Manufacturing data analytics involves using real-time data from machines and operations to make intelligent decisions. Many factories today utilise it to enhance product quality, minimise waste, expedite production, and reduce costs.
For example, manufacturing data analytics makes it possible (and easy) to identify faulty products before they reach the final assembly stage. It can also help cut downtime by tracking machine behaviours and signals.
In this article, you’ll learn everything you need to know about manufacturing data analytics, including how to apply it in your business.
Manufacturing data analytics is the process of reviewing your machine logs, sensor data, and production reports to see what’s happening on your shop floor. It helps you prevent delays, catch and fix defects early, monitor equipment health, and see where time or materials are being wasted.
It simply can be one or all of these, depending on what you want to achieve with it:
There’s always a cost when you’re not seeing the full picture:
And all the while, you’re reacting to problems instead of staying ahead of them.
That’s where data analytics helps. It gives you a clearer view of what’s happening, what’s about to happen, and what to do about it.
When used well, analytics has been shown to drive 20–30% more output, 10–20% better quality, and 5–15% lower costs.
Let’s see a good example of this.
Manufacturers are already using data analytics as a competitive edge in the real world. With the help of our Data Analytics Consultancy, they are seeing results faster and with fewer mistakes.
The examples below are dashboards that the data analysts from Vidi Corp built to help a factory monitor product rejections and sales performance.
This dashboard shows why products are being rejected and who is most affected.
With it, the factory can see the top reasons for rejections (defects, blow holes, sand drops, cracks, etc) as well as the amount of money they are losing from returned items every month or financial year.
They also get a breakdown of the complaints and rejections by customer and part.
All these direct them to where problems occur most frequently and what the costliest issues are, so they can prioritise them, reduce losses, and maintain customer satisfaction. The insights give them better control over product quality.
We have worked with the Head of Strategy of a German manufacturing company doing 8 billion EUR per year in revenue.
The critical analysis for them was sales growth every year. We analysed this sales growth by supplier, product and product group.
Identifying high-growth products gave additional insights into market trends and helped to anticipate the demand in the future. Likewise, idenfitying suppliers whose product sales decreased helped to manage the supply chain accordingly.
A backlog of orders forms when manufacturers sell a product but have to delay the delivery of it. This happens due to many global trends like semiconductor shortages and tarrifs.
When aiming to reduce backlog, it is important to prioritise. As you can see on the screenshot above, analysis of backlog by customer is almost the most important bit of analysis. Knowing which strategic accounts are affected by product backlog helps the team to assign priorities to the manufacturing and supply chain teams.
Measuring backlog by product helps to inform the sales decisions. For example, the sales team can manage expectations with the buyers for when the products will be delivered if they know the backlog of specific products.
Analysing the backlog by root cause helps to drive the management conversations which helps to reduce the backlog over time
Finally, it is important to compare the backlog vs sales to ensure that it is cleared up faster than it’s built up. Analysing average monthly sales vs backlog helps to keep track of how much you add to the backlog vs how much is already there.
1. Production Efficiency
2. Quality
3. Maintenance
4. Cost
5. Supply Chain
We’ve said so much about data and analytics in manufacturing, but where exactly does all that data come from? The table below summarises it.
| Data Source | Data | Usage |
| Machine Logs | Run times, idle times, counts of cycles, error codes | Determine the overall efficiency of a machine and the source of delays |
| Sensor Data (IoT) | Temperature, pressure, vibration, speed | Bring about early failure detection and maintenance of production conditions |
| Production Reports | Output counts, reject rates, rework rates, shift performance | Compare line efficiency and performance of a team or shift |
| Quality Inspection Data | Defect types, measurements, tolerances, pass/fail rates | Trace quality problems to machines, material, or processes |
| Maintenance Logs | Compare the line efficiency and performance of a team or shift | Schedule preventive maintenance based on updates, thereby extending machine life |
| Energy Usage Data | Power consumption by the machine and the production line | Eliminate unnecessary power consumption, thus working towards sustainability |
| Supply Chain & Inventory | Material levels, delivery times, and stock movements | Match production with demand to avoid stockouts or overstocking |
Manufacturing data analytics comprises several key components that work together to make the entire process useful.
1. Real-time data collection
You need systems that collect data from machines and sensors as it happens, not hours or days later. By doing so, you make your decisions and responses in a timely way.
2. Integration of data from multiple sources
Your analytics will not perform if your data is scattered about. Unify all your machine logs, ERP data, maintenance records, and quality checks into one platform.
3. Visual dashboards
Displaying Charts and metrics in a neat way to simplify complex data. A good dashboard shows you the state of your shop floor at a glance.
4. Anomaly detection
Alert systems that notify you of unusual patterns in your data before they become bigger issues.
5. Predictive modelling
This helps you forecast what’s going to happen next using past and current data. Machine failure risk or a supply delay, for example.
1. Early problem detection
Anomaly detection and other similar analytics notify you when something is off, like an early warning, so you can respond before it escalates into a big problem.
2. Better decisions
With enough data-backed insights in your palms, you can easily plan production, schedule maintenance, and assign resources where they will provide real value.
3. Better product quality
Analytics will reveal the patterns behind defects and variations. This means more consistent output and fewer customer complaints or returns.
4. Lower costs
By reducing waste, avoiding rework, and utilising machines more efficiently, you can save money without compromising quality.
5. Faster Time-to-Market
Data analytics helps you fix bottlenecks and move products out quicker.
Manufacturers use data analytics to:
The most important part of implementing data analytics in your manufacturing business comes down to doing these three things right:
Step 1: Define High-Impact Goals
You must be clear on what you want to achieve with analytics. Clear doesn’t mean saying “improve efficiency,” but something specific like “cut downtime on all lines by 25%.” This gives your analytics direction and makes success easier to measure.
Step 2: Assess Data Maturity
The bone of analytics is data. But it’s not just about having a lot of it. You must check:
If your data is messy or incomplete, analytics won’t take you far.
Step 3: Choose the Right Tech Stack
You don’t want to waste your resources (Money and/or time) on tools that do not work well with your current setup or are too complex for your team to use. For dashboards, Power BI or Tableau are great options. You will likely also need custom Power BI connectors or third-party platforms to bring in machine data and keep things running in real time.
A faster route to extracting value from your data is by partnering with a professional data analytics consultancy.
Manufacturing data analytics is no longer a luxury. If you’re serious about cutting downtime, improving quality, and getting the most from your machines, it’s time to get started.
If you need help to analyse your company data, we can guide you through setup, integration, and optimisation so you start seeing results faster, with less trial and error.
Data analytics enables manufacturers to stay ahead of industry trends. By using forecasting models, they can accurately predict customer demand and allocate the necessary workforce and raw materials to meet it. These analytical tools also help optimise product availability and drive revenue growth.
Manufacturing data collection involves capturing information on production processes, equipment performance, and product quality. This data is crucial for streamlining operations, minimising costly downtime, and upholding high-quality standards.
This data is sourced from a range of systems, including equipment sensors, ERP platforms, supply chain logistics, and customer feedback. It can take the form of structured data, like numerical values and categories, or unstructured data, such as audio, video, and text.
