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Manufacturing KPI Dashboard: What to Track and How It Should Actually Work

A manufacturing KPI dashboard is supposed to answer one question at a glance: how are we actually doing right now? In practice, a lot of dashboards fail at exactly that – either because they’re tracking the wrong things, showing the same view to everyone regardless of role, or updating so infrequently that “real time” is more of a suggestion than a fact.

What a Manufacturing KPI Dashboard Actually Is

It helps to separate two terms that get used interchangeably: metrics and KPIs. Metrics are any measurement you collect – cycle time, downtime minutes, scrap pieces, changeover duration. A KPI is a metric that’s specifically tied to a business objective you’re trying to hit. Downtime minutes is a metric; unplanned downtime as a percentage of scheduled time, tracked against a target, is a KPI.

A manufacturing KPI dashboard brings the KPIs that actually matter to your operation into a single visual view, updated close to real time, so decisions get made on current conditions instead of yesterday’s numbers.

The Metrics Most Dashboards Should Include

While every plant’s priorities differ, a handful of metrics show up on nearly every well-built manufacturing dashboard:

  • Overall Equipment Effectiveness (OEE) — availability, performance, and quality combined into one score
  • Unplanned downtime — the share of lost time that wasn’t scheduled or expected
  • Cycle time — actual time to produce a unit versus the standard or ideal time
  • First Pass Yield (FPY) — the percentage of units that meet spec without any rework
  • On-time delivery rate — how consistently production is hitting the schedule that downstream teams are counting on

The goal isn’t to track everything available – it’s to track the handful of numbers that are actually tied to a decision someone will make. A dashboard covered in metrics nobody acts on isn’t more useful; it’s just more cluttered.

A good rule of thumb: start from your current bottleneck, not a generic list. If your biggest constraint right now is unplanned downtime on one line, that line’s OEE and downtime causes deserve the most prominent spot on the dashboard – not because it’s the most impressive metric, but because it’s the one decision-makers will actually act on this week. As priorities shift, which metrics sit front-and-center should shift too.

Not All Dashboards Are Built the Same: Real-Time vs. Business Intelligence

Here’s a distinction that gets glossed over more than it should: a business intelligence (BI) dashboard and a true manufacturing KPI dashboard can look nearly identical on screen – both show charts, gauges, and trend lines – but they’re built on very different foundations.

A BI dashboard is often fed by a nightly or periodic data extract, which means what you’re looking at reflects yesterday’s conditions dressed up as today’s. A real manufacturing KPI dashboard, by contrast, is fed directly from the shop floor as events happen, which is the only way “real-time” actually means real-time rather than “recently updated.” If your dashboard is built on a batch process behind the scenes, an operator staring at it mid-shift isn’t actually seeing what’s happening on their line right now – they’re seeing a snapshot from hours ago with a live-looking interface layered on top.

One Dashboard, Different Views for Different Roles

A dashboard that tries to serve everyone identically usually ends up serving no one well. The information that matters to a plant director isn’t the same as what a line supervisor needs in the middle of a shift:

  • Operators and line supervisors need near-real-time, per-shift visibility — is the line on pace right now, and if not, why?
  • Plant managers need to see bottlenecks and quality issues across multiple lines, usually reviewed daily rather than minute-to-minute.
  • Executives care about higher-level trends — cost, throughput, and performance across the business — reviewed weekly or monthly rather than in the moment.

The best systems let all three views draw from the same underlying data, just filtered and paced differently, rather than requiring three completely separate reporting tools that never quite agree with each other. Mismatching cadence to role causes real problems: an operator forced to interpret a monthly trend chart mid-shift will ignore the dashboard entirely, and an executive drowning in per-minute machine states will do the same for the opposite reason. The fastest way to get a dashboard ignored company-wide is to give everyone the same view and hope it’s useful to all of them equally.

Scaling Across Multiple Locations

Everything above compounds once you’re managing more than one facility. A dashboard that only shows a single plant’s performance leaves you blind to the comparisons that matter most: which location is falling behind on the same product line, which plant can absorb more volume, and where a training gap or aging equipment issue is quietly dragging down results. Company-wide dashboards let you spot those patterns in a single view instead of manually compiling separate reports from each site and trying to compare them by hand.

What to Look for When Building or Choosing a Dashboard

  • Automated data connections. If someone has to manually update the dashboard, it isn’t really real-time – it’s a spreadsheet with better formatting.
  • Threshold-based alerts. A dashboard nobody is actively watching should still be able to notify the right person the moment something crosses a meaningful line.
  • Drill-down capability. The ability to go from a plant-wide number down to a specific line, shift, or machine is what turns a dashboard from a status report into a diagnostic tool.
  • Role-based views. Operators, supervisors, and executives shouldn’t be staring at the same screen expecting it to answer three different questions.
  • A manageable number of KPIs. The most common mistake in dashboard design isn’t showing too little – it’s showing too much. If every metric is on the screen, nothing on it stands out as urgent.

