Big Data & Analytics ETA Featured Savi supply chain disruption

Finally, The Big Data Insights You Need To Make Better Decisions

Obstacles To Visibility Can Be Overcome

“Transparency” and “interoperability” are at the top of the global supply chain industry wish list, as they have been for decades. A 2017 survey by Business Process Innovation Network, found that importers, exporters, carriers, terminal operators, vessel owners and other stakeholders suffer from poor visibility and predictability around shipments. And, due to that lack of partner synchronization and insufficient big data insights, containers sit in ports and incur demurrage, importers pad their inventory, customers experience stock-outs and all through the ecosystem, they are losing money.

There is a disconnect, however, between what the global supply industry needs and wants and what it is able and willing to do to make improvements. Supply chain stakeholders acknowledge the big gaps in collaboration and data integration, with 82% of survey participants in agreement that the level of connectedness and visibility needs to be improved.

In an effort to achieve “connectedness and visibility” there have been calls for the sharing of information between all parties within the supply chain. But, we live in a world of competitive rates, lane optimization, and constant mergers and acquisitions. It is unlikely that competitive carriers in the logistics industry are actually going to openly release the insights of their operational information. Still, transparency within supply chains is possible and solutions exist that allow partners to collaborate to achieve better supply chain orchestration without exposing commercially sensitive information.

Good News: Objective Brokers Provide A Path Forward
Imagine this – you are able to see an accurate estimated time of arrival information on each leg of a shipment well before it occurs – and you don’t have to rely on carriers, data trading partners, cumbersome information feed setup, etc. An objective and neutral third-party broker with no stake in the transaction could provide big data visibility solutions that would give all parties what they’ve been clamoring for – the data they need to make better decisions.

This would allow any participant in the supply chain to combine their own historical data with available current information feeds, accumulating, then applying artificial intelligence (AI) to develop an Estimated Time of Arrival (ETA) that is not strictly bound to the data reported by any particular carrier. In fact, multiple sources of data could be harvested, treated with good data cleansing and stitching practices, normalized, and then associated with AI algorithms that, through machine intelligence, improve accuracy over time. With a solution designed to achieve a high level of accuracy, the dependency on the cumbersome “integration” from others becomes unnecessary. And the value of an accurate ETA can be realized in multiple operational areas in the global supply chain – particularly when crossing an ocean is involved.

Machine Learning-Based ETAs Remove Human Error
Today, supply chain participants rely on the location and ETA projections from the carrier who is hauling the freight. For example, if you are shipping a container of garden hoses from China to the US, you are likely relying on your vessel operator to report the location and estimated time of arrival of your freight. You will get updates as the carrier sees fit. You will be given estimations that are likely coming from a human, and which have not been updated from their initial reporting or ever measured for accuracy.

Conversely, if you are utilizing a machine-learning ETA, you are relying on data, not an individual. The ETA is updated to improve accuracy over the course of the shipment and is continually measured, verified and enhanced at the AI level to ensure constant self-learning and improvement.

Why An Independent Broker Solution Makes Sense
Because of security and competition concerns, the concept of sharing data is a no-go. That doesn’t mean a fair and honest system is impossible though. The next best thing to total transparency can be found in the anonymization and pattern recognition capabilities of modern data science. The latest analytics software can analyze historical data not only to make predictions but to validate them against operational realities and, along with real-time sensor data, improve accuracy.

Right now, few companies are in the position to execute with both speed and accuracy. A third party would need a massively scalable platform that can handle the ingestion of tons of data, including pulling in neutral data off of sensors. It would have to be able to look at patterns, assess a carrier’s performance in an unbiased way and see areas that are ripe for optimization.

Precise ETA Benefits
So, what is the benefit of knowing when your shipment really will arrive at port? Why is it good to know when it docks at interim transport points? Think back to those garden hoses. If you know what time the ship will arrive at the port, you can more accurately schedule pickups and delivery for that last mile. Better yet, you can get more efficient in your decisions about land transportation, because you now have enough information to accurately gauge the docking schedule, and thus the relevant costs. In short, accurate machine-learning ETAs support real-time decision making about the routing and re-routing needed to support just-in-time inventory.

These types of predictive analytics enable the seamless connections that the supply chain industry needs to evolve. And it satisfies the need for transparency without relying on individuals to send incomplete or potentially suspect data. Without analytics, there is no action to take or outcome to predict; just reams and reams of data. Analytics complete the picture by providing the missing piece, insights.

With a real-time carrier neutral visibility and analytics platform in place and operating, supply chain managers finally can have the big data insights they need to make better decisions. Those decisions will allow them to be more agile, reduce logistics costs and improve on-time delivery to customers. To be competitive, this is a necessary tool for supply chains.

Source: BPI Network, “Competitive Gain in the Ocean Supply Chain: Innovation That’s Driving Maritime Operational Transformation”


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