In this episode of The New Warehouse Podcast, Kevin welcomes Marinus Bouwman, Product Manager and Business Development Lead at KNAPP, to discuss the role of master data in warehouse automation. KNAPP, a global leader in intelligent automation solutions, has pioneered advancements in data-driven automation for industries like food retail, healthcare, and general logistics.
Tune in to hear Bouwman share insights into how high-quality data fuels automation, why master data often gets overlooked, and how KNAPP’s software solutions address these challenges.
Why Master Data is the Foundation of Automation
Automation may capture attention at trade shows and on LinkedIn, but behind every successful automated system is robust master data. As warehouses transition from manual to automated processes, the importance of precise product attributes skyrockets.
“You simply don’t get this information very easily, neither from the suppliers nor the retailer,” Bouwman explains. Master data goes beyond dimensions and weight, including attributes like crushability, tiltability, and transparency. Without accurate data, automation systems can fail. “If a robot thinks something is an inch smaller than it really is, it will run into it with two or three tons of force. This can damage the product, and in food retail, that means a lot of cleanup and downtime.”
To mitigate these risks, KNAPP developed KiSoft Genomics, a software solution that collects and refines master data, ensuring that automation systems operate as intended. The challenge, however, is getting businesses to recognize that their existing data often isn’t as clean as they think. “Customers would tell us, ‘Our data is fine.’ Then we run a simulation, and half of their dimensions are completely off,” Bouwman notes.
The Changing Definition of Micro-Fulfillment Centers
Retailers once saw Micro-fulfillment Centers (MFCs) as a silver bullet for rapid e-commerce and grocery delivery. However, as businesses have experimented with different models, the definition of an MFC has evolved.
“Some wanted a small, two-aisle MFC next to a supermarket, while others built larger centralized hubs servicing multiple locations,” Bouwman says. This variance led KNAPP to rethink its terminology. Instead of a one-size-fits-all approach, the company now offers “highly automated solutions for hyperlocal e-grocery.”
These solutions must balance automation with logistical challenges like temperature zones, last-mile delivery strategies, and labor training. “For the retailer, the automation hardware is just a small piece of the puzzle,” Bouwman explains. “It’s about managing people, training, and ensuring the right processes are in place.”
Learning from Data to Enhance Warehouse Performance
Beyond improving master data quality, KNAPP is leveraging real-time data analytics to enhance warehouse performance. The company’s Multi-Site Service Cloud (MSCC) continuously collects and analyzes machine data to predict failures before they happen.
“When conveyors experience frequent use, we can alert the customer that it might fail in the next five days,” says Bouwman. “Replacing a part proactively takes just two minutes and prevents costly downtime.”
By integrating machine learning and historical data, KNAPP is moving toward proactive maintenance, helping warehouses reduce disruptions and operate more efficiently.
Key Takeaways
- Master data is critical for automation success. Poor-quality data can lead to costly system failures and inefficiencies.
- Businesses often overestimate the quality of their data. KNAPP’s simulations frequently reveal incorrect product dimensions and weight.
- Micro-fulfillment is evolving. Companies must tailor solutions based on location, volume, and operational needs—there’s no one-size-fits-all definition.
- Predictive maintenance is the future. Real-time machine data helps prevent breakdowns before they occur.
- The right automation approach depends on business size and demand. Companies should assess their order volume and logistics before investing in automation.
The New Warehouse Podcast
