Manufacturing analytics is a new class of software that brings predictive analytics, big data, industrial internet of things, and mobile-first design to manufacturing companies.
we were motivated by three observations we had on the state of the industry:
1. Most manufacturers lacked visibility into their plant floor.
2. Most ‘traditional’ manufacturing analytics software was way too expensive and complicated to justify the investment for most companies.
3. No one had developed a SaaS Manufacturing analytics product that was affordable or easy to use.
Machine monitoring involves installing web-enabled machine monitoring systems on every machine, throughout the factory floor and on every line. With data being collected continually, on every machine and from every operator in real-time, operators and managers are in a better position to harness that intelligence and make decisions that will improve efficiency across the board. Machines can also effectively self-monitor and flag operators when there is an impending problem, like a component failure.
With wireless monitors connected to easy to use software, the data is collected, analyzed and translated in terms that provide fast and simple results that can be actioned on an ongoing basis. Some monitors are designed to connect easily with all machine types, including Precision CNC, SWISS, Stamping, andDie or Mold.
The data collected includes:
The condition of the machine (including status and faults);
Current work order status;
Downtime data (whether automated or as indicated by an operator)
Boasting the essential tools to reduce costs and generate a greater Return on Investment (ROI), Predictive Maintenance (PdM) has become one of the most effective solutions for asset-heavy organisations to deploy.
The advancement in technologies, such as maintenance management systems and the Internet of Things (IoT), has helped propel businesses into implementing maintenance plans to ensure increased reliability and availability of equipment. Whether that be reactive, preventive (or preventative), or predictive maintenance. The latter being a highly sought-after maintenance program with a predicted market share of 6.3 billion by 2022.
Through condition-based monitoring and sophisticated machine learning, predictive maintenance provides a variety of benefits for all industries and organisations. From reducing unplanned downtime to extending the life expectancy of mission-critical assets. In this article, we’ll cover everything there is to know about predictive maintenance.
Edge analytics refers to an approach to data collection and analysis in which an automated analytical computation is performed on data at a sensor, network switch or another device instead of sending the data back to a centralized data store. What this means is that data collection, processing and analysis is performed on site at the edge of a network in real time.
You might have read dozens of similar articles speculating over the necessity of any new technique, like “Does your project need Blockchain? No!” Is Edge Analytics yet another one of such gimmicky terms?
The truth is, it is really a game changer. At present, organizations operate millions of sensors as they stream endless data from manufacturing machines, pipelines and all kinds of remote devices. This results in accumulation of unmanageable data, 73% of which will never be used.
Overall, edge analytics offers the following benefits:
Data analytics and Machine learning
Data analytics (DA) is the process of examining data sets in order to find trends and draw conclusions about the information they contain. Increasingly data analytics is used with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions. It is also used scientists and researchers to verify or disprove scientific models, theories and hypotheses.
Machine learning is a branch of artificial intelligence that enables systems to find new and better solutions automatically by learning from mistakes and experiences. The more data and experience an algorithm has access to, the better it becomes in the future.
Machine learning systems can largely be divided into two subsets: guided and unguided. Guided systems are supplied with data sets and solutions by humans in the first instance. They are taught which patterns to look for initially and will then get better at identifying those patterns going forward.
The machines haven’t taken over. Not yet at least. However, they are seeping their way into our lives, affecting how we live, work and entertain ourselves. From voice-powered personal assistants like Siri and Alexa, to more underlying and fundamental technologies such as behavioral algorithms, suggestive searches and autonomously-powered self-driving vehicles boasting powerful predictive capabilities, there are several examples and applications of artificial intellgience in use today.
Artificial intelligence is based on the principle that human intelligence can be defined in a way that a machine can easily mimic it and execute tasks, from the most simple to those that are even more complex. The goals of artificial intelligence include mimicking human cognitive activity. Researchers and developers in the field are making surprisingly rapid strides in mimicking activities such as learning, reasoning, and perception, to the extent that these can be concretely defined. Some believe that innovators may soon be able to develop systems that exceed the capacity of humans to learn or reason out any subject. But others remain skeptical because all cognitive activity is laced with value judgements that are subject to human experience.