In the competitive world of warehouse operations, managing labour costs is essential for enhancing productivity and efficiency, particularly in picking and automation tasks. Warehouse and 3PL professionals often struggle with challenges like high travel-time due to poor layout, inadequate training leading to overtime, and limited analytics for forecasting labour needs, resulting in reduced ergonomics and missed kaizen opportunities. At Clarus WMS, we provide innovative solutions that focus on practical automation paths, data visibility, and disciplined slotting. In this article we also include field-tested advice from a warehouse professional to help you pressure-test decisions before you invest.
Traditionally, addressing labour costs involved lengthy manual time-and-motion studies and basic incentive schemes, which could take months to yield minor productivity gains. With our Clarus WMS approach, we enable rapid deployment of analytics and workflow tools, achieving meaningful efficiency improvements in weeks, while minimising overtime and enhancing workforce ergonomics through better layout, smarter pick-face design, and clearer dashboards.
The result of traditional methods:
This article breaks down the failures of outdated labour management methods and explores how Clarus WMS rethinks the process with modern, cloud-based solutions for better visibility and regulatory audit trails. We will address common questions to provide actionable insights for warehouse professionals.
Labour cost management involves tracking and reducing expenses related to workforce productivity, efficiency in picking, and appropriate use of automation to achieve cost savings. This process includes analysing travel-time, implementing slotting optimisation, and using analytics for forecasting labour needs. In warehouse environments with variable demands, optimising labour costs requires cross-training staff to enhance flexibility and reduce overtime reliance.
McKinsey research highlights that transport and warehousing costs in the US have seen significant wage pressure in recent years, creating a “labour mismatch” challenge across the supply chain?web†source?. This trend underscores the importance of focusing on operational efficiency, since external wage inflation is often outside management’s control.
Integrating an LMS style of reporting within a cloud WMS allows for real-time dashboards to monitor incentive programmes and ergonomic risks. In Clarus WMS, teams can use transactional pick data and the API to export picks by SKU and location, then analyse in a BI tool which items are fastest moving and whether they sit near the despatch bay. This makes it easier to decide which SKUs should live in the closest, safest pick faces to reduce footsteps and battery use on trucks.
Automation is useful when volumes, SKU breadth, or service levels exceed what a manual layout can support efficiently. For small-scale facilities with stable demand, manual processes may suffice. As volumes grow, targeted automation such as goods-to-person or automated storage can improve space utilisation and throughput, but it requires a clear payback plan and enough volume to amortise capital expenditure.
A practitioner’s view: large automation projects reduce labour touches and compress travel, but the trade-off is a significant upfront capex and a need to confirm you have the space, replenishment plan, and throughput to pay it back over time. The value comes from storing more in the same footprint, moving more stock per hour, and trimming reliance on manual handling equipment.
McKinsey analysis shows that while many distributors are interested in automation, only around 20 percent of warehouses have meaningfully adopted it—indicating that the ROI case is still selective rather than universal?web†source?.
Voice-picking can provide hands-free guidance and reduce screen interaction in certain environments, such as apparel. However, effectiveness varies by site, and it is not universally adopted. In our expert’s experience across multiple warehouses, voice is far from ubiquitous, and many sites achieve excellent results with well-designed pick faces, good slotting, and disciplined walk sequences. Clarus WMS does not include voice picking as a standard feature.
If you are evaluating voice, run a side-by-side pick trial and compare against improvements from basic layout changes first.
Slotting plays a key role in travel-time reduction by strategically placing high-demand items in optimal locations, enhancing picking efficiency and productivity. In 3PL settings with variable SKUs, slotting should pair with the physical reality of the site. For many e-commerce profiles, tote picking to shelving with min and max rules can work well for many small items. For larger items or pallet movers, configure demand pick faces that are replenished against live order demand so pickers always see the right SKU in the right face without walking deep into reserve.
Seasonal demand: Clarus WMS does not include a specialised seasonal slotting optimiser. Teams can use demand pick faces to cope with peaks, but this may require more pick face space, more replenishment, and more labour during the season. Capacity planning on space and people is essential.
Practical tip: use Clarus transactional data via API to identify your true A-movers and check whether they sit in the closest walk sequence zones. If your fastest mover lives at the far end of the longest aisle, you will pay for it in footsteps and overtime.
Traditional methods often rely on manual time studies for labour management, leading to inaccuracies in productivity tracking and high overtime from poor forecasting. These approaches lack real-time analytics, making slotting and layout inefficient and travel-time excessive. In contrast, Clarus WMS focuses on accurate transactions, clean location walk sequences, and data export so teams can build fit-for-purpose BI and LMS dashboards. Where legacy systems struggle with cross-training visibility, our platform gives you the data to decide who should be licensed on which equipment and to plan holiday cover without scrambling for agency staff.
Teams in warehouses and 3PL often struggle with labour cost due to fragmented systems that hinder productivity and efficiency in operations.
Expert perspective: labour spikes often trace back to simple constraints like not enough licensed drivers for higher racking levels or no holiday cover for the only VNA driver. Training breadth and license refresh cycles are part of labour cost control.
At Clarus WMS, we handle labour cost through a cloud platform that ensures real-time visibility and consistent processes in warehouse productivity. Our approach emphasises accurate master data, robust location structures with walk sequences, and demand pick-face configuration where appropriate. We also surface the transactional history you need to build reports that show pick hotspots, travel-heavy locations, and candidate SKUs for re-slotting near goods out.
In our work with Interspan, we cut reporting time by 90 percent with Clarus WMS, automating workflows and enhancing visibility to improve labour efficiency (Clarus WMS customer story).
Our AI-powered optimisation focuses on reducing travel and touches through smarter location and pick-face configuration. While Clarus does not currently include an automated slotting engine, your team can combine our API data with your BI tool to highlight candidates for re-slotting and to simulate the impact on footsteps and replenishment.
Real-time monitoring in Clarus WMS provides the visibility to spot exceptions, such as rising replenishment activity to far-away pick faces or orders that force multi-aisle walks. Alerts can be configured around process metrics in your BI layer, helping supervisors intervene before overtime accrues.
Scalable adaptation allows the system to grow with operations, handling increased SKU volumes without changing the core processes. A SKU is a SKU, tied to the right account, and added to any receipt or despatch while your operation validates whether you have the physical capacity and the right pick-face mix to handle the added variety. For small-item breadth, tote picking to shelving with min and max works well. For pallet movers, demand pick faces prevent wasted fixed pick-face space.
Full visibility offers end-to-end insight into picking and replenishment, supported by standard transactions and exportable data. This builds confidence in decisions about where items live and which licences your team must hold to keep flow stable during holidays and peaks.
Implementing labour cost optimisation with Clarus WMS can transform your warehouse productivity, reducing overtime and enhancing efficiency without the usual hurdles. Imagine a practical integration that aligns proven layout changes, disciplined cross-training, and selective automation with your operations. Contact Clarus WMS for a demo to explore these benefits firsthand.
