With support from a three-year grant, Kelley School Assistant Professor of Operations Management Amrou Awaysheh is leveraging big data to help manufacturers across the globe save energy and money.
The grant from APICS, the association for supply chain management, allows Awaysheh to work with a Fortune 200 firm to consider various sustainability practices that drive improvements in manufacturing performance.
The firm, which is unnamed, is capturing data from meters on manufacturing lines in their plants. Awaysheh is analyzing that data to understand its value for driving better decision making at various levels of the organization—one of the hallmarks of how researchers at the Kelley School on the IUPUI campus are driving economic access and prosperity in Indiana and beyond.
“Big data holds the potential to unlock answers to questions about sustainability and manufacturing practices,” said Awaysheh. “While there are many questions that can be answered by this data, the possibilities are truly endless. The sheer amount of data available will allow us to answer questions we don’t even know how to ask yet.”
“Think about the large amount of data that exists in an organization. Sometimes, organizations know what they have; sometimes, they don’t. When I work with a company, I tell people to think about it like an iceberg. A company may know the tip of the iceberg, but they don’t know what lies beneath the water. That’s what this grant is getting at – helping companies understand what data they have and what they can do with that data to drive change.”
Researchers define big data as data sets so large that traditional data processing capabilities are inadequate. For instance, the firm Awaysheh is working with has 4.5 billion data points, which collect information on electricity use, water use, discharge and air emissions, as well as employee accidents and well-being.
“We can look at historical data that goes back 10 to 20 years in a company’s history and conduct predictive analytics: What changes when you pull on this lever? What happens when you have a change in manufacturing performance? How does that change impact sustainability? Alternatively, when you improve sustainability, how does it translate to improved manufacturing performance?”
“In fact, we can take it one step further with the addition of AI and machine learning. Such software can do prescriptive analytics, or can tell you what you should be looking at. The only reason we can consider new variables like this is because of the huge scale and size of the data.”
One year into the three-year grant, Awaysheh says his research has revealed two things. First, improved sustainability can continue to improve: There is never a true plateau.
Second, several test sites have realized a 10 to 16 percent improvement in performance after adopting new practices that resulted from understanding their data.
“Big data is going to be integrated more and more into business and managerial decision-making. This research will allow manufacturing executives to better understand how they can use big data in their organizations and, hopefully, become more successful managers.”
Awaysheh says the goal for the second year of the grant is to link manufacturing data to sustainability performance to better understand how they influence each other.
“The ultimate goal of this research is to help manufacturers understand their overall sustainability performance and to show them there is value in investing in these practices,” added Awaysheh.