“Too much data” drives adoption, expectations of predictive analytics
Predictive analytics is not a new technology. In fact, it’s older than the United States. In 1689, Lloyd’s of London began using predictive analytics to underwrite sea voyages.
Since then, predictive analytics has been launched as access to information has become better and faster, with computers providing a major help.
In a modern sense, “they’re similar to RFID tags. They’ve been around for quite some time,” said Rudolf Leuschner, associate professor of supply chain management at Rutgers Business School. “When we take a look at the real algorithms behind predictive analytics, there really isn’t much that is new.”
What has changed is the amount of data and what we do with it.
“We’ve gotten to the point where people aren’t screaming that we don’t have enough data. They’re screaming how much we have too much data,” Leuschner said. Software and technology companies “are doing a better job of collecting this information and doing something with it,” he said.
A ubiquitous wait for ETAs in real time
In “Innovation Driven Resilience,” MHI’s 2021 annual industry report, MHI and Deloitte surveyed more than 1,000 supply chain professionals around the world about investments in supply chain innovation. They found that 31% of respondents say predictive analytics is already being used and 48% say it will be in the next five years.
The success of predictive analytics has already changed expectations.
“We’ve gone from a near zero expectation of things like ETA and what is the likelihood that a shipment will show up within the original timeframe to a reduced expectation,” said Ken Wood, Executive Vice President of product management at Descartes. .
This expectation is pretty pervasive now – for shippers, freight forwarders and receivers, whether that receiver is a business or a consumer.
“Everyone expects to have increasingly narrow guidelines when they can expect the goods to arrive,” said Wood.
Planning, forecasting among the main uses of predictive analytics
Top Uses of Predictive Analytics Over the Next One to Two Years
Technology has also created near real-time visibility in the most advanced supply chains.
“Now that we’re on the way to, you can see where the shipment is relative to your location for the past 30 minutes,” Wood said.
Widespread adoption of the cloud has helped predictive analytics, he added, as they provide easier access to machine learning algorithms and big data processing.
“People aren’t shouting that we don’t have enough data. They are shouting that we have too much data.”
Associate Professor of Supply Chain Management at Rutgers Business School
The more efficient the technologies are at sorting the data, the better the predictive analytics will be, especially when it comes to managing uncertainty, said Wood.
“Predictive analytics works best when it makes predictions under stable circumstances,” he said. “But when the extraordinary happens, and some of these things are driven by external factors, be it the weather, union actions, or geopolitical struggles, [the technology] is not as good at making predictions in these kinds of scenarios. “
Cost barriers of predictive analytics
Predictive analytics remains an expensive technology to implement. While it can reduce costs once up and running, not all businesses can make these kinds of capital expenditures.
According to the MHI report, more than half of companies spend $ 5-10 million on predictive analytics. Three percent spend more than $ 100 million.
Businesses spend a lot on predictive analytics
% of respondents spending within the range indicated for predictive analytics
For some warehouse operators, that kind of expense doesn’t make sense, Leuschner said. Adding predictive analytics to a warehouse can make workers slightly more efficient and a little less error prone, but unless the company operates its warehouses on a large scale, the savings will not be realized at these prices. .
That could change if the systems can be installed as an app on a smartphone without much maintenance, instead of requiring investments in hardware, software, and consultants to get everything working. according to Leuschner.
“If a business can load an app on my phone and build it by use, it’s a lot easier to implement,” Leuschner said.
This story first appeared in our weekly newsletter, Supply Chain Dive: Operations. Register here.