The Impact of Data and Analytics on Sustainable Manufacturing

In the book “Moneyball,” author Michael Lewis chronicles then Oakland Athletics General Manager Billy Beane’s embrace of sabermetrics to transform a struggling franchise into the darling of the advanced baseball analytics movement during its meteoric rise in the 2002 season. Sabermetrics is defined as the empirical analysis of baseball through statistics, which is used to predict the performance of players to give teams a winning edge. While rivals used traditional statistics and scouting reports to build their teams, Beane understood a mountain of existing data could be used to improve the product on the field.

As a result, the Oakland A’s were able to compete in a league of teams with much higher payrolls and household names. Now, most, if not all, Major League baseball teams use some semblance of sabermetrics to evaluate players and build their rosters.

Modern manufacturers use their own data-driven approaches to be the leanest and most efficient operation possible. But it is critical to understand where to find and how to extract insights from that data to get a leg up on the competition. After all, data for data’s sake is just a bunch of numbers.

Data’s Role in Sustainable Manufacturing

The manufacturing sector has become the largest contributor to environmental challenges. Results from a 2022 survey by global technology research firm Omdia were published in the white paper “Industrial Sustainability: Moving Sustainability Forward in Manufacturing,” which revealed that the drive for sustainable business practices has brought pledges and commitments from many leading manufacturers.

Among the respondents, 66% reported that they have set science-based targets and 62% reported carbon neutral targets. But how are these sustainability efforts being implemented and tracked?

The survey data shows that the responsibility primarily falls on the c-suite, with initiatives spread relatively evenly across design, operations, maintenance and supply chain business functions. And that requires the right technology to cultivate and interpret data to drive analysis and measure improvement.

According to the white paper, “Tracking performance metrics such as energy usage, material consumption and waste across different parts of the manufacturing process can support improvement and the evolution of targets.” This requires expertise in data analysis to set key performance indicators (KPIs) and make progress toward their goals.

“A lot of our clients don’t typically know what they don’t know when we start working with them,” said Krishna Kuppuswamy, head of the global supply chain and sustainability practice at Tredence Inc. “They are certain, though, that they are not making enough progress, so they look to outside help for guidance on their sustainability efforts, and that often begins with looking at data.”

Tredence, a data-analytics company with offices across the United States, seeks to put meaningful information into the hands of those who can adopt it and spearhead real business change within an organization. Tredence works with companies across a wide range of industries, helping clients find insights that can be quickly implemented.

The process typically begins by looking at a company from a holistic perspective. This involves talking to key stakeholders, examining already available data and establishing best practices.

“Educating the client is so important because it allows us to show them how they can see some quick return on their investment,” Kuppuswamy said. “It can be frustrating to invest in a sustainability consultancy and not see the fruits of your labor right away, so we make a point of explaining where the work will pay dividends.”

Data is becoming the star of the show when it comes to tracking sustainability, but it can be hard to come by because companies are not set up properly to take advantage of all the numbers they generate. Data specialists are becoming more important in the grand scheme of things because they are trained to turn such information it into something useful.

“A data engineer or scientist will work with a company’s data models and build elaborate data architectures that a company can implement moving forward if they want to get the most out of the data that already exists within their infrastructure,” said Kuppuswamy.

For example, Tredence recently worked with a building material manufacturer that needed to predict short- and long-term product quality in an effort to reduce CO₂ emissions and energy costs. The implemented solution, which was a quality control system for emissions, resulted in a 2.5% reduction in the company’s CO₂ footprint per plant per year, as well as a 5.5% reduction in energy and raw material costs per plant per year. 

Tredence also cited a global communications service provider they worked with to achieve an 18% reduction in energy costs and a 7% reduction in emissions through smart monitoring solutions for energy optimization.

Waste Not, Want Not

Raw materials can be a pain point for many manufacturing companies that want to minimize waste that limits sustainability goals.

Waste comes in many forms. Energy, liquid and solid wastes all cut into the bottom line and need to be reduced. To meet sustainability goals, waste should be one of the first outputs tracked and analyzed.

