Reduce waste by 10% with Data Analytics

Even though there are many standardized tools, specific solutions will always have a place. New challenges demand tailor-made solutions. A cotton manufacturer wanted to work more efficiently and reduce waste. Critical questions spurred that desire on. The production team wondered: why do we do things the way we do them? Why are we throwing away so much good material? Can we do better?

Read more
Open the Table of Contents

Alarm bells

Most problems only become interesting once the money runs out. Or when the margins shrink, or revenue keeps decreasing. That’s when alarm bells start going off. In many cases, that’s too late to turn the tide. The solutions which were thought up under pressure are no longer executable. If only you’d taken action sooner. If only you’d listened to the data.

In the “old” economy, the manufacturing industry, costs are an important performance indicator. Only businesses with a good cost structure can survive a crisis. But how can you ensure that you’re part of the winner’s circle?

An age-old process

Some time ago, I worked for a cotton manufacturer. I had the opportunity to see the production process from up close. In this factory, they manufactured cotton using a centuries-old process. The process of coloring the cotton was simple. A canvas of cotton is simply painted. Some cotton gets lost in the process, but in the end, a final product rolls off the line. Nothing too exciting so far. But it’s a process with an interesting bonus: data.


The company has historically delivered products in multiples of 2 meters. Quality control used to be quite basic. The quality of the product is either sound or it’s not. In the years following, customers became more critical. They checked far more thoroughly and marked every mistake on the canvas. This extra quality control led to more and more waste, however: a lot of waste, due to the machine’s limitations. The machine could only cut products in multiples of two. Even a small printing error led to two meters of waste.

Rewind the data tape

Why didn’t the manufacturer look for a machine without these limitations? Machines that can cut in any configuration, with less waste as a result. The purchasing costs of a new machine are high. So a quick test isn’t an option. We looked for a different method: an improvement method based on machine data, which they had. So why not rewind the tape with this data? Let’s simulate a machine. Build a simulator that pretends to process these orders again.

Higher output with different processing

We built the simulator using Qlikview and Excel. The data is processed using VBA (Visual Basic for Applications). Can this be used to demonstrate that a different method of processing can achieve a higher output? That would justify the purchase of an expensive machine, thus making the business case.

Interesting discovery

Using the simulator, a new problem was discovered: a lot of waste turned out to be the result of product-specific characteristics. The quality manager only looked at the production process and the machine park until that time, but not at the product itself. This interesting discovery wouldn’t have been made without the simulator.

Data Analytics approach to reduce waste

The quality data was incorporated into a Qlikview model from the quality system. The model assesses the quality of every 10-centimeter part of the canvas. The data does need to be edited for this. Sometimes, multiple errors appear on the same piece. Pieces with more than 1 error are rejected. The output is saved as a CSV file. The CSV is processed in Excel by the machine simulation as if it’s an actual order in production.

All data is now processed in the simulator like it is in the machine, per 2 meters. If this piece doesn’t contain any errors, the next 2-meter segment is analyzed. If this one is also approved, a third 2-meter segment follows. If all three segments are approved, it results in a 6-meter product that meets the quality requirements. If 2 sequential segments get a pass, the result is a 4-meter piece. And if only 1 passes, there’s a 2-meter piece with the desired quality.

Dare to change

Afterwards, the limiting factor of 2 meters was left out. In the simulator, everything that’s qualitatively sound is usable. The results are compared to the output as delivered in reality. This other method delivers 5% more high-quality product and a reduction in waste.

The simulation data is entered into QlikView. Using this Data Analytics tool, analyzing for other qualities, such as color recipe, is simple. It’s also easy make analyses between the products and product families. This led to some very interesting insights which weren’t found earlier. The understanding of the processes greatly increased.

Looking at data from a different perspective can be very useful. The simulation leads to a reduction in waste.

