP is not well configured and our planning is still done in excel (every planner uses their own file and the transfer of skills is complicated)
Reduction of the overall stock level
The implementation of Flowlity has enabled a reduction of the overall stock level of 40% for items under own management and for those managed in consignment.
Consignment suppliers saw their average inventory level decrease. It was then easy for them to accept to follow the requests of our client to increase the safety stock in risky periods, thus ensuring a reduction of shortages.
The accuracy of consumption forecasts has also greatly increased thanks to artificial intelligence algorithms. This allows us to communicate to suppliers a more reliable replenishment plan earlier.
Finally, the Flowlity tool has won over users and managers with its pleasant interface and ease of use.
Following this success, we plan to deploy the tool over a larger geographic area.
Optimization of finished products stocks
In this case, we are interested in optimizing stocks of finished products of a manufacturer producing spare parts for the medical industry.
Our customer manages consignment and VMI stocks for his clients who are large european assemblers. Our client manages inventory in its facilities as well as VMI stocks at each of their clients.
Artificial intelligence algorithms to decrease inventory and stock-outs
By using Flowlity on our client’s data and applying a combination of artificial intelligence algorithms, we were able to show an overall decrease in inventory of over 45% and a decrease in stock-outs of over 60%.
In a second step, we plan to deploy Flowlity as a trusted third-party in order to recover the production plans of the different assemblers.
This will allow us to further increase the gains observed during the first stage.
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