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Autonomisation de la supply chain : mythe ou réalité ?

by Emmanuel KOURATORAS

#supplychain #retail #ML #prédictions

At the peak of the epidemic, 104 international retailers were questioned about their level of digital maturity. On the one hand, only 14% of them said they rely on a prescriptive supply chain capable of operating autonomously. On the other hand, only 8% of respondents update their demand forecasting processes in real time using old spreadsheets. Finally, almost 40% want to use AI to achieve complete omnicanality by 2025.

Since the arrival of Coronavirus, the retail sector, already well tested by uncertainty and short-termism before, has been confronted with a radical change in some of our consumption habits. Due to the lockdown and rapid change in demand, some market players have found that their current operations are no longer efficient and need to move towards greater automation.

At the height of the Covid-19 epidemic, between March and April 2020, a study published by the University of Warwick in July 2020 questioned 104 international retailers on their level of digital maturity and the results were astonishing, to say the least. While only 8% of respondents are still updating their demand forecasting processes with spreadsheets, only 14% of them say they rely on a prescriptive supply chain capable of operating autonomously and almost 40% want to use AI to achieve complete omnicanality by 2025. This suggests that many companies are currently in a transformation phase.

In particular, the study shows new trends in the way of understanding the different stages of maturity of the supply chain.

It differentiates 4 different stages of evolution:

  1. Manual and non-recurring automation
  2. The second stage is much more analytical and event-driven.
  3. The use of an advanced continuous prediction system on all supply chain processes.
  4. Complete autonomy thanks to the use of the learning machine to allow to multiply the scenarios exponentially while relying on the result of real-time (or almost real-time) predictions.

Actors in the first two stages require more human effort and suffer more collateral damage from unpredictable external events. Levels 3 and 4 enable real-time prediction and anticipation of future needs, while guaranteeing supply chain autonomy even when major events disrupt the pre-established patterns.

Despite the emergence of technological evolutions to the benefit of the supply chain, the average of the study participants falls to level 2 or lower and only 14% of the surveyed retailers are at level 3 or higher.

Despite this difficult context and the lack of supply chain automation, more than 50% of the study participants say they are in favor of greater anticipation in their systems, hoping to reach a level 3 or higher in the next 5 years.

This growing need for predictive solutions in the sector is explained by the difficulty to adapt to an environment in times of crisis and by the lack of flexibility of the players. Being equipped with predictive solutions would allow them to optimize their pricing, finance, demand and supply as well as their inventory planning, as well as many other strategic flows for top management.

The functioning of the learning machine is not always easy to explain. Many skeptics think that complete autonomy is impossible, even dangerous, because it puts the place of man in the company into perspective. However, it seems that more and more organizations with a rational supply chain understand its importance and are looking towards a more predictive future where AI and machine learning will have a decisive role in decision support thanks to the degree of accuracy provided.

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