A world-class manufacturer of heavy equipment is also in the business of providing support, maintenance and extended warranty. An equipment failure not only causes inconvenience for the client but also has a huge impact on client productivity as well as output. The goal of this manufacturer was to improve profitability by a. improving the up-time of these equipment and b. reduce cost of maintenance.
Data & Analysis
This organization maintained an elaborate database of the installed base as well as the history for each installed equipment. The history contains records of all the maintenance work including parts that have been replaced for each equipment. In addition, all the support tickets relating to when an equipment failed and what were the observations are also captured in the system. The data required to develop the model was already in place.
The team conducted careful analysis of each equipment, its history of failure, intervention by the organization and outcomes. Using machine learning the team was able to develop and train a model that can predict which equipment has the highest likelihood of failure, these equipments get queued for proactive maintenance thus avoiding equipment failure, increasing up-time and reducing cost.
The predictive model has enabled the organization to differentiate itself with the 'predictive maintenance model' and it has improved up-time and profitability for the organization.