Paving the way for AI in supply chains requires foundational levels of digitalisation and connectivity, which are often lacking during pre-retail logistics. This needs addressing first, if businesses are to even get close to creating AI-assisted supply chains in the near future, writes Stuart Greenfield (pictured, below), UK and European Sales Director, for Advanced Supply Chain.
Embracing AI
There’s a growing consensus that AI is a strategic priority for transforming supply chains. Deloitte’s 2026 Retail Industry Global Outlook pinpoints AI as a key enabler for smarter, faster and more resilient supply chains, with 68% expecting agentic AI adoption in the next 12 to 24 months.
Data from Gartner suggests three quarters (74%) of supply chain practitioners identify AI as the primary driver of supply chain transformation in the coming years, while KPMG predicts supply chains this year will start embedding AI in planning and risk management.
Supply chain management and strategies are approaching a turning point, where the possibilities of AI are becoming more of a reality, rather than an aspiration. New opportunities are emerging to optimise stock inventory management, improve demand forecasting and strengthen scenario modelling to better manage supply chain shocks. However, realising such benefits will hinge on the flow of available, reliable data, and pre-retail processes could prove the weakest link.
Walking before running
Many pre-retail operations are still overly reliant on manual processes, often because warehouse operatives are constantly on the move. Paper stock logs and hand-written labels are typically commonplace during the stages of getting products ‘retail ready’ for sale. This causes a huge disconnect, which slows the flow of information and risks errors.
Stock inventory data is often inputted into a system at the end of a shift, meaning a lag of several hours between the processing and movement of goods, and the communication of data. By the time information is shared, it’s already likely to be outdated and unreliable, and possibly incorrect.
Illegible handwriting on labels can lead to mistakes, causing orders to be rejected and rerouted or held up in a distribution centre. The data lag stops real-time or near-time updates, limiting visibility and insight that can negatively impact the whole supply chain. Attempting to apply AI in such operating conditions is like trying to run before you can walk.
Creating connectivity
Manual processes during pre-retail logistics can be quickly replaced and enhanced by mobile, touchscreen kiosks and label printers, which enable automation and digitalisation. Connecting kiosks to a web-based supply chain management solution enriches the flow and accuracy of stock inventory data. Visibility and insight can be created, which, combined with the right IT capabilities and transport management systems, can be used to support end-to-end communication throughout supply chains.
From the moment labels are printed and scanned, information can be communicated to support supply chain optimisation. For example, it’s possible to maximise vehicle loads during both inbound and outbound logistics, while also scheduling vehicle movements to cut dwell time at warehouses and fulfilment centres. Mileage in supply chains and the number of vehicles in transit can be reduced to save carbon emissions and fuel costs. Just-in-time inventory management can also be better planned, minimising stockpiling and the associated energy consumption and costs of warehousing excessive inventory.
Replacing manual processes during pre-retail logistics can enhance efficiencies to help cut lead times and significantly boost speed to market. It’s a step that can yield many advantages from a relatively low investment, and a step that can get pre-retail logistics future-ready for the possibilities of AI.
