09/10/2026
How GEODIS optimizes client warehouse operations using digital twin technology
GEODIS has created a complete digital twin solution for our warehouse clients. The digital twin delivered impressive results for our pilot client including almost $200K in annual labor savings, 15% in headcount optimization, and an 18% increase in capacity.
Key takeaways
GEODIS has developed a "digital twin" solution that optimizes the warehouse operations we manage on behalf of our clients. We use four main technology pillars that bring together predictive labor planning, process intelligence, virtual testing, and real-time monitoring. This article covers the first two of those pillars.
We piloted our digital twin with a large consumer electronics client and achieved some impressive results:
- Labor cost optimization: $197K in annual savings by optimizing headcount and team sizes for our client's warehouse operations
- Capacity expansion: 18% increase in daily throughput from 85,000 to 100,000 units without requiring any additional capital investment from our client
- Process efficiency gains: 12% improvement in pack operations and 6% improvement in pick operations in our client warehouses
- Predictive planning: Data-driven forecasting for a deep understanding of capacity and labor requirements
Our four technology pillars work closely with one another, creating greater compound value and results for our clients. We're investing in technology that helps us deliver superior results for your operations.
This is the first of three articles on how GEODIS uses digital twin technology in the warehouses we run for clients. Part 1 explains what a digital twin is, the problems it solves, and the two pillars that produced our pilot results: predictive labor planning and process intelligence. Part 2 covers virtual testing, near-real-time monitoring, and how all four pillars work together. Part 3 explains how implementation runs and the technical architecture behind it.
Why warehouse efficiency matters
When you work with a third-party logistics provider (3PL), you're counting on them to improve your warehouse efficiency and control your costs. Product launches create demand spikes that can drive labor costs up or service levels down. Capital investments in equipment and layout changes carry real financial risk.
A digital twin helps you and your warehouse provider to understand and manage these impacts.
Consider what happens during a major product launch when your order volumes spike unpredictably. Your warehouse provider faces difficult staffing decisions: how many people do they need, and when? Without accurate demand forecasting, you're either overstaffed or understaffed. Either scenario creates problems.
Introducing new technology carries even higher stakes. A conveyor system upgrade might cost $500,000 or more. Layout changes can cause weeks of disruption. Making these investments without the ability to test them virtually means committing significant capital that you can't validate in advance.
It's also challenging to understand what is happening in complex warehouse operations. Orders get stuck. Bottlenecks emerge during shifts. Process inefficiencies lower productivity but remain hidden. Without real-time monitoring, you and your 3PL are reacting to problems rather than preventing them.
The logistics industry is investing heavily in digital twin technology with the global market growing by 30 to 40 percent annually. Supply chain and logistics applications are among the fastest-growing segments. But many companies are taking a narrow approach. They're using digital twins for single purposes like testing new equipment or visualizing warehouse layouts. That's useful but limited.
GEODIS built something different. Our four-pillar solution combines predictive planning, process intelligence, virtual testing, and real-time monitoring in a single platform. The pillars work together, creating value that point solutions simply can't match. We use this technology every day to optimize the warehouse operations we manage for our clients.
Why are these capabilities essential right now? Consider the pressures you're facing. Ecommerce keeps growing and now represents almost 20 percent of all retail. Warehouse labor costs have surged more than 15 percent since 2020. Your customers expect faster delivery and won't tolerate delays.
Traditional approaches can't keep up. Spreadsheets, historical averages, reactive problem-solving. They're breaking under the weight of these demands.
This series explains how GEODIS digital twin technology works and why it matters for your operations. This article covers the challenges a digital twin addresses, what the technology is, and the two pillars that delivered measurable results in our pilot implementation with a major consumer electronics manufacturer. Part 2 covers the two pillars that test changes before they happen and catch problems as they form, and shows how all four work together to multiply value. Part 3 explains how implementation runs and the technical architecture behind it. You'll see how digital twin technology helps you to make smarter decisions and improve capacity, while reducing your risks and costs.
Do you need the advantage of digital twin technology? Get in touch with GEODIS.
The challenge of modern warehouse complexity
As modern warehouses become more complex, 3PL providers need to handle four fundamental challenges.
Peak season volatility and demand uncertainty
New product releases create surges in demand that can strain workforce planning. Volumes can change dramatically during peak periods. Managing this type of volatility is expensive, and maintaining permanent excess capacity to handle peaks significantly increases costs.
Workforce planning based on historical averages often misses the mark. Each product launch brings different variables. Product mixes change. Timings shift. Market conditions evolve. The financial impact is real. Over-staffing to handle potential peaks wastes labor dollars when volumes run lower than forecast. Under-staffing to control costs risks service failures when volumes spike higher than expected.
Planning methods that adapt to actual demand patterns, rather than relying solely on historical averages, avoid both outcomes.
