Manufacturers globally have needed to document sustainability goals more and more – critical to customer loyalty and investor confidence.

Use AI for a Data-First Approach to Green Manufacturing, Industry Today
Predictive maintenance and digital twin technology – checks optimal sustainability levels and the lifecycle of equipment.

Many organizations worldwide are switching energy sources and acting to reduce product waste throughout their operation. However, properly addressing sustainability goals and achieving what is expected by corporations requires a lot more action and reporting.

Artificial intelligence (AI) can be a major asset to companies looking to do more for sustainable sustainability. For example, equipping maintenance teams with tools to enhance machine efficiency by improving access to data and analytics and innovative monitoring and management technologies to assess performance.

Taking a data-first approach is the first step in conquering these goals. Implementing such tools directly into the manufacturing process will create more sustainable and efficient outcomes ever imagined. 

What Makes Sustainability a Hot Topic?

There are troubling forecasts regarding climate change and the impact it will have on life and our planet as a whole. Corporations, big and small, play a role in tackling these issues based on their significant carbon emissions. Two hundred of the world’s largest companies have vowed to reach net-zero emissions by 2040. Contrary to those promises, a report showed us that many companies are not doing enough to hold up their end of the bargain.  

The goal of this piece is to uncover what more they can be doing.

The Benefits of Being a Data-First Manufacturing Organization

Applying advanced technologies that grasp company data helps companies with the sustainability problems mentioned above in a handful of ways.

Predictive maintenance allows maintenance teams to uncover machinery and equipment problems at the earliest stages. Sensors monitor systems non-stop to ensure proper pressure levels, temperature, humidity, and emission quantities. Any movement from the optimal level will alert the appropriate department. This approach alone allows a company to act and stop a machine from failing altogether or address measurements that don’t represent sustainability objectives.

Many corporations are investing in digital twins to depict the physical assets of their manufacturing operations. Digital twins add intelligence about the equipment and assist with any necessary fixes to reduce downtime and expand the life of machinery.

Proactive approaches highlighted above as a result of deploying innovative technology can be critical in companies going and staying green – an essential attribute to customers, investors, and life on Earth.

Production Processes in a 360-Degree View

A 360-degree view uses AI to bind knowledge from every relevant data source within a company. The return is actionable insights to help identify production-asset faults and operating conditions.  

The connection of data gives companies a clear view of where time and labor are wasted throughout the production process and even highlights where the quantity of raw materials can be minimized.

Combining sensory data, digital twin analytics, and other operational data will give companies a single dashboard with all analytics and insights related to their sustainability benchmarks.  

Reusing Assets with Monitoring Technologies

Predictions on optimal repair and refurbishment cycles can also be executed when working simultaneously with the full scope of manufacturing data. Side by side with predictive maintenance and digital twin technology to check optimal sustainability levels, the lifecycle of equipment can also be anticipated with accuracy and efficiency.

The ability to anticipate failures and level deviation allows organizations to be led down a path of circularity. This path consists of reusing equipment parts and pieces across the supply chain in other areas of the manufacturing operation. Assessing equipment components fittingly can extend the use of manufacturing assets not previously considered. Corporations can reduce their carbon footprint exponentially by recycling parts, reusing materials, and reducing downtime.  

Large organizations have factories and facilities worldwide to manufacture their goods. Reaching circularity in just one major factory could greatly impact sustainability goals and achievements. If attained across the entire supply chain, tremendous fundamental changes can be mimicked by other corporations for their green manufacturing efforts.

What can be Achieved by Going Green and Staying Sustainable?

The use of intelligent technology powered by sensory data and 360-degree views of the manufacturing process will be vital in paving the way for a sustainable future. Hand in hand with monitoring and management technologies, corporations can keep their promises and continue the fight to be greener while increasing efficiency levels.

Acting and deploying innovative technologies will provide practical value to all stakeholders, including investing groups, employees, and customers – all beginning and ending with AI and connecting company-wide data and intelligence.

Use AI for a Data-First Approach to Green Manufacturing, Industry Today
Daniel Fallmann

About the Author:
Daniel Fallmann, CEO & founder of Mindbreeze – Daniel Fallmann founded Mindbreeze in 2005 and as its CEO he is a living example of high quality and innovation standards. From the company’s very beginning, Fallmann, together with his team, laid the foundation for the highly scalable and intelligent Mindbreeze InSpire appliance. His passion for enterprise search and machine learning in a big data environment fascinated not only the Mindbreeze employees but also their customers.

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