LITMUS | June 03, 2021
Today, Litmus, the Intelligent Edge Computing company, announced a partnership with Cantier Systems to integrate Cantier's MES 4.0 with the Litmus Edge platform to assist manufacturers in digitally transforming their shop floor. The combined solution will serve South Asia and India. It will give users deeper insights to rapidly deploy traceability, quality inspection, OEE improvement, predictive equipment maintenance, manufacturing intelligence, statistical process controls, and other IIoT use cases.
Cantier MES 4.0 is configurable and scalable manufacturing software that helps automate shop floor activities with real-time quality information, OEE, planned vs. actual, finished goods, inventory, and other critical manufacturing KPIs. Litmus Edge is a modern edge platform that collects data from any industrial asset, offers pre-built analytics, provides the ability to build and run custom applications, and integrates data with any cloud or enterprise system. Cantier will resell and implement Litmus Edge as a tightly integrated solution for MES and IIoT enablement.
Cantier MES 4.0 integrates seamlessly with Litmus Edge to allow customers to deploy the building blocks necessary for digital transformation quickly. Cantier will also provide implementation to assist customers in deploying the joint solution. The two companies are already deploying the integrated solution to a number one tier 1 automotive OEM supplier in India.
ABOUT CANTIER SYSTEMS
Cantier MES 4.0 is the Next Generation Manufacturing Execution System (MES) that integrates seamlessly with legacy and modern manufacturing environments, combining Execution, IIOT, and Manufacturing Intelligence capabilities into a single platform for real-time operations visibility, predictive decision support, and autonomous actions. Cantier has continuously worked with various customers in the semiconductor, electronics, automotive, aerospace, metal precision and fabrication, food, sugar, and ethanol industries. You can rely on our experience to craft Industry-specific and Configurable MES that achieves faster deployment to realize rapid ROI.
Litmus enables out-of-the-box data collection, analytics, and management with an Intelligent Edge Computing Platform for IIoT. Litmus provides the solution to transform critical edge data into actionable intelligence that can power predictive maintenance, machine learning, and AI.
frost | February 23, 2021
According to Frost & Sullivan's recent analysis, Digital Industrial and Energy Guidebook highlight important market issues. The issues are several me-too solutions, complex market messaging, quick-hit benefits realization, and value-creation uncertainties which restrain organizations from adopting a wide-scale digital solution. The study helps them spot these issues by evaluating solution providers' ecosystems. The guidebook offers a digital solution selection tool. It identifies the top 50 digital best practitioners in various industries. They are characterized in three categories such as industry-specific best practice, enterprise-specific best practice, and function-specific best practice.
As driven by operational excellence, organizations in industries are likely to adopt digital solutions. As a result, the digital solutions market in industrial and energy expects to reach $543.66 billion by 2025 from $449.12 billion in 2020. It will register growth at a compound annual growth rate of 3.9%. On the flip side, the market faced adverse impacts of the COVID-19 pandemic in 2020. With this, it might expect to recover from Q1 2021, with COVID-19 subsidies in Q4 2020, and outshine by 2024.
Rohit Karthikeyan, Industrial Industry Analyst at Frost & Sullivan mentioned that automation is a critical part of the fourth industrial revolution. It will drive the shift to Industry 5.0 as well. Additionally, the established industrial automation companies have an appetite for digitalization. It will feature intelligent systems with context-aware, sensory, and analytics capabilities. This will offer them a competitive edge at a greater scale.
Also, a strong attempt of organizations will further thrust the demand for digital solutions by cutting operational costs, generating higher revenues, and enhance competitive advantage. It will also present lucrative growth prospects for solution providers. In this way, market participants will enjoy components such as
1. Leverage installed information base to understand end users' processes and offer customized digital solutions.
2. Develop extensive plans that cover management capabilities, data access, scalability, flexibility, and innovation roadmaps.
3. Develop a robust digital strategy.
MTC Robotics Engineers | March 08, 2021
Automation experts at The Manufacturing Technology Centre (MTC) were in the news for making the latest through. They made robotics that can handle objects without CAD data or designed grasps to enable faster deployment of robotic solutions.
Mark Robson, Senior Research Engineer, Robotics, MTC, on the latest breakthrough, mentioned that the project demonstrates MTC’s determination to adopt academic developments with the potential to transform robotics in manufacturing. The technique aims to pick new objects and allow MTC to test with customer parts, and advise on implementation strategies quickly.
About the Project
The MTC has demonstrated its ‘state of the art’ technique for bin picking to reduce costs and time. Bin picking is being stated as a common handling task in industries. It is a task in which a single object needs to be separated from an unstructured bulk input. The robotic technique uses a trained model to find the best position to place a vacuum cup. It can also be trained with simulated data to reduce the need for labor-intensive manual data collection and labeling.
With traditional methods, factories use CAD data to identify individual parts accurately and test a range of pre-engineered grasps for feasibility. On this, Dr. Alejandra Matamoros, Technology Manager, MTC, added that MTC Robotics Engineers have proved that design and development can be dematerialized to create cost-efficient intelligent solutions. This will happen to automate operations where predetermined programming is less viable.
The project has been tested by MTC, keeping a range of objects which includes fruit, metal components, and cosmetics containers. The technique performed well with 92% and 94% successful picking, respectively, after being trained with manually labeled data and simulated data.
In the end, MTC ended by saying that the performance of the model trained on purely simulated data showed up as an excellent solution to reduce the burden of data gathering for specific use cases.