How Case Picking Is Impacting Your Warehouse (& How Orchestration Fixes It)

Case picking sits at the center of warehouse operations. It consumes close to half of all labor hours, touches nearly every outbound order, and remains one of the least automated processes on the floor.

Here’s the problem: up to 55% of case-picking time is spent walking between picks instead of handling product. That wasted movement limits throughput, drives up labor costs, and creates congestion that hurts both safety and morale. As order volumes climb and labor markets stay tight, this inefficiency shows up in missed service levels and rising cost per order.

The workflow itself has barely changed since the 1980s. Workers still walk long distances to locate and retrieve cases, even as same-day and next-day delivery have become standard expectations. Tools like voice picking and pick-to-light have added small gains, but they haven’t solved the core issue: when workers spend more time in transit than doing value-added work, productivity suffers.

You don’t need to replace your WMS or rebuild your facility to fix this. You need an execution layer that treats people, robots, and equipment as one coordinated system and directs case picking in real time. That’s what orchestration does.

What Case Picking Is and Why It Dominates Labor

Case picking means pulling full cases or cartons from storage to fulfill orders rather than picking individual units. It shows up across retail distribution centers shipping to stores, wholesale operations fulfilling B2B orders, and omni-channel sites handling mixed case and each-pick orders.

Case picking eats labor hours because many SKUs ship as full cases, pick faces sit far apart, and cases are heavy enough to slow workers down. Legacy warehouse layouts were built for pallet moves and store replenishment, not the high-frequency, varied order patterns that e-commerce generates.

Walk any warehouse floor and you’ll see pickers pushing carts down long aisles, clustering in hot zones during peaks, and backtracking when stock positions shift or inventory runs short. From a metrics view, you see high travel time as a share of the pick cycle, wide variation in picks per hour across workers and shifts, and recurring congestion in the same zones.

Why Traditional Case Picking Fails at Scale

Traditional case-picking models rely on static assignments. The WMS releases batch waves based on cutoff times, assigns tasks to fixed zones, optimizes pick paths once at wave release, and leaves supervisors to handle exceptions manually via phone or radio.

This works at moderate volume. At higher volume with tight service-level agreements, the cracks appear fast.

Excess travel. Workers walk long routes to chase scattered pick lines. Batching logic reduces travel at planning time, but it breaks when volume, inventory levels, or staffing shift mid-shift. Long travel distances drag down picks per hour and inflate cost per pick.

Congestion and blocking. Multiple pickers converge on the same hot SKUs or aisles during demand spikes. AMRs, tuggers, and forklifts share narrow space with case pickers, and there’s no system-level coordination to prevent it. Queues form in front of high-demand locations, raising wait times and collision risk.

Poor response to real-world issues. Short picks, damaged packaging, and moved pallets create detours. Waves and tasks stay fixed even when work piles up in one area. Supervisors intervene manually, but adjustments are slow and inconsistent.

Worker strain. High step counts and repetitive handling lead to fatigue. Rushed walking through crowded aisles raises incident risk. Mental load stays high because workers make constant micro-decisions about where to go next and what to do when exceptions appear.

All of this stems from one root cause: the system plans work in batches but doesn’t control execution second by second.

The Limit of a WMS-Centric Model

Most warehouses run on a WMS designed as a system of record for inventory and orders and a planning engine for waves, routes, and pick lists. For case picking, the WMS creates batches based on rules like customer priority or route cutoffs, assigns tasks to zones and workers, optimizes pick paths once at release time, and sends those tasks to RF devices, voice systems, or pick-to-light tools.

This approach has known limits. The decision horizon is long—minutes to hours. Adjustments are coarse: re-wave, reprint, reassign. The WMS doesn’t continuously rebalance work as conditions change on the floor.

In practice, the WMS tells you what needs picking and where it is. Workers decide in real time how the floor actually runs. There’s no system-level control over traffic, congestion, or task sequencing as inventory shifts or demand spikes.

This gap is where orchestration fits.

What Orchestration Is in Concrete Terms

Orchestration is a software layer focused on execution. It sits on top of or alongside your WMS and does four things:

1. Consumes live data from orders, inventory exceptions, task status, location sensors, congestion levels, and AMR state.
2. Treats workers, AMRs, forklifts, and other assets as resources with known capabilities and current locations.
3. Breaks work into discrete tasks and continuously sequences them based on priority, proximity, and system load.
4. Assigns the next best task to each resource in real time.

Think of it this way: the WMS decides what needs to be picked and where it is. Orchestration decides who does it next, from where, and in what order to keep flow steady and travel low.

