There’s a saying in marksmanship that when shooting a rifle “precision leads to accuracy”. The basic premise being that a rifleman who consistently puts a group of shots on target and close together even without hitting a bullseye is better than a shooter whose shots are all over the place but occasionally hits a bullseye.

The first example demonstrates technique and skill and with a slight mechanical adjustment will consistently and accurately hit their mark, while the other has either undisciplined talent or dumb luck- you’ll never know what you are going to get.

The same rings true in manufacturing analytics. For years, plant managers used the traditional dumb luck methods of downtime tracking in which operators self-reported every hour on the hour their production numbers and downtime. It proved to be a hit or miss affair.

The Problem

Just self-reporting downtime, it turns out, represents a triple threat to plant floor visibility:

It is inconsistent (many downtime events go unrecorded)
It is inaccurate (many downtime intervals are mis-timed)
It is ineffective (many or all downtime reasons are missing)

There are a lot of reasons why an operator might misreport on downtime and we’ll spare the reader our deep philosophical insights into the human mind or musings on human frailty (although we’ve touched on it in the past here). Needless to say, it is an entirely predictable human problem and is in fact, fixable.

The question shouldn’t be, “how do I accurately track machine downtime”, but rather “how do I accurately report on downtime?” By counting machine cycles we can see when a machine is down, but it won’t tell is why it is down. To do that you need consistent, reliable operator input.

Operator self-reporting only. Not accurate, consistent, or effective.

Automated downtime detection without operator downtime reasons. Accurate and consistent, but not effective.

Automated downtime detection with operator downtime reasons. Accurate, consistent, and effective.

The Goal: Reduce or Eliminate Downtime

The goal here is to consistently, accurately and effectively track downtime, so we can stop it when it happens, see its impact on performance, understand what causes it and avoid it in the future. That means categorizing every meaningful downtime event faithfully and correctly.

Remember our target? We don’t want to just see downtime when it happens, we want to understand downtime so we can stop it from happening now and in the future. It starts with reliable detection of downtime, but it doesn’t stop there. Without identifying its root causes, we still won’t understand how to prevent it.

The Fix

  1. Automatically Detect All Downtime
    To avoid misreporting on downtime start tracking all downtime automatically by measuring machine cycles. You’ll have an accurate gauge of the number of downtime events and their length.
  2. Communicate Downtime Plant-wide
    Provide triggered alerts and real-time dashboard and score boards to visibility to the entire plant. Knowing is half the battle.
  3. Mitigate Downtime
    With the clock ticking, the operator is encouraged to fix the downtime issue immediately.
  4. Measure All Downtime
    Prompt the operator to select a downtime category so the event has a reason and its impact can be understood fully, especially if it’s a recurring problem.
  5. Analyze Downtime
    Determine which downtime reasons are preventable and take action to prevent it in the future.
  6. Avoid Future Downtime
    Systematically tackle the issues causing unplanned downtime. Set goals and implement processes to reduce planned downtime.