Case Studies

The Situation

A large consumer goods operation within DHL's logistics network was experiencing significant productivity loss in replenishment activity. Forklift travel times during putaway and replenishment cycles were excessive, the result of putaway decisions that hadn't been designed with replenishment in mind. Stock was landing in locations that made sense for the putaway run but created long, inefficient travel paths when replenishment was later required.

The Approach

The engagement began with a detailed GEMBA walk and data analysis to understand product velocity, current putaway behaviour and the physical layout of the racking environment. The objective was to redesign the Manhattan directed putaway logic so that stock was placed in locations that minimised expected forklift travel at the point of replenishment, not just the point of putaway.

This required working through the full range of product SKUs, their velocity profiles, their replenishment points and the travel distances associated with different putaway location assignments. The revised putaway rules were then configured in Manhattan WMOS, tested against actual replenishment scenarios and validated with the operations team before implementation.

The Situation

An iconic Australian retailer was running a heavily manual, paper-based e-commerce picking operation. Pickers worked from printed pick slips, manually sorted by order and destination, a process that was slow, error-prone and impossible to scale efficiently as online order volumes grew. End-of-Financial-Year (EOFY) Sale periods required an additional 30% casual workforce just to maintain throughput, adding significant cost and operational complexity.

The Approach

The design replaced the paper-based system with a pick-to-tote solution using Manhattan WMS. The critical design element was the cart task creation logic: rather than simply assigning individual orders to pickers, the system was configured to group orders intelligently into tote carts, minimising picker travel paths while maximising carton utilisation across each cart run.

Change management was a significant part of the engagement. The operations team were initially resistant: they saw the scanning of totes and individual items as additional work compared to the familiar paper process. The turning point came when the team understood that every step of the existing manual pick slip sorting process (a time-consuming activity that happened entirely off-system) would disappear entirely. Once they saw what that meant for their actual workload, the team became strong advocates for the new design.

The decision was made to go live during the EOFY Sale period, the operation's most demanding demand spike of the year. This was deliberate: a high-volume, high-pressure environment would expose any weaknesses in the design quickly.

The Situation

In a large dangerous goods operation, forklifts were operating in single-task mode: each machine was assigned to one activity at a time: either putaway, full-pallet picking or replenishment. This meant equipment was regularly travelling with empty tines between task completions, and the operation had no mechanism to capitalise on the natural efficiency opportunities that the physical layout of the facility offered.

The inbound and outbound docks sat side by side on the same wall of the warehouse. A forklift delivering two pallets to the despatch dock was passing the inbound receipt area on every return journey, but with nothing on its tines.

The Approach

Manhattan's task management engine was reconfigured to enable full task interleaving for the reach truck fleet. Rather than being locked to a single task type, each truck was made eligible for putaway, picking and replenishment simultaneously. On completion of each task, the system assigned the nearest, most urgent available task based on the truck's last known warehouse location, ensuring every movement was productive.

The physical layout of the facility was factored directly into the design. Reach trucks carrying two pallets to the outbound despatch dock could immediately collect two inbound pallets for putaway on the return journey, virtually eliminating the dead-running that had previously characterised those movements. The dangerous goods classification of the operation added complexity to the eligibility rule design, requiring careful configuration to ensure compliance was maintained under the interleaved model.

The Situation

Two separate medical device clients occupied the same DHL warehouse facility, each operating with entirely separate, dedicated resource pools. The arrangement meant that when one operation was busy and the other was quiet, there was no mechanism to redistribute labour; resources sat idle on one side of the building while the other struggled to meet throughput targets. Costs were rising and operational flexibility was limited.

The engagement coincided with a robot-assisted picking implementation in the same facility, adding a layer of complexity to the resource design, as the Manhattan task management configuration had to account for how human and robotic picking resources would operate alongside each other.

The Approach

Manhattan task interleaving was configured to operate across both client operations within the same warehouse instance, enabling the workforce to be treated as a single, shared labour pool. Task eligibility rules were designed to respect each client's specific process requirements, compliance constraints and stock segregation requirements, whilst allowing appropriately trained and authorised operators to pick up work for either client based on proximity and priority.

The robot-assisted picking integration required the task management design to distinguish clearly between tasks suited for human operators and those being handled by the robotic system, ensuring neither competed with the other for the same work, and that the overall task flow remained efficient under both resource types.