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Neuton Use Cases for Neural Network and Machine Learning

Hardware maintenance prediction
Challenge:
As various hardware elements continue to converge, the complexity around supporting environments and devices comes at a greater cost impact to the business.
Solution:
Predictive analytics can show when a particular piece of equipment may degrade or malfunction. It also allows scheduling of maintenance to prevent unexpected equipment failures. Other potential advantages include increased equipment lifetime, increased safety, fewer accidents, and optimized spare parts handling.
Why Neuton:
Neuton can immediately bring value to an organization, and is equally effective, whether the organization is looking to monitor existing equipment that has been in the landscape for an extended period of time, with adequate historical data, or with new equipment which when installed has minimal to no data. Neuton is able to bring value and operate effectively in both scenarios due to the fact that Neuton is not dependent on significant amounts of data as other solutions are. Furthermore, due to the compact size of Neuton models, they can be built into IoT devices to more accurately detect equipment anomalies.
Inventory & supply chain optimization
Challenge:
Any retail or manufacturing business is always faced with the problem of supply and storage of products. It can be difficult to keep track of change and carry out optimization of storage facilities. Furthermore, for manufacturers, moving and storing additional stocks is associated with significant costs.
Solution:
Neural machine learning can be used to effectively and dynamically manage uncertainty, and systematically reduce inventory levels across all locations. Artificial intelligence accumulates all historical data, recent trends, optimal costs and makes a forecast regarding the optimization of the supply chain. Automated machine learning also allows you to redirect inventory, increase production speed and reduce costs.
Why Neuton:
Neuton does not require any coding or special data science background, so any inventory manager can use it to make accurate predictions regarding how supplies and warehouse should work to increase operational efficiencies.