25 Warehouse Productivity Secrets: Evidence-Based Ways to Cut Cost, Errors and Turnover

Most warehouses are losing money in places their dashboards never show. The pallet count looks fine, the trucks leave roughly on time, and the shift supervisor is busy all day. Yet the biggest costs hide in plain sight: pickers walking half their shift, inventory records that disagree with the shelf, new starters who leave before they are fully productive, and trucks queuing at a dock nobody scheduled.

Those are not hunches from a consultant's slide deck. They come from decades of operations research, government labour and safety data, and field studies in real distribution centres. We went through the peer-reviewed literature on order picking, slotting, routing, inventory accuracy, labour, turnover, ergonomics, automation and dock flow, plus official data from the U.S. Bureau of Labor Statistics, OSHA and the Department of Transportation.

The result is 25 research-backed secrets of warehouse productivity, grouped into four parts. Each comes with the evidence, an APA citation you can check, and three practical steps a supervisor or operations manager can take this month to cut cost, errors and turnover.

The short version: Warehouse productivity is won or lost in order picking, in the accuracy of your records and in how you treat the people who do the work. Cut walking before you buy robots, fix your data before you trust it, and keep your trained people, because every one who leaves takes your productivity with them.

Key takeaways

• Order picking is where the money goes. Its cost is estimated at as much as 55% of total warehouse operating expense (de Koster et al., 2007).
• About half of a picker's time is travel. A typical breakdown puts travel at 50%, searching at 20% and the actual pick at only 15% (Tompkins et al., 2003, as cited in de Koster et al., 2007).
• Your system is probably wrong more often than it is right. In nearly 370,000 inventory records from 37 stores of one retailer, 65% were inaccurate (DeHoratius & Raman, 2008).
• Warehousing is a high-injury industry. Warehousing and storage recorded 4.8 injury and illness cases per 100 full-time workers in 2024, against 2.3 for all private industry (U.S. Bureau of Labor Statistics [BLS], n.d.; BLS, 2026c).
• Turnover is a productivity problem. In 2025, an average of 4.0% of transportation, warehousing and utilities workers left their jobs every month, compared with 3.3% across all industries (BLS, 2026a).
• Treating people well pays. Stable, predictable schedules raised labour productivity by 5.1% in a randomised field experiment (Kesavan et al., 2022).

How to read this guide: The 25 secrets are grouped into four parts: picking, people, accuracy and flow. Use the table of contents to jump around. Citations in brackets lead to the full reference list at the bottom. Figures describe specific studies, samples and years, and your operation may differ. This article is general information, not engineering, legal or safety advice. For equipment, racking, lifting limits or regulatory compliance, consult a qualified engineer or safety professional in your jurisdiction.

Part 1: Picking is where the money goes

Secret 1: Order picking is your biggest bill, so start there

Receiving, putaway, storage and shipping all matter, but one process dominates the cost of running a warehouse. In their widely cited review, de Koster et al. (2007) describe order picking as the most labour-intensive and costly activity in almost every warehouse, with its cost estimated at "as much as 55% of the total warehouse operating expense" (p. 481). Petersen (1997) likewise called order picking the major cost component of warehousing. A later study of picking technology put the figure at about 60% of operational costs (Guo et al., 2014). The exact share varies by operation, but the lesson is the same: a small percentage gain in picking is worth more than a large gain almost anywhere else.

What leaders should do:

• Break your labour hours down by process (receiving, putaway, replenishment, picking, packing, loading) for one typical week, so you know where the hours really go.
• Rank your improvement ideas by the labour hours they touch. Picking ideas usually belong at the top.
• Track cost per line picked, not just lines per hour, so improvements show up in money as well as speed.

Secret 2: Your pickers spend half their day walking

Picture a picker's shift. Most people assume the work is mostly picking. It is not. A typical distribution of an order picker's time puts travel at 50%, searching at 20%, the actual pick at 15%, setup at 10% and other tasks at 5% (Tompkins et al., 2003, as cited in de Koster et al., 2007). Bartholdi and Hackman describe travel time as waste: it costs labour hours but adds no value (Bartholdi & Hackman, 2005, as cited in de Koster et al., 2007). That makes travel the first candidate for improvement, and it is usually cheaper to cut than any other element.

What leaders should do:

• Walk a picker's route yourself with a step counter or the scanner's timestamps, and measure travel against time at the pick face.
• Treat "searching" as a separate problem: clear labels, consistent location codes and good lighting cut it without changing the layout.
• Set a goal of reducing walking, not of making people walk faster. Faster walking raises fatigue and errors.

Secret 3: A smarter route beats a faster picker

The path a picker takes between locations is a solvable maths problem. Ratliff and Rosenthal (1983) showed that the shortest order-picking route in a standard rectangular warehouse can be calculated efficiently, a rare tractable case of the famous travelling salesman problem. Petersen (1997) compared six routing methods and found that the optimal route was significantly shorter than simple heuristics, but that the best heuristic was on average only about 5% above optimal (as summarised in de Koster et al., 2007). The choice still matters: the "largest gap" method beats the common S-shape (snake) route when there are fewer than about four picks per aisle (Hall, 1993, as cited in de Koster et al., 2007).

What leaders should do:

• Check which routing logic your warehouse management system (WMS) actually uses. Many sites run default settings nobody has reviewed.
• Match the routing method to your pick density. Sparse picks and dense picks need different routes.
• Sequence pick lists in walking order on the scanner or paper list, so pickers are not left to plan routes in their heads.

