Artificial intelligence in supply chain management

Global supply chains are one of the most exciting and challenging issues of our time. Whether it is Covid, the Ukraine war or most recently the earthquake in Turkey, supply chain disruptions can have serious and long-lasting impacts on the economy, society and supply chain management (SCM). This shows once again how complex and susceptible SCM is to disruption - be it due to changes in consumer demand, unpredictable events or social and economic crises.

A recent Accenture study speaks of "supply chain disruptions could cost European economies around €920 billion of their GDP by 2023". This figure is caused solely by a long-running war. Expert:ins calculate a loss of 318 billion euros in 2022 and 602 billion euros in 2023. What happens when other crises (and they are inevitable) are added to this?

To address this, supply chains need to virtually reinvent themselves. Modern supply chain management is about resilience, relevance (customer focus and flexibility) and sustainability. To address these challenges, more and more companies are therefore turning to artificial intelligence (AI) to manage their supply chains more effectively and efficiently. Used correctly, AI can do many things – such as provide insights into consumer behaviour, predict future trends, automate processes and increase transparency across the supply chain network. By using AI, organisations can gain a competitive advantage while ensuring business continuity in times of crisis.

Let's look at some of these potentials and use cases.

Demand Forecasting

At the top of the list for AI in the supply chain is predictive analytics for demand forecasting. Predictive analytics in supply chain management is used to predict future consumer demand so that companies can better manage their resources and adjust their production accordingly. Furthermore, by using predictive analytics, companies can gain insights into customer behaviour, anticipate market trends and prepare for changes in the supply and demand situation. 

Examples of predictive analytics include looking at historical sales data and analysing consumer buying behaviour, as well as using machine learning algorithms to identify correlations between different factors that could impact demand – even going so far as to factor in the weather or political events, for example. Predictive analytics enables organisations to make accurate forecasts and optimise their inventory, production and fulfilment processes, ultimately increasing efficiency in all these areas. Costs can also be reduced. For example, AI-driven automation tools can help companies reduce the manual labour costs associated with demand forecasting.

Inventory management

Inventory management is another crucial component for success in logistics. Today, this management is done in real time (Real-Time Inventory Management). Real-time inventory management is an AI-driven technology that helps companies monitor their inventory in real time. This system uses technologies such as RFID tags, barcodes, sensors and data analytics to collect and analyse data about inventory levels and movements so that companies can make better decisions about their inventory. With real-time inventory management, companies can track the exact location of products, anticipate and proactively manage shortages or overstocks, reduce the costs associated with manual tracking – and improve the customer experience. 

Examples of real-time inventory management are automatic replenishment systems that order supplies when stocks are low or smart shelves that monitor product availability. Furthermore, when combined with predictive analytics, real-time inventory management can predict demand, further optimising the ordering process and shortening delivery times.

Automation of processes and procedures in the supply chain

Speaking of automating processes, automated logistics uses artificial intelligence to manage shipments and deliveries in a fully automated and optimised way. Similar to inventory management within a warehouse, sensors, GPS tracking are used to track shipments, monitor shipment status and identify potential delays or issues with delivery. Thanks to automated logistics plus data analysis, companies can reduce costs associated with manual labour and improve customer satisfaction through shorter and more reliable delivery times. Tracking and updates are also done in real time. 

This is made possible, for example, by intelligent containers that monitor temperature, humidity and other environmental conditions during transport, as well as automated systems that optimise the route for deliveries. Automated logistics is another key component of supply chain management, as it allows companies to track and manage their shipments more efficiently.

Supply chain transparency

In terms of supply chain transparency, it is more than ever about human rights, environmental protection and sustainability. The latter is not only increasingly important for consumers, but is the most important factor when it comes to ESG (Environmental Social Governance) performance. These performance data are used to measure how attractive companies are for investors. Not to mention reputation.

AI helps improve these areas by giving companies more insight into their operations. By using AI-powered solutions to analyse supply chain data, companies are able to identify areas of waste, inefficient processes or underutilised resources. Through these insights, organisations can optimise their supply chain operations, minimise waste, reduce energy consumption and emissions (CO2 footprint) and ensure responsible sourcing of materials. 

In addition, AI can help manage risk by identifying risks in the supply chain and developing strategies to mitigate them. For example, predictive analytics can identify potential ESG issues such as labour or environmental violations by suppliers before they occur. Potential risks can be proactively identified and addressed to ensure that operations comply with the ESG standards of the organisation and potential stakeholders and investors.

Optimisation of delivery routes and faster delivery times

In supply chain automation, we have already touched on shortened delivery times and the overall optimisation of the supply chain. 

By using machine learning, customer demand can be predicted and the entire supply chain from production to shipping can be optimised. This optimisation also allows problems to be identified and resolved more quickly – ultimately, all these measures have a positive effect on customer service and shipping costs are lower overall.

Customer Experience 4.0

Of course, the aforementioned improvements have a strong positive impact on the customer experience as a whole. But it doesn't stop there - AI continues to enable companies to offer their customers an even better experience through personalisation. Here, too, predictive analytics come into play. This allows needs to be anticipated so that tailored product recommendations and discounts can be sent to customers. Thanks to the collected data, automated chatbots can be used to provide personalised customer service - around the clock, of course. All of these solutions help and, most importantly, engage customers to get the most out of their online shopping experience – personalised and tailored to their needs.

And it goes even further. AI can then be used to identify potential customer problems and develop strategies to solve them before they become real problems. Again, predictive analytics can be used to identify anomalies in customer behaviour or to identify potential service issues before they occur. Artificial intelligence can look deep into customer behaviour and anticipate problems.

Brave new world? Complex times need complex answers and solutions, which is why our mission at Pacemaker is to apply tools and technology to challenges in a timely manner. Supply chain and artificial intelligence is in our DNA, and we will delve deeper in the following articles. We look forward to your input and a lively exchange!