Capital investment risk without validation
Equipment investments carry long-term consequences. You're looking at six or seven-figure decisions for material handling systems, conveyor upgrades, or automation equipment. Equipment vendors provide throughput projections and ROI estimates, but validating these claims before implementation? That's difficult.
Traditional approaches rely on vendor documentation and theoretical calculations. The real challenge is understanding how equipment will perform with your specific order profiles and operational constraints before you commit capital and disrupt operations.
Testing equipment performance virtually against actual operational data reduces the risk of major spending decisions.
Limited operational visibility
Much 3PL reporting shows what happened hours or days ago. Daily summaries arrive the next morning. Monthly reviews analyze performance after the fact. This creates a reactive cycle where 3PLs discover problems after they've already impacted operations.
Before implementing real-time monitoring in our pilot, operational issues could go undiscovered. Orders might get stuck between pick and pack, and the operations team wouldn't know until the end of shift reports showed the backup. By then, customer commitments were already at risk.
You should see orders that encounter issues while there's still time to intervene. Bottlenecks forming during a shift should be detectable as they develop, not discovered in end-of-day reports. Process variations that reduce efficiency should be identifiable quickly rather than waiting for quarterly reviews.
Visibility at that level lets an operations team solve problems before they become major issues.
Process inefficiency identification
Efficiency improvements often hide in operational data. Workers may use different pick paths with varying levels of efficiency. Pack procedures may differ between shifts, with some approaches being faster than others. Bottlenecks may only appear when volumes reach certain thresholds. Without systematic analysis of historical performance data, these patterns remain invisible.
Our pilot revealed shift changeover problems that weren't visible through standard observation methods. For example, the 2 PM hour on high-volume days showed consistent productivity drops that had been attributed to "normal afternoon slowdowns." Only when we analyzed months of transaction data did we discover it was actually the shift change process creating the problem.
Continuous improvement traditionally relies on observation and periodic time studies. These methods are valuable, but they may miss patterns that only appear in comprehensive historical data analysis.
How a technology-enabled 3PL answers these challenges
These four challenges separate traditional operators from technology-enabled 3PL providers. GEODIS has built digital twin technology specifically to address these challenges. Our solution provides predictive labor planning based on confirmed order patterns, virtual equipment testing capability, near-real-time operational monitoring, and systematic process analysis using historical data.
Understanding digital twin technology
Each of these challenges demands a fundamentally different approach to warehouse optimization. That's where digital twin technology comes in.
A digital twin is a virtual replica of your physical warehouse operations that mirrors actual warehouse activity with high precision. It's a living model that updates continuously as orders flow through your facility. Every scan, every movement, every process step feeds data into the digital twin, creating a virtual model that's synchronized with your actual operations.
At GEODIS, data flows from the warehouse management system, automation systems, and operational databases into the digital twin every 15 minutes. This near-real-time refresh means the virtual model reflects what's happening almost immediately. The digital twin visualizes this information through interactive dashboards. We can see order status, process performance, capacity usage, and potential bottlenecks as they develop.
Without a digital twin, traditional 3PLs rely on spreadsheet projections and historical averages to plan labor. They introduce equipment changes based on vendor promises they can't validate. Process inefficiencies stay hidden until they show up in end-of-month reviews. Every major decision involves guesswork and risk because there's no way to test outcomes before committing resources.
Digital twin technology gives you and your 3PL a better way to make decisions. Want to test a layout modification? Do it virtually first, before disrupting operations. Considering a major equipment investment? Model it against your actual order data and see real throughput numbers before spending capital. Process inefficiencies hiding in your operation? The digital twin identifies exactly which steps help or hurt efficiency. Need to know tomorrow's staffing requirements? Get precise forecasts based on your confirmed orders, not historical guesses.
You move from making decisions based on assumptions to making them based on validated data.
Discover how GEODIS can use digital twin technology to help you compete better. Get in touch with GEODIS.
The four-pillar digital twin solution
Our digital twin solution combines four integrated technology pillars. Each pillar deals with specific operational challenges, and together they create a system where the whole delivers more value than the sum of its parts.
Here's how the first two pillars work and what they deliver for the operations we manage on your behalf. Part 2 of this series covers the other two.
Pillar 1: predictive labor planning and capacity management
This pillar forecasts the staffing and workforce needs for the warehouse operations that we manage for you. It lets us plan your warehouse labor based on actual order data rather than historical averages.
How it works
Our system analyzes your order forecasts using machine learning algorithms. Unlike traditional spreadsheet forecasting that uses simple averages, these algorithms (Prophet and NHiTS models) can detect patterns in your data that change over time. They automatically factor in external variables like holidays, peak dates, and product launch schedules that impact on the demand for goods. This means our forecasts adapt to your changing business reality rather than just repeating last year's patterns.
The forecasting works across multiple time horizons. Our operations teams get strategic planning with 365-day forward visibility, that's refreshed every week. We also get tactical planning at the "next day, next hour" level for immediate staffing decisions. The system calculates labor requirements by job category, breaking down exactly how many pickers, packers, and other roles we need in the facility.