Secret 4: Slotting your fast movers close is the cheapest upgrade you will ever make

Where you store each SKU decides how far people walk to pick it. Random storage is simple and uses space well, but it spreads popular items across the building. Class-based storage groups items by popularity, often A, B and C classes, and puts the fastest movers nearest the dispatch point. Simulation research summarised by de Koster et al. (2007) found that full turnover-based storage cut travel most, but recommended class-based storage with two to four classes as the practical choice. Petersen and Aase (2004) found that class-based or volume-based storage delivered nearly the same savings as batching, while being less sensitive to changes in order size.

What leaders should do:

• Run a simple Pareto analysis of picks by SKU for the last 90 days. Typically a small share of SKUs drives most of your picks.
• Re-slot your top movers into the easiest, closest, waist-height locations, and schedule re-slotting at least seasonally.
• Store items that are often ordered together near each other, and keep enough empty slots in each class to avoid overflow.

Secret 5: Batch small orders, or pay for the same walk twice

When orders are small, sending one picker on one trip per order wastes enormous amounts of walking. Order batching groups several orders into one picking tour. In Petersen and Aase's (2004) simulation, batching produced the greatest savings of all the policies tested, especially when smaller orders were common. The de Koster et al. (2007) review notes that even simple batching methods lead to significant improvement over first-come, first-served picking. The trade-off is that batches need sorting afterwards, either at the cart or downstream.

What leaders should do:

• Profile your orders by lines per order. If most orders have only one to three lines, batching should be your first experiment.
• Use multi-tote carts or pick-and-sort so batched items go straight into the right order container, cutting downstream sorting errors.
• Set batch release windows that respect your carrier cut-off times, so batching never delays urgent orders.

Secret 6: Layout is a decision, not an inheritance

Many warehouses run on a layout chosen years ago for a different product mix. Research shows layout has a measurable effect on picking time. Roodbergen and de Koster (2001) found that adding cross aisles to a parallel-aisle warehouse can reduce order handling time by lowering average travel. But they also found that adding too many cross aisles can increase travel again, because the space they occupy must be crossed too. A comprehensive review of warehouse operations research by Gu et al. (2007) shows the same pattern across receiving, storage, picking and shipping: design and control decisions interact, so changing one without the others limits the gain.

What leaders should do:

• Before any re-rack, model a few layout options using your real order data, including the number and position of cross aisles.
• Check the location of your pick-up and drop-off point. Petersen (1997) found it affects picking efficiency.
• Review layout whenever your order profile changes sharply, for example when e-commerce orders replace store replenishment.

Secret 7: E-commerce broke the old picking model

Traditional warehouses were built to pick cases and pallets for stores. E-commerce brings huge numbers of time-critical orders, each with just a few lines and small quantities. Boysen et al. (2019) found that traditional picker-to-parts warehouses are often ill-suited to these conditions. Online retailers respond with automated picking workstations, robots and organisational changes such as mixed-shelves storage, dynamic order processing, batching, zoning and sorting systems. Research also shows that task complexity and market dynamics drive how sophisticated warehouse planning and control needs to be, and how specific your WMS must be (Faber et al., 2013).

What leaders should do:

• If your order mix has shifted towards small online orders, review your picking method instead of simply adding staff.
• Consider mixed-shelves or scattered storage for e-commerce SKUs, so any given item is close to more pickers.
• Choose WMS functionality based on your real complexity. Over-buying adds cost, while under-buying forces workarounds on the floor.

Part 2: People, standards and the turnover trap

Secret 8: Your pickers are not interchangeable, so stop assigning them as if they were

Planning models often assume every picker works at the same speed. Real data shows they do not. Matusiak et al. (2017) used data from a Finnish retailer and found that taking individual skill differences into account improved state-of-the-art batching and routing by almost 10%. Even compared with assigning work based on each picker's overall productivity, using skill differences saved 6% of total time. In a field experiment with 101 participants, de Vries, de Koster and Stam (2016a) found that personality traits and age helped predict performance with different picking tools, and that higher productivity was possible without sacrificing quality.

What leaders should do:

• Use your WMS data to understand which zones, product types or tools each picker handles best.
• Assign work by fit as well as availability, but rotate tasks too, so people build skills and avoid repetitive strain.
• Never use individual differences to label people. Use them to match work and target coaching.

Secret 9: New starters are slow and at risk, and that is predictable

Every new picker goes through a learning curve. Grosse and Glock (2013) collected data in a manufacturer's picking process and found clear learning effects, which several standard learning-curve models described well. That means a new hire's output is predictably lower for a period, and planning should allow for it. The risk is not only output. In workers' compensation data, Breslin and Smith (2006) found that workers in their first month on the job were over four times more likely to have a lost-time injury claim than workers with over a year in their current job, with stronger effects in manual occupations.

What leaders should do:

• Build ramp-up targets into your labour standards, so new starters are measured against a realistic curve, not veteran rates.
• Start new workers in lower-hazard tasks and areas, and pair them with an experienced buddy for their first weeks.
• Track productivity and injuries by tenure. If both cluster in the first 90 days, fix onboarding before you hire more people.

Secret 10: Engineered standards work, but only with the right goals and feedback

Engineered labour standards set an expected time for each task based on measured work content, such as travel distance, number of picks and handling. They give supervisors a fair yardstick. The motivation research explains why they can lift performance. Locke and Latham (2002) reviewed 35 years of studies and found that specific, difficult goals lead to higher performance than vague "do your best" goals. Feedback matters too, but it is not automatically positive. A meta-analysis of 607 effect sizes found that feedback interventions improved performance on average (d = .41), yet more than a third of them actually reduced performance. Feedback worked worse when it shifted attention away from the task and towards the person (Kluger & DeNisi, 1996).

