Smart OTEA
Optimizing an automotive wholesaler stock management
Modelling, Simulating and Optimizing the stock of a spare parts wholesaler.
COMPANY
EcoMT Ecomanagement Technology
RESEARCH CENTRE
ITMATI. Spanish Network for Mathematics & Industry (math-in)
PRODUCTIVE SECTOR
Energy and Environment
Problem description
Implement improvements in the OTEA platform, developing an algorithm which predicts incidence risk in the air-conditioning system, taking the machine’s operating mode into account (cooling or heating)
Challenges and goals
Representation of the forecast of incidents on a map to create a heat map for air conditioning systems.
Mathematical and computational methods
Proposed model.
Use of the generalized additive models, GAM, to predict response variables from observations:
- As covariables, for modelling the cooling regime, the maximum daily ambient temperature of the previous day, the average daily climate consumption before opening and the member of the last days with incidents are included.
- The model for the heating regime includes the number of the last five days with incidents, the average daily temperature and the daily average climate consumption before the opening.
Results and Benefits
The knowledge foundation that has been created is exploited to represent on the map the geographical distribution of the incidents according to size.
It shows, with a high degree of reliability, which commercial premises could pose problems throughout the day. Through the forecasting of potential incidents, which can be obtained before opening hours, and taking into account the number of machines each shop is equipped with, a list of the stores that are most at serious risk are provided.
