• Written by Azhar Aliyar, Director

A logistics manager checks the shipment tracking system to investigate a delay, only to find that the data is incomplete or inconsistent, making it difficult to pinpoint the cause of the issue. This is a common problem in logistics, where the complexity of the supply chain and the variability of carrier performance can make it challenging to maintain accurate and reliable data.

Logistics complexities that affect data quality

The logistics industry is inherently complex, with multiple stakeholders, varying transportation modes, and a multitude of factors that can impact delivery times and costs. Carrier performance variance, dwell and detention, and exception handling at scale are just a few of the challenges that can affect data quality. For instance, a shipment may be delayed due to unforeseen circumstances, such as weather conditions or traffic congestion, which can make it difficult to accurately track and record the shipment's status.

Moreover, the logistics industry is characterized by a high degree of variability, with different carriers, routes, and shipment types, which can make it challenging to establish a standardized data collection and management process. This variability can lead to inconsistencies and inaccuracies in the data, making it difficult to rely on the data for decision-making purposes.

Field-level meaning and data documentation

One of the common mistakes made in logistics data management is assuming that column names are sufficient documentation for the data. However, this is often not the case, as column names may not accurately reflect the meaning and context of the data. For example, a column named "shipment_status" may contain a variety of codes and descriptions that are not immediately clear, making it difficult to understand the true meaning of the data.

Field-level meaning is critical in logistics data management, as it ensures that the data is accurately interpreted and used for decision-making purposes. This requires a deep understanding of the business context and the specific requirements of the logistics operation. By taking the time to properly document and define the data, logistics managers can ensure that the data is reliable, consistent, and accurate.

Systems that get left out of scope

In many cases, logistics data management systems are designed to focus on specific aspects of the supply chain, such as transportation management or warehouse management. However, this can lead to a narrow focus that neglects other critical systems and processes, such as customs clearance, freight auditing, or supply chain visibility. By leaving these systems out of scope, logistics managers may be missing critical data and insights that can impact the overall efficiency and effectiveness of the logistics operation.

For example, a transportation management system may not take into account the impact of customs clearance on shipment delivery times, which can lead to inaccurate estimates and disappointed customers. By considering the full range of systems and processes involved in the logistics operation, logistics managers can gain a more complete understanding of the data and make more informed decisions.

Gaps in the data that can teach a model the wrong lesson

Stockouts, backfills, and other gaps in the data can have a significant impact on the accuracy and reliability of logistics data management systems. These gaps can occur due to a variety of factors, such as inventory discrepancies, shipment delays, or data entry errors. If not properly addressed, these gaps can teach a model the wrong lesson, leading to inaccurate predictions and decisions.

For instance, a model that is trained on data that contains stockouts may learn to predict that a particular product will always be out of stock, even if this is not the case. By identifying and addressing these gaps in the data, logistics managers can ensure that the model is trained on accurate and reliable data, leading to more informed decisions and better outcomes. To learn more about how AsscherAi can help with data management, visit our website at www.asscherai.com.

When AsscherAi is not the right choice

While AsscherAi can be a powerful tool for logistics data management, it is not the right choice for every situation. For example, if the data is highly unstructured or requires significant manual processing, AsscherAi may not be the best fit. In these cases, other solutions, such as manual data processing or custom software development, may be more suitable.

In other cases, the logistics operation may be relatively simple, with minimal complexity and variability. In these cases, a more basic data management system may be sufficient, and AsscherAi may not be necessary. To determine whether AsscherAi is the right choice for your logistics operation, contact us at www.asscherai.com/contact-us to discuss your specific needs and requirements.