DATA MANAGEMENT FOR WAREHOUSING
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users can all trust.” Mohammad says more companies are expecting data to be provided directly from their WMS and TMS operational platforms so they can interweave it with their own data resources and gain a holistic view of their business. “As customers gain greater analytical capability, they are demanding access to raw data rather than relying solely on prescribed outputs,” he adds.
Enterprise AI, allowing organisations to enhance the entire supply network rather than just internal silos.” Richard Skelson, managing director at Field Ascend, says that AI is changing data management from something that looks backwards to something that supports day-to-day decisions. “By analysing large volumes of live and historical data, AI can forecast demand, improve routing, automate replenishment and highlight maintenance issues before they cause disruption, helping teams move away from constant firefighting,” he says.
Contributors
Martin Tombs
qlik.com
AI impact Of course, AI is impacting on data
management too. “AI is adding value once reliable data is available but it’s only as good as the data it receives, which is why accurate data capture at source (labels, RFID, mobile data collection) remains critical,” notes Benoit. “In warehousing and logistics, AI is increasingly used to identify patterns and anomalies in inventory movement, support predictive maintenance and forecasting and improve demand planning and resource allocation.” Manik says that with rapid digitisation, the warehousing and logistics sector has become incredibly data rich. “This is characterised by a high volume of different systems, formats and frequencies,” he says. “While AI and modern technology excel at consuming this multi-modal data at speed and scale, the real value lies in its application. “There is no AI without PI (Process Intelligence). AI promises to reinvent operations, but without the essential context provided by PI, companies cannot unlock its full potential. PI provides the ‘common language’ and context needed to translate this massive amount of data into a true digital twin of operations. “When implemented this way, AI becomes more than a tool, it becomes a trusted partner. It doesn’t just identify issues; it uses that dynamic digital twin to help teams understand why they occur and how to prevent them. This turns fragmented data management into true
Work closely With warehousing and logistics businesses
often still getting to grips with data, resellers have a crucial role to play.
“Resellers need to work closely with their customers first to fully understand their processes before mapping technology to meet their operational needs,” says Benoit. Benoit adds that the focus should be on end-to-end data flow, not just individual devices, the importance of data quality at the point of capture, a future-ready approach that supports barcode and RFID and solutions that reduce manual intervention and human error. Martin says resellers should focus on the business impact of good data management, not just the technology. “Data quality, visibility and governance directly affect how efficiently a business operates and how well it can respond to change,” he says. “Ongoing support is just as important. Automated checks, monitoring pipelines, and maintaining compliance may not be flashy, but they make sure the data stays reliable as operations grow. Making that link between strong data practices and real-world outcomes helps position resellers as long-term partners rather than one-off suppliers.” Paul agrees that resellers should start with the business, not the tech. “Warehousing and logistics are complex,
Manik Sharma
celonis.com
Richard Skelson
field-ascend.com
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