The volume of data within companies is increasing. Machine, supplier, and product data are becoming more accessible. It is necessary to collect all this relevant data to make it usable for analytics. By gathering relevant data, businesses are provided with insight and directions on how to become more competitive.
An absolute necessity in the age of IoT, where more and more machines are connected with each other. We analyze all the different types of data (machine, sensor, forecast, stock, etc.) which allows us to build and deploy solutions that are able to predict, improve organizational efficiency, and minimize operational risk and cost.
More and more data is arising from the use and maintenance of machines. Structural analysis of this data can help in predicting when a machine or a system will malfunction. This enables smart maintenance planning which can often be done prior to the device breaking down.
Moreover, through the analysis of the data, companies can determine with a certain level of accuracy what is the remaining lifetime value of their machine or sensor components.
With the use of ERP info, 'event logs' and other types of structured and unstructured data, we can identify and improve different organizational and machine processes. It's particularly interesting for companies that want to identify bottlenecks, revise outdated procedures and improve overall performance.
On top of that, through the analysis of this data, different KPI's can be measured and focused.
With this solution the aim is to tackle the major logistics problems that manufacturers face.
Through analyzing product usage, purchase behavior, and seasonal peaks, we can create forecasting models for organizations that want to predict when a certain product/article will run out of stock and what is the optimal delivery method which won't lead to extra costs.
This will allow for both stock level optimization and improved inventory planning.