20 Dec
2019

Machinery Reliability, OEE and Manufacturing Business Performance KPIs

Learn how important manufacturing KPIs are related and how improving one in the hierarchy of of manufacturing kpis impacts the others.

Production Monitoring
Machinery Reliability, OEE and Manufacturing Business Performance KPIs

There is a hierarchy of factory productivity KPI measures that are nested within one another in ways that can be a bit confusing to understand at first. Near the top of this hierarchy is OEE, or Overall Equipment Effectiveness, and near the bottom is data on individual machine reliability, such as MTBF (Mean Time Between Failure). The data near the bottom of this hierarchy feeds upward to production insights at higher levels of information.

It’s important for manufacturing professionals to remember that even though they may be measured on improving manufacturing KPIs, what their bosses really care about is how these KPI achievements contribute to macro-business results at the top of this hierarchy.

The Manufacturing Business KPI Reporting Hierarchy

Overall Financials - Revenue, Margins, ROI

Manufacturing Performance - Labor Costs per Unit, # Units Produced and Shipped

TEEP - Total Equipment Effective Performance

OEE - Overall Equipment Effectiveness

MTBF - Mean Time Between Failure - Machinery Reliability

 

Starting at the very top of this hierarchy, when measuring the net performance of a manufacturing business, financial measures indicate the health of the business. The ones that most manufacturing senior leaders pay attention to are Revenue, Profit Margins and ROI or Return on Investment. These numbers indicate the competitive strength of the business and can help senior leadership to compare your business to other similar businesses in your industry. These numbers are the result of non-manufacturing related factors (marketing effectiveness perhaps, or product design and recalls) as well as manufacturing related factors such as labor efficiency, scrap rates, delivery performance etc.

Moving further down through the manufacturing KPI reporting hierarchy within the financial results are the manufacturing related performance metrics such as labor cost per unit and overall number of units produced and shipped.

As we move down to the next level of manufacturing performance metrics, it gets more interesting and useful for manufacturing professionals. In most manufacturing businesses there are 3 primary drivers of cost. These are labor, inventory and plant and equipment. Labor and inventory costs get a lot of attention, as they should, however since so much capital is usually tied up in plant and equipment for a manufacturing business, improving the return on this capital investment can be a critical factor for competitive advantage.

The ROI of your capital investment in manufacturing equipment is significantly affected by equipment performance and reliability.

Not only will a machine that’s performing well produce visible ROI through the revenue generated from it’s production, but it will also reduce the need for adding overtime or adding more machinery to increase capacity, saving the costs of additional facility space, heat, insurance and energy as well as the cost of adding more operators.

Drilling further down into our manufacturing KPI hierarchy we get to factory machinery performance metrics. The highest order factory machinery performance measure is TEEP or Total Equipment Effective Performance. Compared to OEE, TEEP is generally a lesser known metric. TEEP provides you with the true potential capacity of your manufacturing operation while OEE provides you with the effectiveness of your equipment when it is scheduled to run. TEEP is a function of both OEE and Scheduling Losses.

The difference between TEEP and OEE is that OEE measures performance against Planned production time and TEEP measures capacity against ALL time. TEEP is an important measure of machinery capacity utilization overall. OEE is an important measure of equipment performance when the equipment has been scheduled to run. If you just measured OEE as a measure of productive capacity, you might conclude erroneously that your capacity has been reached.

For instance, it’s a widely cited statistic that best in class OEE is 85%. However, according to one source, a typical packaging line’s overall OEE is about 45 - 55%, which means that about half of its' planned production time is actually productive. If you’re a food products manufacturing manager, you can see that by addressing common issues of packaging equipment downtime and reliability with a Smart Factory Analytics solution, you can quickly improve the OEE of that area. Let’s say that you’re successful and achieve 85% OEE with your packaging line. You might believe that you’ve reached full capacity in that area of the plant. In actuality TEEP may reveal that there's more capacity available.

TEEP reveals the ‘invisible plant’, that is, what capacity is still available after you’ve maximized OEE. 

 

TEEPSchedule LossesPlant Not OpenPlant Not ScheduledOEE - 6 Big LossesAvailability / DowntimeSetup and AdjustmentsMaterial shortage, machine changeover, process warm-up after shutdown, operator error in setupBreakdownsEquipment failure, unplanned maintenance, damaged tooling or accessoriesQuality / Rejects

Startup Defects

Setup issues, product rejects, improper assembly or packagingProduction DefectsProduct damage / scrap, out of tolerance, contaminatedPerformance / StopsSmall StopsMisfeeds, cleaning, material jams, quality checks, tolerance adjustmentsReduced SpeedOperator efficiency / capacity, equipment age / lack of maintenance

 

Moving further down the manufacturing KPI reporting hierarchy, machinery reliability is nested within OEE and is a contributing factor to low OEE. A typical measure of machinery reliability, MTBF or Mean Time Between Failure is measured by dividing Operating Hours by Number of Failures.

Failures are usually thought of as Unscheduled Downtime. However, a more nuanced definition of Equipment Failure is ‘occurrences where the asset failed to fulfill its' function’. So while most people might assume that a ‘machine failure’ means the machine has completely broken down, the actual indicator may be that it’s prone to mis-feeds, operating more slowly than it should, or otherwise underperforming. 

So how do you get started in improving and impacting your manufacturing hierarchy of KPIs?

In our article Strategic Roadmap for IIoT Success we recommend that when you’re implementing a Smart Factory Analytics solution, that you walk before you run. We also recommend that you embrace a quality improvement process to guide your data collection strategy and your responses to data and insights as they come in.

Historically, TPM or Total Productive Maintenance is a Lean Manufacturing philosophy that is used to improve OEE. TPM addresses the causes of low OEE while creating an environment where operators can feel ownership over the machinery operations. TPM is a set of guideposts to help you to reduce Downtime, which is a component of OEE. Your overall goal may be to improve OEE, which will drive higher order financial performance, but by focusing on a smaller goal using machinery analytics, and reducing downtime, you’ll make faster progress towards ROI and still be addressing the larger goal.

One way to get at downtime is by collecting the MTBF of critical machinery.  Smart Factory Analytics tools such as Worximity are ideal for measuring and improving machine reliability metrics such as MTBF and downtime.

If you’re interested in getting to the heart of the causes of machinery reliability in your facility and improving your OEE, reach out and schedule a Worximity Demo!

















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