When I’m explaining how digital machine health programs help manufacturers increase OEE (Overall Equipment Effectiveness), management expert Peter Drucker’s famous observation that “efficiency is doing things right; effectiveness is doing the right things,” almost always comes to mind.
Many manufacturers are already doing the right things in tracking OEE to improve their processes, and essentially “make a good thing better.” But what I'm really interested in is taking things to the next level. I'm interested in how to make a “better” thing, like efficient manufacturing, truly world-class.
Many factories, including in the beverage industry, already have machine health improvement processes in place, like a preventive maintenance or even predictive maintenance program. They might follow preventive maintenance schedules recommended by original equipment manufacturers, or manually check their machines’ vibration levels with a handheld vibration tool. However, all too often existing measurements aren’t enough to take productivity to the next level. When our digital machine health system is installed, we usually find that about 30% of a facility’s machines have some sort of problem, and 10% of those are severe. I often wonder, why wasn’t it discovered earlier?
It's partly because technicians must spend time, above and beyond their regular work, gathering machine health data. Without enough (or any) vibration analysts on site, there’s not enough expertise to analyze the results they eventually collect. When manufacturers come to us, they’re often discouraged, because they haven't seen value in a manual data-collection and analysis program, regardless of their investment in time and money. No matter how advanced existing predictive maintenance and reliability programs are, they can’t take the facility to the next level of availability, performance, and quality, because traditional OEE measurements only provide lagging indicators— they're basically reporting yesterday’s news.
That’s where machine health initiatives come in. When added to OEE measurements, condition based monitoring provides that extra competitive boost, improving productivity, and making a good team even better.
Machine health initiatives help get ahead of impacts to OEE by comparing machine health data from the line with a database of similar machines system-wide, and potentially even from other facilities. By analyzing vibration, magnetic field, pressure, temperature and more, a machine health system can proactively flag events like those that have caused scrap or failures in the past on previously healthy machines. These early warnings prevent scrap events from happening in the first place, instead of just measuring the damage later. What you’re left with is a team that is fixing a dull blade, or worn bearing, or loose belt before it becomes an expensive mess.
Because our predictive machine health software keeps learning what’s normal for healthy machines, and what tolerances and variations are allowable while still maintaining machine health, technicians can often do fewer manual checks and inspections for specific failure modes—increasing worker safety, as well as freeing workers to address more complex tasks.
Condition based monitoring also contributes to OEE by improving the availability of healthy machines. Predicting and preventing interruptions in production gives operators advance warnings about required repairs, improving machine availability by allowing operators (or, if necessary, maintenance technicians) to return the machine to an acceptable condition during planned downtime. In the end, more healthy machines remain available for use, with fewer surprises or unplanned downtime.
However, the value of machine health programs goes beyond preventing downtime. Companies typically see value in their digital machine health solution within the first month of installation, especially when severe malfunctions are identified early.
Knowing in advance what needs to be fixed saves on the cost of repairs and replacements. For example, by taking action early parts can be ordered for regular shipment, instead of being expedited; labor hours and overtime can be controlled because predictive maintenance data lets managers plan repairs and maintenance; scrap is often reduced, while increasing worker safety. Many facilities see full payback within six months.
Doing the right things, with liquor
Here's an example from the beverage industry, where digital transformation is rapidly taking hold. A well-known beverage manufacturer averted disaster soon after installing a digital machine health system. To make a popular liqueur, they relied on their emulsifier mixer to combine ingredients that were delivered once a day, which became unusable if they sat for longer than 24 hours. Every batch of raw material was worth $55,000--and would be lost to scrap for every day the emulsifier was out of service.
The machine health system quickly detected a pump bearing misalignment causing structural looseness in the emulsifier. Technicians monitored the equipment and planned ahead to replace the pump bearing. The machine returned to an acceptable condition, and the factory avoided an expensive emergency because they could see what was coming—two months before it might have caused disaster.
Clearly, digital transformation success leads to more success. A virtuous cycle is created where repairs are successfully made causing the machine learning algorithms driving this machine health system to keep improving, which helps OEE, motivates the team to continue implementing repair recommendations, and leads to more proactive repairs.
And as teams increasingly hit productivity targets that demonstrate greater overall equipment effectiveness, the better they feel about their work, and the more confidence they feel, both in the benefits of machine health monitoring, and in their own improved outcomes. Productivity, averted downtime, and even improved worker safety are all direct results of improved machine health. In essence, the team grows stronger, because it can rally around improved outcomes that everyone can see.
Digital machine health technologies are here — they are mature, stable, increasingly widespread, and offer rapid paybacks. While any significant operational change can feel daunting, there's no longer a substantive reason to delay your investment. It’s the right thing.
