Manifest Vegas: Q&A with Easy Metricsā Dan Keto
The buzz at Manifest Vegas 2026 was impossible to ignore. More than 7,200 industry leaders descended on The Venetian this past February, captivated by promises of AI-powered warehouses and next-generation automation.
Yet, amid all the flashy tech demos and bold predictions, a sobering reality went largely unspoken: most warehouses don't actually know how well they're performing.
The metrics are scattered. The data is fragmented, and critical decisions are still being made on gut instinct and outdated spreadsheets.
Enter Dan Keto, Co-Founder and President of Easy Metrics, who's team is helping operations leaders build the data foundations necessary to compete. Through warehouse performance management and labour management solutions, Easy Metrics is bringing clarity to one of the supply chain's most complex environments ā transforming how facilities track, analyse and improve their operations from the ground up.
How is the company evolving to meet the changing needs of the modern warehouse?
What we do is focus on warehouse optimisation. We started off as a labour management company, but with the increased investment in different technologies – specifically robotics, automation and other software systems – the warehouse has become a much more complex environment.
We are evolving into what we call warehouse performance management, where we ingest data from all these different sources to give a unified picture of the warehouse from a cost, profitability and performance perspective for every single transaction in the business
What are the primary challenges you’re seeing in the marketplace regarding AI and data integration?
The challenge we're seeing right now in the marketplace is everyone's very excited about AI, but AI without the right data foundation is going to be very expensive and it's not going to give you the results you need.
What we've seen over the last year is a big push to move into AI, but now these companies are realising they haven't solved their data problem. There is quite a process you have to go through to build what we call the 'unified data model' within these organisations. It involves a heavily transformed data set – bringing in all the different transactional data systems from that operation – and aligning that with the stakeholders who understand what their KPI requirements are.
Once you build that foundational layer, AI can create magic for you. But if you haven't done that, it's going to be extremely expensive and you're not going to get the results that you want.
Looking ahead, how do you see the supply chain landscape shifting over the next 18 months?
Supply chain is turning into more of a manufacturing operation. I don't think it's going to happen in 18 months; I think the next 18 months will be the data story.
Companies are going to have to get their hands on their data properly and transform it properly.
Ultimately, as we're bringing more and more automation into these distribution centres, you cannot operate the distribution centre by just throwing more labour at the problem anymore. You now have to optimise the throughput of the facility like a manufacturer does, because robots only operate at a certain pace, whereas with humans you can kind of dial them up and dial them down. Automation operations have set throughput capacities. T
he paradigm shift is going to be more of a holistic engineering model for the distribution centre, versus optimising specific performance on specific processes.