AI Adoption: The case of Supply Chain & Manufacturing.

24/06/2020 230

In a recent Mckinsey Study of AI adoption worldwide, results show a 25% increase in AI use compared to the previous year, and a significant increase in AI use in new and different areas. In particular, four business areas stand out: marketing and sales, product and service development, supply chain management, and manufacturing.

In supply chain management, we can see a significant impact on the increase in sales due to the use of AI in the processes of forecasting demand and sales, and a reduction in costs due to the use of AI in the analytical processes of expenses and optimization of the logistics network. Manufacturing areas also stand out with a significant reduction in costs due to the application of AI in predictive maintenance, energy control and optimization of the production cycle.

Studies show that the adoption of Artificial Intelligence worldwide has increased by 25%

This study classifies AI capabilities in organizations into 9 categories:

1- Robotic process automation (RPA)

2- Computer vision

3- Machine learning

4- Natural language text understanding

5- Virtual agents or conversational interfaces

6- Physical robotics

7- Natural language speech understanding

8- Natural language generation

9- Autonomous vehicles

It is very interesting to observe in this study that the use of AI capabilities varies between sectors. For example, companies in the consumer packaged goods sector report much higher use in industrial robotics compared to other types of capabilities. In other words, the priority in this sector is aimed at its assembly processes.

Pharmaceuticals, medical products and the automotive sectors follow this same priority, confirming the importance of AI in their manufacturing processes. In transportation and logistics, the use of virtual agents and RPA stands out. In the retail sector, a very balanced use of all AI capabilities, except autonomous vehicles, is observed, with a slight superior use of natural language understanding.

Table. Adoption of AI Capabilities by Industry.

By connecting the results of this study with the experience of companies, we can identify several success stories in each of these sectors.


The automotive sector is the industry with the largest number of robots, responsible for more than 30% of robot installations worldwide, followed by the electronics industry. While in the automotive sector, cost reduction is the main motivator of investing in robots, in electronics it is improving quality.

In the pharmaceutical sector, accuracy is the main reason for acquiring this capacity. The types of industrial robots range from articulated robots, through collaborative robots, autonomous guided vehicles (AGV), to exoskeletons (arms or mechanical joints connected to the human body).

In this sector we can also mention the use of intelligent virtual assistants within vehicles. Sherpa is a very good example with its agreement with car manufacturers such as Porsche.

The automotive sector is the industry with the largest number of robots, responsible for more than 30% of robot installations worldwide


A few years ago, Amazon surprised us with the AmazonGO model. Using AI capabilities (computer vision, sensor fusion, virtual agents) to eliminate checkout activities in convenience stores. This model of AmazonGO came to reinforce the contribution of AI value to eliminate the friction between the customer and the service provider.

Logistics and Transportation:

In 2018, DHL introduced the possibility of tracking shipments through Amazon Alexa and Google Assistant, in a clear commitment to the development of conversational interfaces in logistics.

The use of autonomous vehicles is another of the AI ​​capabilities applied in transportation and logistics. First, we can mention electric trucks, autonomous and capable of creating a road train (platooning). In other words, a group of automated vehicles that increases the transport capacity of motorways, reduces emissions, and improves safety.

Tesla recently promised a first delivery of these vehicles by 2021. Second, we can mention Toyota’s e-palette concept for mass-producing multi-purpose autonomous and electric vehicles. For example, merchandise delivery vehicles in cities, people transport vehicles while doing other activities (for example, eating a pizza, or holding a meeting via video conference.)

In conclusion, we are witnessing an accelerated advance in the adoption of AI in all sectors of the economy, with special interest in supply chain processes and manufacturing, and with a progressive use of the different capabilities of AI.