Manufacturing analytics: the metrics you need to enjoy a worry-free vacation

The modern manufacturing industry is more data-driven than ever before. By analyzing data on everything from production efficiency to supply chain performance, manufacturers can make informed decisions that lead to increased productivity and profitability. Without it, you’re just shooting in the dark. Which of the dozens of metrics should you keep track of, and what is really worth measuring in a manufacturing company?

In today’s article, we’ll show you what metrics you can use to have “peace of mind” when you are away from the facility – because you will know that your people and company are working as they should.

Let’s start with the assumption that you already have a company somehow organized, i.e., with Prodio or another manufacturing management tool, and you collect manufacturing data. Your workers already know what and how to do.

The basic production data you need to prepare to introduce some metrics

Please note: We will certainly not talk about some very advanced KPIs, the OEE type, which is introduced in large production plants. Because, first of all, they are very difficult to introduce, and you would have to collect a huge amount of data. And secondly, we do not know any company with less than 20 workers where introducing this famous OEE would bring some benefits proportional to the amount of work that would have to be put into it.

Let’s start with preparing data on which we will build our metrics. And before we talk about this data, we think it is worth saying what measuring means for a small manufacturing company.

What does it mean to measure something on the shop floor vs. do you want to type in tons of unnecessary data?

Many company owners imagine that if they build complex manufacturing orders, their workers will carefully fill the line by line what they did, how much they did, and enter 15 other parameters with a pencil, pen, or anything. Later after, e.g., three weeks, another person in the office will paste it all into Excel, and they will have a perfect measuring tool.

It has nothing to do with measuring. Why? Because if you have data older than 72 hours, it is only a mass of letters in practice. You cannot take in a manufacturing company after a week, look at some orders, and see that it took longer than it should or someone had low productivity because, after this time, you cannot work out what happened there. Workers will always tell you that they do not remember or it was a machine error, so such data will only stress you.

To sum up: Remember that more than 72 hours break from the time the data was collected makes the data useless because, well…it is…. out of date.

Data we need – minimum package

Working time on operations (in connection with products)

The first and the most basic thing you need is the simplest combination of who, what, how much time, and how many pieces they made while working. And this data must be combined together. You need to know that Tome Jones, on CNC machine number 1, made ABC products and that two pieces were made within 38 minutes. Only if you have this data combined can you pull out some analysis.

Operation comments

Another parameter, which is a production “must have” to analyze the data well, are workers’ comments. If, in this line, on this sheet of paper, or just like in Prodio, you add a simple text box with space for comments, it will save you tens of hours of work because you will not have to assert why the operation was poorly performed. If, in this line, on this sheet of paper, or just like in Prodio, you add a simple text box with space for comments, it will save you tens of hours of work because you will not have to assert why the operation was poorly performed. 

Your workers will leave information that at that time they were unloading the car from or that there was a breakdown of the machine, so there is no need for painstaking “investigations” because you have everything clearly explained and accounted for in the comments.

Time and Attendance

The third super useful data that you should have to draw KPIs is the time and attendance of people in the company.

You might have today some kind of Clocking software where people use PIN codes, key fobs, or QR codes to clock in, and you are happy because at the end of the month, you can pull data in the form of a report and send it to your HR department or accountant. We suggest that you either combine both systems (the one for time and attendance tracking and the one which collects data from the shop floor) or duplicate them. Why? You will see when we move onto simple metrics.

Additional data that is worth having to cover 90% of needs

If we already have the basic data you need, let’s add three more parameters, thanks to which you will cover 90 percent of all needs regarding KPIs.

Deficiencies

The first additional parameter is the number of deficiencies that arose in the production process. So in practice, you will know that John Smith, who worked for 38 minutes on machine tool A on the ABC product, made two deficiencies.

Raw materials consumption

The next parameter is the amount of raw materials consumed during each operation.

Breaks from machine work

The third parameter is often a “bone of contention” because we think it is not a perfect indicator. However, many clients love it and thoroughly analyze the break time. These include all cigarette breaks, or in general, i.e., time for machine service or machine settings.

How can we collect data?

You can either use the easiest way, for example, Google Docs surveys, and program it accordingly, or use a simple system to collect RFID-friendly data, such as Prodio. It will take less than two or three working days for someone who would only transfer this data to Excel.

Never extract more data than you need and break down your analysis step by step. Otherwise, you will be flooded with information, and the crew can start rebelling calling you a slave-driver, because you count every second on the shop floor.

Since we already have basic data, we can move on to simple manufacturing metrics.

