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Will Big Data Help Us Predict the Future?

How confident can we be in experts’ predictions and would they be able to make better predictions if they had more data?

By Patrick Davison

It’s spring and the start of the 2018 Major League Baseball season. With the start of the new season comes the pundits’ predictions of who will win the World Series in October. Even though the season hasn’t begun yet, the so-called experts are predicting the Dodgers as five-to-one favorites to win it all in 2018, followed by the Indians and Astros at six-to-one. But how confident can we be in their predictions? How confident are the experts in their predictions? And what information did they use to make their predictions, and would they be able to make better predictions with more data?

More importantly, what does this have to do with Material Handling & Logistics U.S. Roadmap 2.0?

According to the Roadmap, “Big Data,” is predicted to shape everything from final-mile routing to long-term infrastructure planning to freight forwarding to ultimate decision-making to network optimization, all of which is anticipated to improve forecasting. GE’s Jeff Immelt was reported to postulate, “the marriage of Big Data analysis and Industrial engineering promised a nearly unimaginable range of improvements.”

The promise of Big Data will almost certainly help transform our industry and help bring about new technologies, such as autonomous vehicles. But how much of those “improvements” will actually help to predict future activities? Will more data computation capabilities help us make better forecasts, or simply provide us with more confidence in the forecasts we would have otherwise made with existing data?

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According to the recently released 2018 MHI Annual Industry Report, “Overcoming Barriers to NextGen Supply Chain Adoption,” eight out of ten survey respondents believe these supply chains will be the predominant model within just five years. However, the report found that the adoption of some of these technologies was slower than originally reported when MHI started the annual report in 2014. The report cites three top barriers to adoption of these technologies: 1. Making the business case for NextGen supply chain investments. 2. Tackling the supply chain skills gap and workforce shortage. 3. Building trust and security in digital, always-on supply chains. This issue of MHI Solutions focuses on the adoption of these digital solutions, from best practices in robotics and artificial intelligence to blockchain and innovations in last mile delivery.

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