Visibility blind spots increase widening gaps in supply chain operations, which usually lead to supply chain disruptions, including delays and inconsistent delivery processes, or can even impact customers’ services. This is why real-time visibility is becoming a significant challenge for transportation and logistics operations, a necessity to undertake better and accurate corrective actions.
The main difficulty lies in being able to remediate unplanned disruptions quickly before the situation gets worse. In light of these ever-growing supply chain challenges, the emergence of advanced technologies in this picture seems to address this growing need for accurate and predictive information.
The Internet of Things (IoT), Artificial Intelligence (AI), and Machine learning seem to be top of minds for supply chain managers, but how reliable is that frenzy of innovation?
Predicted Estimated Times of Arrival (ETAs) introduction appears to be a real game-changer to gain a more proactive approach thanks to real-time visibility, but how accurate and credible is the information collected?
This article discusses the actual effectiveness of predictive ETA in supply chain and logistics operations to optimize supply chain performance.
Predictive ETA, effective for chronic transport disruptions
When does predictive ETA actually work?
As we have seen on countless occasions and more than ever these days, logistical transport comprises recurrent hazards that can deviate from the planned timeline. Supply chain efficiency depends on the timely delivery of goods for every leg of the journey. When shipments differ from the scheduled timeline, reliable predictability is the key to making intelligent decisions for remediation. But How?
Actually, the dynamic ETA is a regular update with realistic arrival times that help you understand schedule changes. The technology will be based on data recovery made available by the maritime companies and the additional data retrieved by IoT trackers. The objective is to recover the information as early as possible to be able to take action quickly.
It’s a fact; predictive ETA is, therefore, effective in the case of « controllable » and « known » disturbances such as missed ships.
When a container misses a ship, the solution will be able to analyse the frequency of available vessels at the disposal of the shipping company to know when the container can be shipped.
And finally, in some specific cases, the IoT based track&trace, using technologies such as machine learning, can provide additional data to get even closer to a realistic ETA.
Actually, in the case of a ship deviation or unscheduled transhipment, the trackers will quickly identify the deviation. Based on historical data, real-time location, and historical lane performance, the solution will recalculate the time of the new route and propose a new ETA.
So let’s be honest, predictive ETA is not « magic » data. It is effective in « regular » unexpected events and allows reliable information to be provided as quickly as possible to undertake better and quicker corrective actions.
The limits of Predictive ETA, limits where the outcome is uncertain
Whether it is digital Track & Trace or IoT-based track&trace, solutions such as the recalculation of ETA are based either on information provided by shipping companies or on historical data.
Specifically, we know the departure time of a new ship when there is a missed shipment; we can estimate additional waiting time when there is congestion at a port.
However, the device cannot estimate when EverGreen will unblock the Suez Canal or when a hurricane will subside.
Therefore, we can say that in the case of unpredictable events whose end is not known or « controllable », it is difficult to provide a reliable predictive ETA.
However, and this is the whole point of a real-time monitoring solution. Its ability to provide reliable alerts as quickly as possible. A real-time alert, a map of the exact location of the boats, helps to eliminate the black box effect.During an incident, we are able to follow its evolution in real time: to be alerted as soon as possible and to have reliable information almost continuously on the evolution of the incident and its resolution.
The example of the Suez Canal is very useful. An automotive supplier used the EasyTrack solution to provide real-time alerts of canal congestion and, therefore, to reroute ships that were not yet blocked.
Knowing which goods were blocked helped to anticipate a need for restocking and request another mode of transport (train, plane) if necessary. This visibility allows for quicker corrective actions and therefore predicts stock shortages.
Some vessels decided to bypass the canal and go through Africa, off the coast of Somalia. The tracking solution detected this deviation and alerted the customer that there would be a longer lead time. And thanks to the data history, we know that to bypass Africa it is 7 days of lead time.
The fast-growing need for enhanced visibility and control throughout the supply chain has made predictive ETA a subject of growing interest in supply chain management. Predictions are made to provide insights about potential future disruptions and give you the ability to anticipate and take corrective actions. However, the accuracy of predictive ETAs truly lies in the integration of actionable data.
Therefore, we can say that the nature of the unexpected event will directly impact the reliability of the predictive ETA.
Without being in any way half-hearted in closing this article, I think that predictive ETA should be taken for what it is. It is a means of providing reliable information in the case of events with a known endpoint. And it is then that we need to see a monitoring solution for what it is, a means of recovering data as soon as possible (alert, geolocation, predictive ETA) and, therefore, beyond gaining visibility, to be able to make corrective decisions based on reliable and timely data.
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