Tag: process control

Back-to-Back Success for Neewee’s Digital Twin Technology.

Posted on by Neewee

Predicting manufacturing defects and malfunctions in the process.

In quick succession to our story of how Neewee got listed as a Digital Twin Operator in an influential business and technology report, here is yet another. We are elated by the newest feather in our metaphorical hat!

The “Smart Factory Trend Report”, distributed by the Japanese TECHBLITZ, lists eight categories of digitalization services that will shape the future of manufacturing. We are proud to share that Neewee features as a representative in the ‘Design & Simulation’ category for our Digital Twin technology.

An article published online as a summary of the report points out that AI-powered design data visualization delivers accuracy in simulation. It also improves the iterative process efficiency. All crucial for product development.

Read the overview of the full report published here. 8 CATEGORIES OF SMART FACTORIES THAT ARE THE FUTURE OF MANUFACTURING – TECHBLITZ

Transformational Impact Through Connectivity

The industrial internet of things (IIoT) is the ecosystem of devices, people, and processes that are connected and interoperable. To briefly define the Digital Twin, it is a virtual representation of all those physical objects and processes in the entire manufacturing value chain. IIoT enables the data collection from various sensors, and Neewee’s AI app helps process it in real-time. The digital twin creates simulations of every step in the production process, unlocking valuable insights that help improve decision-making. The digital twin technology optimizes process efficiency in different manufacturing areas—product design and production to marketing— impacting business outcomes significantly. Thus, the potential for increased productivity using digital twins is immense as it transforms the entire industrial operations into intelligent manufacturing.

Neewee’s Digital Twin Technology for Predictive Analytics and Monitoring Production Performance.

Manufacturing is a cost-intensive and time-sensitive process. Predictive analytics in manufacturing can help companies forecast process variations, reduce waste, and control costs. Predictive analytics can also help manufacturers improve quality and provide more personalized customer service.

Dynamic, data-powered simulation of the physical world with the digital twin technology is a boon for predictive analytics. Getting an organization-wide view of shop-floor realities is key to effective decision-making. Statistical and analytical models help production managers predict and control future events in the process workflow. Predictive analytics uncovers patterns in production data to detect dysfunctional processes, which can cause anomalies in the output. Manufacturers can mitigate such risks if they receive timely actionable insights.

The digital twin also helps forecast deviations or disturbances in the manufacturing process before they occur. For example, if it predicts an issue with the equipment, an alert gets sent out to the production team. That helps them fix the equipment before the issue escalates and becomes an actual problem. The production team gets saved from unexpected downtime, which could have caused delays in batch-cycle time. Thus, a digital twin operator can help manufacturers save money by preventing any small or big interruption of the production line. Aside from monitoring production performance and catalyzing production flexibility, predictive analytics with the digital twin supports maintenance tasks too.

By aligning Neewee’s Digital Twin to the value you expect from your manufacturing processes, you can optimize production efficiency and enhance quality with a targeted approach.

Gartner’s survey revealed Digital Twins are entering mainstream use. Benoit Lheureux, research vice president at Gartner said, “Over two-thirds of companies that have implemented IoT will have deployed at least one digital twin in production.” This rapid growth in adoption is because digital twins are delivering business value and have become part of digitalization strategies. [1]

On the other hand, Thomas Kaiser, SAP Senior Vice President of IoT said, “Digital twins are becoming a business imperative, covering the entire lifecycle of an asset or process, and forming the foundation for connected products and services. Companies that fail to respond will be left behind.” [2]

You do not want to be left behind. Act now! Book a Demo.

For those who missed the detailed enumeration of “What Is a Digital Twin?” and the value it brings to manufacturing, read our previous post here Forrester Lists Neewee Amongst Digital Twin Service Providers in 2022 Report – Neewee

References:

[1] STAMFORD, Conn. (2019, February 9). Gartner Survey Reveals Digital Twins Are Entering Mainstream Use. Gartner. https://www.gartner.com/en/newsroom/press-releases/2019-02-20-gartner-survey-reveals-digital-twins-are-entering-mai

[2] Marr, B. (2017, March 6). What Is Digital Twin Technology – And Why Is It So Important? Forbes. https://www.forbes.com/sites/bernardmarr/2017/03/06/what-is-digital-twin-technology-and-why-is-it-so-important/?sh=1b2ef4c52e2a

Reinforcement Learning, the Effective Solution for Process Control Optimization.

Posted on by Neewee

Gradual and deliberate ‘change for the better’ eventually improves productivity and facilitates efficient processes.

Kaizen, the Sino-Japanese business philosophy, urges you to strive for continuous improvement. Applying the principles of Kaizen to manufacturing focuses on improving the four main elements of manufacturing — product, process, people, and environment. Kiichiro Toyoda, the founder of Toyota Motor Corporation, championed the philosophy by saying, “The ideal condition for making things gets created when machines, facilities, and people work together to add value without generating any waste.” [1] 

Harnessing the power of data analytics thus becomes crucial to a company’s continual process improvement strategy. Manufacturing process control optimization drives you toward achieving several business goals, from improved production efficiency to reduced production costs and increased customer satisfaction. However, integrated data analytics by solely relying on limited human capabilities is not only a terrible challenge but can also be misleading and flawed. Measurement and monitoring of Key Performance Indicators (KPIs) are essential for process control, but with limited analysis capabilities, it gets difficult to control wastage completely.  

The manufacturing sector has thus undergone a radical transformation over the last few years with the advent of Artificial Intelligence (AI). Machine learning is a subfield of AI that enables machines to become intelligent with precise data analytics. It makes manufacturing process optimization possible by utilizing a simulation-based dynamic programming method called Reinforcement Learning (RL). 

Reinforcement learning is an unsupervised machine learning algorithm that has revolutionized the continuous manufacturing scenario. It enables an autonomous system that learns from interactions with the industrial environment in real-time, trial and error, and does not rely on preprogrammed training. RL catalyzes SMART manufacturing by learning from accomplished tasks, incorporating changes in behavior based on feedback about the results of previous actions.  

The biggest challenge for manufacturing optimization lies in collecting and cleaning vast amounts of data from different sensors every day while maintaining consistent uptime of production facilities. In many manufacturing systems, continuous state variables and discrete actions get detected. Therefore, process optimization becomes a highly complex task. Breaking down siloed data is essential and controllable parameters need monitoring and adjustment as they directly affect the production throughput.  

Though these nonlinear dynamics can make process optimization challenging, it is not that difficult for RL algorithms to converge toward optimal control policies. Since experimentation on the shop floor could get expensive, the best action path gets derived through simulation. A digital twin of the physical production system gets created by domain experts who understand how the system works. The algorithm then learns the complex relations between the various parameters besides their effect on production and output.  The feedback loop helps the goal-oriented algorithm learn and make sequential decisions while building a framework for the ideal process flow to generate desired results. In operations, RL-based approaches have delivered consistently impressive results.  

Manufacturing is a complex system, where failure at balancing the customer’s demands with production costs, inventory, batch-cycle time, and quality can result in catastrophic outcomes. Manufacturers need to take a data-driven approach while developing robust, adaptive, and scalable solutions. Reinforcement Learning is an effective tool for predicting and solving specific problems in manufacturing processes. In fact, today 40% of all potential value created by data analytics comes from AI and ML techniques. In totality, machine learning can account for about $3.5 trillion to $5.8 trillion in the annual value — as per a McKinsey report. [2] 

To summarize, RL enables data-driven and flexible processes in production domains. It has, therefore, already been deployed in production systems, proving to be an effective solution for process control optimization.