Orchestration is event-driven. It reacts to new orders, completed tasks, exceptions, and sensor signals. It optimizes in short cycles—seconds to minutes instead of hours. It’s policy-driven, so you set the rules for priorities, zones, safety constraints, and labor allocation, and the system executes within those boundaries.

How Orchestration Differs From a Traditional WMS

A WMS is the system of record. It plans work, manages inventory, and releases waves or tasks based on rules and cutoff times. It provides route optimization mostly in batch mode.

Orchestration is the real-time execution brain. It continuously sequences tasks, assigns resources, and reroutes work as conditions change. It explicitly targets flow, utilization, and congestion.

The WMS operates on a decision horizon of minutes to hours with waves and batches planned in advance. Orchestration operates on a horizon of seconds to minutes with constant micro-adjustments to tasks and routes.

When disruptions happen, the WMS flags exceptions and waits for supervisors to manually reassign or re-wave work. Orchestration auto-reroutes tasks around jams, stockouts, or down equipment without human intervention.

The WMS assigns workers to zones or waves with limited mid-shift rebalancing. Orchestration dynamically rebalances pickers and zones to wherever demand and congestion require it.

For automation coordination, the WMS sends orders to robots or a warehouse control system and receives confirmations, but there’s limited joint optimization. Orchestration coordinates people, AMRs, and equipment as one system to cut travel and queues.

Most high-performing sites use both. The WMS remains the inventory and order backbone. Orchestration sits on top to continuously optimize case-picking workload. For many operations, the practical path is to keep the existing WMS and add orchestration where travel, congestion, and priority management are chronic problems.

How Orchestration Changes Case Picking in Practice

Dynamic task allocation. Without orchestration, workers have fixed pick lists or waves. They complete the list, then ask for the next one or wait to be reassigned. One zone gets swamped while another runs light.

With orchestration, tasks are broken into smaller units: individual case pulls, short picks, exception tasks. The system assigns the next task to each worker based on current location, skills, and priority. Zones are rebalanced continuously.

The result: fewer long empty walks back to a start point, less time waiting for reassignment, more consistent picks per hour across workers.

Picker-AMR pairing. In traditional patterns, pickers walk to locations, pick cases, and push carts or pallets to staging or the dock. With orchestration and AMRs, the robots handle long moves between zones and staging. Pickers stay in short, dense pick zones. Orchestration decides when to send a robot, which robot, and which picker it meets.

The system assigns an AMR to a route and attaches tasks for multiple SKUs on that route. As the AMR approaches a zone, tasks are queued to nearby pickers. Pickers pick to the robot, then move to the next nearby location while the robot continues its route.

Walking distance per picker drops sharply. Pickers handle more lines per hour without extra physical strain.

Congestion management. Traditional systems have no control over who enters which aisle when. Hot zones clog up during peaks, and there’s no mechanism to prevent it.

With orchestration, the system tracks active tasks per aisle per time window. It limits overlapping entries or staggers tasks into a busy area. It can divert tasks to alternative pick faces when appropriate.

You set tactical levers: maximum concurrent workers per aisle or zone, priority rules for heavy equipment versus pedestrians, time-based throttling during high-volume intervals. This improves throughput stability in known bottleneck areas and raises safety since fewer people and machines compete for the same space.

Exception handling. Exceptions slow manual case picking more than most executives realize. Without orchestration, a worker hits a short pick or damaged case, stops, calls or flags a supervisor, or leaves the area. Work-in-process piles up and the wave falls behind.

With orchestration, the worker flags the issue on their device. The system creates one or more new tasks: alternate location pick, inventory check, replenishment request, or substitution. Those tasks are assigned automatically to the best available resource. This reduces idle time for the original picker, eliminates unplanned detours, and cuts manual follow-up by supervisors.

Workforce and training impact. Because orchestration drives the next task and route, workflows become easier to learn. New workers focus on following simple screen or voice prompts. Veterans can be deployed to higher-value exceptions and coaching.

Training can shrink from weeks to days because workers no longer need to read the floor and plan their own routes independently.

Technical Architecture: How Orchestration Fits Your Stack

The main concern for executives is how orchestration fits your current systems and what changes it requires.

System roles. The WMS stays the system of record for inventory and orders. If you have a warehouse control system, it continues to control conveyors, sorters, and AS/RS at a low level. Orchestration sits beside them as a real-time decision engine for work execution.

Data flows. The WMS sends orders, tasks, inventory status, and constraints. Orchestration subscribes to events: order creation, allocation, stock changes, exceptions. Orchestration returns task assignments, status updates, and completion events. AMRs and other automation send location, availability, and task state back to orchestration, not just the WMS.

