“Now we can leverage the Internet of Things to determine a more precise ETA, and then manage dynamically. That’s what we’ve done here at Brooks and so far it’s been really successful. The predictive accuracy is really good. It’s a relatively low cost and easy-to-implement solution.”
– Chase Mueller, Manager, Supply Chain Visibility, Brooks Running, P2L Customer
Retailers are in the midst of an unprecedented period of change, precipitated by dynamic consumer buying behavior, competition from next day delivery competitors, increasing exposure to supplier and other risks and the related delivery expectations and price pressures that these challenges bring.
With the advent of omni-channel and the always-on economy, consumers now order goods through a host of channels, expect next day or two-day delivery options, and they are price sensitive. They also want the ability to adjust an order that has been placed any time up until delivery.
Ten years ago, this operating model was untenable for the vast majority of retailers, but it is now table stakes, driven largely by Amazon and innovative fast fashion retailers.
By applying advanced technologies and business processes, and vertically integrating, Amazon is pushing the pace of play. Increasingly, the company is introducing more private label brands, and using its supply chain as a weapon to grow market share.
Lacking the significant supply chain innovation budget of an Amazon, and already behind in the race, most retailers are looking for new ways to compete. Retailers have determined that their supply chains must be more agile, real-time and predictive.
Paralleling and driving these market changes have been technology advances. Two of them—real-time big data from the Internet of Things (IoT) and machine learning algorithms—are enabling innovative retailers and suppliers to successfully adapt to these drastic market changes.
Predict the Future
TransVoyant’s Precise Predictive Logistics (P2L) solution boasts one of the largest repositories of real-time big data in the world, collecting and processing over one trillion events each day. The solution applies advanced machine learning algorithms to these massive big data sets to produce real-time and predictive insights that enable retailers to compete in the new economy.
P2L is enabling retailers and retail suppliers to do the following:
Gain real-time visibility of inventory, both static and in-motion, from n-tier supplier to customer door, across all modes of transportation
Accurately predict the ETAs of shipments and orders, factoring the impact of external events and dynamically evolving conditions
Confidently allocate inventory in-transit, in response to dynamic demand
Reduce buffer stock levels, unlocking value that can be redeployed to other strategic priorities
Reduce expedited freight by optimizing mode selection and simultaneously improving on-time delivery performance
Predict and mitigate the impact of supply chain disruptions from weather, port congestion, traffic, natural disasters, supplier issues, labor strikes and other external events
Reduce supply chain risks by having real-time visibility, and the ability to quickly shift to other modes, routes, etc. when alerted to predicted disruptions
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