Real-time tracking through IoT and AI have completely revolutionized supply chain management... but today the holy grail of logistics is prediction.

Because while having real-time information is essential today, predicting certain events is a strategic asset... now at your fingertips. Logistics prediction is now a subject and a real need in the world of the supply chain, to better manage its resources and operations. But today, is it possible to see “the future”? Without calling on a fortune-teller, how does a supply chain anticipate events?

This is where AI comes in...

Already omnipresent in our daily lives — on Facebook to create news feeds, in the banking world to detect fraud, or even in the health sector — AI offers enormous potential to businesses that adopt it, and transport is no exception.

According to the Report by the consulting firm McKinsey & Company, Logistics would be one of the sectors to benefit the most from the contributions of AI: we are witnessing a paradigm shift towards predictive and proactive logistics operations, in order to optimize decision-making.

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What are the benefits of transport forecasting?

Real-time management of the supply chain is now essential for optimal logistics. The traceability of its flows represents key data for logisticians in order to better manage their operations.

But Can We Do Even Better? Real-time visibility is good, but being able to predict possible delays or other factors that may slow down your supply chain means improving your logistics management!

Real time makes it possible to manage daily life, but prediction makes it possible to be even more proactive, improving operational productivity, but also the making of strategic decisions.

Because we know that logistics is above all a question of costs and deadlines: it is necessary to manage your supply chain well, in order to have the best ROI, by managing fleet and flow management effectively, the latter constantly evolving according to demand.

Logistics prediction can focus on various trends or contexts that directly impact supply chain operations:

• Demand forecasting : Using AI, identify the requests and products that sell the most or the fastest by looking at the sales history. This makes it possible to model inventory and reduce surpluses. Planning an inventory would also reduce delivery times and thus contribute to better customer satisfaction.
• The prediction of its logistics Makes it possible to have a proactive management of unforeseen events and hazards such as the forecasting of ETA and the prediction of asset availability. Predictions of external events are also possible, such as weather conditions, social conflicts (strikes), local events, or even predictive maintenance.
• Predictive analytics Therefore allow companies to produce usable insights and thus have proactive decision-making in order to improve customer satisfaction. This automation of decisions obviously increases the profitability and productivity of the company.

Calculating logistics forecasts and trends has therefore become a real opportunity in this sector, to better guide its operations and therefore significantly reduce costs, the main challenges of the Supply Chain of the future.

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Why is AI relevant for transport prediction?

As we have seen, forecasting in transport remains complex, due to the quantity of data to be taken into account, as well as to the high quality required for this data.

However, this trend prediction is possible thanks to AI and Machine Learning. These cognitive computer systems learn about the business and intelligently and effectively identify industry trends and consumer needs that traditional analytics can only identify with difficulty.

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The Importance of More Qualified Data

The problem of prediction here is that of qualifying the data. Indeed, having information on events is possible today, but this data is not always perfectly qualified.

To be able to enrich these events, it will then be necessary to rely on several examples or on scenarios already experienced.

The difficulty here is to integrate data qualified as context, in order to adjust the desired estimates, which are the real needs of logistics players today. AI offers them this data and thus allows them to contextualize events and unforeseen events, allowing them to be more reactive.

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Big Data: Too much data to process

Forecasting is possible thanks to AI, and AI is possible thanks to Machine Learning. Machine Learning allows computer systems to learn independently, and to discover patterns to make predictions thanks to a series of examples already experienced (through supervised learning or not).

We know that one of the performance indicators of transport management is flexibility. With flows and demands that are constantly changing, forecasting calculations face large and complex volumes of data. But the simple fact of collecting a large amount of data is no longer enough today to produce a result. In addition, too large a volume of data does not allow for proactive and reactive “human” decision-making.

And that's where AI is relevant. Thanks to Machine Learning, AI has a strong capacity to process large data information. It is therefore easy to predict the availability of its assets under different scenarios.

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Will AI soon be indispensable in logistics?

AI should lead the new economy, which has been described as the “fourth industrial revolution” or the “second age of the machine.”

And we can already say that the supply chain will benefit a lot from AI and logistics prediction. Today, IoT represents a real convergence between Big Data and AI. Thanks to AI, the supply chain industry is moving from reactive actions to a proactive and predictive, automated and personalized model. The key word for tomorrow's logistics is to move from analytics to predictive analytics.

This new model allows a better understanding of activities, with a reduction in costs.

Everysens is setting up transport optimization software via IoT and AI.

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