The simplest metrics for manufacturing companies

There will be no sophisticated numbers, only the simplest things that, unfortunately, 90% of companies before implementing Prodio do not have.

Workers’ efficiency in different operations

What is efficiency? Efficiency shows how fast workers perform a particular operation.

To calculate such efficiency, we simply have to compare the time they worked with the number of pieces made. That is, if they made 10 pieces for 60 minutes, then such efficiency was 10 pieces per hour.

As well you can use norms in minutes, seconds, and hours. Some customers, for example, when producing large complex blocks on CNC machines, even use norms in days. But you need to know such efficiency.

Why do you need to know it? It is important: not to chase your people but to push them to work more efficiently, faster, etc. Because it is not where the savings are, first of all, you need efficiency to check whether the norms you use for the calculation or that your manufacturing engineer set up, somewhere you have written in notes, are right. You priced your products and services correctly and don’t accidentally pay up for them. Are the standards you set long ago still up to date?

If you have efficiency, you can compare your workers and check whether your norms of pieces per hour are realistic.

Summary of how much time it took to make a particular order item

Why is the particular item so important, not the entire order? Because most of the programs available on the market are based only on the entire order and, for example, if the customer has committed 30 products, they will be treated as a single basket, that is, a whole. You will not be able to break it down into individual items.

We suggest that each time you should focus on individual items of a given order separately because it is much easier to find the potential for optimization (price change), or improvement in manufacturing technology. Otherwise, you will have a lot of data, but you won’t know what can be improved.

The cost of a working hour per order

If you look at the work history of all people broken down into specific operations and products, then you can say that 100 pieces of the product were produced in 6 hours, with 2 hours being spent on machine tool number 1, 3 hours on welding, and 1 hour on polishing.  This approach not only allows you to see what was the greatest cost, but if you multiply these times by the working hour cost of each machine (or as in Prodio, you enter such cost in the machine settings), then you will see the total labor cost of a given order item.

Automatically, by adding together all items of a given order, you will see how much this order costs, for example, 100,000 USD. Worse, if it turns out that its manufacturing cost you 110,000 USD, you paid up to it.

Total of working hours per day

Going further and analyzing simple sums from the work history, we can examine the number of working hours in a given day, where you see who worked on which machines. You see how many hours people worked effectively on machines during that day. You can also see the total for a given month.

Total of working hours per machine

You can filter the analysis not only by workers but also machines or in any other way you want. Prodio customers love a convenient daily shop floor report and periodic report that allows you to export all the data we are talking about here with one click of a button.

Simple numbers used in 90% of manufacturing companies

Productivity

This is our favorite metric – productivity. This is nothing different than comparing how long your workers spent on the shop floor (based on Time and Attendance tracking) with the machine time, so how long they spent working on different operations.

In practice, this is the most “eye-opening” metric for the owners of manufacturing companies because it turns out that often for 180-200 hrs spent on the shop floor hall, where people have a lot of overtime, they work effectively only 140-120 hrs. And no, this is not their fault: it only shows how much there is to be optimized.

Where do these unproductive hours come from? There are different reasons: i.e., workers are looking for elements, they walk around the production floor looking for certain parts to be mounted on machines, and they do not know what to do because tasks have not been delegated. sometimes they deal with side operations such as cleaning, servicing, and unloading the car. You need to include these in the calculations of your products.

Productivity of about 80% is already a good result, but you can still improve it.

Deficiency

If we combine the history of products and filter it by workers, machines, or orders with the number of deficiencies that arose, you will be able to see the true deficiency, that is, how much % of the products had to be corrected/thrown away.

What can you do if you know deficiency metrics?

1. Try to spot the pattern – maybe one person has more deficiencies than others? You might consider providing some extra training for them to minimize the number of faulty products.
2. Maybe it is a particular machine that you should blame for many deficiencies and be replaced or set differently.
3. Start including deficiencies in your calculations. Some deficiencies are part and parcel of the manufacturing process, so maybe the number you have is an industry standard.

Break time

This metric shows how much manufacturing time people spend on breaks or non-productive activities. Thanks to Time and Attendance, where breaks are included, you not only know who is at work but who actually works on machines, who is on a cigarette break, and who is currently available.

Percentage of breaks in the working day

Another interesting but controversial metric is the percentage of breaks on the shop floor during the day.

The difference between productivity and the percentage of beaks is that in the case of productivity, you combine machine time with the time that people were present on the shop floor, and you see, for example, that 20% of the time, they did nothing that would bring value.

Regarding breaks, we can list specific reasons, e.g., how many percent the services took, breakfast breaks, how much time it took to clean the shop floor, unload the car or help in logistics.

