I’ve been a leading and very vocal advocate of the practical use of machine learning and predictive analytics in eDiscovery, Information Governance, marketing and general business for over 10 years. Based on SciFi hits like 2001: A Space Odyssey, I realized early on that technologies like machine learning were a bit scary and therefore would require a long adoption cycle. I just did realize how long. Well, the adoption rate that I had always envisioned is starting to pickup up steam. According to a new market research report, “Predictive Analytics Market by Business Function, Applications (Risk Management, Operations Management, Sales Management, Supply Chain Management, Workforce Management), Organization Size, Deployment Model, Vertical, & by Region – Global Forecast to 2020”, published by MarketsandMarkets, the global market will grow from USD 2.74 Billion in 2015 to USD 9.20 Billion by 2020, at a Compound Annual Growth Rate (CAGR) of 27.4% during the forecast period.
According to the Deloitte Global Council on Competitiveness study, Advanced Technologies Initiative Manufacturing & Innovation, “Predictive data analytics is the most important advanced manufacturing technology needed for driving future competitiveness according to US manufacturing executives.” The study is a key element of the Deloitte Touche Tohmatsu Limited (Deloitte) and Council on Competitiveness multi-year Manufacturing Competitiveness Initiative and interviewed nearly three dozen Chief Technology Officers (CTOs), Chief Research Officers (CROs), Chief Executive Officers (CEOs), and company presidents from various manufacturing sectors, in addition to nearly a dozen directors of US national laboratories and research facilities.
Another key takeaway from the Deloitte study is artificial intelligence and machine learning is projected to be a $36B market this year increasing from $.9B in 2013. Combining analytics, artificial intelligence, and machine learning enabling contextual intelligence and insight from the shop floor to the top floor is beginning to revolutionize manufacturing. Applying cognitive computing to the complex challenges of multi-site manufacturing, multi-tier distribution, product configuration, distributed order management, and aftermarket service also has the potential to revolutionize manufacturing with greater accuracy, customer responsiveness, and speed.
Those are some big numbers and they wouldn’t be that big if there wasn’t a tremendous ROI for those enterprises that have invested in machine learning and predictive analytics. Therefore if machine learning and predictive analytics isn’t on your short list of technologies to embrace in 2016, you need to re-evaluate your list.
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