What leaders should do:

• Base standards on the real work content of each task, so a picker with a long route is not judged against one with a short route.
• Give feedback about the task ("your travel was high in zone C; let's look at the slotting"), not the person ("you're slow").
• Review standards whenever layout, slotting or product mix changes. An outdated standard destroys trust fast.

Secret 11: Pay for performance can lift output, but design it carefully

Incentive pay is common in warehouses, and the evidence shows why. When Safelite Glass switched installers from hourly pay to piece rates, output per worker rose by 44%, partly from greater effort and partly from attracting more able workers (Lazear, 2000). The design of the scheme matters. Bandiera et al. (2005) studied fruit pickers and found that productivity was at least 50% higher under individual piece rates than under relative incentives that rank workers against each other. Under relative pay, workers held back to avoid hurting colleagues, especially friends.

What leaders should do:

• If you use incentives, reward individual or team output against a fair standard, not a ranking that pits people against each other.
• Include accuracy and safety in the scheme, so nobody is paid to cut corners.
• Explain the scheme in plain language and show people how their pay is calculated each week.

Secret 12: Watching people more closely does not make them work better

Modern warehouses can track every scan, step and idle minute. It is tempting to assume that more monitoring means more output. A meta-analysis of 94 independent samples and 23,461 workers found no evidence that electronic performance monitoring improves worker performance. It did find that monitoring is associated with increased stress, whatever its characteristics. Organisations that monitored more transparently and less invasively saw more positive attitudes (Ravid et al., 2023). A University of California, Berkeley Labor Center report on warehouse technology warned that tools can raise work pace, surveillance and health risks if they are introduced without involving workers (Gutelius & Theodore, 2019).

What leaders should do:

• Use tracking data to fix processes, such as slotting, replenishment and congestion, before you use it to judge individuals.
• Tell workers exactly what is measured, why and how it is used. Transparency reduces the stress cost.
• Remove metrics that nobody acts on. Monitoring with no purpose only adds pressure.

Secret 13: Turnover quietly destroys your productivity gains

Every leaver takes their learning curve with them. Turnover is high in this sector: in 2025, an average of 4.0% of transportation, warehousing and utilities employees separated from their jobs each month, against 3.3% across all industries (BLS, 2026a), and the monthly quits rate was 2.2% (BLS, 2026b). Repeated over twelve months, a 4.0% monthly rate is equivalent to separations of roughly half the workforce in a year (our calculation; the sector figure includes utilities, which have low turnover). A meta-analysis of 110 independent correlations found that higher turnover rates are linked to lower organisational performance (ρ = −.15; Park & Shaw, 2013), and another found turnover is more strongly related to nearby operational outcomes than to distant financial ones (Heavey et al., 2013). In the 2025 MHI and Deloitte industry survey, workforce and talent shortages were cited by 35% of supply chain leaders as a top trend affecting their operations (MMH Staff, 2025).

What leaders should do:

• Calculate your own monthly separation rate and the cost of each leaver, including recruiting, training and lost output on the learning curve.
• Ask every leaver, and every stayer, why. Exit and "stay" interviews reveal fixable problems such as supervisors, schedules and equipment.
• Follow Ton's (2014) "good jobs" logic: invest in well-trained, well-paid people and simplify the operation around them, instead of treating labour only as a cost to cut.

Secret 14: Standard work protects you when people leave

You cannot eliminate turnover, but you can limit the damage it does. Ton and Huckman (2008) studied 48 months of data from the stores of a major U.S. retail chain. On average, turnover was linked to lower profit margins and worse customer service. But at stores with high process conformance, where managers consistently applied standardised procedures, turnover had no negative effect on performance. At low-conformance stores, the harm was pronounced. The study was in retail, but the lesson suits warehousing well: when work is repetitive, clear standard procedures let new people perform like experienced ones sooner.

What leaders should do:

• Write simple, visual standard work for each core task: receiving, putaway, picking, packing and loading.
• Audit adherence regularly and fix the procedure when people work around it, because a workaround usually signals a real problem.
• Use standard work as the backbone of onboarding, so every new starter learns the same best method.

Secret 15: Stable schedules and sane shifts are a productivity tool

Scheduling is often treated as an admin task. The evidence says it is a performance lever. In a randomised field experiment in 28 Gap stores over nine months, responsible scheduling practices (more consistency, predictability and employee control) increased labour productivity by 5.1%, through higher sales and lower labour hours (Kesavan et al., 2022). In a large survey of U.S. hourly service workers, unstable and unpredictable schedules were more strongly associated with psychological distress, poor sleep and unhappiness than low wages were (Schneider & Harknett, 2019). Shift design matters for safety as well: a review found that safety declines over successive night shifts, with increasing hours on duty and with longer gaps between rest breaks (Folkard & Tucker, 2003).

What leaders should do:

• Publish schedules as far in advance as you can and limit last-minute changes, especially cancelled shifts.
• Limit runs of consecutive night shifts and build in regular rest breaks, particularly on long shifts.
• Give workers some control, such as shift swaps through an approved system, so flexibility works both ways.

Part 3: Accuracy, errors and the truth about your inventory

Secret 16: Most pick errors are designed in, not careless

When a wrong item ships, the instinct is to blame the picker. The research points to the system. Setayesh et al. (2022) reviewed 46 studies and interviewed 38 order pickers at 14 companies in four countries. They identified eight design-related failure modes behind pick errors: physical workload, physical fatigue, mental fatigue, complexity, memory demand, vision, hearing and motivation. The interviews added two more that the literature had missed: communication and supervision. A content analysis of order-picking research found that it had focused mainly on management efficiency and largely ignored human factors (Grosse et al., 2017). A related framework argues that building human factors into planning models improves both system performance and worker welfare (Grosse et al., 2015).

