Picking is one of the most time-consuming and resource-intensive activities in a non-automated warehouse because it involves not only the physical picking of items, but also operator travel, location searches, task management, any required checks, movement to the packing area, and all the waiting time caused by tools, equipment, or processes that are not perfectly coordinated.
To assess the efficiency of this activity, it is therefore not enough to know the total number of packages prepared, because the same result can be achieved with very different levels of labor, time, and space utilization. Two shifts may prepare the same number of packages, but one may require more hours, more operators, or a significantly higher number of tasks and movements.
The Picking dashboard in B-AI Semplice makes it possible to analyze this data in a structured way, connecting operator productivity with task characteristics, picking methods, customers served, work shifts, and the warehouse areas involved.
The goal is not simply to determine whether the warehouse is operating quickly or slowly, but to understand which factors are driving performance and where improvements can be made to increase productivity without compromising service quality.
Productivity per Operator
One of the main indicators available in the dashboard is productivity per operator, which can be measured through the number of packages prepared per working hour and the related hourly cost.
This data makes it possible to compare operator performance, but it must be interpreted according to the type of activity performed. An operator preparing simple orders, with many items concentrated in the same area, works under very different conditions from someone handling fragmented orders, bulky items, or specific packaging requirements.
For this reason, productivity should not be analyzed in isolation, but together with the number of tasks completed, the average number of picks included in each task, the picking method used, and the customer being served.
If an operator records a lower-than-average number of packages per hour, the dashboard helps determine whether the cause is actually related to individual performance or instead to the complexity of the assigned work, the characteristics of the area in which they operate, or the presence of external delays.
This distinction is also important for workforce management, because it prevents inefficiencies caused by warehouse layout, task configuration, or the availability of work equipment from being automatically attributed to the operator.
Hourly Cost and the Cost of Productivity
The number of packages prepared per hour measures physical productivity, while hourly cost makes it possible to connect operational performance with its financial impact.
When costs remain constant but the number of packages prepared decreases, the unit cost of each package increases. Conversely, improved productivity allows labor costs to be spread across a greater number of completed activities.
This type of analysis can be used to compare different shifts, teams, or periods and determine whether organizational changes have produced measurable benefits.
For example, after changing the slotting of a warehouse area or the way picking tasks are structured, the manager can check whether the number of packages prepared per hour has increased and whether the operating cost per unit of work has decreased.
The dashboard therefore makes it possible to move beyond general assessments such as “the shift went well” or “we worked slowly today,” translating performance into indicators that can be compared over time.
The Number and Structure of Picking Tasks
Picking productivity depends significantly on how picking tasks are organized.
The dashboard shows how many tasks were completed by each operator, the average number of picks included in each task, and the total number of packages handled.
This data makes it possible to understand whether the warehouse is generating many tasks with only a few picks or a smaller number of larger tasks.
In the first case, operators may be forced to repeat the same routes frequently, increasing travel distances and the time required for each package. In the second case, fewer trips are required, but excessively long or complex tasks can increase the risk of errors, make container management more difficult, and slow down subsequent activities.
There is therefore no optimal number of picks per task that applies to every warehouse, because the result depends on the layout, item dimensions, order structure, and picking method used.
However, the dashboard makes it possible to compare different configurations and identify the one that provides the best balance between the quantity prepared, time required, and number of movements.
Picking Times by Customer
Not all customers generate the same workload, even when their order volumes are similar.
A customer may require customized packaging, specific labels, additional checks, separation by store location, or specific documentation, increasing the time required to prepare each package.
The dashboard makes it possible to analyze picking times for different customers, revealing operational complexity that often does not emerge when looking only at the number of orders or revenue.
This information can be used to assess the actual resources required to serve each customer, compare different management approaches, and determine whether specific requirements generate disproportionate costs compared with the volumes handled.
The data can also be useful when defining commercial terms, because it helps distinguish between a high-volume customer with standard requirements and a customer with similar volumes but a much higher level of customization.
The dashboard does not, of course, replace a complete profitability analysis, but it introduces an often-overlooked factor into the evaluation: the actual logistics time required to serve each customer.
Picking Times by Picking Method
B-AI Semplice makes it possible to compare the times recorded for different operating methods, such as multi-order picking, pick and pack, put to light, or stock picking.
This comparison helps determine whether the method being used is appropriate for the characteristics of the orders being processed.
Multi-order picking, for example, can reduce travel distances by allowing operators to pick items for several orders at the same time. However, it can also create additional sorting activities and increase the risk of errors when the process is not properly organized.