Frequently Asked Questions

How many KPIs should be on a manufacturing dashboard?
 Fewer than you’d think. A dashboard with 20 metrics buried together makes it harder, not easier, to spot what actually needs attention. Most effective dashboards limit the primary view to a handful of KPIs tied directly to current priorities, with the option to drill into more detail as needed.

Is a manufacturing KPI dashboard the same as an OEE dashboard?
 OEE is usually one of the most important KPIs on the dashboard, but a full manufacturing KPI dashboard typically covers more ground — downtime, cycle time, quality, and delivery performance alongside OEE, not instead of it.

How do I know if my dashboard is actually real-time or just looks like it?
 Check where the data is coming from. If it’s pulled from a nightly extract or a periodic batch job, it’s a BI-style dashboard no matter how live the interface looks. A genuinely real-time dashboard is fed directly from machine and shop-floor data as events happen.

Why do people stop looking at their dashboard after a few weeks?
 Usually because it wasn’t built around a decision they actually make. A dashboard that shows the same generic KPIs to everyone, regardless of role or current priorities, tends to get ignored fastest — the fix is narrowing what’s shown to what’s genuinely actionable for the person looking at it.


Want a dashboard that’s actually built on live shop-floor data, not a batch extract dressed up to look real-time? See how Thrive’s dashboard works, or learn more about automated production reporting.

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Manufacturing Downtime Data Analysis: Turning History Into Fewer Repeat Problems

Collecting downtime data is the easy part. The real value only shows up once you actually analyze it – turning a log of stoppages into an understanding of why they’re happening, which ones cost you the most, and what’s actually worth fixing first. That’s what downtime data analysis is for, and it’s a different skill from simply tracking downtime as it happens.

What Downtime Data Analysis Actually Involves

At its core, downtime data analysis is the systematic collection, categorization, and interpretation of data about when your equipment stops producing. Done well, it answers four questions for every meaningful downtime event: What type of downtime occurred? How long did it last? How often does it happen? And what did it actually cost?

That last question matters more than it might seem. A downtime event isn’t just measured in minutes – it’s measured against your theoretical or rated production capacity. Quantifying the gap between what you actually produced and what you could have produced is what turns a downtime log into a business case for fixing the problem.

The Metrics That Make Downtime Data Analyzable

Raw downtime logs are hard to act on. A handful of specific metrics turn that raw data into something you can actually compare, track, and improve:

  • Mean Time to Repair (MTTR) — the average time it takes to get equipment back up and running after a failure. A rising MTTR usually signals maintenance delays or a lack of spare parts on hand.
  • Mean Time Between Failures (MTBF) — how often a given piece of equipment fails. This is your best early signal for equipment that’s approaching the end of its reliable life.
  • Downtime percentage — the share of scheduled production time lost to downtime, which lets you compare machines, lines, or shifts on equal footing regardless of how much each one actually ran. This matters because raw downtime minutes alone are misleading: a machine that ran for 20 hours and lost 2 is in a very different situation than one that only ran for 4 hours and lost the same 2, even though the raw number looks identical.
  • Pareto analysis — ranking downtime causes from highest to lowest impact. In most operations, a small number of causes are responsible for the majority of total downtime, so this is usually the fastest way to find out what’s actually worth fixing first.

Categorizing Downtime So Trends Actually Mean Something

None of the metrics above are useful if the underlying data is inconsistent. This is where categorization – sometimes called reason codes – earns its place. Common categories include mechanical failure, electrical issues, tool or product changeovers, operator error, material shortages, and scheduled maintenance.

The distinction between planned and unplanned downtime matters especially: planned downtime (maintenance, changeovers, cleaning) is expected and can be scheduled around, while unplanned downtime (breakdowns, shortages, unexpected stoppages) is what actually erodes your capacity unpredictably. If your team doesn’t apply these categories consistently – one shift calling something a “changeover” while another logs the same event as “downtime” – your trend data ends up comparing apples to oranges, and the resulting analysis is only as reliable as the categorization behind it.

This is exactly where manual categorization tends to break down. Because it depends on whoever’s on shift remembering to apply the right code in the moment, consistency is hard to enforce after the fact. Systems that prompt an operator for a reason code automatically at the moment a stop is detected – rather than relying on someone to remember and log it later – tend to produce far more usable data than a system that depends entirely on manual entry.