What leaders should do:

• When an error happens, ask "what made this error easy?" before asking "who did it?"
• Separate look-alike SKUs, improve lighting and labels, and reduce what pickers have to remember.
• Include supervisors and communication in your error reviews. Unclear instructions are a failure mode too.

Secret 17: Paper pick lists are costing you accuracy

The tool a picker uses changes both speed and accuracy. Guo et al. (2014) compared four picking methods: a head-up display, a cart-mounted display, pick-by-light and a paper pick list. They measured accuracy, error types, task time, workload and preferences, and found the head-up and cart-mounted displays were superior on all metrics to the paper and pick-by-light methods. In de Vries, de Koster and Stam's (2016a) field experiment, pick-by-voice outperformed RF-terminal picking. Battini et al. (2015) compared five paperless systems and showed that the best choice depends on the setting, with some solutions better for low-level warehouses and others for multilevel shelving, once the cost of errors is included.

What leaders should do:

• If you still pick from paper, pilot a paperless method in one zone and compare errors and time per line.
• Choose technology for your storage type and order profile, not because a competitor bought it.
• Include the cost of errors, such as returns, re-shipping and customer credits, in every technology business case.

Secret 18: Your system is probably lying to you

Every replenishment order, pick list and customer promise assumes the inventory record is right. Often it is not. DeHoratius and Raman (2008) examined nearly 370,000 inventory records from 37 stores of one retailer and found 65% were inaccurate. Inventory auditing practices reduced inaccuracy, while complexity and the distribution structure made it worse. In earlier work, Raman et al. (2001) estimated that inventory record and misplaced-SKU problems reduced retail profits by more than 10%. They also found that performance varied widely between stores of the same chain using identical information technology. In other words, the difference was execution, not software.

What leaders should do:

• Measure inventory record accuracy at the location level, not just total units, because a right count in the wrong place is still a miss.
• Find your biggest sources of error, such as receiving mistakes, unscanned moves or unrecorded damage, and fix them at the source.
• Compare accuracy between shifts, zones or sites. The gap between your best and worst areas shows what better execution is worth.

Secret 19: Small losses snowball into big stockouts

A few missing units sound harmless. Kang and Gershwin (2005) showed with analytical and simulation models that even a small rate of stock loss that the system does not detect can disrupt replenishment and create severe out-of-stock situations. The revenue lost to those stockouts can far outweigh the value of the lost stock itself, and the effect is stronger in lean, low-inventory systems. A related problem is "freezing", where the record shows stock that is not physically there, so the system never reorders (DeHoratius et al., 2008).

What leaders should do:

• Investigate "phantom inventory" alerts, such as locations where the system shows stock but picks repeatedly fail.
• Make it fast and easy for pickers to report a short pick, and trigger a count immediately when they do.
• Track shrink and damage by cause, so you fix the leak instead of writing it off.

Secret 20: Count smarter, not more

Annual wall-to-wall stocktakes disrupt operations and correct errors only once a year. Cycle counting spreads counts across the year, but how you choose what to count matters. Kök and Shang (2007) modelled inventory systems with inaccurate records and proposed a cycle-count policy that triggers inspection when recorded stock falls below a threshold. It performed almost as well as the optimal policy. DeHoratius et al. (2008) showed that an audit policy based on a probability model of true inventory significantly outperformed the popular "zero balance walk" approach. Technology can help: in field experiments, RFID-enabled visibility reduced inventory record inaccuracy by about 26% in one study, with results varying by category in another (Hardgrave et al., 2013).

What leaders should do:

• Count more often where errors cost most: fast movers, high-value items and locations with frequent exceptions.
• Trigger counts on events such as a short pick, a near-zero balance or an adjustment, rather than only by calendar.
• Use count results to find root causes. A count that corrects a number but changes nothing will be needed again.

Part 4: Flow, automation and safety

Secret 21: The dock is where your schedule falls apart

A warehouse can only work as fast as trucks are unloaded and loaded. When docks are unscheduled, trucks queue, labour peaks clash and drivers wait. The U.S. Department of Transportation's Office of Inspector General (2018) estimated that driver detention reduces annual earnings of for-hire truckload drivers by $1.1 billion to $1.3 billion. Using limited 2013 data, it also estimated that each 15-minute increase in average dwell time raises the expected crash rate by 6.2%. The report stressed that better data is needed. Operations researchers treat dock scheduling as a core planning problem with a substantial literature (Boysen & Fliedner, 2010). The Warehousing Education and Research Council's 2025 DC Measures report lists dock-to-stock cycle time among its five most popular metrics (Lamerson, 2025).

What leaders should do:

• Introduce dock appointments and measure arrival-to-departure time for every truck, by carrier and by shift.
• Staff receiving to the appointment plan, so labour matches the trucks you actually expect.
• Measure dock-to-stock time and set a target, because stock that is received but not put away cannot be sold.

Secret 22: The fastest pallet is the one you never store

Cross-docking moves goods from the receiving dock to the shipping dock with little or no storage in between (Van Belle et al., 2012). Apte and Viswanathan (2000) explain that it can substantially reduce transportation costs without increasing inventory, while maintaining customer service and shortening order cycle time. It is not suitable for everything. It needs reliable inbound timing, good information about what is arriving and where it is going, and tight coordination with carriers. The review by Van Belle et al. (2012) classifies the many design and scheduling decisions a cross-dock involves, from strategic layout to daily truck sequencing.

