Closing Warehousing’s Signal-Response Gap – Without the Robotics Price Tag 

The majority of my career has been spent in or around distribution and fulfillment operations. I’ve spent time with companies and third-party providers whose capabilities span the paper-and-pencil game all the way through to advanced automation and modern warehouse management system (WMS) deployment. So now, when I catch a whiff of some corrugate dust, it’s like going home. But the crack of a tape gun is not just a sound I know well, but a starting horn signaling a significant change of pace for warehousing.  

Customer expectations now require near-real-time adjustments to product availability in addition to ever-increasing delivery speed. Proximity to the customer is critical, but so is fixed-asset utilization, and getting this network design wrong has consequences. A prime example just this week: Ocado’s closure of customer fulfillment centers in the US, which resulted in its share price falling by more than 19% as it admitted the density of demand wasn’t keeping up, and fulfilling from existing store footprints is more cost-effective.  

At the same time, shrinking the cycle time for producing a consumer good is all for naught if the warehouse can’t match the flexibility and agility needed to get it out the door. Gone are the days when a master configurator could periodically review bottlenecks and re-sequence work to meet throughput targets. Now, an entire “flexible warehousing” industry has emerged to handle seasonal spikes and social media-induced surges that exceed standard network capacity. Variability is the constant and order profiles and item velocity a moving target. Fulfillment operators have a new charter: adapt.  How? Using AI to increase the pace of data ingestion and speed up decision reflexes.  

AI – With or Without Robotics  

For many operators, AI means automation and robotics – which often means multi-year implementations, high capital investment, and extreme levels of leadership alignment. One of our customers put it this way: “It’s really hard to pilot a subset of orders in a corner of a legacy warehouse with robotics and get enough results to wow anyone.”   

The numbers bear this out: a study projects that 74% of warehouses will still not be automated by 2027, leaving the vast majority of facilities out of the game when it comes to AI. 

Robotics and materials handling equipment (MHE) automation should be explored for most large fulfillment networks. But AI advancements can improve execution in the face of high variability – even without a full-fledged robotics implementation.  

At Zero100, we keep tabs on the digital maturity of software solution options across supply chain. As the market evolves, certain solutions have risen to the top of the maturity curve, offering benefits without disrupting operations or delaying automation implementation. Below are some of the most sophisticated non-automation capabilities we’ve seen within warehousing, organized by “Jobs to be Done.”   

In Practice 

Delving into some real-life use cases, here’s what we found. 

Dynamic Slotting and Picking Task Optimization: Getting product into the optimal and most efficient spot to reduce pick times and keep product flowing has typically been manual to allow for frequent updates with a yearly overhaul or to reactively resolve bottlenecks. Newly available AI-based solutions allow for the optimal placement of items and can be reviewed continuously based on daily or even hourly changes in ordering patterns, congestion, equipment, labor, etc.  

To improve slotting, an automotive parts distributor reduced storage requirements by 20% with optimized slotting for over 20,000 parts with volatile demand spikes. Solutions like this bring decision intelligence, efficiency, and savings to the picking floor with little disruption and no MHE install needed, showing results with 10-25% throughput increase, and ROI achieved in 9-15 months.  

Task Assignment and Labor: Optimizing labor faces the same challenges as slotting and picking, with AI-enabled solutions managing the variations inherent across a day to dynamically direct workers and increase productivity. Typically, these solutions require a WMS as a starting point – but we’re also seeing warehouse execution systems (WES) filling software gaps and improving labor usage (among other things), working in concert with existing systems 

Uniform company Cintas, for example, used Logistiview to improve labor and increase productivity by 10-15% with a rapid implementation, bringing data out of offline handwritten processes and into a centralized view using tablets to capture, track, and optimize labor for value-added services, standardizing across all DCs.  Solutions in this space are showing 10-15% labor cost reduction and overtime reduction, with 12-18 months to ROI.  

Don’t Let Automation Envy Keep You from AI Benefits 

While warehousing and fulfillment operators sometimes feel like the last on the list to get investment approval, AI is changing the game – and quicky. These solutions can bring competitive advantage without a massive price tag or shop floor overhauls.  

Today’s speed-to-response demands require progress now. The barrier to entry for data-driven dynamic optimization is lower than I’ve ever seen. It’s time to shed the capital project burnout, put fresh eyes on the options, and identify your path to AI-driven results in warehousing.