You want event-based APIs or message buses rather than batch file drops. You need clear definitions of who owns each decision: WMS for planning, orchestration for task sequencing, WCS for motion control.

Resource model. Orchestration needs a clean model of workers (skills, shift times, certifications, device types), equipment (AMRs, tuggers, forklifts, put walls, stations), zones and locations (pick faces, aisles, staging, docks), and task types (case picks, replenishments, exception tasks, audits).

Decisions are built on top of this model, so a key implementation step is normalizing this data across systems.

KPIs That Move First

Orchestration shifts a small set of execution KPIs significantly.

Travel and pick productivity. Travel time as a share of the pick cycle drops because work is dynamically routed and robots or buffers absorb long hauls. Average distance per pick falls. Picks per labor hour rises as more of each minute is spent handling product rather than walking.

Throughput and service. Overall throughput—lines or cases per hour—increases as orchestration smooths flow and avoids local bottlenecks in hot zones or aisles. Order cycle time from release to ready-to-ship shortens because urgent work can be re-prioritized and resequenced in real time instead of waiting for the next wave or manual intervention. On-time completion against cutoffs or carrier departures improves.

Cost and utilization. Cost per case or line picked declines as travel and idle time fall and the same volume is handled with fewer labor hours. Labor utilization becomes more balanced across zones and shifts, reducing overtime and the peaks-and-valleys pattern in workload. AMR and equipment idle time drops because orchestration explicitly manages traffic and task timing.

Quality and safety. Picking accuracy can improve modestly as workers face fewer distractions and clearer, system-driven task queues, which reduces mis-picks. Safety KPIs such as recordable incident rate or near-miss count benefit from less cross-traffic, fewer rushed long walks, and better ergonomics from shifting effort away from walking to controlled handling. Overtime and fatigue indicators improve.

You should tie any orchestration project to a small, clear set of these metrics from the start.

How to Prove Impact: A Tactical Test Plan

To keep vendors honest and focus your team, treat orchestration like any other automation program.

Step 1: Define scope. Pick a bounded area: a set of case-pick zones, a building, or a flow tied to one customer channel. Define the workflows in scope: case picking only, or case picking plus replenishment and exceptions.

Step 2: Establish a baseline. Capture four to eight weeks of data for your target KPIs. Record context: volume, mix, headcount, seasonal effects. Freeze other major initiatives in that area during the baseline window if you can.

Step 3: Run a controlled experiment. Options include A/B by area (one group of zones with orchestration, one group without), A/B by team (one picker cohort on orchestrated tasks, one cohort on traditional waves), or time-based (orchestration on during some shifts or days, off during others, under similar volume).

Compare travel time share, picks per hour, congestion events or idle time, and cycle time and service levels.

Step 4: Normalize and attribute. Normalize to order volume and mix. Exclude periods where other changes occurred, such as layout work or big promotions. Tag tasks as orchestrated versus non-orchestrated for clean comparisons at the transaction level.

Step 5: Convert to financial impact. Translate travel time reduction into labor hours freed. Convert picks per hour gains into capacity gains or headcount reduction. Translate cycle time gains into on-time performance and potential revenue retention. Aim for ranges rather than one exact number, and tie them back to the KPIs you set at the start.

How to Start: A Practical Playbook

For executives ready to move, a simple playbook looks like this:

Diagnose. Measure travel time share in case picking. Map congestion hot spots and exception patterns. Benchmark picks per hour and cost per pick.

Prioritize. Select one or two high-impact areas where travel, congestion, and labor cost are most visible. Confirm that existing systems can integrate with an orchestration layer.

Design policies. Define priorities by orders, customers, lanes, or carriers. Define safety and congestion rules by zone. Define pairing rules for workers and AMRs.

Prepare data. Clean location and zone definitions. Assign worker skill attributes. Align WMS task definitions with orchestration’s task model.

Implement and iterate. Start with orchestration in assist mode, where it recommends tasks and supervisors can override. Move to direct mode as confidence grows. Review KPIs weekly and adjust policies.

Scale. Extend orchestration from initial zones to more flows. Add more automation such as AMRs or automated buffers once execution is stable. Refine rules by time of day, season, and account.

Why This Matters Now

Case picking itself hasn’t changed much since the 1980s. The environment around it has.

E-commerce and omni-channel demand create more orders with smaller, more varied lines. Same-day and next-day delivery expectations are standard. Labor markets stay tight and turnover remains high. Tariffs and supply variability introduce irregular volume and inventory position shifts.

You can’t rely on adding more people each peak season to cover inefficiency. You also don’t need a full system replacement to gain control.

Orchestration gives you higher throughput from the same footprint and headcount, more predictable performance under volatile demand, safer and less stressful work for pickers, and clearer levers for supervisors and planners.

