The advancement and utilisation of Artificial Intelligence (AI) is poised to make a similar impact in the 4th Industrial Revolution we are currently experiencing as Henry Ford’s assembly line did over 100 years ago. A convergence of machine learning algorithms, big data analytics, and connectivity between machines due to Internet of Things (IoT) capabilities are impacting and reshaping industry and business around the globe. Here is a broad overview of some of the contexts within remanufacturing these advances are rapidly being applied. By Joseph Zulick
Design for Remanufacturing
Barriers for remanufacturing can always be traced back to the initial product design stage. If products were better designed to accomplish the goals of the remanufacturing process, massive improvements and efficiencies can be accomplished. The adoption of ubiquitous information and communication technologies (ICTs) thanks to elements of advanced AI as described above continue to blur the lines between virtual environments and the real world to create more sophisticated cyber-physical production systems (CPPSs).
Advanced Remanufacturing Processes
Artificial intelligence technologies are exponentially expanding computing power and connectivity which results in greater volumes of data that can be analysed in a more robust manner than ever before. This will allow remanufacturers to think big and push the envelope to develop more ambitious goals and objectives for their programs. Lack of data or advanced robotics capabilities will no longer be impediments for remanufacturers to successfully process a higher percentage of product components and materials.
Robotics in Remanufacturing
Robots have already proven their capabilities in remanufacturing under certain conditions with relatively small and simple batches of components that usually involve some significant human oversight. Advances in AI are moving the needle in identifying and creating new patterns in the way humans and machines interact. This application of emerging technology shows significant promise to expand the capabilities of robotics in remanufacturing to tackle progressively more complex scenarios with less and less human interaction with greater efficiency.
Critical Failure Prediction
In industrial manufacturing settings, there is continuous pressure to improve efficiency, increase productivity, and reduce costs. IoT connectivity and other elements of AI are being brought to bear in this environment to improve predictive maintenance and avoid machine failure during critical phases of production. These same benefits of monitoring automated equipment on the front-end of the manufacturing process can also deliver the same benefits to the remanufacturing setting as well. Not only can unexpected downtime be eliminated, but the ability to plan and schedule preventive maintenance more proactively and efficiently can occur as well.
One of the most significant challenges all remanufacturers face is predicting how much demand there will be for returned products with the flow of returned items coming into the remanufacturing process. Of course, the quality of the materials being returned can make a significant difference as well. AI technologies can greatly improve upon existing forecasting models that attempt to predict product returns. Elements of Big Data and Machine Language Learning can leverage and up-date real-time data on sales, product usage and warranty activity and more accurately predict product life expectancy and the rate and timing of returns into the remanufacturing process.
Resilient Remanufacturing Networks (ReRuN)
Sustainability is the objective of remanufacturing in a world that has shifted from a linear model where products used to end up in a landfill once they are no longer functioning for their intended use. As a society, we continue to grow more aware of the finite nature of our natural resources that has led companies to produce products according to a circular model where as many components of an item are reused as many times as is practical.
As stated in the points above, AI and other emerging technologies are already making significant improvements in all phases of the product life-cycle that occur prior to remanufacturing. By embracing a ReRuN mindset that is calculated as early as the product concept/design phase, remanufacturing outcomes are positioned for greater outcomes due to improved forecasting in all elements of the remanufacturing process.
Closed-Loop Supply Chain Management
There can be no true resiliency for remanufacturing unless a complete closed-loop supply chain management strategy is employed. In-depth studies on remanufacturing are just now starting to take place and raise awareness of the opportunities to be leveraged during the remanufacturing process to impact economic and environmental sustainability. The advances in AI and all emerging technologies will help put remanufacturing on equal footing with all other phases of product life cycle. Because this emphasis on remanufacturing is just starting to expand and receive attention, it also holds the most potential for impacting the entire product lifecycle.
The Future is Now
In the news, every day we continue to see advancements in the development of products and processes that seem to be right out of science fiction movies and shows of the 1960s and 1970s. From flying cars to putting a colony of people on Mars, humankind is entering a bold new era where we know have the technology to execute just about anything we can imagine. This coupled with increased global awareness of our finite resources and need to be good stewards of our planet, will continue to bring greater emphasis and attention to remanufacturing in all phases of the product cycle. AI and other emerging technologies are finally catching up and giving industry the tools to create this new reality.
Joseph Zulick is a writer and manager at MRO Electric and Supply. MRO Electric and Supply maintains a comprehensive stock of FANUC CNC and FANUC Robotics parts which are used in several industries including but not limited to engineering, manufacturing, packaging, and plant automation.