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

Because if having real-time information is essential today, predicting certain events is a strategic asset... now at your fingertips. Logistics prediction is a subject and a real need in the world of supply chain, to better manage its resources and operations. But today, is it possible to see "the future"? How does a supply chain anticipate events without the help of a soothsayer?

This is where the AI comes in....

Already ubiquitous in our daily lives - on Facebook to create news feeds, in the banking world to detect fraud, or even in healthcare - AI offers enormous potential to companies that adopt it, and transportation is no exception.

According to the McKinsey & Company report, logistics would be one of the sectors to benefit the most from AI: there is a paradigm shift towards predictive and proactive logistics operations, with the aim of optimising decision making.

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What are the benefits of transport prediction?

Real-time supply chain management is nowadays 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 better? Real-time visibility is good, but being able to predict possible delays or other factors that could slow down your supply chain means improving your logistics management!

Real time allows for day-to-day management, but prediction allows for even greater proactivity, improving operational productivity, but also strategic decision making.

As we know, logistics is above all a question of costs and lead times: it is necessary to manage your supply chain well, in order to have the best ROI, by piloting fleet and flow management efficiently, the latter constantly evolving according to demand.

Logistics prediction can address different trends or contexts that directly impact supply chain operations:

• Demand forecasting: Using AI, identify which demands and products are selling the most or the fastest by looking at sales history. This allows inventory to be modelled and surpluses to be reduced. Forecasting inventory would also reduce delivery times and thus contribute to better customer satisfaction.
• The prediction of its logistics allows for proactive management of unforeseen events and hazards, such as the anticipation of ETAs and the prediction of asset availability. Predictions of external events are also possible, such as weather conditions, social conflicts (strikes), local disturbances, or predictive maintenance.
• Predictive analytics allow companies to produce actionable insights and thus make proactive decisions to improve customer satisfaction. This automation of decisions obviously increases the company's profitability and productivity.

Calculating logistics forecasts and trends has therefore become a real opportunity in this sector, to better direct its operations and thus reduce costs considerably, 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, prediction in transport remains complex, due to the amount of data to be taken into account, as well as the high quality required for these data.

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

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The importance of more qualified data

The problem of prediction here is that of data qualification. Indeed, having information on events is possible today, but these data do not always have a perfect qualification.

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

The difficulty here is to integrate qualified contextual data, in order to adjust the desired estimates, which are the real needs of logistics actors 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

Prediction is possible thanks to AI, and AI is possible thanks to Machine Learning. Machine Learning allows computer systems to learn autonomously, and to discover patterns to make predictions thanks to a series of already experienced examples (through supervised or unsupervised learning).

We know that one of the performance indicators of transport management is flexibility. With constantly changing flows and demands, forecasting is faced with large and complex volumes of data. But simply collecting a large amount of data is no longer enough to produce a result. Moreover, too much data volume does not allow for proactive and reactive "human" decision making.

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

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AI, soon to be a must in logistics?

AI should lead the new economy, which is referred to as the "fourth industrial revolution" or the "second age of the machine".

And we can already say that the supply chain will gain 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 customized model. The keyword of tomorrow's logistics is to move from analytics to predictive analytics.

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

Everysens implements transportation optimization software via IoT and AI.

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