• AI has the potential to create $1.4T to $2.6T of value in marketing and sales across the world’s businesses, and $1.2T to $2T in supply-chain management and manufacturing.
  • By 2021, 20% of leading manufacturers will rely on embedded intelligence, using AI, IoT, and blockchain applications to automate processes and increase execution times by up to 25% according to IDC.
  • Machine learning improves product quality up to 35% in discrete manufacturing industries, according to Deloitte.
  • 50% of companies that embrace AI over the next five to seven years have the potential to double their cash flow with manufacturing leading all industries due to its heavy reliance on data according to McKinsey.
  • By 2020, 60% of leading manufacturers will depend on digital platforms to support as much as 30% of their overall revenue.
  • 48% of Japanese manufacturers are seeing greater opportunities to integrate machine learning and digital manufacturing techniques into their operations than initially believed according to McKinsey’s landmark study, Digital Manufacturing – escaping pilot purgatory.

Bottom Line: The leading growth strategy for manufacturers in 2019 is improving shop floor productivity by investing in machine learning platforms that deliver the insights needed to improve product quality and production yields.

Using machine learning to streamline every phase of production, starting with inbound supplier quality through manufacturing scheduling to fulfillment is now a priority in manufacturing. According to a recent survey by Deloitte, machine learning is reducing unplanned machinery downtime between 15 – 30%, increasing production throughput by 20%, reducing maintenance costs 30% and delivering up to a 35% increase in quality.

The following are ten ways machines learning is revolutionizing manufacturing in 2019:


  • AI has the potential to create $1.4T to $2.6T of value in marketing and sales across the world’s businesses, and $1.2T to $2 in supply-chain management and manufacturing. McKinsey predicts AI-based predictive maintenance has the potential to deliver between $.5T to $.7T value to manufacturers. McKinsey cites AI’s ability to process massive amounts of data, including audio and video, means it can quickly identify anomalies to prevent breakdowns. Machine learning can determine whether a specific sound is an aircraft engine operating correctly under quality tests or a machine on an assembly line about to fail. Source: McKinsey/Harvard Business Review. Most of AI’s business uses will be in two areas by Michael Chui, Nicolaus Henke, and Mehdi Miremadi. March 2019
  • Manufacturers are gaining new insights into how they can become more sustainable using machine learning and predictive analytics that scale on cloud platforms. Process manufacturers are using Azure’s Symphony Industrial AI to deploy equipment models from a template library that includes heat exchangers, pumps, compressors, and other assets process manufacturers rely on. Symphony AI’s Process 360 AI helps users create predictive models of their processes. A process is defined at the high level as the items (such as chemicals, fuels, metals, other intermediate and finished products) in production through the equipment. Process template examples include an ammonia process, an ethylene process, an LNG process, and a polypropylene process. Process models help predict process upsets and trips — which equipment models alone may not be able to predict. Source: Microsoft Azure blog, Implement predictive analytics for manufacturing with Symphony Industrial AI,
  • Boston Consulting Group (BCG) found that manufacturers’ use of AI can reduce producer’s conversion costs by up to 20% with up to 70% of the cost reduction resulting from higher workforce productivity. BCG found that producers will be able to generate additional sales by using AI to develop and produce innovative products tailored to specific customers and to deliver them in a much shorter lead-time. The following graphic illustrates how AI will bring increased flexibility and scale to production processes based on BCG’s analysis. Source: Boston Consulting Group, AI in the Factory of the Future, April 18, 2018.