What leaders should do:

• Identify flows that are already pre-allocated to customers or stores. They are your best cross-dock candidates.
• Ask suppliers for advance shipping notices with accurate contents, so freight can be routed before it arrives.
• Start with a pilot lane and measure dwell time, damage and on-time dispatch before expanding.

Secret 23: Lean works in warehouses when waste is measured, not preached

Lean thinking began in manufacturing, but its focus on removing waste fits warehouse work well (Womack & Jones, 2003; Liker, 2004). Shah and Ward (2003) studied manufacturing plants and found that bundles of lean practices explained about 23% of the variation in operational performance after accounting for industry and context. Warehouse-specific evidence is emerging. In a survey of Middle Eastern warehouse operators, Abushaikha et al. (2018) found that higher levels of waste reduction were linked to better warehouse operational performance and distribution performance, which in turn were linked to business performance. The waste is familiar: excess travel, waiting, double handling, searching and rework.

What leaders should do:

• Run a "waste walk" with your team, listing every instance of waiting, double handling, searching and unnecessary travel.
• Pick one waste at a time, measure it before and after, and share the results with the people who helped fix it.
• Use daily short huddles at a visual board to surface problems early, rather than relying on end-of-week reports.

Secret 24: Automation is not a shortcut around bad processes

Robots and automated storage get the headlines, and adoption is rising. In the 2026 MHI and Deloitte industry report, 39% of supply chain leaders rated the impact of robotics and automation as significant or greater, up 16 percentage points (MHI, 2026). Yet more automation does not automatically mean more efficiency. Hackman et al. (2001) benchmarked 57 warehouses and found that smaller facilities and those with lower levels of automation tended to be more efficient. Researchers reviewing robotic systems found that many new systems have hardly been studied, and that design, slotting, batching and routing must all be revisited for robotised warehouses (Azadeh et al., 2019). Robots also change the work. In firm-level data, robot investment was associated with more total employment but fewer managers, and with adoption driven by quality more than labour savings (Dixon et al., 2021).

What leaders should do:

• Fix slotting, data accuracy and process flow before automating. Automating a bad process makes the problem faster.
• Build the business case on your own order profile, peak volumes and growth plans, with realistic utilisation assumptions.
• Plan the people side early: new roles, maintenance skills, retraining and how you will involve workers in the change.

Secret 25: Safety leadership and ergonomics make warehouses faster, not slower

Warehousing and storage had a total recordable injury and illness rate of 4.8 cases per 100 full-time workers in 2024, and 32 fatal injuries (BLS, n.d.). The rate for all private industry was 2.3 (BLS, 2026c). OSHA's (2023) National Emphasis Program targets warehouse hazards including powered industrial vehicles, material handling and storage, walking-working surfaces, means of egress and fire protection, with heat and ergonomic hazards considered in every inspection. Good safety does not cost productivity. A systematic review found that in 95% of studies with clear effects, human factors improvements moved worker well-being and system performance in the same direction (Neumann & Dul, 2010). In 78 Dutch warehouses, safety-specific transformational leadership predicted fewer accidents even more strongly than hazard-reducing systems (de Koster et al., 2011). A follow-up study of 87 warehouse managers found it had no detrimental impact on productivity or quality (de Vries, de Koster & Stam, 2016b).

What leaders should do:

• Assess lifting tasks with the revised NIOSH lifting equation (Waters et al., 1993), and redesign heavy, low or far-reach picks first.
• Make sure every forklift operator is trained, re-trained after incidents or changes, and evaluated at least once every three years, as U.S. rules require (Powered Industrial Trucks, 29 C.F.R. § 1910.178(l)).
• Lead safety visibly: talk about it at every huddle, act on near-miss reports and thank people who stop unsafe work.

Your 10-point action plan

1. Map your labour hours. Know how many hours go to each process, and start improvement where the hours are, usually picking.
2. Measure travel. Find out how much of each picker's shift is walking, and set a target to reduce it.
3. Re-slot the top movers. Run a Pareto analysis and put your fastest SKUs in the closest, easiest locations.
4. Batch and route smarter. Review your WMS routing logic and test batching for small orders.
5. Fix your data. Measure inventory record accuracy by location and use event-triggered cycle counts.
6. Redesign for fewer errors. Treat pick errors as system signals and pilot paperless picking where it fits.
7. Set fair standards. Use engineered standards with ramp-up curves and task-focused feedback.
8. Protect your people. Publish stable schedules, onboard new starters carefully and use monitoring transparently.
9. Schedule the dock. Introduce appointments, measure dwell and dock-to-stock time, and look for cross-dock lanes.
10. Lead safety and lean daily. Run short huddles, waste walks and ergonomic reviews, and automate only once the process is sound.

Ready to lead a faster, safer, more accurate operation? Explore BLTI's logistics, transportation and operations credentials to build the skills behind these results, and take our free Leadership Flex Check to see how you lead your team under pressure.

Frequently asked questions

What is the biggest cost in a warehouse?

For most warehouses it is order picking. A major literature review estimates that picking can account for as much as 55% of total warehouse operating expense (de Koster et al., 2007). Within picking, travel between locations is typically the largest share of a picker's time.

How can I improve picking productivity without buying robots?

Start with the cheapest levers: re-slot fast movers close to dispatch, batch small orders, use a better routing method and sequence pick lists in walking order. Research shows batching and class-based or volume-based storage each deliver substantial travel savings (Petersen & Aase, 2004). Then fix data accuracy and standard work before considering automation.

What is a good inventory record accuracy rate?