Letting the Past Inform the Future

Once your data is categorized consistently, historical trend charts become genuinely useful rather than just descriptive. Trending your equipment’s performance by day, week, month, or year lets you see whether a change you made months ago actually stuck, or whether a piece of aging equipment is quietly declining in reliability.

It’s worth remembering that meaningful improvement rarely comes from one dramatic fix – it’s usually a series of smaller, cumulative changes that add up over time. Historical data is what proves that progress is actually happening, even during the frustrating stretches where day-to-day numbers don’t seem to be moving. It’s also what lets you compare shifts, lines, or entire plants against each other on a level playing field – a comparison you simply can’t make by looking at any single day in isolation.

From “We Have a Downtime Problem” to “Here’s the Cause”

Good downtime analysis follows a consistent loop: log every event with a timestamp and reason code, categorize it consistently, analyze the resulting data for patterns, take a targeted corrective action, and then keep watching to see whether that action actually worked.

The analysis step is where drilling down matters most. Rather than looking at a single plant-wide downtime number, break results down by machine, line, shift, or operator. Does the night shift experience meaningfully more downtime than the day shift – and if so, why? Is one specific machine responsible for a disproportionate share of stoppages, and is it creating a bottleneck for everything downstream of it? These are the kinds of questions a well-categorized, drillable dataset can actually answer, where a single aggregate number can’t.

How Often Should You Be Reviewing Downtime Data?

One of the most common mistakes in downtime analysis isn’t a data problem at all – it’s a cadence problem. Weekly or monthly reviews are common, but by the time a pattern surfaces in a monthly report, the window to prevent it from recurring has usually already closed several times over. The more frequently a pattern-level review happens, the faster a team can act on what the data is actually showing – which is exactly why real-time visibility and historical analysis work best as complements to each other, not substitutes.

Frequently Asked Questions

How is downtime data analysis different from downtime tracking?
 Tracking is the act of capturing when and why a machine stopped. Analysis is what happens after – using that captured data to find patterns, calculate metrics like MTTR and MTBF, and identify where corrective action will have the biggest impact.

What’s the fastest way to find what’s actually costing us the most?
 A Pareto analysis of your downtime causes. In most operations, a small number of causes account for the majority of total downtime – ranking caused by total time lost (not just frequency) usually points straight at what to fix first.

Do we need special software to do this kind of analysis?
 Not necessarily, but consistent, automatically-categorized data makes it far more reliable. Manually logged data is prone to the same inconsistency problems described above – different people categorizing the same event differently – which undermines the trend analysis before it even starts.

Does planned downtime need to be analyzed too, or just unplanned?
 Both are worth tracking, but for different reasons. Unplanned downtime is where the biggest, most urgent losses usually hide. Planned downtime – changeovers, scheduled maintenance – is worth analyzing separately to see whether it’s taking longer than it should, since even “expected” downtime has room for improvement.


Want your downtime data captured and categorized automatically instead of pieced together by hand? See how Thrive’s downtime tracking system works, or learn more about automated production reporting.

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Real-Time Production Monitoring: Catch Problems Before They Cost You a Shift

Most manufacturers aren’t short on data — they’re short on timely data. Machines are generating signals constantly, but if that information only reaches a person at the end of a shift, it’s already too late to act on the problem it describes. Real-time production monitoring closes that gap, giving you an accurate picture of what’s happening on your shop floor at the exact moment it’s happening.

What Real-Time Production Monitoring Actually Means

Real-time production monitoring is the continuous tracking of machine states, output, and performance as it occurs – not a summary compiled after the fact. Instead of waiting for someone to notice a bottleneck and write it down, sensors and connected systems capture the event immediately and make it visible right away.

The distinction from historical reporting matters here: historical data tells you what already happened and helps you spot trends over time, while real-time monitoring tells you what’s happening right now, while there’s still time to respond. A strong operation needs both – but real-time visibility is what lets you catch a problem in the moment instead of reading about it tomorrow.

Machine Monitoring vs. Production Monitoring

These two terms get used interchangeably, but they describe different layers of the same system. Machine monitoring is the foundational layer – automated, real-time data collection from individual pieces of equipment (often the process constraint) that builds an accurate record of what each machine is actually doing.

Production monitoring sits on top of that foundation. It takes the raw machine data and turns it into something people can actually use to make decisions – reviewing performance within a structured framework, spotting where output is falling behind plan, and taking action based on what the data shows. In short: machine monitoring builds the data; production monitoring puts it to work.

