Cool and modern tools?

LOGISTICS MATTERS presents insights on digital tools, such as AI and machine learning, and what impact they can have in the  supply chain.

Claire Charlton, head of W², Wincanton

“Connecting your technologies enables intelligent decision making, predictive insights, and the automation of complex tasks and processes. But start with the problem you’re trying to solve, wholly engage your people and customers in the journey and be prepared for some failures along the way. Organisations need to consider supply chain technologies within the context of their wider digital strategy. It is important that these are part of a connected landscape, ensuring that organisations aren’t implementing pieces of technology in isolation. The principles of test and learn are also central to this. At Wincanton our W² Lab’s programme sees early-stage businesses invited to pitch proposals which use digitalisation to drive change across supply chains. Once successfully selected, we support and collaborate with those businesses to develop and scale the innovative supply chain solutions of the future. Many solutions we have supported are now essential, everyday parts of our customer offering. It’s not just about the technology; successful organisations need all stakeholders, to embrace digital at the heart of their culture.”

Edward Napier-Fenning, sales & marketing director, Balloon

“Quite suddenly, AI is everywhere. As with the early days of many other revolutionary technologies, there is a lot of overclaiming, and a lot of what is currently touted as ‘AI-enabled’ is really only a sequence of, admittedly very fast and very clever, algorithms, following logical pathways devised by the humans. The ability to process immense amounts of ‘big data’ at lightning speed is impressive, but it doesn’t of itself constitute AI. True AI has the ability to learn from historic data and from current activities, and, in a sense, rewrite its own algorithms. True AI is beginning to be able to look at pick path optimisation more intelligently: where goods are in the warehouse, what goods can or cannot be combined on a given trolley or container (and where those containers are), what the priority orders are, and thus building the most efficient pick routines possible.”

Gerry Power, UK head, TMX

“Generative AI - through personalised virtual try-on experiences, custom product recommendations and real-time sizing assistance - can reduce online returns. For inventory and supply chain optimisation, AI can help with distribution routes, demand forecasting and decisions on inventory levels. This ensures that products are available when and where customers need them, reducing the likelihood of returns due to stockouts or delivery delays. Returns predictive analytics can analyse historical return data and generate predictive models to anticipate which products are more likely to be returned. Retailers can then take proactive measures, such as targeted marketing or product improvements, to mitigate returns.”

Stephan Sieber, CEO, Transporeon

“The era of Excel spreadsheets, manual searches, and endless route and rate browsing have become now relics of the past. Now is the time for enterprises to pivot from mere data collection and embark on the process of generating transactions with the data at their disposal. Automated, data-driven decision-making within a collaborative and interconnected network, leveraging historical patterns, real-time data, and future predictions, will enhance transportation operations.”

Chris Jones, EVP, Descartes

“The advent of powerful, but intuitive and low-cost analytics platforms such as Microsoft PowerBI gives deep insight into plan versus actual performance. Machine learning can more accurately identify actual stop location, drive, service and stop times, and other patterns such as changes in stop sequence. These recommendations can be applied to the optimised planning solution to create more accurate and productive route plans.”