AI is big business-US venture capital investment in the sector reached $6.6 billion in the first three quarters of 2018, compared to $3.9 billion in the same period the year before.
Instrumental is attacking the 20 to 35 cents of every $1 spent in manufacturing that is wasted. That waste comes from literal scrap at the various factories in the process, returns, mistakes, extra experimentation, travel, and engineering time that wasn't used efficiently..
We still have a long way to go before we can truly enjoy the countless benefits AI has to offer. Not only that, but for AI and ML (machine learning) to gain real, feasible meaning in manufacturing and beyond, there are a few steps the industry needs to take.
Among our early initiatives is a plan to create a self-driving forklift for the production floor, and to develop AI technology-based robots to improve the outgoing quality of products, enhance turnaround time and reduce costs.
Artificial intelligence is an essential factor in the industrial sector when used wisely. Its adoption has brought many advantages to the manufacturing industry. It has made work easier. Let us embrace technology and optimize it in our day to day activities.
The manufacturing industry is facing a hiring crisis. According to Deloitte, by 2025, the manufacturing skills gap will result in a need for 3.4 million skilled workers, and 2 million of those roles will go unfulfilled.
IHS Markit recently published the "Manufacturing Technology Vertical Intelligence Service", including a manufacturer survey for the transformative technologies adoption in the semiconductor, display and electronics manufacturing sectors.
World Economic Forum: A.I. and robotics will create almost 60 million more jobs than they destroy by 2022
Saheli Roy Choudhury for CNBC: The outlook for job creation is more positive today because companies better understand what kind of opportunities are available to them due to developments in technology, according to WEF.
The Industrial Internet of Things (IIOT) is bringing us the factories of the future with the implementation of AI to help optimize and scale operations through predictive maintenance, improved safety amongst others.
Application of advanced AI (defined as deep learning models) in manufacturing and supply chain have the potential to create $1.2-2 Trillion in annual economic value.
VTT's proof of concept demo showed that a range of sensors or smart identifications can be added to 3D printed metal parts during manufacture, in order to track the performance and condition of machines or devices, or verify the authenticity of the parts.
Steel mills are rich in data, and learning algorithms only get better as they learn. Steel mill owners stand to improve their quality and margins dramatically from AI investment. AI can also reveal unexpected business opportunities as well as operational efficiencies.
Will Knight for MIT Technology Review: Perceiving dynamic actions could be a huge advance in how software makes sense of the world.
Modern systems are paving the way to allow our customers to make products people want in the way they want them, instead of making products and spending our time trying to make people want them.
Many businesses are already using continuous monitoring technologies - like Internet of Things (IoT) connected devices - which is a good start; but the key lies in not just simply monitoring the output of various data (which is how many companies use it today), but by taking the next step and employing advanced algorithms and machine learning to take action from real-time insights and anticipate future outcomes.
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A reactive manufacturing cycle of building, inspecting, and correcting is no longer sufficient to meet rising customer wants and demands. To break this ineffective cycle, organizations must have a robust data-driven solution that combines a proactive focus on quality with integrated analytics and automation capabilities to turn quality control from a liability into an asset.