There is no single universal benchmark, so compare yourself with your own best areas and with industry benchmarking studies such as WERC's DC Measures. Be aware that inaccuracy is often far higher than managers assume: one large study found 65% of records were inaccurate (DeHoratius & Raman, 2008). Measure accuracy at the location level and track it over time.

Why is warehouse turnover so high, and how do I reduce it?

Turnover in transportation and warehousing runs above the all-industry average (BLS, 2026a). Common drivers include unpredictable schedules, physically demanding work, poor onboarding and the quality of supervision. Evidence supports stable scheduling (Kesavan et al., 2022), careful onboarding (Breslin & Smith, 2006) and strong standard work that limits the damage when people do leave (Ton & Huckman, 2008).

Does warehouse automation always pay off?

No. Benchmarking research found that smaller, less automated warehouses tended to be more efficient (Hackman et al., 2001), and many new robotic systems are still under-studied (Azadeh et al., 2019). Automation can pay off, especially for high-volume e-commerce, but only with a sound process, accurate data and a business case built on your own order profile.

Do safety programmes slow down warehouse productivity?

The evidence says no. Studies of warehouse managers found that safety-focused leadership was linked to fewer accidents without harming productivity or quality (de Koster et al., 2011; de Vries, de Koster & Stam, 2016b), and a systematic review found ergonomic improvements usually improve performance as well as well-being (Neumann & Dul, 2010).

About this research

This guide was compiled by the Better Leaders Training Institute (BLTI) research team. Every source below was checked for authors, year, title, publisher and DOI or stable link, using publisher records, Crossref and official government pages. We prioritised peer-reviewed studies, meta-analyses, official government data and established industry surveys. Where a figure comes from an industry survey, a press release, a trade-press summary or a secondary citation inside another paper, we say so in the text. Some studies were conducted in retail stores or manufacturing rather than warehouses, and we flag this where it matters. Findings describe averages and specific samples; your operation may differ. This article is general information, not engineering, legal or safety advice.

References

Abushaikha, I., Salhieh, L., & Towers, N. (2018). Improving distribution and business performance through lean warehousing. International Journal of Retail & Distribution Management, 46(8), 780–800. https://doi.org/10.1108/IJRDM-03-2018-0059

Apte, U. M., & Viswanathan, S. (2000). Effective cross docking for improving distribution efficiencies. International Journal of Logistics Research and Applications, 3(3), 291–302. https://doi.org/10.1080/713682769

Azadeh, K., de Koster, R., & Roy, D. (2019). Robotized and automated warehouse systems: Review and recent developments. Transportation Science, 53(4), 917–945. https://doi.org/10.1287/trsc.2018.0873

Bandiera, O., Barankay, I., & Rasul, I. (2005). Social preferences and the response to incentives: Evidence from personnel data. The Quarterly Journal of Economics, 120(3), 917–962. https://doi.org/10.1093/qje/120.3.917

Battini, D., Calzavara, M., Persona, A., & Sgarbossa, F. (2015). A comparative analysis of different paperless picking systems. Industrial Management & Data Systems, 115(3), 483–503. https://doi.org/10.1108/IMDS-10-2014-0314

Boysen, N., de Koster, R., & Weidinger, F. (2019). Warehousing in the e-commerce era: A survey. European Journal of Operational Research, 277(2), 396–411. https://doi.org/10.1016/j.ejor.2018.08.023

Boysen, N., & Fliedner, M. (2010). Cross dock scheduling: Classification, literature review and research agenda. Omega, 38(6), 413–422. https://doi.org/10.1016/j.omega.2009.10.008

Breslin, F. C., & Smith, P. (2006). Trial by fire: A multivariate examination of the relation between job tenure and work injuries. Occupational and Environmental Medicine, 63(1), 27–32. https://doi.org/10.1136/oem.2005.021006

de Koster, R., Le-Duc, T., & Roodbergen, K. J. (2007). Design and control of warehouse order picking: A literature review. European Journal of Operational Research, 182(2), 481–501. https://doi.org/10.1016/j.ejor.2006.07.009

de Koster, R. B. M., Stam, D., & Balk, B. M. (2011). Accidents happen: The influence of safety-specific transformational leadership, safety consciousness, and hazard reducing systems on warehouse accidents. Journal of Operations Management, 29(7–8), 753–765. https://doi.org/10.1016/j.jom.2011.06.005

de Vries, J., de Koster, R., & Stam, D. (2016a). Exploring the role of picker personality in predicting picking performance with pick by voice, pick to light and RF-terminal picking. International Journal of Production Research, 54(8), 2260–2274. https://doi.org/10.1080/00207543.2015.1064184

de Vries, J., de Koster, R., & Stam, D. (2016b). Safety does not happen by accident: Antecedents to a safer warehouse. Production and Operations Management, 25(8), 1377–1390. https://doi.org/10.1111/poms.12546

DeHoratius, N., Mersereau, A. J., & Schrage, L. (2008). Retail inventory management when records are inaccurate. Manufacturing & Service Operations Management, 10(2), 257–277. https://doi.org/10.1287/msom.1070.0203

DeHoratius, N., & Raman, A. (2008). Inventory record inaccuracy: An empirical analysis. Management Science, 54(4), 627–641. https://doi.org/10.1287/mnsc.1070.0789

Dixon, J., Hong, B., & Wu, L. (2021). The robot revolution: Managerial and employment consequences for firms. Management Science, 67(9), 5586–5605. https://doi.org/10.1287/mnsc.2020.3812

