Taking pro-active measures based on advanced data analytics to predict and avoid machine failure
Knowing well ahead of time when an asset will fail avoids unplanned downtimes and broken assets. On average, predictive maintenance increases productivity by 25%, reduces breakdowns by 70% and lowers maintenance costs by 25%. It is based on advanced analytics and marks a new way of organizing and implementing maintenance on an industrial scale. Deloitte has developed an approach to smoothly introduce predictive maintenance into business processes in a customized and structured manner.
In times of highly interconnected and tightly synchronized production processes, industrial organizations face the risk of machine failure including costly downtimes, quality defects and idle productivity potential, if they neglect the importance of maintenance.
On the (r)evolutionary path to Industry 4.0, productivity of machines and interconnectedness of production chains have dramatically increased, which required immense investments in assets. In 2015, German automotive OEMs for instance invested more than 14 billion Euros in tangible assets. Despite all efforts to prolong lifecycles, the risk of machine failure cannot be completely eliminated and thus, contribute to an estimate of $50 billion costs for unplanned downtimes and a reduced production capacity each year.
It is therefore crucial for producing companies to consider maintenance not solely as reactive necessity after a machine failure, but instead to consider taking pro-active measures based on advanced data analytics in order to predict and avoid machine failure. Especially, for highly connected processes, predictive maintenance can be a significantly powerful strategy, which’s outcome, i.e. cost savings, an increased equipment availability as well as productivity gains, increases with the underlying maintenance costs. To reach this point, we apply well-established techniques from machine learning, that enable intelligent algorithms to learn how to detect failures at an early as possible stage.
Predicting failures via advanced analytics can increase equipment uptime by up to 20%.
Employing expensive machinery that is likely to fail, predictive maintenance could help you to manage maintenance more efficiently. But keep in mind that not all enterprises require the same level of reliability from their assets.
A good place to start the assessment for your enterprise is to look at mission requirements and maintenance program maturity. We suggest that you ask yourself the following questions:
- How reliable do your assets need to be – what are your availability targets?
- What is your machines’ current failure-rate?
- How high are your current maintenance costs?
- Do you have the right spare parts in the right place at the right time?
- How you we determine when it is time to replace an asset rather than maintain it?
- Have you identified the critical assets in our production system?
- Do you have the needed technological expertise in house to develop a predictive maintenance program?
- Do you have advanced analytics experts in house?
We at Deloitte Analytics Institute already helped several clients on their way to an insight driven Industry 4.0 company by implementing predictive maintenance programs and tailoring solutions that perfectly fit to our clients’ needs, requirements and data availability.
For further information on predictive maintenance download our Position Paper or get directly in touch with
Dr. Björn Bringmann
Chief Data Scientist | Deloitte Analytics Institute