How Real-Time Data Gets Collected

There are a few common ways manufacturers capture this data:

  • Direct machine integration — communicating with a machine’s existing controller to pull data with no additional hardware needed.
  • Added sensors or PLCs — for equipment that doesn’t already report data on its own, an inexpensive sensor or PLC can be added to capture run/stop/error states.
  • IoT-connected devices — increasingly common on newer equipment, these stream data continuously to a cloud-based dashboard without requiring a dedicated server on-site.

Whichever method you use, the goal is the same: get machine state, part counts, and downtime events into a system that updates continuously, not one that waits for someone to log in and refresh a report.

The Metrics Worth Watching in Real Time

Not all data is equally useful moment-to-moment. The metrics that matter most in a real-time context are the ones tied directly to whether a line is on pace right now:

  • Machine uptime vs. downtime — the most immediate signal that something needs attention.
  • Units produced vs. plan — tells you whether you’re on track to hit a shift or daily target while there’s still time to course-correct.
  • Overall Equipment Effectiveness (OEE) — availability, performance, and quality combined into a single score.

That last point matters more than it might seem: OEE calculated from delayed or incomplete data is only as accurate as the data behind it. If downtime events are logged hours late or categorized inconsistently between shifts, your OEE score reflects guesswork, not reality — which means decisions based on it are only as good as the data feeding them.

Why Real-Time Visibility Changes Behavior on the Floor

One of the most underrated benefits of real-time monitoring isn’t the data itself — it’s what visibility does to accountability. When operators can see their own line’s performance updating live, rather than finding out how they did at the end of the day, it naturally shifts behavior. People catch small issues before they compound, and it becomes obvious — to operators and supervisors alike — who might need a bit more training on a specific piece of equipment or process.

This works both ways: it also surfaces which operators are consistently performing well, giving you real, current information rather than a gut-feel impression formed weeks ago.

Turning Live Data Into Something Usable

Collecting real-time data is only valuable if someone can actually interpret it quickly. This is where dashboards and visual displays do the heavy lifting — translating a stream of raw numbers into a chart, gauge, or Pareto view that shows, at a glance, whether a line is on pace and what’s causing the biggest losses when it isn’t.

Many real-time systems also support automated alerts — a notification sent the moment a machine goes down or a threshold is crossed, rather than requiring someone to be staring at a dashboard at the right moment. That combination of visual clarity and proactive notification is what actually makes real-time data actionable instead of just available.

What to Look for in a Real-Time Monitoring System

  • No dependency on manual entry. If someone still has to type in downtime reasons after the fact, you haven’t actually achieved real-time visibility.
  • Compatibility with the equipment you already have. A system that requires replacing legacy machines to get connected isn’t practical for most plants — look for sensor or PLC options that work with older equipment.
  • Alerts, not just dashboards. A screen nobody is watching doesn’t help; look for systems that can push notifications to the right person when something needs attention.
  • Simple, accessible reporting. The best real-time systems present information in a way that doesn’t require training to interpret — if your team needs a manual to read the dashboard, it’s working against you.

Frequently Asked Questions

Is real-time production monitoring the same as automated production reporting?
 They’re closely related. Automated production reporting is the broader system — capturing and distributing data on downtime, output, and performance. Real-time monitoring specifically refers to seeing that data the moment it happens, rather than in a scheduled report.

Do I need to replace my existing equipment to get real-time monitoring?
 No. Most systems can connect to a machine’s existing controller directly, or use an added sensor or PLC for older equipment that doesn’t already communicate on its own.

How does real-time monitoring affect my OEE score?
 It makes the score more accurate. OEE calculated from delayed or manually logged data reflects guesswork as much as reality; real-time data capture removes that gap.


Want to see what real-time visibility looks like on your own shop floor? Explore Thrive’s downtime tracking system or learn more about automated production reporting.

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The Benefits of OEE (Overall Equipment Effectiveness) — and How to Actually Capture Them

Overall Equipment Effectiveness distills three things that matter most on your shop floor – availability, performance, and quality – into a single score. A perfect 100% means you’re manufacturing only good parts, as quickly as your equipment allows, with zero stop time. Nobody actually hits that number, and that’s fine – OEE isn’t a scoreboard you’re trying to max out. It’s a diagnostic tool, and the real benefits show up in what you do with what it tells you.