Faber, N., de Koster, M. B. M., & Smidts, A. (2013). Organizing warehouse management. International Journal of Operations & Production Management, 33(9), 1230–1256. https://doi.org/10.1108/IJOPM-12-2011-0471

Folkard, S., & Tucker, P. (2003). Shift work, safety and productivity. Occupational Medicine, 53(2), 95–101. https://doi.org/10.1093/occmed/kqg047

Grosse, E. H., & Glock, C. H. (2013). An experimental investigation of learning effects in order picking systems. Journal of Manufacturing Technology Management, 24(6), 850–872. https://doi.org/10.1108/JMTM-03-2012-0036

Grosse, E. H., Glock, C. H., Jaber, M. Y., & Neumann, W. P. (2015). Incorporating human factors in order picking planning models: Framework and research opportunities. International Journal of Production Research, 53(3), 695–717. https://doi.org/10.1080/00207543.2014.919424

Grosse, E. H., Glock, C. H., & Neumann, W. P. (2017). Human factors in order picking: A content analysis of the literature. International Journal of Production Research, 55(5), 1260–1276. https://doi.org/10.1080/00207543.2016.1186296

Gu, J., Goetschalckx, M., & McGinnis, L. F. (2007). Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177(1), 1–21. https://doi.org/10.1016/j.ejor.2006.02.025

Guo, A., Raghu, S., Xie, X., Ismail, S., Luo, X., Simoneau, J., Gilliland, S., Baumann, H., Southern, C., & Starner, T. (2014). A comparison of order picking assisted by head-up display (HUD), cart-mounted display (CMD), light, and paper pick list. In Proceedings of the 2014 ACM International Symposium on Wearable Computers (pp. 71–78). ACM. https://doi.org/10.1145/2634317.2634321

Gutelius, B., & Theodore, N. (2019). The future of warehouse work: Technological change in the U.S. logistics industry. UC Berkeley Center for Labor Research and Education; Working Partnerships USA. https://laborcenter.berkeley.edu/future-of-warehouse-work/

Hackman, S. T., Frazelle, E. H., Griffin, P. M., Griffin, S. O., & Vlasta, D. A. (2001). Benchmarking warehousing and distribution operations: An input-output approach. Journal of Productivity Analysis, 16(1), 79–100. https://doi.org/10.1023/A:1011155320454

Hardgrave, B. C., Aloysius, J. A., & Goyal, S. (2013). RFID-enabled visibility and retail inventory record inaccuracy: Experiments in the field. Production and Operations Management, 22(4), 843–856. https://doi.org/10.1111/poms.12010

Heavey, A. L., Holwerda, J. A., & Hausknecht, J. P. (2013). Causes and consequences of collective turnover: A meta-analytic review. Journal of Applied Psychology, 98(3), 412–453. https://doi.org/10.1037/a0032380

Kang, Y., & Gershwin, S. B. (2005). Information inaccuracy in inventory systems: Stock loss and stockout. IIE Transactions, 37(9), 843–859. https://doi.org/10.1080/07408170590969861

Kesavan, S., Lambert, S. J., Williams, J. C., & Pendem, P. K. (2022). Doing well by doing good: Improving retail store performance with responsible scheduling practices at the Gap, Inc. Management Science, 68(11), 7818–7836. https://doi.org/10.1287/mnsc.2021.4291

Kluger, A. N., & DeNisi, A. (1996). The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119(2), 254–284. https://doi.org/10.1037/0033-2909.119.2.254

Kök, A. G., & Shang, K. H. (2007). Inspection and replenishment policies for systems with inventory record inaccuracy. Manufacturing & Service Operations Management, 9(2), 185–205. https://doi.org/10.1287/msom.1060.0136

Lamerson, R. (2025, September 18). WERC releases 2025 DC Measures report with a focus on combining vision with vigilance. MHI Solutions. https://www.mhisolutionsmag.com/index.php/2025/09/18/werc-releases-2025-dc-measures-report-with-a-focus-on-combining-vision-with-vigilance/

Lazear, E. P. (2000). Performance pay and productivity. American Economic Review, 90(5), 1346–1361. https://doi.org/10.1257/aer.90.5.1346

Liker, J. K. (2004). The Toyota way: 14 management principles from the world's greatest manufacturer. McGraw-Hill.

Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist, 57(9), 705–717. https://doi.org/10.1037/0003-066X.57.9.705

Matusiak, M., de Koster, R., & Saarinen, J. (2017). Utilizing individual picker skills to improve order batching in a warehouse. European Journal of Operational Research, 263(3), 888–899. https://doi.org/10.1016/j.ejor.2017.05.002

MHI. (2026, April 15). New MHI and Deloitte report finds AI is biggest disruptor of supply chains over the next decade [Press release]. Business Wire. https://www.businesswire.com/news/home/20260415926416/en/New-MHI-and-Deloitte-Report-Finds-AI-is-Biggest-Disruptor-of-Supply-Chains-Over-the-Next-Decade

MMH Staff. (2025, March 19). New MHI and Deloitte report focuses on orchestrating end-to-end digital supply chain solutions. Modern Materials Handling. https://www.mmh.com/article/new_mhi_and_deloitte_report_focuses_on_orchestrating_end_to_end_digital_supply_chain_solutions

Neumann, W. P., & Dul, J. (2010). Human factors: Spanning the gap between OM and HRM. International Journal of Operations & Production Management, 30(9), 923–950. https://doi.org/10.1108/01443571011075056

Occupational Safety and Health Administration. (2023). National emphasis program on warehousing and distribution center operations (Directive No. CPL 03-00-026). U.S. Department of Labor. https://www.osha.gov/enforcement/directives/cpl-03-00-026