If case picking consumes a large share of your labor spend and you lack real-time control of how work flows across the floor, orchestration is the next logical step. It turns a static, walk-heavy process into a coordinated, data-driven workflow that can keep pace with the demands your warehouse faces now.

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Frequently Asked Questions

What is the main difference between a WMS and orchestration software?

A WMS is the system of record that plans what needs to be picked and when. Orchestration is the real-time execution layer that decides who picks what next, from where, and in what order to minimize travel, congestion, and idle time. The WMS operates in batch mode with waves planned in advance. Orchestration operates second by second with continuous task rebalancing.

Can orchestration work with my existing WMS?

Yes. Most high-performing sites keep their existing WMS as the inventory and order backbone and add orchestration on top to optimize execution. Orchestration integrates via event-based APIs or message buses. It doesn’t replace the WMS; it fills the execution gap the WMS was never designed to handle.

How long does it take to see ROI from orchestration?

Many sites see measurable impact within weeks of deployment. Travel time as a share of the pick cycle drops quickly when robots handle long hauls and workers stay in productive zones. Picks per hour rise as more time is spent handling product rather than walking. Start with a controlled test in one zone and track KPIs weekly to validate impact before scaling.

What happens when an exception occurs, like a short pick or damaged case?

Without orchestration, the worker stops, flags a supervisor, and waits for manual intervention. Work-in-process piles up. With orchestration, the worker flags the issue on their device and the system automatically creates new tasks: alternate location pick, inventory check, replenishment request, or substitution. Those tasks are assigned to the best available resource without stopping the flow.

Do I need AMRs or other robots to use orchestration?

No. Orchestration delivers value by coordinating workers and tasks in real time even without automation. That said, orchestration and AMRs work best together. The orchestration layer decides when to send a robot, which robot, and which picker it meets, so both humans and machines operate at peak efficiency.

How does orchestration reduce congestion in high-traffic aisles?

Orchestration tracks active tasks per aisle per time window and limits overlapping entries. It staggers tasks into busy areas and can divert tasks to alternative pick faces when appropriate. You set tactical rules like maximum concurrent workers per aisle, priority rules for heavy equipment versus pedestrians, and time-based throttling during high-volume intervals.

What KPIs should I track to measure orchestration’s impact?

Focus on a small, clear set: travel time as a percentage of pick cycle, picks per labor hour, order cycle time from release to ready-to-ship, cost per case or line picked, labor utilization across zones, AMR idle time, and safety KPIs like near misses or recordable incidents. Tag tasks as orchestrated versus non-orchestrated so you can compare performance at the transaction level.

How do I prove that gains come from orchestration and not other initiatives?

Run a controlled experiment. Use A/B by area (one group of zones with orchestration, one without) or A/B by team (one picker cohort on orchestrated tasks, one on traditional waves). Establish a clean baseline, freeze other major initiatives during the test window, normalize to order volume and mix, and tag tasks so you can compare at the transaction level. Present results as ranges tied back to the KPIs you defined at the start.

Does orchestration make training easier for new warehouse workers?

Yes. Because orchestration drives the next task and route, workers follow simple screen or voice prompts instead of reading the floor and planning routes independently. Training can shrink from weeks to days. Veterans can be deployed to higher-value exceptions and coaching instead of spending time on navigation decisions.

What’s the first step if I want to explore orchestration for my warehouse?

Start by diagnosing your current state. Measure travel time share in case picking. Map congestion hot spots and exception patterns. Benchmark picks per hour and cost per pick. Then prioritize one or two high-impact areas where travel, congestion, and labor cost are most visible and confirm that your existing systems can integrate with an orchestration layer.

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Key Takeaways

• Case picking consumes close to half of warehouse labor hours, with up to 55% of that time spent walking instead of handling product.
• Traditional WMS-based models plan work in batches but don’t control execution second by second, creating gaps in congestion management, exception handling, and real-time task rebalancing.
• Orchestration acts as a real-time execution layer that coordinates workers, AMRs, and equipment as one system to minimize travel, smooth traffic, and handle exceptions automatically.
• High-performing sites keep their existing WMS and add orchestration on top to optimize case-picking workload without full system replacement.
• Orchestration moves specific KPIs fast: travel time share drops, picks per hour rise, order cycle time shortens, cost per pick declines, and safety incidents fall.
• Prove impact with controlled experiments using A/B testing by area or team, clean baselines, and transaction-level tagging of orchestrated versus non-orchestrated tasks.
• Start by diagnosing travel time share, mapping congestion hot spots, and selecting high-impact zones where orchestration can deliver measurable ROI within weeks.