The Core Benefits of Tracking OEE

The most immediate benefit is visibility: instead of a vague sense that “the line isn’t running as well as it should,” you get a specific breakdown of why. Unlike watching a handful of isolated KPIs – throughput here, cycle time there, changeover time somewhere else – OEE combines everything into one number that exposes losses none of those individual metrics would catch on their own. That specificity pays off in a few concrete ways:

  • Downtime becomes a dollar figure, not a feeling. Once you know your line’s throughput and margin per unit, a few points of lost availability translates directly into a number leadership can act on. If a line runs 200 units an hour at a $12 margin, a 3-point drop in OEE has a specific, calculable revenue impact – not just an unlogged sense that “we lost some time today.”
  • Quality issues get caught earlier. Pinpointing when and where defects happen – including parts that end up needing rework – means you can fix the underlying cause before it produces a pile of rejected parts, rather than discovering the problem after the fact.
  • You stop guessing where to focus. Because OEE separates loss into three distinct buckets, it tells you where your next improvement project should actually start – rather than everyone independently guessing, or worse, competing for attention on separate KPIs.
  • Maintenance and operations start speaking the same language. A drop in availability tends to point to maintenance; a drop in performance often sits with operations; quality can span both. Sharing one number means the conversation about what’s dragging performance down happens with data instead of blame.

Reading Your OEE Score: A Diagnostic, Not Just a Number

One of the more underused benefits of OEE is what the shape of your three sub-scores tells you, beyond the overall percentage. If availability is weak but performance and quality are solid, that usually points to downtime or slow changeovers. If availability and quality are fine but performance lags, you’re probably looking at speed loss – worn tooling, conservative settings, or an aging asset. If both availability and performance are strong but quality is the outlier, that’s typically process drift or a setup issue.

This matters because the score by itself is only a diagnosis. The actual benefit – reduced downtime, lower repair costs, higher-quality output – only materializes once that diagnosis triggers a specific fix. A team that tracks OEE meticulously but never acts on what it reveals will have cleaner reports and the exact same production problems a year later. The more mature version of this is closing the loop entirely: when a stop is detected, the right person gets notified immediately, rather than the event sitting in a report that gets reviewed days later. Shrinking the gap between “something went wrong” and “someone is fixing it” is where a lot of the compounding value of OEE actually lives.

Common Mistakes That Blunt These Benefits

A surprising amount of the value of OEE gets lost before it ever reaches a decision-maker, and it’s worth naming the usual culprits directly:

  • Measuring over too short a window. Pulling OEE data from a single shift, or even a single day, doesn’t tell you what “normal” looks like for your line – one unusually long downtime event can skew the number into something meaningless. A month or more of data gives you enough of a baseline to separate a real trend from ordinary variation.
  • Relying on manual data entry. Operators jotting down stop times on paper or in a spreadsheet is slow, easy to skip during a busy shift, and prone to the kind of human error that makes an OEE score more fiction than fact. A short stoppage that felt too minor to log still counts, and enough of those add up to a genuinely misleading number.
  • Underestimating the small stuff. Minor stops, slight speed reductions, and small defects rarely look significant in the moment, but they accumulate over a shift or a week into some of the largest losses on the line – often larger than the one big breakdown everyone remembers.
  • Rolling it out without buy-in. If operators and supervisors see OEE tracking as something being done to them rather than a tool that makes their job easier, adoption stalls regardless of how good the software is. Showing the team a clear, early win tends to do more than any policy memo.

Why Automation Is Where the Real Benefit Starts

Every mistake above gets easier to avoid once data capture is automated instead of manual. Automating that capture removes the guesswork and frees your team to spend their attention on actually fixing problems instead of documenting them. It’s also worth pairing automated data collection with a bit of human context: encouraging operators to log a quick note when something unusual happens (what caused it, what they tried) preserves the “why” behind the numbers in a way raw timestamps never will – and that historical context becomes genuinely valuable the next time a similar issue shows up.

The best OEE tools also make the resulting data usable by anyone, not just whoever built the spreadsheet. A good manufacturing KPI dashboard turns raw numbers into something a plant manager, a maintenance lead, and an operator can each look at and immediately understand – which matters because the whole point of automating this is to get insight in front of people faster, not just to collect cleaner data for its own sake.

Getting the Full Benefit Means Implementing OEE Across Your Entire Line

A common mistake is treating OEE as something that only applies to your handful of “critical” machines. That leaves you with a partial picture – you know how your headline assets are performing, but you’re blind to the compounding losses happening on everything else. The real value of OEE compounds when it’s implemented plant-wide: you can compare shift-to-shift efficiency, break performance down by SKU, and even compare performance across multiple locations if yours is a larger operation.

It also helps to have one point of ownership – ideally a single person who’s already respected on the floor, comfortable working with data, and given the standing responsibility of monitoring the trend and pushing improvements – rather than leaving it to whoever happens to notice a problem that day. The tool itself should support that person, not create more work for them: if a system requires a small team of specialists to interpret its output, it’s adding a layer of friction that works against the whole point of tracking OEE in the first place.

