Is your data lying to you — or are your colleagues worried that it might be?
This concern plagues supply chain teams worldwide, where crucial decisions are made based on conflicting metrics, outdated reports, and stubbornly relying on old ways of working because “This is how we’ve always done it.” While organizations invest millions in sophisticated data systems, the bitter truth remains: most of your employees still can’t access the insights they need when they need them.
In addition to frustration, this disconnect is costing you cold, hard cash. With supply chain leaders reporting resilience issues, organizations are bleeding money through excess inventory, expedited shipments, and missed opportunities, all because the right data isn’t reaching the right people at the right time.
Natural language query (NLQ) tools are emerging as the bridge across this divide, allowing anyone to ask questions about their supply chain in plain, conversational language
Here’s how these tools are transforming work for five key roles who have traditionally been separated from the data they need:
The Pain Point: “I know our inventory numbers are off, but I don’t have time to wait for IT to run another report.”
Supply planners are trapped in a perpetual dilemma: order too much and waste capital, order too little and risk stockouts. Many rely on gut feeling or outdated spreadsheets because accessing accurate data across systems is too cumbersome, which means organizations maintain excess inventory while still experiencing stockouts.
The Transformation: Imagine a supply planner named Alex who needs to make inventory decisions for an upcoming season.
Without NLQ tools, Alex would compile data from multiple systems, manually analyze trends, and probably add a “safety buffer” to account for uncertainty.
With NLQ tools, Alex simply asks: “Show me seasonal demand patterns for products with the highest stockout rates last year, compared to current inventory levels.”
The system instantly visualizes the patterns, helping Alex make precise adjustments rather than blanket increases.
When Alex’s colleague in another region insists they should order more of a particular component based on “experience,” Alex can quickly counter with data: “Actually, the last three times we increased that order we ended up with excess that took six months to clear.”
The Pain Point: “By the time I find out about a supply issue, my production line is already down.”
Production managers live in a world where every minute of downtime costs thousands. Yet many spend hours hunting for information across systems or waiting for reports that arrive too late to prevent disruptions. With limited real-time tracking, they’re often the last to know about supply issues that directly impact their lines.
The Transformation: Consider Maria, a production manager at a manufacturing plant. Traditionally, she’d discover material shortages only when the line was about to run out or worse, after it already had.
With NLQ tools, Maria starts her day by asking: “What inbound deliveries are delayed that will impact today’s production schedule?” She immediately sees three critical components running late and proactively adjusts the production sequence.
When a new production issue arises, Maria doesn’t rely solely on tribal knowledge. Instead, she asks: “When was the last time we experienced this problem, and what was the solution?”
The system retrieves historical data that helps her team resolve the issue faster than trying to track down the employee who “fixed it last time.”
The Pain Point: “Everyone wants to know where their shipment is, but I’m still waiting for the carrier to return my call.”
Logistics teams have traditionally operated reactively, responding to issues after they occur and struggling to provide accurate information to internal customers. When ETA accuracy issues affect operations, they’re often caught between angry customers and unresponsive carriers.
The Transformation: Take James, a transportation manager handling hundreds of shipments daily.
Before NLQ tools, he’d respond to status requests by manually checking multiple carrier portals, making phone calls, or simply saying, “I’ll get back to you.”
Now, James can instantly answer questions like: “Which of our priority shipments are currently delayed, and what’s the impact on customer delivery promises?” He not only sees the delayed shipments but also which customers will be affected and by how much.
When the VP of Sales challenges why they’re not using a particular carrier anymore, James doesn’t rely on anecdotes. He asks: “Show me on-time performance and damage rates for Carrier X compared to our current providers over the past year.” The data tells a clear story that ends the debate.
The Pain Point: “I know it’s in the warehouse somewhere—we just need to find it.”
Warehouse managers juggle space constraints, labor shortages, and constantly changing priorities, often with systems that don’t talk to each other. With inventory accuracy typically between 89-99% (below the 99.5% benchmark for best-in-class), they’re constantly fighting discrepancies that ripple throughout the supply chain.
The Transformation: Meet David, a warehouse supervisor managing a 500,000-square-foot facility.
Traditionally, when asked about a specific order or inventory discrepancy, he’d need to dispatch someone to physically verify or spend time reconciling multiple system reports.
With NLQ tools, David responds to challenges immediately. When asked why an order is delayed, he queries: “Show me the current status and location history of order #45678.” He instantly sees that the items were received but placed in an overflow area during a period of high volume.
When peak season approaches, David doesn’t just copy last year’s staffing plan. He asks: “What were our busiest days last peak season, what caused the highest labor demands, and how did our staffing align?” He discovers patterns that help him create a more precise plan based on data, not just institutional memory.
The Pain Point: “I can tell you what we spent, but not whether it was necessary or effective.”
Finance professionals are expected to control costs while enabling the business, but they often lack visibility into the operational realities behind the numbers. With working capital management challenges requiring 10-15% more capital than necessary, they struggle to connect financial outcomes to supply chain decisions.
The Transformation: Consider Sarah, a finance director responsible for supply chain costs.
Traditionally, she’d see expenses after they occurred and struggle to influence future spending without understanding the operational context.
With NLQ tools, Sarah can ask: “What’s driving the increase in expedited freight costs this quarter, and which products and lanes are most affected?” She immediately sees that delays from a specific supplier are causing most of the expediting, information she can use to have a targeted conversation with procurement.
When the CFO questions rising inventory levels, Sarah doesn’t just report the numbers. She asks: “Show me inventory turns by product category compared to forecast accuracy.” The visualization reveals that poor forecasting in specific categories is driving most of the excess, allowing for a focused improvement effort.
The ability to ask questions in plain language and get immediate answers transforms how organizations operate. According to Blume Global, NLQ tools are “removing much of the administrative overhead in managing the supply chain” by allowing users to query complex datasets using everyday language.
This democratization of data addresses fundamental challenges that have plagued supply chains for decades:
In supply chain, disruptions are constant, which means that accessing and understanding data is critical. The global connected logistics market is expected to grow rapidly, reaching $123.5 billion by 2032.
Natural language query tools are a key component of this growth, enabling organizations to build more resilient, efficient, and customer-centric supply chains by putting the power of data in everyone’s hands — not just analysts and IT specialists.
For organizations looking to implement NLQ tools, success depends on:
The future belongs to organizations where everyone, from the warehouse floor to the C-suite, can ask questions and get answers without technical barriers. In this future, decisions aren’t based on who speaks the loudest or has been around the longest, but on what the data actually reveals.
At FourKites, we built FourSight because we saw firsthand how supply chain teams struggled with data accessibility. Instead of waiting days for reports or battling spreadsheets, your team can now simply ask questions like “Which carriers had the most delays last quarter?” or “Show me dwell time by facility” in any of 24 languages and get instant visual answers.
When everyone from planners to warehouse managers works from the same reliable data, those painful cross-departmental debates about whose numbers are right simply disappear. FourSight not only saves time, but fundamentally changes how teams collaborate and make decisions. No specialized skills needed, no complex setup, just straightforward answers that let you focus on solving problems instead of finding data. Curious about how natural language queries might work with your supply chain data? Check out all the details on FourSight.
