with demand planning is huge,” Quinn said. “Too much inventory makes the warehouse less efficient, drains capital, and decreases turns. When you have the right amount of product — not so many ‘just in case’ items — cash flow is healthier, turns are higher, and profit margins improve.”


5. Reduce Safety Stock Planning traditionally operates on fixed weekly or


monthly cycles with safety buffers absorbing variability. AI-enabled real-time analytics are reducing that depen- dency. Oca described the shift as a “tremendous change in terms of how inventory, pricing, and service will now be managed.” Instead of waiting for the next planning run, real-time inferencing reacts to continuously updated demand signals with dynamic readjustment of reorder points and lead times. What that translates into is the ability to lower safety


stock without eroding service. Consider a chain restau- rant procuring supplies ahead of a limited-time menu roll- out, a situation where demand often ramps up faster than projected. With real-time analytics, inventory rebalancing can happen proactively instead of only after product goes out of stock.


6. Optimize Slotting Where traditional slotting is often reactive and based


mostly on velocity, AI-driven slotting is predictive and accounts for more subtleties. “You don’t want to change your layout frequently because it’s a chore,” Quinn said. “But looking at maybe your top 100 items once a month and adjusting based on seasonality and those sorts of things. Instinctively you think you’re going to sell more air conditioners in spring and summer and more heaters in winter. But it’s more nuanced than that. AI can pick up patterns that humans can’t.” MLMs can be used to predict the best slotting location


for new products by analyzing factors such as dimensions, weight, and similarities to existing SKUs. They’re able to highlight correlations between products that could be missed with manual analysis. If 75% of customers who order a specific oven also order the same cleaning acces- sory, for example, the system would recommend placing them closer together to reduce pick-path distance and cross-zone congestion. Using that same pattern recognition, AI models can


also analyze error history and identify products that are prone to picking errors, such as situations where two SKUs have similar packaging, naming, or item num- bers. In those cases, the AI may recommend physically


separating the products to reduce the chance that em- ployees pull the wrong item from the shelf.


7. Align Labor with Volume Labor challenges are often attributed to headcount,


but sometimes the real challenge is timing. AI labor forecasting can help supervisors align staffing levels with projected inbound and outbound volume to reduce congestion and improve throughput. For instance, a midweek inbound surge can be anticipated rather than being reacted to. Armed with that foresight, distributors may choose to shift replenishment resources to receiving in advance and expand picking coverage ahead of shipping cutoffs.


8. Prevent Equipment Downtime AI can use telemetry from conveyors, forklifts, and


sortation systems to automatically predict equipment failures before they disrupt operations. Transitioning


Digitize Your Foodservice Equipment & Supplies Freight Needs With a Customized LTL Solution


B2B and B2C eCommerce has already advanced the FE&S industry, however customer expectations are driving new transportation solutions. We manage more than 600,000 FE&S shipments each year and know that gains in transportation cost and efficiency go a long way in driving your long-term success.


FE&S margins are slim. Use freight as a margin generator and competitive advantage.


us.kuehne-nagel.com/-/services/road-transport/ ltl-ecommerce


September 15-18, 2026 • Grand Hyatt Deer Valley • Park City, Utah Spring 2026 13


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72