Our capacity planning application maps the warehouse into operational blocks and identifies where bottlenecks will appear before they happen. Our operations teams can adjust parameters, test different scenarios, and see visual representations of capacity usage across your facility. The dashboards compare forecasted labor needs against current staffing levels, making gaps immediately visible to the team that's managing your operations.
We integrate all of this data directly with the GEODIS Warehouse Management System running your operations. The system pulls real performance data and feeds it back into the forecasting models, creating a continuous improvement loop. As productivity changes or processes improve, the forecasts automatically adjust to reflect actual capability.
Results from our pilot
During our pilot with a consumer electronics client, this pillar made a big difference to labor optimization. We reduced headcount in their operations from 324 positions to 275, a 49-position reduction representing 15% workforce optimization. That translated to $197,000 in annual labor cost savings for the client.
Service levels also stayed consistent throughout. We weren't cutting corners or risking capacity. We were finally matching staffing to what operations actually required rather than padding numbers based on worst-case assumptions.
What this means for you
Labor is typically your biggest controllable warehouse expense. [URL] When your 3PL can accurately forecast staffing needs, those savings flow directly to your bottom line. One of the main advantages for you is predictability.
Your product launches no longer mean your 3PL is guessing about staffing needs weeks in advance. Peak season planning shifts from reactive scrambling to confident forecasting. We know exactly what capacity you'll have available and what it will cost, letting you meet your commitments to your customers with confidence.
The forecasting also identifies when we're approaching capacity limits in your facility, giving you lead time to make decisions. Do you need to expand operations? Adjust fulfillment strategies? Have conversations with us about scaling? We'll help you make those decisions proactively with data, not reactively when you're already maxed out.
Pillar 2: process intelligence and optimization
This pillar analyzes your historical operational data to spot any inefficiencies in the operations we manage for you. It examines months of performance data to find patterns that daily observation misses. Where are orders getting stuck? Which process variations slow throughput? What's causing the bottlenecks our operations teams can't see?
How it works
We use Celonis, a process mining platform that analyzes how work flows through warehouses. It audits every transaction in the warehouse management system. Celonis tracks three critical data points: order IDs, activities performed on those orders, and timestamps for every action.
The system analyzes this data to identify performance issues, cycle time problems, process variants, and recurring errors. It compares what's actually happening in the warehouse against best practices and optimal workflows. This is "conformance checking," where we can see exactly where operations deviate from the expected process, down to the individual scan level.
Many operations teams can tell you about problems they're already tracking. Process mining finds the efficiency issues you had no idea existed. During our pilot, we discovered shift changeover inefficiencies and specific scanning errors that weren't on anyone's radar but were costing capacity.
The information overload challenge
Early in the pilot, we learned an important lesson. When we first activated Celonis for our client's operations, the volume of findings was overwhelming. The system identified hundreds of process variations and potential improvements. Our operations team didn't know where to start, and the wealth of data actually slowed decision-making rather than improving it.
We solved this by adding a dedicated process analyst to filter the Celonis output. The analyst reviews all findings and identifies the top five most actionable improvements. These are the changes that will have the biggest impact with reasonable implementation effort. Our operations teams now get a focused list of priorities rather than drowning in possibilities.
This is an important part of how we've implemented process intelligence. The technology is powerful, but it needs expert interpretation.
Results from our pilot
The pilot analysis highlighted some interesting findings. Shift changeovers were creating productivity drops, particularly during the 2 PM hour on high-volume days. Pack and audit processes showed scanning errors concentrated among specific workers, indicating training opportunities rather than system problems. The flow between pick completion and pack start showed delays suggesting bottlenecks in handoff processes.
These weren't guesses. They were patterns that we identified through systematic data analysis of millions of transactions. Our team made changes based on these findings.
Pack operations efficiency improved 12%, which enabled capacity to increase from 85,000 to 95,000 units daily. Pick process efficiency improved 6%, pushing capacity from 95,000 to 100,000 units daily. When combined together, these improvements translated into an 18% capacity increase without adding floor space or major equipment to the client's facility.
What this means for you
Many 3PLs tell you they're committed to continuous improvement. Process intelligence shows you the proof. We're systematically analyzing operations to find efficiency gains that traditional observation methods miss.
This matters because small process variations accumulate into significant capacity losses. A scanning error that adds 10 seconds per order doesn't seem meaningful. But multiply that across thousands of daily orders, and you've lost substantial throughput. Process intelligence quantifies these impacts and prioritizes which improvements actually move the needle.
The 18% capacity increase from our pilot shows what's possible. That's handling 15,000 more units per day through optimization rather than capital investment. For you, it means we can absorb volume growth without expanding your facility footprint or adding expensive automation.
Paul Maplesden
Lead Content Strategist
Next in this series: Part 2, testing changes before they happen and catching problems as they form. It covers virtual testing, near-real-time monitoring, and how the four pillars work together.