23 Key Metrics You Can't Ignore In Your Manufacturing Dashboard

These key performance metrics keep your industrial machines and processes on track

In this blog post, we show you what the most important metrics are, how to collect the necessary data and how you can transform it into valuable information.

Efficiently running machines and optimized business processes are important elements for successful business operations. To measure, analyse and improve effectively you need certain metrics. Each job role needs different information. To ensure larger business goals you often need to combine several metrics.

In this article, we explain what manufacturing metrics are, why you need a dashboard and what the 23 key metrics are that matter most for different business goals and objectives. To make it a bit more hands-on, we introduce a tool you can use to build and monitor your own dashboards so you can keep track of your own machine performance using live condition monitoring.

Tip! Sign up for our free product tour and see live data dashboards in action.

What are manufacturing metrics?

A manufacturing metric or KPI (Key Performance Indicator) is a well-defined measurement to monitor, analyse and optimise production processes regarding their quantity, quality as well as different aspects like time, turnover and costs. These metrics need to be aligned to business goals and objectives and need to be defined in a SMART way (Specific, Measurable, Actionable, Realistic, Time-Based). 

To improve your metrics you need a continuous improvement methodology – a cycle that is never fully finished. For continuous improvement, you require a manufacturing dashboard with these key metrics included.

Why do you need a manufacturing dashboard?

A manufacturing dashboard helps to monitor and visualise the most important KPIs. It enables manufacturers to track and optimise production quality and at the same time gain insight in specific operations by different machines. This makes a dashboard a very valuable analytics tool to manage all related operations efficiently.

These seven reasons explain the benefits of a manufacturing dashboard:

1. Use data to make informed decisions.
2. Manage real-time efficiency.
3. Identify problems before they happen.
4. You gain complete insights of production lines or specific machines.
5. Resolve equipment downtime and issues promptly.
6. Eliminate manual registrations, like downtime sheets.
7. Engage staff (or suppliers) and improve productivity.

Here is a list of the most common metrics / KPIs in the manufacturing industry to update the performance and quality of processes and machines and condition monitoring.

23 key manufacturing metrics that matter in your reports

Improving Production & Efficiency

1. Production Volume – Track the quantities that you are able to produce
2. Production Downtime – Analyze and optimize your maintenance level
3. Production Cost – The actual total cost to produce one item
4. Production Time – Total amount of actual product time
5. Time to Make Changeovers – Measures the speed or time it takes to switch a manufacturing line or plant from making one product over to a different one. By tracking this metric you can identify how and where you could improve your changeover time, for example, by using equipment that is easier to set up and configure.
6. Throughput – Measures how much of a product is being produced on a machine, line, unit, or plant over a specified period of time.
7. Overall Equipment Effectiveness (OEE) – This multi-dimensional metric is a multiplier of Availability x Performance x Quality, and can be used to indicate the overall effectiveness of a piece of production equipment, or an entire production line.
8. Total Effective Equipment Performance (TEEP) – A performance metric that provides insights as to the true capacity of your manufacturing operation. It takes into account both Equipment Losses (as measured by OEE) and Schedule Losses (as measured by Utilization). Calculate Total Effective Equipment Performance by multiplying Availability, Performance, Quality and Utilization. TEEP is calculated by multiplying four factors: Availability, Performance, Quality, and Utilization.

Improving Quality

9. First Pass Yield – Indicates the percentage of products that are manufactured correctly and to specifications the first time through the manufacturing process without scrap, re-run or rework.
10. Rate of Return – Measure how many items are sent back. Also known as Customer Rejects.
11. Supplier’s Quality Incoming – A measure of the percentage of good quality materials coming into the manufacturing process from a given supplier.
12. Defect Density – Track the damaged items.

