AI technology heralds major promise for industrial companies

24 March 2021 4 min. read

Artificial intelligence could help optimise the entire industrial manufacturing value chain – across production, supply chains, research & development and sales. Experts at Emerton and Startupinside have laid out the role of AI in post-pandemic industrial processes.

The context is a disruptive economic climate, where optimisation has become the core of business prosperity. Industrial manufacturing is notoriously time and capital-intensive, leaving plenty of room for improvement during and after the Covid-19 crisis.

Aimé Lachapelle, managing partner of Emerton’s data science & artificial intelligence practice, highlighted the challenges faced in 2020. “Production came to a standstill, the global supply chain broke down, and the women and men at the center of these industries were greatly destabilised.”

AI technology heralds major promise for industrial companies

The time has come to rebuild, and the experts position AI as the future. Emerton and Startupinside break the industrial manufacturing value chain into four parts – production, supply chain, R&D and sales – each of which can benefit from AI.


An often-cited use of AI in production is predictive maintenance – where AI collects and examines vast pools of production data for patterns that lead up to failures. In reality, the researchers note that failures occur a handful of times per year, giving AI algorithms little data to work with. As it stands successful predictive maintenance might be some way down the line.

Instead, the report highlights the value of using AI to optimise production – a priority for 60% of industrial leaders. AI can help with better planning, boost productivity, optimise energy consumption and improve production parameters. Per the report, the best means to this end is to deploy AI at the heart of the production processes.

Supply chain

“The supply chain, by its complex nature at the interface of many processes, offers a fruitful playing field for artificial intelligence algorithms that allow the simulation and optimization of a large number of scenarios,” explained Lachapelle.

For instance, the predictive nature of AI and machine learning can be used to forecast customer demand or delivery times, enabling accuracy, flexibility and agility across the entire supply chain. Other applications include the use of AI to inform critical decisions, or its deployment to make the decision itself.


The adoption of AI in R&D is minimal, although the applications are limitless. AI can be deployed to conduct tests; select the most relevant laboratory experiments; and even predict their outcomes. Aviation, food, chemical and pharmaceutical are some of the industries where AI-driven R&D is finding its legs.

And there are applications in design too. The technology can generate plans for a car part based on specifications, for instance, or predict if a perfume will be chemically stable. Combined with advances in 3D printing, these methods could accelerate the prototyping process according to Lachapelle.

Sales & marketing

“Some industrial companies such as more B2C oriented consumer goods, are starting to deploy AI tools to improve their sales. In particular, this is the case in the cosmetics and perfumery sectors which use algorithms for hyper-personalisation of products or the analysis of online reviews,” said Lachapelle.

Overcoming barriers

So AI has tremendous value to bring for industry, although a gap exists between this potential and the scale of implementation. Some barriers exist – most notably data security issues around vast pools of production data being stored in the digital space; variations across production sites that make it challenging to develop scalable solutions; and resistance from the workforce owing to high complexity and a lack of trust.

These challenges present a clear roadmap for industrial companies. Infrastructure solutions must be designed for secure data storage, collaboration and harmonisation can lead to scalability, and a human-centric approach must be adopted when implementing AI. With these obstacles cleared, industrial businesses could gain substantial value as they adapt to the new normal.