Data quality: essential for your warehouse (with Manhattan)

Item dimensions, weight, description, hazardous nature… These are just a few examples of the critical data that ensures the smooth operation of a logistics warehouse. Yet, the quality of this data is often overlooked, a mistake that can lead to significant consequences Rémi Coolen, Director of Business Solutions at Manhattan France, a leading software provider for supply chain management, explains why data quality is so essential.

“In a warehouse, the most crucial data to manage is stock levels,” explains Rémi Coolen. “We must always have a clear, accurate picture of available stock, including the types of items, quantities, and their status. It’s equally important to know which stock is unavailable, in transit, or being received. ”

Stock data is, of course, vital, but it’s far from the only type of data that’s needed. A Warehouse Management System (WMS) requires a wealth of other information to ensure effective warehouse operations.

“To operate efficiently, receive goods, inspect them, and place them in stock, the WMS needs precise data about each item, including its dimensions, durability, hazardous properties, and exact location…”, Rémi Coolen continues. This information dictates the type of packaging used and the processes for managing the items—whether they’re small, fragile, or require temperature control.

And there’s more. “The trend towards digital twins in logistics is accelerating”, Rémi Coolen adds. “The purpose of this warehouse management system is to model the reality of the physical warehouse, in other words, to create a virtual twin.” The idea? To optimise order preparation by, for example, reducing the number of journeys made by operators. This means knowing the exact position of products and the precise configuration of the warehouse. In short, reliable data is essential.

Reliable data in a warehouse context is data that closely reflects reality. It is crucial for the management system, enabling informed decision-making and timely actions, especially if it can be adjusted in real-time.

The system will select a particular box based on the product, its size, whether it must remain upright, etc. “What’s interesting is that the WMS itself is also a key factor in improving data reliability”, explains Rémi Coolen “Suppliers provide specific characteristics of their items: for example, this product weighs 18g and is 5 cm high. Upon arrival at the warehouse, the item is scanned using a 3D scanner to verify these details and determine its exact dimensions, and weighed on a scale to check its weight… because the system relies on this data to perform its tasks. This reliable data ultimately drives the processes. ”

Although the system is automated, operators continue to play an important role in improving data reliability. Measurements are still taken manually, whether by individual piece or by pack. While sensors may be used for measurement, data validation remains the responsibility of the operators.

Poor data quality can have far-reaching consequences within the warehouse. If left unaddressed, the repercussions will reverberate throughout the supply chain, up to the point when goods leave the warehouse.

Rémi Coolen emphasises the importance of managing data quality before it is entered into the WMS. Without this, the hours spent by teams correcting and verifying data will multiply, causing productivity to suffer and adding unnecessary costs.

For example, if an item’s dimensions are entered incorrectly as too large, the packaging suggested by the WMS will also be oversized. To prevent the product from shifting in the box, operators will have to use fillers. However, the cost of these fillers, as well as the time required to place them, is not accounted for in the process. This can have a direct impact on customer experience, causing delays or compromising the expected quality.

Reliable data is fundamental for optimising logistics processes. Reliable position data, for example, enhances productivity by reducing staff movement or the need for checks, adapting operations in real-time, or providing historical data to improve warehouse organisation and decision-making.

Data reliability is also critical for meeting regulatory requirements: traceability of item batches, tracking numbers, compliance with the cold chain, and so on. This information is particularly important for products stored in controlled environments. “In concrete terms, improved data quality leads to better service levels within the warehouse and a reduction in disputes and returns”, concludes Rémi Coolen. “Implementing a WMS that can enhance data reliability, through sensors or visual recognition tools, can be highly beneficial. It is not uncommon to see a reduction in disputes by up to four times and productivity gains of 10 to 15%. ”

In a warehouse, the best way to obtain reliable data is to use the appropriate tools:

• WMS (Warehouse Management System) for warehouse management
• OMS (Order Management System) for order management
• TMS (Transport Management System) for transport management
• WES (Warehouse Execution System) for order execution

Above all, a well-defined strategy must be implemented. This final step is often overlooked, yet it is essential for smooth warehouse operations.

The ideal time to define this strategy is during the WMS project implementation. This is when the warehouse’s digital maturity should be assessed, and if necessary, an upgrade project should be initiated alongside the WMS implementation.

The challenge is that those responsible for data collection are not always directly impacted by its poor quality. “The people receiving the goods are tasked with measuring and weighing them”, explains Rémi Coolen, “but it is the operators preparing the orders who are affected by the choice of suitable packaging boxes and their fill rate.”

That’s why it is crucial to implement quality controls for data collection and maintenance, and, above all, to establish a continuous improvement loop. The goal is to always know how data is being captured, monitored, and even corrected.

This effort to ensure data reliability is even more important as packaging becomes increasingly automated. “Our customers often don’t fully appreciate the impact of data on their processes”, insists Rémi Coolen. “That’s why it is essential to define performance indicators (KPIs) that clearly show how input data affects warehouse operations.” Determining these KPIs, identifying those affected by the data, and revising them annually to reflect the evolving reality of the warehouse takes time, but it greatly streamlines operations.

Collecting quality data and managing it efficiently are central to warehouse performance. Supply chain automation improves data reliability and customer service levels.

Learn more about WES Deepsky, the warehouse execution system developed by Exotec.