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CLS is just a few months away from completing its transition to a new industrial center in Mazarefes, Viana do Castelo. The company, specialized in personal protective equipment, is working on a robotized and semi-automatic warehouse integrated with state-of-the-art technologies for managing logistics flows, optimizing storage, and digitally monitoring processes.
Diogo Amorim, a computer engineer responsible for the development and implementation of the new technologies, states that the warehouse “was designed from scratch to automate flows, reduce operational waste, and guarantee full traceability in real time.” “We estimate that, once stabilized, the system will operate with around 80% of logistics decisions automated,” he clarifies.
The system is based on nine Kardex automatic devices, configured with highly optimized shelf layouts based on rotation algorithms and picking density, designed to store stock and ensure autonomy for a full month of sales, with the minimum number of distinct layouts and maximum storage density.
“We integrated the flows with our ERP, with direct synchronization of minimum quantities, replenishment, and picking priorities,” explains the manager. The goal of this investment is undoubtedly to have a larger and smarter warehouse: “It’s about better control, faster response, and fewer errors,” he clarifies, pointing out scale as the biggest challenge.
All the logistics flow logic was developed internally, enabling: automatic fitting algorithms for containers, with controlled rotation and stacking; stock turnover forecasting based on actual sales; validation of physical product fittings by box type; dynamic generation of picking, replenishment, and supply files per operator and machine; monitoring of usable space occupancy; full integration with the ERP, ensuring stock synchronization, minimums, and automatic orders; and automatic preparation of weekly performance reports.
“A visual simulation module was also created to virtually test layout changes,” he explains. “With the structure we built, we have the basis to triple operations without tripling the chaos,” he sums up. Projections indicate that CLS Brands will be able to reduce average order preparation time by 40 to 50%; decrease picking errors to margins below 0.1%, thanks to digital traceability per operator and machine; and achieve 30% gains in storage efficiency.
“This transition will allow our teams to focus more on analytical tasks, decision-making, and technical support,” he clarifies to T Jornal. “We implemented a team training plan — some sessions have already been delivered — oriented by area (logistics, purchasing, IT, commercial), with direct monitoring and practical simulations.”
Management also foresees the creation of new roles in the areas of: “logistics data management and system parameterization; operation and maintenance of robotized warehouses; development and support of internal tools; and creation of technical content for marketing and digital communication.”
This is also an important step for CLS Brands in exploring the potential of Artificial Intelligence. It is being used for forecasting stock needs based on sales patterns and seasonality; optimizing shelf layouts by density and turnover; detecting patterns of logistic errors or waste; and simulating fitting and physical occupancy scenarios based on consumption variations.
“All the algorithmic base and forecasting models were developed internally by our programming team, using languages like Python, direct integration with the ERP (Primavera), and connection to visualization and simulation modules. Technological independence has been a pillar from the start: building custom solutions with the capacity to rapidly adapt to our business,” he concludes.
In terms of lessons learned, Diogo Amorim highlights three points that can serve as references for other companies: “The system logic must be designed to serve the real operation; programming, logistics, and business vision must go hand in hand; it is preferable to start with a simple, well-defined, and reliable base, and gradually sophisticate as data and teams mature.”