| |OCTOBER 202219Artificial Intelligence enablement is most commonly used by manufacturing sectors for increasing the efficiency as well as yield of the equipmentBENEFITS THAT ARTIFICIAL INTELLIGENCE IS BRINGING TO SMART MANUFACTURINGArtificial Intelligence comprises the capabilities of learning systems which are regarded/perceived as intelligent by humans. Artificial Intelligence & ML technologies are the top priorities in the manufacturing industry as they help organizations in altering several business models, discover operational paradigms for supporting those models, & most importantly, monetize information to achieve higher levels of productivity.Artificial Intelligence works as it accumulates considerable benefits for the manufacturing industry, which include enabling smart production, developing predictive as well as preventative maintenance, providing supply chain optimization, improved safety, product development as well as optimization, facilitating Augmented Reality or Virtual Reality, reducing cost, quality assurance as well as enabling green operations and many. Artificial Intelligence enablement is most commonly used by manufacturing sectors for increasing the efficiency as well as yield of the equipment. Artificial Intelligence is also being leveraged normally as a tool for improving productivity, improving quality as well as consistency which helps in forecasting the results accurately.It is safe to say that the manufacturing industry continues to be driven by AI and Ml technologies. The critical use case of Artificial Intelligence in changing operations, enhancing the overall product quality as well as decreasing costs via several methods that include smart operation, design prediction, quality assessment of products and many. REASONS WHY ADOPTION OF ARTIFICIAL INTELLIGENCE IN MANUFACTURING IS ACCELERATINGThere are few merits that make the adoption of Artificial Intelligence specifically suitable as launching pads for manufacturers for embarking on their journey with cognitive computing­ intelligent maintenance, intelligent demand planning & forecasting as well as product quality control. Although the deployment of Artificial Intelligence is a complex process with many facets of digitization, it has still not stopped from moving forward. The ability to grow as well as sustaining the Artificial Intelligence initiative in a manner that produces huge value for the organization is most likely to be crucial for achieving early milestones on an Artificial Intelligence adoption journey.Manufacturing enterprises are adopting Artificial Intelligence & Machine Learning with such speed since by leveraging these cognitive computing technologies, companies can actually leverage their analytics capabilities, in order to make better forecasts & reduce the inventory costs. Improved/enhanced analytics capabilities facilitate organizations to shift to predictive maintenance, decreasing maintenance costs & downtime.HOW IMPORTANT IS PREDICTIVE MAINTENANCE IN MANUFACTURINGThe use of Artificial Intelligence facilitates manufacturers in predicting when or if functional equipment as well as repairs can be scheduled in advance. It is highly critical since machines can operate even more efficiently as well as cost-efficiently when AI-powered predictive maintenance is implemented. What makes Artificial Intelligence excellent for maintaining reliable equipment & maintaining smooth production is its ability of predicting breakdowns as well as optimize scheduling before the equipment fails. We observe that scaling Artificial Intelligence implementations beyond a POC level is one of the major challenges that manufacturing as well as several other industries such as logistics, healthcare, insurance, finance & audit face.HOW CAN DIGITAL TWINS REVOLUTIONIZE THE MANUFACTURING INDUSTRYDigital twins can be leveraged in several ways for improving manufacturing operations as well as enabling engineering, production, sales & marketing in order to work together by utilizing the same data, for making better decisions. The digital twin helps in changing the manufacturing process as part of quality management for identifying variances in all parts of the process coupled with the use of better materials as well processes. For instance, supply chains, fleet managers as well as route efficiency are measured using digital twins. A digital twin displays several variances in either the equipment or the manufacturing processes which indicate the requirement for maintenance prior to something big happens. In fact, the ease of access to operational data via digital twins enables collaboration, better communication along with faster decision-making.
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