And then again: if you want to run on pen and paper, it is much more complicated because you’ll need someone who can copy all data into Excel.

Prodio solves it quite simply: there is a button on the production schedule, which workers click when registering a break. They can use a key fob, scan a bar code, or enter a PIN code, and o break is automatically in the system. In the daily shop floor report, created at the end of the day, these breaks will be listed (not only globally during the whole day but also filtered by workers or break reasons).

Raw materials vs. costs

Now, if you add to all these metrics the raw materials mentioned at the beginning of this article, you can track the labor cost for a given order and the total cost of this order.

You can also compare: labor costs with the cost of used raw materials, and then you will get the total cost you have incurred to complete the order. You can determine whether it was profitable and to what extent, or you will see that you spent more than you earned and need to renegotiate prices or change the production technology.

Global efficiency

The last thing you can learn from these indicators is global efficiency. Sometimes it is also called global productivity, but in Prodio, we use global efficiency.

To calculate such global efficiency, you not only need to know how efficiently your workers worked (and as we have said before, you can calculate it by comparing the number of pieces made to the working time), but you also need to have manufacturing norms for your products.

If you know that you or your manufacturing engineer wrote in the product technology that it takes 5 minutes to create 1 given element on that particular machine, you can start to compare the time planned for that operation with the lead time. Since the production of 1 element takes 5 minutes, the production of 100 elements, that is, the entire order should take 500 minutes. If it took much longer, let’s say 700 minutes (instead of 500 minutes), you could compare the norms globally for the entire company, the entire order, or individual orders to see if there are followed.

By dividing these 2 numbers, you will get an efficiency indicator. This will show you how efficiently your workers have worked in relation to the established norms.

Everything below 100% will show that workers have worked below the assumed norms, so it is possible that the standards can be improved. And all above 100%, e.g., 110%, will show you they have worked above the assumed norms. While planning, you can analyze these norms and conclude that, i.e., you need less/more time to complete your order.

See how our Prodio can make gathering and analyzing your metrics far more convenient? Everything can be collected and stored just through one app – and you can access the automatically updated data whenever you want.

Want to see it for yourself? Then, how about scheduling a demo presentation or giving our free 14-day trial a test drive? We’ll show you that, as with our many other clients, you soon won’t imagine your manufacturing facility without our cloud service Prodio.

Conclusion

How many times you had the feeling that the moment you will leave the company (for example, to go on holiday) everything will fall apart? Too many to count? We hope our manufacturing guide will help you (at least in some part) to get rid of the anxiety and make you enjoy your well-deserved time off. Because it really is simple to do – you just need to know what you should be measuring in a manufacturing company and have a handy helper in the form of Prodio that will ensure that everything works like a well-oiled machine. And whether you will be away on a business trip or on a beach, you’ll have all the information you might need, right inside one Prodio app – updated 24/7.

So, when will you be planning your well-deserved time off? 🙂

Manufacturing metrics FAQ

Why is measuring manufacturing metrics important?

Measuring manufacturing metrics is crucial for evaluating and improving the overall performance of a manufacturing operation. It helps identify areas of strength and areas that need improvement, enabling informed decision-making and targeted strategies for enhancing productivity, reducing costs, and optimizing resource utilization.

What are some commonly used manufacturing metrics?

Manufacturers commonly analyze production output, cycle time, equipment uptime, yield rate, defect rate, overall equipment effectiveness (OEE), inventory turnover, on-time delivery, and customer satisfaction. These metrics provide valuable information about different aspects of the manufacturing process and help monitor performance over time.

How can predictive analytics be used to measure manufacturing metrics?

Predictive analytics uses historical data and machine learning models to forecast future outcomes. In manufacturing, it can be applied to measure metrics such as demand forecasting, production capacity planning, and equipment maintenance scheduling. By analyzing patterns and trends in data, predictive analytics helps optimize production processes, reduce downtime, and enhance overall efficiency.

How can real-time monitoring contribute to measuring manufacturing metrics?

Real-time monitoring involves continuously collecting and analyzing data from connected devices and sensors in the manufacturing environment. It provides instant visibility into key performance indicators (KPIs), allowing manufacturers to track metrics such as production output, equipment performance, energy consumption, and quality in real-time. This enables proactive decision-making, timely interventions, and continuous improvement.

How can measuring manufacturing metrics improve product quality?

Measuring manufacturing metrics, such as defect rate, yield rate, and customer satisfaction, provides insights into the quality of products being manufactured. By closely monitoring these metrics, manufacturers can identify potential issues, implement corrective actions, and continuously improve their production processes, leading to higher product quality and customer satisfaction.