More waste

On the client’s side there’s profit to gain, too. The client makes clothing out of the purchased materials, but they don’t always use the full length of the material. If they only need 5.4 meters, do they want to pay for a 6 meter piece and cut off 60 centimeters? Why should we cut up a good 5.4 meter product in production? Because of the limitations of the machine? That’s a waste! This situation stimulated us to look for alternatives to get these products on the market.

Win-win situation

In the next simulation, we let go of the pre-set product length. No more rigid 2, 4, and 6 meter pieces only. In this simulation we looked at all possible lengths. The customer should be able to shop themselves. They have to be able to look for the measurements they want, that best suit their needs. They should only pay for what thye need.

Logistical puzzle

A new way of working brings with it new challenges. The logistical process has to be redesigned. Different product lengths are stocked. This change, diversity in measurements, isn’t just regarded as an opportunity. This challenge is a bridge too far. Given the years-long tradition, revolutionary, even. But what if the business put the production process first?


By processing the data in a simulator, mistakes were discovered. Not all products achieve a higher output in the simulation and reduce waste. Some products even produced less. Because this isn’t technically possible, we did a deep dive. There were administrative errors in the system. Several defective products were erroneously delivered as good products.

Some orders containing mistakes that appeared regularly across the entire order were discovered. The mistakes occurred at a high frequency. For this type of order, robotic design would not lead to an improvement. The length of first-choice material was too short for these orders in every case. But this led to useful information. There are apparently product families with similar challenges. Product families that are interesting when a quality breakthrough is found. So, focus on market groups. Don’t just look at products that sell at a loss. The products are technically very different. Yet, at that time, all eyes were on the quality department. Is that the reason that real breakthroughs weren’t happening?


The returns on high quality can be increased by new ways of working. Using historical quality data, it’s easy to build a simulator. The simulator led to several interesting insights. Some insights weren’t predicted. Insights that indicated that there was more at play than was initially assumed. Insights that justified the purchase of a robotic design machine. More than 5% extra output proved to be achievable, without doing anything about the quality. It’s hard to imagine why no one else thought of that.

What if the business also decided to stop selling only pre-determined measurements and sold any possible measurement? An improvement of 10%, with no problem at all.

Lessons learned

Work differently. Don’t just keep pushing the current method. The argument “that’s how we’ve always done it” isn’t valid. Look at things from a different perspective, without adhering to the existing rigid rules. Look for new methods of working to reduce waste. This will lead to ideas that provide new information – information that you may not even have been looking for.

The simulator indicates that it’s smart to buy a robotic design machine which can cut per 10 centimeters. With the extra revenue generated, that money is quickly earned back. The existing machine is still usable. The current design can continue to exist. For several products, it continues to serve its function. Because of this, there’s plenty of capacity. It’s impossible to let the new machine process everything. The capacity of one robotic machine is insufficient for the entire offering. But now it turns out that purchasing two new machines isn’t necessary. The current machine still serves its purpose. That method of working isn’t completely replaced. There’s just one new one added to it.

A perfect balance, created by keeping the existing machine and method of working and adding one robot to it. That will save you some money.

The right tools

Clearly, this is a complex puzzle. But it’s a solvable one. Even difficult puzzles can be solved with the right people and resources. The right approach will get you far. Don’t look for the “god-tool”. Fill your toolbox with multiple tools, and choose the right one based on the current situation.


Passionned Group’s consultants and advisors are always looking for new information, or new ways to simplify processes and reduce waste. They look at data from every possible perspective. If you want a new perspective on your data, contact us. We’d love to see how we can help you improve.

A selection of our customers

Become a customer with us now

Do you also want to become a customer of ours? We are happy to help you with data-driven working or other things that will make you smarter.

Daan van Beek - Business Partner


Business Partner

Contact me directly

Fact sheet

Number of organizations serviced
Number of training courses
Number of participants trained
Overall customer rating
Number of consultants & teachers
Number of offices
Number of years active