What a “Good” OEE Score Actually Looks Like

It’s worth setting realistic expectations here. World-class OEE performance generally sits around 85%, and most manufacturing operations start out well below that mark. That’s not a discouraging fact – it just means there’s real room to improve almost everywhere, and a lower starting score isn’t a sign that something is broken. It’s the normal starting point for the work OEE is meant to guide, and consistent, incremental progress toward that number matters far more than hitting any particular figure quickly.

One Honest Caveat

OEE is genuinely one of the best tools available for understanding shop-floor productivity, but it isn’t a silver bullet. Leaning on it too heavily, without keeping sight of customer needs or the broader production context, can occasionally push a team toward decisions that look good on the OEE score but don’t actually serve the business – for example, optimizing machine utilization at the expense of flexibility a customer actually needs. Used well, OEE is a guide for where to look next, not the only number that matters.

Ready to Get More Out of Your OEE Score?

Understanding the benefits of OEE is one thing – actually capturing them requires accurate, automated data you can trust. Thrive’s downtime tracking and OEE reporting tools are built to remove the manual guesswork so your team can spend their time on fixes, not paperwork. Schedule a demo to see it in action.

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Want to Reduce Your Production Costs? Improve Your OEE Score  

Regardless of the industry that you’re operating in, manufacturers are always looking for opportunities to cut costs without sacrificing quality. This is especially true as many industries like pharmaceuticals and even food and beverage become increasingly competitive all the time.

Thankfully, there is already an opportunity to do precisely that – it’s just one that far too few business leaders are currently taking advantage of. It’s called Overall Equipment Effectiveness or OEE and it brings with it a host of different opportunities to cut costs that are absolutely worth a closer look.

The Key to Greater Efficiency is Here

To truly get an understanding of how your OEE score impacts your production costs, you must first begin to think about those costs as more than just line items on a balance sheet somewhere. Yes, producing X number of parts costs Y dollars per part – but it’s also a great deal more sophisticated than that.

When you improve your OEE score, you make meaningful gains towards eliminating unplanned downtime and reducing unnecessary stops. You learn the actionable information you need to make the types of changes that will improve line changes and retooling techniques. In other words, you boost availability – something that will allow you to produce more parts without necessarily adding resources like new equipment.

Speaking of that equipment, improving your OEE score also means that you’re improving not only the performance of that which you’re already working with, but the utilization as well. It helps guarantee that all machines are operating at their maximum potential – or at least as close to it as you can get. This, too, helps realize the full potential of your existing infrastructure, thus cutting costs significantly along the way.

Not to mention the fact that if you have a high OEE score, it means that you’re doing your part to avoid poor quality parts that need to be rejected as soon as they come off the assembly line. It means that you’re avoiding going through unnecessary procedures just to arrive at a less-than-desirable conclusion.

When taken together, all of this means a few important things. Your OEE score is a direct indication of how much waste you currently have in your manufacturing process and the more you can eliminate, the easier it becomes to produce A) high quality parts and other components B) as quickly as possible. Once you get to that point, you can start to see the highest possible return on investment of the materials you’re working with, the machines you’re using and even the people you’ve paid to run them – which in and of itself is the most important benefit of all.

If you’d like to find out more information about how improving your OEE score can help significantly reduce your production costs over time, or if you just have any additional questions you’d like to discuss in a bit more detail, please don’t hesitate to contact Thrive today.

 

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How to Extract as Much Value From Your OEE Score as Possible  

Overall Equipment Effectiveness or OEE is one of the most important metrics for manufacturers in the modern era. However, it’s so much more than just a simple number. To truly get as much insight from it as possible, you need to understand what that value actually means. Getting to that point requires you to have an essential context surrounding what you’re measuring and, more importantly, why it matters that you’re paying attention to these qualities to begin with.

What the Data is Really Trying to Tell You

To truly get a sense of what your OEE score is revealing about your operations, you need to come to a greater understanding of how this score is calculated in the first place.

Consider your quality score, for example. Let’s say you produced 20,000 parts in a given period, but had to eliminate 5,000 due to issues with their viability. 20,000 parts minus 5,000 would be 15,000 which, when divided by that original 20,000 production run, would equate to a quality score of roughly 75%.

Now, to be fair – this is not a bad score at all. It’s not necessarily possible to reach an OEE score of 100%, as that would mean that everything was operating at total efficiency – a tall order, to be sure. But it does indicate that there is room for improvement in terms of the quality of your output and once you know that you have the starting point of something far more important.

Unplanned stops could be an issue, for example – those periods where key pieces of equipment fail, when an operator isn’t available to run a machine or when unplanned maintenance takes place. It could even be an issue with planned stops – shift changes are taking too long, machines aren’t being as cleaned as quickly as they should be, etc.

