Availability vs Reliability: Same Uptime, Different Machine

Two definitions

Reliability is a probability tied to a length of time. R(t) is the probability that a machine runs for time t without a failure. It says nothing about how long a failure takes to fix, so a machine can have almost no chance of a full shift without a stop and still be up most of that shift, as long as each stop is short.

Availability is a fraction of time: the share of time the machine is able to run. In steady state it follows from the two averages.

Availability = MTBF / (MTBF + MTTR)

Availability blends how often a machine stops with how long each stop lasts, so on its own it cannot tell you which of the two you have.

For an exponential time to failure, R(t) = exp(−t / MTBF). For other distributions R(t) comes from the fitted distribution, or straight from the data as a survival curve.

Same availability, very different reliability

Stops per shift = 480 / (MTBF + MTTR). R(t) = exp(−t / MTBF). P(stop > 10 min) = exp(−10 / MTTR). Stop time beyond the buffer = stops per shift × MTTR × exp(−10 / MTTR), using the memoryless property of the exponential.

By availability, A and B are the same machine. By reliability, B is far better: close to an even chance of a whole shift without a stop, against almost none for A. On the line the picture turns over again. Most of A's stops are short enough for the buffer to ride out, while most of B's outlast it, so B passes about six times as much stop time to the next machine.

Neither number alone picks the better machine. A's frequent restarts cost operator attention and, on many lines, startup losses. B's long stops are the ones that reach the rest of the line. Which matters more depends on where the machine sits and how much accumulation surrounds it, and a real buffer is not always full when a stop begins.

Inherent vs operational availability

"Availability" also changes meaning with what you count as downtime. Reliability engineering separates several versions:

• Inherent availability, A_i = MTBF / (MTBF + MTTR), where MTTR is active corrective repair time only. It describes the equipment under ideal support, with a technician and parts ready the moment it fails.
• Achieved availability, A_a = MTBM / (MTBM + M), adds preventive maintenance. MTBM is the mean time between all maintenance actions, corrective and preventive, and M is the mean active maintenance time.
• Operational availability, A_o = MTBM / (MTBM + MDT), where the mean downtime MDT also includes waiting: for a technician, for parts, for sign-off. It describes what the plant actually experiences.

A stop log records operational downtime. A logged stop runs until the machine restarts, response and waiting included, so an MTTR computed from line event data sits closer to mean downtime than to active repair time. The gap is a finding in itself: if much of a failure mode's downtime is waiting, faster response may return more than engineering the failure out.

Machine availability in an OEE report is different again. OEE Availability compares run time with planned production time, so setups and changeovers count against it along with failures. And a machine that sits idle because it is starved or blocked is not down. It is waiting on another machine, and counting that time as its downtime charges it for somebody else's stops.

Why line output depends on both, plus buffers

A reliability block diagram combines machines with series and parallel rules. Five machines in series, each 95% available, with no buffering between them, give a line availability of 0.95⁵, about 77.4%. That is the tightly coupled extreme, where any stop stops the line and every machine's downtime adds up.

Buffers move a line away from that extreme. Accumulation between two machines absorbs stops shorter than it can cover, so short, frequent stops stop reaching the line, while stops longer than the buffer still pass through. With plenty of accumulation and matched speeds, output tends toward the limit set by the least available machine rather than the product of all five. Real lines sit between the two limits. Where a line sits depends on the run and repair distributions of each failure mode, the buffer sizes, the rates and where the constraint is.

That is why a line model needs full distributions for each failure mode rather than one availability per machine, and why a reliability block diagram falls short for a buffered line. ReliaSim's guide How big should a buffer be? works through the buffer side, and OEE simulation covers the model that ties rates, buffers and interrupts together.

Measure both from your stop data

• Interrupt Quick Start (free): pick an equipment failure pattern and get an availability figure and a stops-per-shift readout.
• Interrupt Designer (free): set a TTF + TTR pair and see availability alongside the PDF, CDF and survival curves and a working/failed timeline over a shift.
• Interrupt Explorer: survival curves R(t) by cause from a stop log, with each run measured from the previous stop of any cause, plus the chronic stop rate.
• ReliaStats desktop app: fit TTF and TTR distributions for each cause group and export them for ReliaSim, as part of a ReliaStats subscription.

To read what a fitted uptime distribution says about reliability, see Weibull analysis of downtime data. For the averages behind availability, see MTBF vs MTTR.

Frequently asked questions