“Any facility that burns fossil fuels to make steam and heat water within their facility is dumping energy somewhere in the manufacturing process,” noted Patricia Provot, president of the Americas, Armstrong International Inc., a Three Rivers, Mich.-based company that specializes in thermal utility issues. “A lot of this energy, however, can be recovered and used elsewhere and have a positive impact on the bottom line.”

While there might be a desire to revamp the entire process, the easier step—and a more prudent one—is to work with what you have, because it is often the fastest way to improve. That’s when you need to crunch the numbers.

“As much as 80% of the energy that is being used in a manufacturing facility gets dumped before it can be recouped and used elsewhere,” Provot asserted. “Our engineers are tasked, then, with working closely with our customers to map everything within the facility and churn out data that helps us make better, more informed sustainability decisions for the client.”

Creating a data foundation can be eye-opening. Manufacturers that choose to work with third-party resources to build their sustainability processes know there is gold to be mined within their facilities, but it can be surprising how much energy can be repurposed back into existing processes.

The ability to fully monitor such data didn’t exist before, especially when it came to thermal systems. These systems were, and are, some of the most outdated in manufacturing, often being used a decade past their expiration date. Too much time is spent fixing machines and making sure they are operational to heat and cool a building. Tracking data isn’t feasible on these relics.

For Armstrong, this is low-hanging analytical fruit because it bears so many immediate benefits. Flow meters are a key tool. Knowing that a lot of energy escapes from archaic thermal systems, Armstrong places flow meters in strategic areas to pull data.

“The organizations that are committed to decarbonization will see these flow meters as the first step toward that goal,” Provot explained. “This allows them to quickly and easily extract data that they can then export easily, no matter what type of product they manufacture.”

Cutting Off Waste at the Pass

Waste is often equated to unused raw materials throughout the manufacturing process. But that’s only part of the problem.

“For better or worse, humans err when it comes to creating processes during the manufacturing process,” said Jason Walker, Hexagon AB’s general manufacturing practice lead. “We work closely with our clients to show them where they can implement technology tools earlier and bridge the gap when it comes to tech adoption.”

Hexagon, a multinational giant that strives to create sustainable value across ecosystems. This includes employing digital tools that let manufacturing customers conduct analysis up front before prototyping begins, which boosts overall efficiency by reducing scrap and process time.

Sustainability involves much more than just reducing waste. It can have long-lasting and positive effects across the board.

“When you are able to use digital tools at the onset, and you are pulling the right data, iteration time goes down and production can be reduced from weeks to days in some cases,” Walker explained. “When a lot of our customers are also bemoaning higher costs they have to deal with, they start to see the value in data collection being a boon for their bottom line, beyond sustainability.”

What Does the Future Hold?

You can’t read anything about the future of manufacturing, data and sustainability without addressing artificial intelligence (AI). While manufacturers might eventually get to a place where AI is the norm when it comes to mining data, some experts believe we need to pump the brakes and focus on more tangible and realistic goals.

“We are still in the early days when we look at how artificial intelligence is going to impact the manufacturing industry, regardless of how many breathless accounts are being written about the technology’s capabilities,” said John McEleney, a founder of PTC Inc.’s Onshape.

Based in Boston, Onshape helps clients accelerate product development with the power of cloud computing, real-time collaboration and built-in product data management systems.

One way AI could help a company meet its sustainability goals is when it comes to creating new parts without reinventing the wheel.

“Every manufacturer knows that building new parts is an expensive process when existing designs would do the trick without having to make unnecessary investment,” McEleney said.

This is where AI can step in. Rather than spending money building new parts for a new or smaller order, AI can be trained to dig through old or forgotten platforms suitable for reuse. When AI has access to relevant information within a manufacturer, it can sift through and find the right platform.

“This is why it’s important to capture as much data as possible,” said McEleney. “In this case, the AI can comb through the data to find the right part, which leads to less material used (e.g., not having to create new molds and tooling), and a more sustainable business case can be made for incorporating this technology.”

The road to sustainable manufacturing requires a thorough understanding of business practices to identify inefficiencies and set realistic environmental, social and governance (ESG) goals.

With more manufacturers prioritizing ESG efforts and publishing annual reports for compliance reasons, as well as to bolster public trust in responsible business practices, there’s no turning back when it comes to examining data and analytics to set and track sustainability goals, make better decisions and drive results.