Park, T.-Y., & Shaw, J. D. (2013). Turnover rates and organizational performance: A meta-analysis. Journal of Applied Psychology, 98(2), 268–309. https://doi.org/10.1037/a0030723

Petersen, C. G. (1997). An evaluation of order picking routeing policies. International Journal of Operations & Production Management, 17(11), 1098–1111. https://doi.org/10.1108/01443579710177860

Petersen, C. G., & Aase, G. (2004). A comparison of picking, storage, and routing policies in manual order picking. International Journal of Production Economics, 92(1), 11–19. https://doi.org/10.1016/j.ijpe.2003.09.006

Powered Industrial Trucks, 29 C.F.R. § 1910.178 (2025). https://www.ecfr.gov/current/title-29/subtitle-B/chapter-XVII/part-1910/subpart-N/section-1910.178

Raman, A., DeHoratius, N., & Ton, Z. (2001). Execution: The missing link in retail operations. California Management Review, 43(3), 136–152. https://doi.org/10.2307/41166093

Ratliff, H. D., & Rosenthal, A. S. (1983). Order-picking in a rectangular warehouse: A solvable case of the traveling salesman problem. Operations Research, 31(3), 507–521. https://doi.org/10.1287/opre.31.3.507

Ravid, D. M., White, J. C., Tomczak, D. L., Miles, A. F., & Behrend, T. S. (2023). A meta-analysis of the effects of electronic performance monitoring on work outcomes. Personnel Psychology, 76(1), 5–40. https://doi.org/10.1111/peps.12514

Roodbergen, K. J., & de Koster, R. (2001). Routing methods for warehouses with multiple cross aisles. International Journal of Production Research, 39(9), 1865–1883. https://doi.org/10.1080/00207540110028128

Schneider, D., & Harknett, K. (2019). Consequences of routine work-schedule instability for worker health and well-being. American Sociological Review, 84(1), 82–114. https://doi.org/10.1177/0003122418823184

Setayesh, A., Grosse, E. H., Glock, C. H., & Neumann, W. P. (2022). Determining the source of human-system errors in manual order picking with respect to human factors. International Journal of Production Research, 60(20), 6350–6372. https://doi.org/10.1080/00207543.2021.1991022

Shah, R., & Ward, P. T. (2003). Lean manufacturing: Context, practice bundles, and performance. Journal of Operations Management, 21(2), 129–149. https://doi.org/10.1016/S0272-6963(02)00108-0

Ton, Z. (2014). The good jobs strategy: How the smartest companies invest in employees to lower costs and boost profits. New Harvest.

Ton, Z., & Huckman, R. S. (2008). Managing the impact of employee turnover on performance: The role of process conformance. Organization Science, 19(1), 56–68. https://doi.org/10.1287/orsc.1070.0294

U.S. Bureau of Labor Statistics. (n.d.). Warehousing and storage: NAICS 493 [Industry at a glance]. U.S. Department of Labor. Retrieved October 24, 2026, from https://www.bls.gov/iag/tgs/iag493.htm

U.S. Bureau of Labor Statistics. (2026a). Table 20. Annual average total separations rates by industry and region, not seasonally adjusted [Data table]. Job Openings and Labor Turnover Survey. https://www.bls.gov/news.release/jolts.t20.htm

U.S. Bureau of Labor Statistics. (2026b). Table 22. Annual average quits rates by industry and region, not seasonally adjusted [Data table]. Job Openings and Labor Turnover Survey. https://www.bls.gov/news.release/jolts.t22.htm

U.S. Bureau of Labor Statistics. (2026c, January 22). Employer-reported workplace injuries and illnesses, 2023–2024 (USDL-26-0101) [News release]. https://www.bls.gov/news.release/archives/osh_01222026.htm

U.S. Department of Transportation, Office of Inspector General. (2018). Estimates show commercial driver detention increases crash risks and costs, but current data limit further analysis (Report No. ST2018019). https://www.oversight.gov/reports/audit/estimates-show-commercial-driver-detention-increases-crash-risks-and-costs-current

Van Belle, J., Valckenaers, P., & Cattrysse, D. (2012). Cross-docking: State of the art. Omega, 40(6), 827–846. https://doi.org/10.1016/j.omega.2012.01.005

Waters, T. R., Putz-Anderson, V., Garg, A., & Fine, L. J. (1993). Revised NIOSH equation for the design and evaluation of manual lifting tasks. Ergonomics, 36(7), 749–776. https://doi.org/10.1080/00140139308967940

Womack, J. P., & Jones, D. T. (2003). Lean thinking: Banish waste and create wealth in your corporation (Rev. ed.). Free Press.

Videos

CRE-MSD UW. (2022, February 3). NIOSH: 6 factors that affect lifting [Video]. YouTube. https://www.youtube.com/watch?v=6x21qhGh5hM

MIT Center for Transportation & Logistics. (2023, March 22). Automation in distribution centers: Key considerations and technology frontiers [Video]. YouTube. https://www.youtube.com/watch?v=9ND5KilZMuE

MIT Open Learning. (2025, October 2). The explosive growth of automated warehouses [Video]. YouTube. https://www.youtube.com/watch?v=AEr-BnTRGas

Mountz, M. (2015, July 15). What happens inside those massive warehouses? [Video]. TED. https://www.youtube.com/watch?v=9UiK4llJOXc

Ton, Z., & Massey, C. (2022, May 17). Creating good jobs is a strategy: 2022 Wharton Future of Work Conference [Video]. Wharton School. https://www.youtube.com/watch?v=UnGxs09nyXE