Artificial intelligence in the supply chain

A global supply chain under pressure

Recent history has placed extraordinary stress on global supply chains. The pandemic produced upheaval, shutdowns, and shortages in labour, goods, and materials. Consumer behaviour and product demand were upended, leading to unpredictable whiplash effects for demand and supply of many goods and services.

Since the pandemic, geopolitical tensions have provoked trade tariffs and a widespread trend towards reshoring and economic nationalism creating a need for increased supply chain resilience. Leading to mass reconfiguration of manufacturing operations and supplier relationships, as well as energy shortages and extreme price volatility.

Transport and materials costs have soared, while worsening climatic conditions have made disruption events more frequent. Just-in-time manufacturing practices have waned in popularity as firms have reacted to their newly exposed supply chain fragility.

Together, the compound impact of these challenges has created an overwhelming need for greater efficiency, and enhanced inventory and working capital management throughout the supply chain – challenges that AI can be used to overcome.

Christopher Brown

Partner, Head of Strategy

KPMG in Ireland

mail
call

The potential of AI for supply chains

As a data-rich environment characterised by high levels of complexity, the demands of global supply chains could be met by the widespread deployment of AI, with abundant use cases across the product life cycle, from manufacturing to last-mile delivery and Maintenance and Repair Operations (MRO).

Sector leaders are already showcasing AI’s capacity to yield powerful benefits in:

Analytics and AI provide more precise forecasting, stock holding, pricing, and identification of slow-moving items, which helps to reduce overstocking and stock-outs, and ensure the right products are available at the right time.

Patrick Corbett

Managing Director

DHL Supply Chain Ireland

AI can help manufacturers and retailers increase forecasting accuracy and optimise replenishment. E-commerce pioneers such as Amazon and Alibaba are already using machine learning to predict future demand for products based on consumer behaviour and seasonal or environmental factors, allowing for automated placement of product into fulfilment centres. 1

While Unilever has developed an AI-powered customer connectivity model that enhances collaborative planning, forecasting and replenishment process that has achieved a 98% product availability in a trial with Walmart and reduced human effort by an estimated 30%. 2

Similarly in Ireland, An Post, the national postal service has been exploring AI to optimise delivery routes and demand forecasting. 3

In the agri sector, chemical companies have used similar models and environmental data to predict which regions will need particular chemicals and nutrients at which times, allowing for enhanced stock positioning and capacity planning. 4

Computer vision-enabled AI can allow manufacturers to detect product defects in real time, enhancing fault detection and reducing costs from scrap and reduced yield. For example, Foxconn recently integrated Google’s Vision Inspection AI to its manufacturing process and has reported enhanced quality as a result. 5

Tesla similarly makes liberal use of computer vision and AI in its ‘unboxed’ manufacturing method, enabling it to identify and correct defects in real time 6, whilst AstraZeneca claims to have ‘reduced manufacturing lead times from weeks to hours’ through smarter manufacturing processes. 7

Similarly General Motors has used AI in the design for manufacture process to reduce component weights by 40%, while increasing strength by 20%. 8

In Ireland, companies across a range of sectors, including manufacturing and resource management, are working with research organisations to test new methods of production, develop innovative materials and ultimately rethink how they manufacture for more efficient operations and greater product circularity.

Throughout the product life cycle, AI can accelerate adoption of circular practises. From virtual testing to reduce waste from prototypes and process optimisation through to enhanced end of life.

Revolve, a member of Circuleire - Ireland’s first national circular innovation network has created a technology-driven supply chain solution to match reusable auto part and waste materials with appropriate users, this helped An Garda Síochána, the Irish police force, to save the equivalent of approximately 38,000 kg of CO2 in 2022 by acquiring over 550 reclaimed vehicle parts of various makes and models. 9

Dr. Geraldine Brennan, Head of Circular Economy and Circuleire Lead at Irish Manufacturing Research, noted, "Many companies are yet to understand the role of AI in enabling greater circularity and manufacturing efficiencies. AI can help companies to identify opportunities for better material and resource utilisation in manufacturing, while also supporting reuse and remanufacturing."

