We were contacted by a client who had gathered a lot of data on the storage and movement of dispensers with garments for medical staff. The client wanted to use this data for the optimization of garment placement and movement processes for hospitals in Europe. This was successfully achieved as the result of building this data analysis app.
All hospital employees should wear specific work clothes. These clothes are stored in dispensers. Every employee uses multiple types of cloth: gowns, gloves, T-shirts, etc. After the work is done, the clothes are put in a container. Since every piece of work clothes has an RFID tag, its usage can be logged by the tracking system: who took it, when, where, and when it was released.
The problem was – a lack of some types of cloth and excess of another. As a result, employees were unable to acquire all the necessary garments set in one dispenser and had to move to another part of a hospital to get it. This is where a data analysis app was supposed to help.
Our model successfully predicts the usage of inventory work clothes by employees of an enterprise for the next week using two-weeks exploitation history as an input (which is around 2 million records) and supports the business processes of medical organizations in Europe.