Regardless – once you know that you have an issue, and once you know what it is, you have the actionable information you need to fix it. This, in essence, is what OEE is all about.

All told, OEE is the gold star metric for manufacturing organizations – but looks can be deceiving. Don’t necessarily assume that you can get to a score of 100%, because you can’t. Nobody does. But even if you come out of the gate with a low score, so long as it is always ticking upwards you can be confident that you’re moving in the right direction. Once you hit a score of 85% or above, you can rest easy knowing that you’re doing everything you should be – which is the most important thing of all.

If you’d like to find out more information about how to extract as much value from your OEE score as possible, or if you just have any additional questions that you’d like to see answered in a bit more detail, please don’t delay – contact the team at Thrive today.

 

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Want to Learn More From Downtime Tracking? Pay Attention to These Key Attributes  

At its core, downtime tracking doesn’t just help you better understand the current state of the equipment in your manufacturing facility. It also helps you gain visibility into what that equipment is capable of, and what the potential is when you get as close to peak productivity as possible.

The more information you’re tracking, the more insight you have – and the better choices you can make in terms of optimization.

According to one recent study, it’s estimated that about 70% of all organizations don’t actually know the full extent of their unplanned downtime events. They may know that they have an issue, but they’re not sure why – which means they can’t prevent it from happening again.

Thankfully, downtime tracking solutions like those available from Thrive can help relieve that issue – provided that you pay attention to a few key attributes along the way.

Harnessing Downtime Tracking to Your Advantage

To make sure that you’re deriving the most value from your downtime tracking software, you need to be as detailed in terms of the data that you’re collecting as possible. This means that in addition to capturing the process area or specific line where the downtime event occurred, you also need to make a note of things like the machine itself as well.

Equally important are things like the product that was being worked on at the time of the event. Sometimes, it’s not so much a matter of an issue with the machine as it is an issue with the process itself.

Of course, the length of the event is also paramount to helping better understand what is going on. If you know when an event happened and when it was eventually corrected, you should be able to see this number get shorter over time as you make steps to improve. Operator comments will be equally important to that end, as they can help provide additional insights into an event including what the conditions were and what steps were taken to ultimately fix the problem.

Overall, downtime tracking is about painting a more complete picture than you can arrive at on your own. It’s not just about what happened – it’s about who was involved, when the event occurred and where it took place. All of these help answer the most crucial question of all: “why is this issue happening?” Is it a one-time affair, or is it recurring? What is the context surrounding the issue? Once you’re able to answer those questions, you have nearly everything you need to emphasize continuous improvement across your manufacturing lines – leading to greater efficiency and even greater profits as well.

If you’d like to find out more information about the key attributes that you should be paying attention to in order to get the most out of your downtime tracking efforts, or if you’d like to speak to someone about your own needs in a bit more detail, please feel free to contact Thrive today.

 

 

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When It Comes to Downtime Tracking, Your Data is Only the Beginning

While downtime tracking solutions are undoubtedly powerful, it would be a mistake to consider them a “silver bullet.” Simply investing in such a solution in and of itself is not enough to consistently improve your manufacturing efforts. It’s not an end point – it’s the beginning of something much larger.

This means that to drive real, positive change, you need to be prepared to analyze that downtime tracking data in a variety of different ways, all of which are worth a closer look.

Taking Downtime Tracking to the Next Level

By far, one of the most important steps to take in order to get the most out of your downtime tracking efforts involves conducting a thorough analysis of what your data is trying to tell you.

Case in point: machine failures. Yes, downtime tracking can instantly tell you when a machine goes offline and can likely define a reason why. But how long does it actually take your employees to fix these issues? Is this a number that can be improved as well and, if so, how do you best go about doing it? The answers to these questions cannot only make sure that machine failure events happen less frequently, but that they’re resolved as quickly as possible when they do.

Along the same lines, it’s also important to understand your data within the context of your own manufacturing operations. The volume of data that a solution like Thrive can provide you with is enormous, so you need to pick more specific metrics in order to gain actionable insights about your company. Which metrics matter the most to you? Why is it important to gain real-time visibility into these qualities? These, too, are answers that will vary depending on the organization, which is why it’s so important to answer them as early on in the process as possible.

Finally, consider the end result of downtime tracking – meaning what action you’re actually going to take as a result of your findings. Yes, you want to reduce unplanned downtime events – but you also want to accomplish more than that. Downtime tracking can be a viable way to identify waste in your production efforts, for example, which can and should lead to thoughtful process changes. It may even uncover opportunities for the implementation of new technologies, allowing your employees to work “smarter, not harder.”

