6 Habits of Data Driven Manufacturers

Manufacturers are not new to utilizing data.  For decades, many manufacturers have used data to to drive operational improvements, improve quality, reduce waste, and increase throughput. For instance, Six Sigma, a set of statistical tools, analyzes data to understand the source (root cause) of defects in order to improve quality.

However, manfacturers now have unprecedented amounts of data collected at speed through advanced analytics, machine learning and IoT enabled devices. Where gathering data was once a labor intensive and slow process – perhaps collected manually by an operator and written in logbooks - it is now generated and accessible in real time.

Additionally, advanced machine learning algorithms allow manufacturers to “find correlation and covariance in larger, noisier data sets,” according to Tulip, a software provider that connects people, machines, and devices.

Advanced manufacturers are tapping into the potential of all this data to improve productivity, efficiency, quality and drive the next level of operational performance.

Predictive analytics, for instance, can help anticipate when a machine is about to fail and alert operators before it results in significant equipment damage or a prolonged work stoppage.

But, despite the enormous potential that data offers to manufacturers, many are still struggling to make use of it.

“The manufacturing industry continues to suffer as a result of the disconnect between data collection and data usage, hindering or altogether eliminating valuable use cases,” writes Graham Immerman, Vice-President, Marketing, MachineMetrics, in an article on the World Economic Forum.

"Despite the massive amounts of data collected, the industry continues to be a laggard amongst its peers, showing the lowest levels of task automation in data management.”

Here are 6 habits of data driven manufacturers, gathered from industry experts and speakers at some of our recent conferences and webinars:

#1: They recognize the importance of culture

Data is only useful if people make it part of their behaviour and daily interactions with people, machines, and systems. If data is not trusted and integrated into the daily behaviours of all employees, it will not be useful.

Colgate-Palmolive, for instance, recently integrated a digital twin into their manufacturing process. Darren Haverkamp, Technical Director at the company said that the most difficult part of the implementation was getting people to change their behaviours to act on the data (rather than tribal knowledge and personal experience).

“Engagement should be your first step,” says Havercamp adding that it’s important to get people to “think differently.”

That’s something that resonates with Matt DiGeronimo, Plant General Manager at International Paper. It’s one thing to have data and another to act appropriately based on what that data is telling you. Data and curiosity are the key to effective operations and continuous improvement.

“We want to be a thoughtful, inquisitive organization that has a questioning attitude when faced with data,” says he said in an interview with IX Network.  “A questioning attitude is at the heart of operational excellence. Let's ask the hard questions, even if we think we know the answer. And let’s get used to assuming that the status quo can be approved upon.” 

#2: They don’t make it too complicated

Too much data can quickly overwhelm operators and managers. Too little data and the information is not effective. Data requires a Goldilocks-style ‘just right’ approach.

“You want to provide just the right amount of data to allow people to react. Create the right KPI reports and make those reports visible to the appropriate audience,” says Havercamp.

You don’t want users to feel like they have yet another complicated application to learn to use. Tools should be simple and intuitive and give employees the right information at the right time to make good operational decisions.

#3:  They provide their employees with the skills to use data effectively

It’s not a viable option to have a data scientist in every part of the organization. But employees need the skills to understand what data means and how to use it. This means boosting soft data skills like asking good questions and understanding data context, according to the authors of an article published in Harvard Business Review.

“We need underlying advanced data skills, but what our research found is that it is these associated, attendant soft skills that make an even bigger difference,” write the authors.

Workers on the shop floor should have some basic training on how to ask the right questions, interpret what variables have an impact in a process, and basic understanding of how algorithms work so they don’t blindly accept what the computer is telling them.

#4: They make data visible

Making work “visible” has long been a principle of Lean manufacturing. If you can’t see it and measure it, you have no way of knowing whether you’re on track. Solutions can be as simple as Kanban boards on the shop floor that display work in progress and other key metrics.

New digital technologies enable manufacturers to go further with this concept.  

At Nestle Purina Petcare, Terry LeDoux, VP of Digital Manufacturing, describes how his company uses digital to “provide information and technology at the fingertips of every associate so they can deliver results anytime, anywhere.”

In an IX Network online presentation about Nestle’s approach to digital transformation Le Doux explains that digital technology allows you to tailor information displays to each individual.

“How am I performing? How is my team performing? How is my factory performing? How is that information presented in a way that is useful to your employees?” he says.  

#5: They eliminate data silos

The reality is that accessing high quality, reliable data is still difficult at many manufacturers. Data lives within different systems, machines and departments of an organization and can be subject to human input error.  

“The modern factory is a collection of technology and processes. One of the challenges is that so many different solutions exist. For example, with the production line you might have several different technologies [operating] in a siloed way. Even the data collection is happening in a siloed way,” observes Elena Alikhachkina, Global Vice President Data and Analytics, speaking about data enabled manufacturing at IX Network’s Factory of the Future event last year.

Data offers the most insight when it is accessible across machines, departments, and processes rather than hoarded by individuals. This requires a solution that is part governance, and part technical. Many organizations have data restrictions between departments and individuals.

Alikhachkina says that you need to create “a layer of data excellence so that technology vendor talks to technology vendor.” Further, some loosening of data governance rules and restrictions may be necessary to allow access to a wider data pool.

#6: They have leaders who value data

None of these habits would be possible without senior leadership that understands the value of data and leads by example. A culture that rewards data driven behaviours and the technological infrastructure that makes it happen needs to be modelled and enabled from the top.

"Data-driven organizations are led by open-minded, creative executives who run their businesses with passion and curiosity," says Sergey Patsko, Engagement Leader, Data Science Services at General Electric, quoted in an article on TDWI.  "[…] They are willing to make bold moves and invest in IoT platforms to create an infrastructure that enables data-driven processes, and not just set the list of KPIs to look after. They are curious to learn from the data, and they challenge traditional, often empirical, approaches that exist within their own organizations."

What do you think? Are there any other habits that you think it takes to truly become a data driven manufacturer?

Interested in learning more about this topic? 

Join our upcoming webinar as industry analyst, Craig Resnick from ARC Advisory Group, presents how operations leaders are reimagining their operations and driving business value by creating new opportunities for industrial process improvement. Register here.