Warehouse labor analytics is the practice of measuring, forecasting, and optimizing warehouse work, using engineered labor standards and productivity data to plan workforce coverage, balance workload, and reduce cost per unit. It turns labor from a fixed cost into a planned, measurable driver of throughput.
InfoSun turns operational opportunities into scalable, data-driven solutions for the warehouse and the network. We deliver warehouse labor analytics, warehouse solution design, slotting and pick-path optimization, and supply chain real estate analytics, so new and existing facilities run at higher productivity and lower cost-to-serve. Analysts and AI models work together under human supervision to size labor, design layouts, and validate the numbers before you invest.
Each service runs on the same human-governed, AI-native operating model. Explore any one for what it covers, the outcomes it drives, and how it is delivered.
Labor analytics and engineered standards raise warehouse productivity, often 20-40%.
Solution design and slotting cut travel and match capacity to real demand.
Footprint analytics place capacity where it lowers cost-to-serve.
Model layouts and labor before you commit to a new distribution center.
Typical ranges across supply chain, logistics, and enterprise engagements, baselined against your current numbers at the start.
Choose the processes together, and analyze them first
Stand up the team, governance, and KPIs
Where supervised AI suits the work, and where it does not
Agents draft and perform; your people approve
Tune against live KPIs with your team
Runs on Manhattan, SAP EWM, and Blue Yonder, plus your TMS and ERP. No rip-and-replace.
| Build in-house alone | Contract it out | Run it with InfoSun | |
|---|---|---|---|
| Speed to value | Slow ramp: hire, tool, and train first | Fast start | Fast start, on the systems you already run |
| Who owns the capability | You, eventually | The vendor | You: playbooks, models, and process transfer to you |
| Process knowledge and data | Stays in-house | Accumulates with the vendor | Stays in your systems and your data |
| AI in the operation | You build it yourself | On the vendor's roadmap | AI-native from day one, human-supervised |
| Measured on | Effort and milestones | Activity and SLAs | Outcomes against your baseline |
India hosts 1,700+ global capability centers, and 83% of GCCs are already engaging generative AI.
NASSCOM and Zinnov India GCC Landscape; EY GCC Pulse, 2025. Market context, not InfoSun results.
No logos to borrow, no invented results. We prove value the only way that counts: on your baseline, on your dashboards.
We measure your current cost, accuracy, and service together at the start, so every target is grounded in your reality.
We set the outcome numbers with you before work starts. No moving goalposts.
We prove the model on a single workflow or site before scaling, so risk stays small.
You see the movement on your own metrics, monthly, not on ours.
We run on the WMS, TMS, and ERP you already own.
AI agents propose, your experts approve. Never a black box.
Get the baseline and business case before you commit.
Measured on cost, accuracy, and decision speed, on your P&L.
A selection of delivered outcomes from InfoSun engagements. Client names and financials are withheld.
Results from real InfoSun engagements, baselined at the start of each engagement. Client names and financials are withheld by request.
Warehouse labor analytics is the use of data and engineered labor standards to measure, forecast, and optimize warehouse work. It tracks productivity by task, area, and shift, forecasts the workload a facility will face, and identifies where time and cost are lost. The result is better workforce planning, balanced workload, and lower cost per unit.
You optimize warehouse labor productivity by measuring work against engineered labor standards, then removing the causes of lost time: excess travel, poor slotting, unbalanced workload, and idle periods. Better slotting and pick-path design cut walking distance, while labor forecasting matches coverage to demand. Combined, these levers commonly lift productivity in the 20-40% range.
Warehouse slotting optimization is the practice of placing SKUs in the best locations based on demand, velocity, size, and handling needs. Fast movers go where they are quickest to pick, and complementary items are grouped to shorten pick paths. Good slotting reduces travel time and congestion, which directly raises picking productivity and throughput.
Industrial real estate determines how close inventory sits to customers, which drives transportation cost, delivery speed, and service levels. The right number, size, and location of facilities lowers total cost-to-serve, while the wrong footprint adds freight cost and delay. Supply chain real estate analytics models these trade-offs so network and facility decisions are data-driven.
Designing a new distribution center starts with demand and SKU profiling, then defines throughput requirements, storage media, material flow, labor model, and automation fit. Engineers model layouts, slotting, and workforce plans, then validate throughput and cost before construction. The aim is a facility that meets service targets at the lowest cost-to-serve and scales with growth.
Engineered labor standards are calculated by breaking each task into its elements, measuring the time each should take under defined conditions, and adding allowances for fatigue and delay. The result is a fair, data-based expectation of how long work should take. Standards then anchor labor forecasting, performance measurement, and productivity improvement.
Written and reviewed by the InfoSun operations team. Last updated July 14, 2026.
In a 30-minute assessment we map one high-cost workflow against your baseline and show the path to it.
30 minutes. Scoped to your operation. No obligation.
