The Practimum-Optimum (P-O) algorithm represents a paradigm shift in developing automatic optimization products for complex real-life business problems such as large-scale manufacturing scheduling. It leverages deep business domain expertise to create a group of virtual human expert (VHE) agents with different "schools of thought" on how to create high-quality schedules. By computerizing them into algorithms, P-O generates many valid schedules at far higher speeds than human schedulers are capable of. Initially, these schedules can also be local optimum peaks far away from high-quality schedules. By submitting these schedules to a reinforced machine learning algorithm (RL), P-O learns the weaknesses and strengths of each VHE schedule, and accordingly derives reward and punishment changes in the Demand Set that will modify the relative priorities for time and resource allocation that jobs received in the prior iteration that led to the current state of the schedule. These cause the core logic of the VHE algorithms to explore, in the subsequent iteration, substantially different parts of the schedules universe and potentially find higher-quality schedules. Using the hill climbing analogy, this may be viewed as a big jump, shifting from a given local peak to a faraway promising start point equipped with knowledge embedded in the demand set for future iterations. This is a fundamental difference from most contemporary algorithms, which spend considerable time on local micro-steps restricted to the neighbourhoods of local peaks they visit. This difference enables a breakthrough in scale and performance for fully automatic manufacturing scheduling in complex organizations. The P-O algorithm is at the heart of Plataine Scheduler that, in one click, routinely schedules 30,000-50,000 tasks for real-life complex manufacturing operations.

Industry 5.0 is a relatively new concept that refers to the integration of human intelligence with advanced technologies such as artificial intelligence, machine learning, and robotics. It represents a new stage in the evolution of industry and manufacturing, where human creativity and problem-solving abilities are combined with cutting-edge technologies to create innovative and efficient manufacturing processes. In the context of Manufacturing Execution Systems (MES), Industry 5.0 refers to the use of advanced technologies to optimize and streamline manufacturing operations, while still leveraging the unique skills and insights of human operators. MES are software systems that track and manage the production process from raw materials to finished products. They provide real-time information on production status, inventory levels, and quality control, and can help identify bottlenecks and inefficiencies in the manufacturing process.

Manufacturing execution systems (MES) have undergone many transformations in the past several years--from simple point solutions to comprehensive shop floor systems that are now mission-critical to manufacturing operations. As I mentioned in part one of this series, the union between MES and smart manufacturing technology gives manufacturing enterprises access to new, advanced capabilities. From increased operating margins to decreased costs, manufacturers can leverage this smart combination and find themselves with a significant competitive edge globally. In part one and part two of this three-part series, we looked at four significant new aspects of MES: mobility and the use of artificial intelligence (AI), track-and-trace database capabilities and the use of many applications. The IIoT is becoming synonymous with smart manufacturing.

Doug Lawson is the chief executive officer of ThinkIQ. The United States is the third largest manufacturer in the world, trailing only China and the European Union. With hundreds of thousands of manufacturing businesses, the sector accounts for about 11% of America's total gross domestic product and serves a vital role in the global economy. Manufacturing yards serve as the lifeblood of this industry, but these properties pose unique security challenges. Companies want to protect their manufacturing operations, materials and intellectual property while ensuring employee safety and building an efficient operation.

Digitising operational processes is critical for manufacturers to cope with the demands and challenges of the crisis, and is considered a natural step forward in the evolution of manufacturing," said Jonathan Whiteside, Principal Technology Consultant at Dept. There are three key areas where manufacturers can implement AI to become more resilient and improve the bottom line: forecasting, conditional maintenance and communication." The speed and accuracy of machine learning forecasts provide multiple benefits, with the improvement to customer experience as a result of improved availability being just the tip of the iceberg. Improving demand forecast accuracy is demonstrating solid results across all industries with Consumer Packaged Goods manufacturers leading the pack. Machine learning and AI are not only influencing how companies manufacture, but also what they manufacture.

There has recently been an increased emphasis on reducing energy consumption in manufacturing, driven by the fluctuations in energy costs and the growing importance given to environmental impact of manufactured goods. Lots of attention has been given to the reduction of machine tools energy consumption, as they require large amounts of energy to perform manufacturing tasks. One area that has received relatively little interest, yet could harness great potential, is reducing energy consumption by planning machine activities between manufacturing operations, while the machine is not in use. The intuitive option --which is currently exploited in manufacturing-- is to leave the machine in a normal operating state in anticipation of the next manufacturing job. However, this is far from optimal due to the thermal deformation phenomenon, which usually require an energy-intensive warm-up cycle in order to bring all the components (e.g.

Matt Kirchner spent his career running manufacturing companies. Now, he shares his knowledge with listeners of Webinar Wednesday and the TechEd Podcast. In this article, Matt gives us 11 predictions for how artificial intelligence will totally transform manufacturing. To view this article as a video presentation, click here. You know, there's a handful of things that have differentiated world class manufacturers from average ones in the last several decades. The organizations that embraced change surged ahead, while the others were left behind.

Putting this idea into practice is anything but simple. Even two of the same pieces of equipment (i.e. two of the same model of drill in a manufacturing operation) can have unique patterns of data – and thus require unique calibration and diagnosis. Sensors to detect heat may not work well in colder seasons, or may fall off or lose their sensitivity when exposed to extreme conditions. Determining precisely what data should be used as the diagnostic data stream for a specific piece of equipment is also not easy, and may require expensive and time-consuming iteration. Even the most well-funded firms in industrial AI are still pivoting in order to find the right applications for machine learning in order to deliver consistent results for clients.

In a constantly changing world where virtual and physical environments are converging, ever-evolving digital technologies continue to disrupt the medical device industry. Besides, an uncertain environment, resulting from the ongoing COVID-19 outbreak has forced healthcare device manufacturers to adopt advanced technologies and tap into enormously growing medical device markets. At the same time, the increased connectivity between medical devices, physicians and consumers is necessitating OEMs to design and develop technologically advanced medical solutions. The digitization of manufacturing is, thus considered to be one of the core adjustments for healthcare manufacturers and OEMs to provide better patient care and remain competitive in the medical device markets. Advanced technologies like IoT, AI/ML, cloud, RPA, augmented and virtual reality hold a great potential to revolutionize the medical device manufacturing processes.

Since the next gen technologies became mainstream about three to four years ago, the IoT industry has gone through a significant journey. Needless to say, IoT has been the focal point of many transformation-related conversation in the asset heavy industries. In the beginning, most organizations were struggling with the question of how to interpret IoT in the context of their business. Since the possibilities were endless, so was the dilemma of where and how to begin. The industry ultimately chose to go with a case-based approach and the technology service providers played a key role in launching it.