AI’s Influence on Supply Chain Efficiencies

Software, Media & Technology


While OpenAI’s GPT-4 may have some believing Artificial Intelligence (AI) is finally here, it has been in continuous development since the 1950s, although its advancement has certainly sped up in recent years. The first chatbot, a means now used (much to everyone’s annoyance) by countless customer support channels, was created in 1966 – and when there’s efficiencies to be gained, businesses will look for ways to integrate innovative technologies.


Supply chains and AI

Supply chains, progressively complex and globally integrated, have been in and out of news cycles in recent years after multiple, widely known disruptions. There are vast efficiency gains to be made within the supply chain and AI is already playing a role.

Starting with Manufacturing & Distribution as an example (one of Polestar’s sectors), demand-planning enhancements are being achieved through improved visibility and monitoring of information and goods flow from procurement to customer. This results in forecasts with greater reliability, which is improved through Machine Learning (ML), a subset of AI, that increases the accuracy of predictive models through algorithms.


Case studies

In industry, BMW Group and NVIDIA are working together to create a new production platform where humans and robots work seamlessly side by side. The virtual factory planning tool will allow data to be collected live and collated from multiple databases to accurately plan highly complex systems, without compatibility issues. Integrating with suppliers and logistic planning will bring these gains into the supply chain, from planning through to production.

In logistics, the cost of transporting goods worldwide is on the rise, according to Bloomberg. In 2020, the cost of shipping goods by sea increased by 12%, the highest level in the previous five years.

To combat this, companies such as Echo Global Logistics provide an AI platform to secure better shipping and procurement rates, manage carrier contracts, and identify opportunities to enhance profits within supply chains. By accessing a centralised database accounting for all aspects of its supply chain, users, such as shippers and carriers, receive advice on transport management and financial decision-making.


Other uses

For last-mile logistics, AI-driven optimisation can find the best route for drivers who may have hundreds of drop-offs per day, a task that would be unsustainable through human processing and interpretation. While improving the customer experience, who expect online orders to be delivered on time, route optimisation brings the added benefit of reducing mileage – leading to savings through lower fuel consumption and vehicle maintenance. Innovations in AI within the supply chain are paving the way for a future where autonomous, AI-powered vehicles are a reasonable possibility.

According to the 2021 Gartner CEO and Senior Business Executive Survey, CEOs anticipate that AI will have the most significant impact on their industries through 2025. AI is no longer merely an aspiration but is now a well-defined objective for supply chain leaders, with companies projecting an exponential increase in automation throughout various supply chain functions.


In conclusion

Advancing AI maturity is no longer a question of “if,” but “when.” It’s an opportunity facing every industry, every organisation, and every CEO. This is not to say there won’t be challenges along the way. As new generations enter the workforce alongside AI, automated processes will remove learning opportunities at a time when key skills and knowledge are developed. On the other hand, perhaps these roles will be redundant sooner than we think.


By Conor Barrett on 21/03/2023