AI provides manufacturers with a range of options to optimise warehouse operations, including inventory management, space utilisation, automated packing, and order tracking.

FedEx and DHL have already rolled out AI-enabled sorting robots, while DB Schenker is piloting computer vision and AI in its warehouses to offer real-time pallet tracking and ultra-accurate dock-to-stock cycle times for inbound pallet management. 10, 11, 12

Mengniu Dairy, in China, has used AI to automate supplier order scheduling and vehicle dispatching, increasing inventory turnover by 73% and operational efficiency by 8%. 13

Patrick Corbett, Managing Director, DHL Supply Chain Ireland, noted, “As the demand for faster and more accurate fulfilment has grown, business needs to leverage more advanced technology to scale operations and improve overall efficiency. Analytics and AI provide more precise forecasting, stock holding, pricing, and identification of slow-moving items, which helps to reduce overstocking and stock-outs, and ensure the right products are available at the right time. With the use of AI throughout our customers supply chain, we’re aiding in developing end-to-end automation capabilities that give our customers full control and flexibility over their logistics and supply chain operations.”

AI, coupled with appropriate equipment sensor data, can autonomously predict when machines are likely to fail or require maintenance, which reduces unplanned downtime, extends equipment lifespan, and reduces maintenance costs by flagging issues before they lead to significant breakdowns.

GE, Bosch, and Rolls Royce are just some of the major engineering companies already using such technologies to analyse data from thousands of in-service units to detect anomalies, facilitate proactive maintenance and reduce lifecycle costs for equipment they have produced.

GE’s SmartSignal, for instance, is one such predictive analytics tool that employs digital twins to allow users from mining to manufacturing to diagnose, forecast, and prevent equipment failures ahead of time. 14 ,15, 16

Kieran Kelly, ubloquity (see case study below), noted, "Preventative maintenance is evolving from reactive to proactive strategies using AI to gain an understanding of failure patterns and accurately identify conditions for changing components."

Case studies

MRO case study: Rolls Royce

Rolls-Royce has developed a range of advanced predictive maintenance solutions that harness AI to boost engine efficiency and reliability. Cutting-edge engine monitoring systems measure a range of parameters as standard to provide deep insight into engine health, in real time, and allow for proactive maintenance decisions.

AI-powered inspection tools, such as their Intelligent Borescope, use AI technology similar to facial recognition to analyse images of engine components and identify inconsistencies or irregularities, reducing the time required for data processing during inspections.

Such innovations help Rolls-Royce to enhance engine reliability and operational efficiency by reducing unexpected maintenance downtime.

Last mile delivery and route optimisation: AI has already dramatically improved goods delivery through enhanced route optimisation based on real-time traffic and weather analyses, all of which are now mainstream technologies in leading logistics providers like DHL, Amazon, and UPS. 17, 18, 19
Reporting and compliance: AI can help companies streamline and simplify the collection of data necessary to satisfy the growing regulatory burden of directives such as the Corporate Sustainability Reporting Directive and subsequent standards (CSRD, CSDDD) which require companies to measure hundreds of KPIs and tens of thousands of underlying data points. Technological solutions, like Microsoft’s Azure Open AI, offer companies a way to simplify and automate much of this process.20

Platform case study: ubloquity

Northern Ireland based supply chain startup ubloquity is an innovative company applying AI to solve complex knots in the supply chain – in this case, the difficulty of navigating new cross-border controls demanded by the Northern Ireland Protocol, which threatened to interrupt the formerly seamless transit of goods between Great Britain and Northern Ireland.

ubloquity was part of the joint venture (with Fujitsu, Entropy, and B4B) tasked with developing the digital infrastructure to facilitate the new trade arrangements. In response, they developed Secure Freight, an end-to-end system that deploys AI elements like digital twins and smart seals to verify shipment security during transit.

Integrating these solutions with GPS data and blockchain technology allows logistics providers to comply with required border checks in as robust, transparent, and frictionless a manner as possible.

ubloquity’s solution has been used across sectors for example, to reduce downtime and facilitate just in time delivery in aviation, to monitor cold chain logistics and transportation, and to enable predictive maintenance on touring musicians’ instruments.