Reducing Costs & Improving Profitability

13. Asset Turnover – Acknowledge your assets in relation to your revenue.
14. Unit Costs – Track and optimize your units costs over time.
15. Return on Assets – See how profitable your business is relative to its assets.
16. Maintenance Costs – Evaluate your equipment costs in the long run.
17. Right First Time (RFT) – Understand the performance of your production process.
18. Manufacturing Cost as a Percentage of Revenue – A ratio of total manufacturing costs to the overall revenues produced by a manufacturing plant or business unit.
19. Net Operating Profit – Measures financial profitability for all holders for a manufacturing plant or business unit.
20. Energy Cost per Unit – A measure of the cost of energy (electricity, steam, oil, gas, etc.) required to produce a specific unit or volume of production.

Improving Field Service

21. Mean Time Between Failures (MTBF) – Indicates the average operational times between failures. The MTBF helps businesses understand the availability of their equipment (and if they have a problem with reliability). The measured value depends heavily on the operating conditions on site (ambient temperatures, start/stop cycles, servicing intervals, etc.). 
22. Mean Time To Repair (MTTR) – The average repair time after a system breakdown. This value indicates how long it takes on average to detect and locate a failure and replace the defective component. 
23. Mean Down Time (MDT) –  The average amount of time needed to repair a failure after a breakdown. Unlike the MTTR, the MDT includes the entire duration of repairs and maintenance as well as any delays caused by arrival and delivery times, replacement parts logistics, and failed attempts during unplanned maintenance. 

Machine manufacturers set the base to facilitate this kind of metrics by using the parameters from the machine.

From Machine Metrics to Manufacturing Metrics

It all starts with machines since they are at the heart of any manufacturing operation. A standalone machine or machine which is part of a larger process, generates a lot of data. Changes in machine state, production counters or sensor measurements are logged locally on the industrial controller (e.g. PLC, robot or HMI) and are usually defined in variables or parameters in the software. 

To keep track of these machine metrics, and use it to calculate manufacturing metrics to monitor condition, production or quality, the data needs to be transmitted securely to a central environment where data from several sources come together. That is where IXON Cloud comes in. IXON Cloud allows you to pull data from industrial equipment in an easy a secure way and store it in our SaaS platform for analysis. 

Pairing Big Data from Manufacturing Machines with Visual Analytics

Depending on which machine metrics you consider important, in the web-based IXON Cloud platform you configure which data you want to collect. Data can be logged on change, trigger or interval. Mining millions of data points can provide tremendous insights. Data analysts know how to sort through your data to find the game-changing information you need and present it in a way that business users can understand.

IXONs IXrouter acquires and monitors the variables at the edge, prepares the data and transmits it to the IXON Cloud environment. Machine builder or machine users can use this smart technology to create simple reports on massive amounts of data without any help from IT. 

By pairing big data with visual analytics, organisations can use all their data and all their variables. They can explore the data quickly and visually to see relationships and trends to focus on.

How to visualise your machine metrics in a dashboard?

You can configure IXON’s data logging and create a live monitor or historical data dashboard with the gathered machine metrics. This data can be visualised in a customisable dashboard (or live monitoring report). This means you can explore variables doing simple “drag and drop” selections to see what emerges visually. When something interesting emerges, it can be passed on to send alerts or exchanged with BI-tools for in-depth analysis with combined data. 

These steps turn metrics into customisable manufacturing dashboards:

1. Create your company portal in IXON Cloud
2. Connect and manage your industrial equipment to the IXON Cloud using the IXrouter or IXagent
3. Collect the data by configuring the variables and trigger criteria
4. Create a dashboard and add widgets to visualise variables
5. Share dashboards with other users or field service engineers
6. Install the mobile app to monitor machines on your smartphone

Building manufacturing dashboard templates in IXON Cloud

Our users love IXON Cloud because it empowers them to build their own dashboards from scratch in their own IoT platform. It takes time to get it right, but it’s definitely worth it. To reduce repetitive work you can create dashboard templates and apply them to other machines.

Create and duplicate your performance dashboards for continuous improvements of your manufacturing operations. Or combine our SaaS platform with Business Intelligence tools like Power BI or Tableau.

Please get in touch to request examples of manufacturing dashboards.

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