“The bathtub curve is widely used in reliability engineering. It describes a particular form of the hazard function which comprises three parts:
– The first part is a decreasing failure rate, known as early failures.
– The second part is a constant failure rate, known as random failures.
– The third part is an increasing failure rate, known as wear-out failures.
The name is derived from the cross-sectional shape of a bathtub.”
Through the data they collect, your OSS tools are able to provide useful predictive failure rate analysis for your operations and procurement teams to work with. The bathtub curve shown above gives a theoretical sample for defect rates across the life-cycle of a product range.
Your OSS can collect statistics that show actual defect patterns in your network and environment.
For example, your OSS can assist with the following measures:
- Identification of the level of spares needed to be held for each device type in the network. This may decrease as a device type “beds in,” but then decrease again as it ages
- As a device type ages, statistical analysis can be made on the level of wear and tear to predict useful life and plan replacement programs well in advance
- Provide statistics to help refine routine and predictive maintenance schedules
- Identification of trends that impact defect rates. Climatic conditions such as rain, humidity, wind, heat, etc can impact relative defect rates, so maintenance schedules may differ across different parts of a network. For example, external plant (eg mobile base station towers / antennae) in sea-side areas may require more frequent maintenance than other parts of your network because of the corrosive nature of salt-laden air / mist.
- If the CSP outsources break-fix, then accurate historical trends can be provided to outsources for quoting against
Do your maintenance planning teams ever make use of the data you collect? Alternatively, do you ever investigate this type of data to present findings to them?