While () is one of the most disruptive technologies impacting manufacturing, it is also one of the most controversial. The race to implement is accelerating faster than ever before, and we are already witnessing major progress in the Robotics, Machine Learning and Predictive Analytics as a result.
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From speeding shop floor operations and automating routine decisions, to advising on executive-level decisions — is already creating value in manufacturing by automating and streamlining the entire ecosystem. In fact, it has become so instrumental that some wonder if factories will rely on machines altogether, eliminating jobs.
While this can be a sensitive subject, managers should weight the benefits and risk of and learn they can best deploy it in their enterprise. To stay competitive, manufacturers must understand the latest trends, keep pace with change, and not let fear of the unknown impede progress. is evolving rapidly and the companies that choose to leverage this technology will quickly transform with it.
The State of Today
technology is advancing at such a rapid rate that it is difficult to stay up-to-speed on best practices. It is being embedded within a wide range of technologies, from ERP solutions with built-in Business Intelligence capabilities, to Networked Supply Chains using Predictive Analytics. is also nudging users along guided paths and supplementing decision points with tips and results from previous similar incidents. In such cases, the user may not even realize that is providing the insights and simply see the guidance as what is expected of modern computing.
More specifically, here is how is applied in modern manufacturing:
Automation. In industrial manufacturing, several routine and administrative tasks can be automated by . Users can create workflows, which allow data points to create reports, place re-orders, signal notices, trigger reactions, flag potential hazards, dispatch crews, reserve parts or reroute materials. These cases of automation range from modest time-savers to huge improvements in uses of time and resources.
and IoT. and the Internet of Things (IoT) work together to interpret and determine the seriousness of flagged data points received from sensors. Sensors generate such vast amounts of data, it would be useless without the ability to sort and identify the significant points. Using , the system can spot anomalies, such as early warning signs that an asset may require maintenance.
Personal Assistants. An excellent example of how is improving manufacturing is the Personal Assistant. This technology uses Natural Language Processing to answer questions, perform functions, interact with the user and provide sound recommendations. Not only are Personal Assistants convenient, they can be valuable safety aids for hands-free questions and data queries, such as when the user is driving a forklift or servicing machinery on an elevated platform.
Facial Recognition. This technology offers the capability of identifying and verifying individuals based on images, which can greatly benefit the manufacturing industry by issuing security clearances. Even more impressive, applications for recognizing images can provide visual confirmation of quality control. The same type of technology that recognizes faces can determine if red jelly beans are being routed to the correct packaging. […]