Regardless, downtime tracking itself is only the beginning. The true impact rests not just with the data that you’re being provided with, but the actions you take and the decisions you make as a direct result of it.

If you’d like to find out more information about how to dive beneath the data from your downtime tracking solution to uncover the true story of what’s going on with your business, or if you have any additional questions you’d care to talk about in a bit more detail, please don’t delay – contact Thrive today.

 

 

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Why Manufacturers Should Be Embracing the Internet of Things  

Imagine if nearly every device on your shop floor had a sensor built into it. One that was collecting performance and quality data at all times. One that was also sharing data with not only every other machine and sensor, but with organizational leaders as well. The types of insight that you could derive from that information would be enormous – it would generate a type of real-time visibility into your operations that would virtually guarantee continuous improvement on a daily basis.

Thankfully, we’re talking about a category of technology that already exists. It’s called the Industrial Internet of Things (or IIoT for short), and the market is predicted to reach an enormous $124 billion in value by the end of 2021.

Yet despite this, there are still manufacturers out there that insist on doing things “the old-fashioned way” – meaning with manual, time-consuming processes that add little value to the production. All told, embracing the Industrial Internet of Things is not only a good idea, but it’s also one that can dramatically improve your OEE scores as well.

OEE and the IoT: The Perfect Pairing

Short for Overall Equipment Effectiveness, OEE is essentially the most important metric for any manufacturer. Once you begin carefully tracking quality, availability and performance, you gain an incredible level of insight into what you need to do to improve your company moving forward.

The Industrial Internet of Things can help with these efforts in a wide range of different ways. As you collect more and more data from machines and operators, you can go back and look at historical data to optimize virtually all areas of your business. You can see which machines are most likely to go offline, allowing you to spend your budget wisely in terms of what you replace and what you repair. You can see when demand is high and create schedules for employees that avoid bottlenecks. You can make sure important resources are where they need to be, exactly when they need to be there – absolutely no exceptions.

All of this creates something of a perfect storm in the best possible way. Maintenance costs are reduced almost immediately because you can see the “bigger picture” of what is going on. You’re spending less on materials and other resources because you have a better understanding of exactly what you need in a given situation. Equipment is more available and is being better utilized: significantly increasing your return on investment, too.

Quality. Availability. Performance. Those are the three areas of your operations that can be significantly boosted by the Industrial Internet of Things, and they’re the ones that will increase your OEE score as well.

If you’re interested in finding out more about why manufacturers should be embracing the Internet of Things with open arms, or if you have any additional questions that you’d like to go over in a bit more detail, please feel free to contact the team at Thrive today.

 

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Effective Downtime Tracking Requires You to Dive Beneath the Data  

One of the most important things to understand about downtime tracking software is that it is not the “be all, end all” solution that many think it is – at least, not on its own.

Yes, it’s valuable to get an automated alert whenever a critical piece of equipment goes offline. But if you don’t know why, meaning you also don’t know what to do about it, what value are you actually getting?

Therefore, if you really want to practice the most effective downtime tracking possible, you’ll want to keep a few key things in mind.

Getting to the Heart of All Those 1s and 0s

By far, one of the most important steps you can take to dive beneath your downtime tracking data involves making sure you understand exactly what it is that you’re tracking in the first place.

A downtime tracking solution like Thrive will allow you to separate events based on a number of different categories so that you see more than just when something is offline – you get visibility into why, all so that you can put yourself in the best possible position to do something about it.

An unplanned downtime event is one when a machine is not working as it should be because of some type of issue you didn’t predict. Obviously, this is different from a scheduled period of downtime which may be some planned event like maintenance or a shift changeover.

It’s also important to acknowledge when a particular machine is not operating because a separate machine upstream in the production line is down. This is similar to when a machine might be blocked – meaning that a downstream machine is offline and something of a bottleneck has developed.

All of these examples are technically downtime instances, yes – but they’re very different situations that require totally different resolutions. If you treat them all as if they’re the same – meaning that if you’re only paying attention to whether or not a machine is online – you’re only getting one small part of a much larger story.

Similarly, you need to make sure that your operators are prepared to assign detailed downtime reasons to all events. This is crucial to the ongoing problem-solving process. Many solutions can help assign downtime reasons automatically, but operator notes are still important to again provide that essential context. Without it, you’ll likely face the same recurring issues again and again – you’ll be treating the symptom, not the disease, so to speak. With accurate downtime reasons, you’ll have total visibility into what is happening and why – all so that you can make meaningful steps to improve your operations in the future.

If you’d like to find out more information about why effective downtime tracking requires you to dive beneath the data to uncover the true story of your business hiding in plain sight, or if you just have any additional questions that you’d like to get answers to, please feel free to contact Thrive today.