Abstract
In this technical paper, we address the issue of predicting cash dispenser (addressed as ‘Device’ henceforth) failure by harnessing the power of humungous data from service history, logs, metrics, transactions, and plausible environmental factors. This study helps increase device availability, enhanced customer experience, manage risk & compliance and revenue growth. It also helps reduce maintenance cost, travel cost, labour cost, downtime, repair duration and increase meantime between failures (MTBF) of individual components. This study uses a cognitive prioritization model which entails the following at its core; a) Machine Learning engineered features with highest influence on machine failure, b) Observation Windows, Transition Windows and Prediction Windows to accommodate various business processes and service planning delivery windows, and c) A forward-looking evaluation of emerging patterns to determine failure prediction score that is prioritized by business impact, for a predefined time window in the future. The model not only predicts failure score for the devices to be serviced, but it also reduces the service miss impact for the prediction windows.
Highlights
Maintenance is a vital area that controls major cost savings and revenue
Lack of a formal commitment and the easiness with which consumers can switch to competitors make the process of building trust among consumers even tougher for retail bankers
This study introduces predictive maintenance capabilities that can immensely help
Summary
Maintenance is a vital area that controls major cost savings and revenue. According to the International Society of Automation (ISA), more than $647 billion is lost each year due to downtime. All businesses have strived to achieve cost effective maintenance, high availability and customer satisfaction. They too have grappled with maintenance processes to alleviate downtime for a long time. Lack of a formal commitment and the easiness with which consumers can switch to competitors make the process of building trust among consumers even tougher for retail bankers. The consumers need to be able to get the service they want on a desired time. 66% of them will switch banks on account of an unfulfilling service needs
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