Manufacturers and corporations worldwide have increasingly needed to document sustainability goals. These efforts are crucial in maintaining customer loyalty and confidence in investors and invested groups.
Many organizations are taking legitimate steps to switch energy sources and reduce product waste throughout their entire operation. However, this is not the magic bullet to addressing the full extent of ongoing sustainability efforts.
Technology, specifically artificial intelligence (AI), can do more for companies in this area, especially in manufacturing. Outlined in this article are other areas AI can assist. These practices include using technology to enhance equipment maintenance efficiency through improved data and analytics and dynamic monitoring and management technologies to assess performance continuously.
The highlighted efforts begin with implementing a data-first approach directly into the manufacturing process to create the most efficient and sustainable practices for operations.
Why is this Important?
There are concerning predictions surrounding the impact climate change will have to life on our planet. Major corporations have a massive part in combatting these concerns based on their significant carbon emissions. With that said, upwards of 200 of the world’s largest companies have promised to reach net-zero emissions by the year 2040. A recent report showed us that a large portion of these companies are not doing enough to keep that promise and reach that goal.
So, what more can they be doing?
Taking a Data-First Approach to Manufacturing and Maintenance
The umbrella of doing more for this problem comes down to using advanced technologies that prioritize the much-discussed data-first approach.
Prioritizing manufacturing data can accelerate the efficiency of equipment maintenance and optimize manufacturing processes.
Predictive maintenance allows companies to detect defects in machinery and equipment at the earliest stages. Systems are continuously monitored through sensors to ensure proper levels are being achieved – pressure, temperature, humidity, and even emission levels. Any deviation from the norm or optimal level will be reported. With this approach, any company can take steps to stop a machine from failing before they do so or be alerted if a certain level is veering away from their sustainability goals.
For example, many companies use digital twins to represent the physical components of their manufacturing operations. The digital twins are used to add intelligence about specific parts and help with technical fixes to equipment – reducing downtime and expanding the life of certain assets.
A proactive approach and the utilization of digital technologies can be fundamental in corporations actually being true to their word about going green. Of course, this holds importance to customers and investors wanting to be in business with sustainable organizations but also to our planet and the existence of sustainable life.
A 360-Degree View of Production Processes and Conditions
A 360-degree view connects intelligence from all data sources and uses AI to do so. AI can give insights and help identify production-asset faults and operating conditions with the most proficient accuracy and efficiency.
A high level of data connection can pinpoint companies where time and labor are wasted in the production process and where they can reduce the number of raw materials used.
Combined with sensory data related to optimal machine maintenance, a single dashboard can be generated to bring intelligence around all things sustainability to the forefront.
The use of AI tells companies where labor and materials are being wasted and provides proactive insights on how to address them to match going green and efficiency benchmarks.
Monitoring and Management Technologies for Proper Reuse of Assets
Parallel to taking a data-first approach and achieving a 360-degree view of manufacturing data and the production process, predicting optimum repair and refurbishment cycles is achievable. Hand in hand with predictive maintenance to confirm optimal sustainability levels, the equipment lifecycle can be accurately predicted.
These predictions lead manufacturing companies down a journey of circularity. This journey consists of reusing equipment parts and pieces of machinery in other areas of the manufacturing process. Proper equipment assessment can fully extend the use of all manufacturing assets. Whether it be reducing downtime, recycling parts, or reusing materials in other spaces, the carbon footprint of a single factory is significantly decreased over time.
In large organizations, hundreds of facilities across the globe could be used to manufacture goods. Taking steps to reach a high level of circularity in just the largest factory may have a notable impact on sustainability goals. Imagine if you attained this across the entire supply chain – the numbers would revolutionize roadmaps to green manufacturing.
Digital technologies, sensory data, and 360-degree views of the manufacturing process with the help of monitoring and management technologies are the key to corporations holding up their end of the bargain in the fight for a sustainable and green future.
Taking firm action and deploying the right innovative technology will only provide greater value and confidence to your customers, investors, employees, and all other key stakeholders within your enterprise. Then, AI and machine learning can connect all your data to show the incredible impact on your business and the climate.