Kieran Kelly of ubloquity noted, “By using AI and blockchain technologies we enable zero stops during transit, which enhances efficiency and enables logistics providers to deliver clear service level agreements (SLAs) regarding delivery times. We have seen customers reduce stock holding by 30% to 40% due to the just-in-time manufacturing facilitated by the logistics process discussed.

For every 1% saved in stock holding, some customers save upwards of 10 million euros, indicating significant financial implications of the logistics improvements. The single trade window connects all documents and steps through a single interface, improving visibility and efficiency in logistics operations.”

For every 1% saved in stock holding, some customers save upwards of 10 million euros, indicating significant financial implications of the logistics improvements.

Kieran Kelly

CEO

ubloquity

Unintended consequences of AI

While AI, paired with greater digital strategies, has the potential to disrupt and transform supply chains, there are challenges too:

Complexity of implementation

Siloed technology solutions are of interest but developing a strategy for implementation into existing supply chains holistically, getting the full benefit of effort, can be complex.

Not every form of AI will yield significant gains and while the market is flooded with many solutions, there is still a need for an emergence of a “trusted toolkit” of technology.

Recent research conducted by Irish Manufacturing Research (IMR) funded by the Environmental Protection Agency (EPA) report highlights some of the key barriers reported by Irish stakeholders who participated in the study regarding AI to implement circular economy practices.

The biggest barriers cited were organisational (33%), technical (25%), financial (25%) and societal and ethical (17%). Positively, 92% of stakeholders documented an interest in using AI in their business, with a sizable minority already using some form of AI in workflows. 23
Skills

Both for implementation and use of AI, and considering how AI might offset existing workforces, what should happen to those workers, and how can organisations develop a strategy that promotes adaption rather than displacement of resourcing.
Environmental and ethical

How will AI be powered and how will that energy source benefit wider society. Equally, beyond immediate displacement of roles, where is it ethical to use AI in supply chain and manufacturing, and where does it pose challenges to societal norms?

Irish businesses need to be aware of the risk of “burden shifting”. Whilst substantial gains can be made from AI utilisation, these positives come with an inherent environmental footprint.

Dr Geraldine Brennan

Head of Circular Economy and Circuleire Lead

Irish Manufacturing Research

Dr Geraldine Brennan says, “It is important not to assume that AI adoption will automatically result in more sustainable outcomes. Irish businesses need to be aware of the risk of “burden shifting”. Whilst substantial gains can be made from AI utilisation, these positives come with an inherent environmental footprint (e.g. consumption of energy, water and minerals to e-waste) associated with software / data processing and servers / data centre hardware.

Renewable energy use and deployment of remanufactured equipment are a necessary start to reduce negative impacts and both direct and indirect ethical risks need to be actively discussed and mitigated.”

For best results, start now

Reaping the benefits of AI is easier said than done. Integrating AI into the supply chain successfully is a significant project whose success depends on overcoming a range of challenges, including data quality and integration; talent acquisition; cultural resistance; cybersecurity, and a fluid regulatory environment.

Nonetheless, the effort is worth making. According to the World Economic Forum, investment in AI for manufacturing is expected to grow by 57% by 2026, from $1.1 billion in 2020 to $16.7 billion by 2026.24 In this context of rapid evolution, companies that fail to deploy AI where compelling use cases exist will quickly become obsolete.

KPMG’s 2024 Global Tech Report found that 87% of executives report higher profits thanks to their tech investments, and that 74% believe AI is already improving their organisation’s overall performance.25 Despite the enormity of this promise, however, in a recent YouGov poll of transport and logistics professionals, only 25% stated that their organisation leveraged AI capabilities, suggesting significant untapped potential in the sector – and a huge opportunity for fast movers.26

Supply chains of the future will be more efficient, resilient, and autonomous, with AI providing unprecedented levels of situational awareness and transparency. Companies will not realise this vision overnight, but through a process of iterative technological adoption. Such a process demands careful planning and execution against a coherent strategy, which ambitious leaders should be developing now.