Automation is today the top priority for logistics leaders, according to Toyota Material Handling’s Logistics Trends 2026/2027 report. Technology can multiply the performance of a well-run operation, but it can also amplify the inefficiencies of one that isn’t. Today, warehouse productivity doesn’t depend on a single machine or software, but on the balance between space, flow, product, data, and people.
Is productivity solely dependent on technology?
In many operations, productivity losses don’t begin with a lack of technology, but earlier—in how space is allocated, in the path each SKU travels from entry to exit, and in the data available to decide. Therefore, Toyota Material Handling Spain analyzes the most common mistakes to watch out for before starting any automation project.
1. Design around the available space, not around the flow
Many projects start with the building, focusing on the meters available and how to fill them. However, the capacity of a facility depends not only on how much space is occupied, but on how materials and people move through it. Because material flows must align with the facility’s design.
A single warehouse can host several flows at once: inbound from different suppliers, movements between storage zones, replenishments, order picking, outbound shipments, or returns. When those relationships aren’t studied, you get interference between routes, redundant movements, or bottlenecks.
“When we analyze a facility, the first thing is to understand the routes and listen to the people who run them every day. That is often where the explanation for much of the problems attributed to a lack of technology lies,” explains David García Amandi, Automation Sales Manager at Toyota Material Handling Spain.
2. Maintain a layout designed for demand that has already changed
The placement of items is usually defined during the initial warehouse design. However, demand is not static. Seasonality, catalog changes, the evolution of e-commerce, and the growth of certain channels can significantly alter the order profile. An item that occupied a secondary location can become one of the most frequently picked items, while other products may lose rotation.
The layout of the warehouse and demand must evolve hand in hand. The result is a facility optimized for an operation that no longer exists. The choice between fixed or dynamic locations follows the same logic. A fixed location can simplify management but consumes capacity when an item’s occupancy varies. A dynamic location lets you use space more efficiently but requires systems and processes capable of guaranteeing inventory traceability. There is no single valid configuration. The layout should respond to the real demand behavior and be reviewed when that behavior changes.
3. Automate a process that is not yet resolved
Automation can increase capacity and reduce repetitive tasks, but it must operate on processes that have been previously analyzed. Automating a process with unnecessary routes, unidentified variations, or a poorly defined task sequence does not eliminate those problems. It may embed them into the automated system and add an investment that can hinder later modification.
The pertinent question, therefore, is not which technology is more advanced, but how much variability the process to be automated can tolerate. Before automation, technologies such as simulation or digital twins allow testing different configurations and analyzing how an operation will behave under different scenarios, like demand spikes, changes in volume, or distribution modifications.
4. Decide without data and with isolated systems
Without real-time data on inventory, equipment, and processes, it’s impossible to know where productivity is being lost, and improvements end up being applied where symptoms are perceived rather than where the root cause lies. Digital transformation is not limited to automation; it encompasses data-driven decision-making and, above all, the integration of systems, linking algorithms with warehouse management, robotics, and fleet management, which must exchange information to coordinate the operation.
Without that integration, every system optimizes its own domain and the whole doesn’t improve. The quality of decisions will depend on the quality of the available data. So the dashboards that unify productivity, inventory accuracy, and efficiency are, more than control tools, diagnostic instruments.
«Before asking what technology a facility needs, it’s worth answering two other questions: what problem do you want to solve and what data are you using to measure it. Without that early conversation, a project may work technically and not improve plant productivity», says David García Amandi.
5. Bring in technology without redesigning the team’s roles and competencies
A more automated warehouse does not require fewer people, but people with different roles and skill sets. When you invest in automation without accompanying training, the expected performance can be hindered. Ongoing training of teams is not just a supplement to efficiency; it is a prerequisite to achieving it. A well-trained operator can better leverage any equipment or system, supervise and manage incidents, and perform tasks that require decision-making.
The sector’s concrete best practices include defined career paths, internal certification programs, retraining toward technical profiles (operators who configure AGVs, control-systems specialists, energy-management coordinators) and simulation-based training before working on production systems. Companies investing in their staff development see higher retention, better efficiency, and faster adoption of next-generation systems.
Also often overlooked is warehouse ergonomics and load handling. Poorly designed jobs cause fatigue, slow down processes, and increase the risk of incidents. Caring for the human factor isn’t just a matter of worker well-being; it’s also a driver of operational performance.
6. Optimize a part of the operation without analyzing the entire supply chain
Finally, a common mistake is treating the warehouse as a closed compartment, disconnected from the rest of the supply chain. Decisions inside the facility affect transportation, production, and customer service, and vice versa. Optimizing a part of the process without considering the whole can yield local improvements that actually shift the problem to another point in the chain. The design should account for all flows, from storage and picking to intermediate zones and reverse logistics.
Returns are the clearest example, as e-commerce has turned them into a high-volume process that cannot be treated as a separate circuit. If managed as a standalone loop, they can create new bottlenecks, inventory errors, and difficulties in rapidly recovering products.
Automation must, therefore, analyze the relationships between processes and not only the performance of a single technology or a specific area. The goal isn’t to make an individual system reach its maximum productivity, but to improve the performance of the whole.
