In a 2017 article entitled “Artificial intelligence isn’t the scary future. It’s the amazing present,” the Chicago Tribune quoted a statement that may have sounded insane back then. The article quoted Computer Science Professor David Gelernter as saying:
“The coming of computers with true human-like reasoning remains decades in the future, but when the moment of “artificial general intelligence” arrives, the pause will be brief.”
He went on to highlight the unimaginable possibilities that such intelligence will present. Widely acknowledged to be the key propellant of the fourth industrial revolution, artificial intelligence (AI) holds limitless potential for humanity as a whole.
However, does it have what it takes to push the manufacturing sector to the next level? Since the Great Recession of the 2000’s, the industry has undergone some major upheavals, which have stunted growth. On the bright side though, it has always been quick to adopt new technology. Since the 1960s, industrial robots and drones have been a part of its operations.
It is clearly ripe for a revolution and machine learning together with SharePoint might well hold the key to the next revolution. With the adoption of the technology, the sector is already reaping massive benefits and therefore has every chance of experiencing growth. Consider below some of the ways they can revolutionize the manufacturing sector.
How the Manufacturing Sector Can Benefit from Machine Learning
Using machine learning, manufacturers are gaining insights into the use of predictive analytics to drive sustainability. For example, they are deploying equipment models based on industrial equipment.
These include compressors, pumps, heat exchangers and other manufacturing assets. They then create process models, which assist in predicting upsets as well as trips. This is something that equipment models alone might not manage to predict.
There are template models in use based on Azure’s Symphony Industrial AI.
Another area that holds a lot of potential is predictive maintenance. Statistics predict that this alone can create between $1.2 to $2 trillion of value in manufacturing and supply chain management. For the manufacturing sector alone, it can yield $0.5 trillion to 0.7 trillion.
The reason for these high expectations is its high ability to process huge amounts of data. This includes documents, audio and video which make it possible to quickly identify anomalies so as to prevent equipment breakdowns.
Predictive maintenance models often work hand-in-hand with Internet of Things (IoT) sensors to relay data. Machine learning models then use this data in real-time for analysis and present their findings.
Reduction of Production Costs
According to research by Boston Consulting Group, the use of the technology can reduce conversion costs for producers by a whopping 20%. From this cost reduction, about 70% results directly from an increase in workforce productivity.
How is this possible? Machine learning makes it possible to draw actionable insights from available data. Hence, for companies that are keen on collecting data in central repositories, it becomes easy to base business decisions on such insights.
As such, they can use such information to develop and produce products innovatively tailored to suit the precise needs of specific customers. Additionally, it allows for delivery in much shorter lead times.
The use of product defect detection, as well as quality assurance algorithms based on AI and machine learning, is also another practical application. The technologies have inherent advantages in finding anomalies in product design and packaging using automated approaches.
The application of this approach will bring an end to having defective items leave a production facility. They hold a significant advantage over human inspection, facilitating improvements of up to 90% in some cases.
And these benefits are not limited to large manufacturing plants alone. Even small businesses can make use of cloud services from providers such as Microsoft to take advantage of them. Such models are easy to train as they simply require image references of good and defective products. The higher the level of exposure, the better they become based on this supervised learning approach.
Improving Energy Consumption and Throughput
Process and discrete manufacturers are also making use of automated AI approaches to deal with energy consumption. These are mostly large producers who make use of heavy equipment and large scale machinery.
They are always on the lookout for ways to cut down consumption while enhancing throughput as well as sustainability. Machine learning can automate complex tasks, allowing the machinery in question to run efficiently on autopilot. In turn, this facilitates what they refer to as lights-out manufacturing on single or multiple production shifts.
In addition to saving on consumption expenses, this also provides precise set points which offer optimum yields and consistency in the production process.
Securing and Scaling Operations
Another top concern for the industry’s stakeholders has to do with security. They are thus turning to automated models in search of solutions. Using such models reduces the costs associated with securing every threat surface while at the same time boosting performance.
For manufacturers, security covers a much wider space than for most other sectors. To be effective, it needs to cover all networks, cloud-based platforms, on-premise applications, operating systems and the entire production and supply chain network.
Reducing Labor Shortage / Increasing Employee Retention
Among the top bottlenecks hampering the growth of the manufacturing sector is a chronic labor shortage. Manufacturers, in fact, rank it as one of the top three issues that constrain growth the most in this industry.
A company known as Eightfold has devised an innovative way to tackle this challenge using machine learning. They have a platform based on artificial intelligence, Talent Intelligence Platform, which makes use of a blend of supervised and unsupervised learning approaches.
The trained algorithms match candidates’ strengths, experience and capabilities to available job openings. As a result, they enjoy more effective recruitment exercises for better outcomes than what they would previously get using a traditional approach.
The Role of Sharepoint in the Machine Learning-based Manufacturing Revolution
Based on the above practical points, machine learning evidently holds remarkable potential for the manufacturing industry. But can SharePoint have a role to play in this?
SharePoint is a professional content management system that allows metadata to be associated with content. In SharePoint environments with large structured lists and files, they may benefit from training models. Machine learning, thrives on big data that are properly classified and tagged. So the big question is ‘Can the two worlds ever meet?’ Will the shift in data storage render it useful in machine learning applications?
The answer is a resounding yes. Organizations in all sectors including manufacturing are moving to a proactive approach from a reactive one. Rather than making use of historical data, they rely on real-time information for diagnostic analyses. SharePoint is central to this approach as it offers reliable business intelligence (BI) based on its collaborative platforms. Through the use of machine learning and SharePoint, it is possible to create algorithms based on a centralized repository of relevant BI.
The marriage of the two can also come in handy in text analytics, supporting sentiment analysis, tone evaluation and even keyword extraction. A third benefit has to do with evaluating the supply chain and customer journey. Users can easily assess the impact of actions like marketing, pricing and distribution to enhance performance.
SharePoint online already has OCR Image Text Extraction abilities to automatically collect text from images. We should expect the ability to soon auto classify images the same way Google, eBay, and some phone apps like auto classify images you upload. This method of image tagging could help auto identify manufacturing parts if trained correctly from previously tagged images (through SharePoint metadata).
Overall, machine learning algorithms can in play a key role of data tagging and processing while SharePoint provides the required repository of that information.
The Future is Now
Statistics estimate that by 2021, 20% of the world’s leading manufacturers will rely on some form of artificial intelligence. With the marriage between machine learning and SharePoint, that future prospect is already unraveling right before our eyes and can only keep getting better.
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