Pick and pack can eliminate subsequent handling steps because the item is picked and placed directly into the shipping container. However, it may be less efficient when orders are particularly complex or when packaging requires additional operations.
Put to light can speed up the sorting of items across different orders, but its effectiveness depends on volume, process repeatability, and the correct replenishment of workstations.
By analyzing time, packages, and tasks for each method, managers can determine which approach delivers the best results under different operating conditions, avoiding the use of a single procedure for orders with very different characteristics.
Performance by Day, Shift, and Time Slot
Another key feature of the dashboard is the ability to analyze picking performance by day, shift, and time slot.
This analysis makes it possible to identify when activities are most concentrated, when productivity decreases, and when available resources are either higher or lower than the actual workload.
If the number of picking tasks consistently increases during certain hours while the number of operators remains unchanged, the warehouse may accumulate delays that are later recovered through overtime or unplanned staff reallocation.
Conversely, a time slot with a limited number of tasks and a high number of operators may indicate that resources are oversized compared with the actual workload.
The dashboard makes it possible to determine whether these situations are occasional or recurring, providing a more reliable basis for shift planning.
Managers can decide to bring some employees in earlier, postpone other shifts, reinforce a critical time slot, or temporarily assign part of the workforce to other activities such as receiving, replenishment, inventory counting, or workstation organization.
In this way, workforce planning is based on actual flow data rather than solely on experience or established organizational habits.
Analyzing Travel Paths and Slow Areas
In manual picking, a significant amount of time is spent moving between storage locations.
The dashboard helps identify warehouse areas where picking tasks take longer and the stages of the process where delays are concentrated, such as entering the picking area, reaching the storage location, picking the item, moving it to packing, or closing the order.
An area may be slow because it contains high-demand items stored in hard-to-reach locations, because aisles are congested, because storage locations are difficult to identify, or because operators are forced to follow inefficient routes.
The analysis makes it possible to determine whether picking sequences force operators to move in a zigzag pattern, repeatedly returning to the same aisles, or whether more linear or U-shaped routes could be introduced.
This information can be used to improve slotting by moving the most frequently handled items to more accessible areas and assigning more distant locations to slow-moving products.
The connection with Inventory ABC Analysis is clear: Class A items should be stored in locations that minimize access time, while Class C items can be placed in less central areas without significantly affecting overall productivity.
Identifying Bottlenecks
An increase in picking times is not always caused by the picking activity itself or by operator speed.
Slowdowns may be caused by having too few scales for the number of workstations, a printer shared by too many operators, a handheld terminal that frequently loses connection, a shortage of available picking carts, or a packing station that cannot handle the incoming workload.
The dashboard makes it possible to identify the stages and areas where processing times increase, allowing managers to investigate the causes directly on the warehouse floor.
If picking is completed within the expected time but orders accumulate in front of the packing station, adding more picking operators could make the problem even worse by increasing the flow toward an activity that is already at capacity.
In this case, the right decision might be to add another workstation, relocate a printer, provide a second scale, or change the way activities are distributed.
The value of the dashboard lies precisely in its ability to identify the actual constraint within the process before investing in additional staff, equipment, or organizational changes.
Training and Performance Management
Operator-level data can also be used to design more targeted training activities.
When an operator performs below average, the manager can determine whether the difference is concentrated in specific warehouse areas, particular picking methods, or certain types of orders.
This analysis makes it possible to organize targeted coaching rather than relying on generic training programs.
One operator may struggle with multi-order picking tasks, another may not be sufficiently familiar with certain warehouse areas, while a third may lose time when using handheld terminals or completing closing activities.
Similarly, KPIs can be used to recognize top performers and potentially introduce incentive systems, provided that performance is not assessed solely on speed.
To avoid counterproductive behavior, productivity should be evaluated together with work quality, the number of errors, compliance with procedures, and the complexity of the tasks assigned.
Rewarding operators exclusively on the basis of packages per hour could encourage them to prioritize speed, increasing picking errors, product damage, or rework that may ultimately cost more than the productivity gains achieved.
From Measurement to Operational Improvement
The B-AI Semplice Picking dashboard connects productivity with the factors that drive it, bringing together operators, picking tasks, picking methods, customers, shifts, time slots, and warehouse areas.
Logistics managers can use this information to optimize shift staffing, improve slotting, reduce travel distances, choose the most appropriate picking method, eliminate bottlenecks, and implement targeted training activities.
The value of the dashboard therefore lies not only in knowing how many packages have been prepared, but in understanding how they were prepared, how much they cost, and which actions can improve performance.