Uptime drives productivity and profitability, while downtime incurs significant operational costs.
According to Pingdom, 98% of organizations estimate that an hour of downtime exceeds $100,000 in costs, varying depending on the industry, the size of the production line and the type of items produced.
As a result, manufacturers must measure machine uptime and reduce machine downtime. Two important metrics in the quest for improved productivity are availability and reliability.
While both assess operational health, they reflect different causes of downtime. Measuring both can help improve overall equipment effectiveness (OEE) and align maintenance operations around performance goals instead of relying on a reactionary “firefighting” approach.
In this piece, we’ll define availability and reliability in the context of manufacturing, speak to their key differences, examine how both impact OEE and explore the impact of technology on bridging the gap to boost productivity.
What is availability?
Availability measures how often equipment is ready and able to produce items.
To calculate your availability percentage, use this formula:
Availability = (Actual operating time / Planned production time) x 100
For example, if your planned production time is 60 minutes but machines only operate for 55 minutes, your availability is 91.6%
Several factors play a role in availability. They include:
- Repairs — Planned and unplanned repairs impact availability.
- Changeovers — Changeover time to remove old materials and productions and transition to a new production run reduces availability.
- Operator delays — Operator or user delays tied to skill issues or mistakes can limit availability and negatively affect the overall user experience.
- Material shortages — Materials shortages prevent machines from operating, which means they’re not available until the issue is resolved.
What is reliability?
Reliability measures the probability that equipment runs without failure while performing its intended function. In other words, it is a measure of performance over time.
One of the most common ways to calculate reliability is mean time between failure (MTBF).
MTBF = Total operating time / Number of failures
This means that if machines run for 500 hours and experience 10 failures, the MTBF is 50 hours. Higher MTBF values are better because they indicate that machines are more reliable.
Companies may also choose to track failure rates, which is the inverse of MTBF:
Failure rate = Number of failures / Total operating time
In the example above, the company experiences 0.02 failures per hour.
Reliability is tied to larger error and failure patterns within the organization, such as power outages or resource limitations. It is also impacted by overall asset health and maintenance effectiveness. For example, older machinery is often more prone to failure, reducing its reliability. When it comes to industrial maintenance service solutions, it’s critical to address the root causes of equipment failure rather than symptoms to boost reliability.
Availability vs. reliability: Side-by-side comparison
Availability and reliability are two sides of the same coin: While availability measures the total amount of uptime (and downtime), reliability tracks the frequency. Both metrics are critical to improving production line performance.
Factor | Availability | Reliability |
Focus | Time machine is online | Time between failures |
Causes | Delays, setups, planned downtime | Breakdowns, worn parts |
Maintenance influence | Scheduling and responsiveness | Preventive strategies |
Pain point | Lost production time | Recurring failures |
KPI role | OEE component | Reliability engineering |
Consider a machine that requires regular calibration. If this calibration takes longer than expected, the machine’s availability is shortened even if it performs flawlessly once it’s up and running.
If a machine requires minimal oversight and adjustment but regularly experiences faults during operation, it is unreliable. If these failures are minor and easily resolved, however, the equipment may still appear available.
How availability and reliability affect OEE
OEE is comprised of three pillars: availability, performance and quality.
To improve OEE, manufacturers must optimize both reliability and availability. This is because reliability directly impacts high availability; unreliable machines are less likely to be available when needed. In addition, frequent minor stoppages reduce both overall system performance and quality.
Data-driven maintenance improves all three pillars. Using tools such as computerized maintenance management systems (CMMS), connected sensors and remote monitoring software to monitor performance in real-time, companies can pinpoint when machines become unavailable, how often they are unreliable and what steps they can take to improve manufacturing KPIs.
Put simply, reliability improves the quality of run time while availability increases the quantity of run time. Together, they improve OEE.
Technology’s role in bridging availability and reliability
Visibility is critical to bridge the gap between availability and reliability. While knowledge of one or the other provides some context, manufacturers need both to get the big picture.
Connected systems provide a single source of truth for both metrics, allowing companies to take targeted action. Key technologies include:
- IIoT sensors — IIoT sensors collect operational data to predict component wear patterns, enable proactive machine health monitoring and support reliability-centered maintenance programs.
- Remote monitoring — Remote monitoring helps surface loss patterns and identify common factors in machine failures.
- CMMS — CMMS tools automate workflows to shorten repair cycle time.
- MES — Manufacturing execution systems (MES) can optimize maintenance schedules and maximize machine capacity through manufacturing capacity analysis.
Ensure long-term improvement and governance
One-off improvements and short-term fixes aren’t enough to drive better OEE. Instead, companies need long-term solutions that deliver consistent outcomes. Four best practices can help create solid strategies:
1. Conduct cross-functional meetings. IT and OT teams should be part of cross-functional meetings during both planning and execution stages to ensure alignment and accountability.
2. Consider scaling across multiple facilities. Data from one facility is useful. By scaling to collect data from multiple facilities, manufacturers can identify company-wide failure patterns and opportunities for improvement.
3. Ensure maintenance, operations and technology alignment. Maintenance keeps machines up and running. Operations teams help optimize processes, and technology deployment collects and applies key data. Alignment of all three is necessary to increase OEE.
4. Prioritize data transparency. More accurate and available data improves reliability, availability and maintainability (RAM) across production lines, including better tracking of MRO spare parts to reduce downtime.
Availability measures operational readiness, while reliability measures failure-free performance. Both are necessary to meaningfully improve OEE, enhance industry competitiveness and increase ROI.
Maximize uptime and reliability with ATS. Connect with us to explore tailored solutions.
References
Pingdom Team. (2023, January 9). Average cost of downtime per industry. Pingdom. https://www.pingdom.com/outages/average-cost-of-downtime-per-industry/
