Accuracy and timeliness: Two critical measures that define the quality of data in logistics

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In our previous article about connecting supply chain data, we talked about the five key challenges to link all different sources of data. That is the first step towards accomplishing a complete overview of your logistics operations. When connecting supplier data, what helps guarantee your investment will have the expected benefit is the quality of the inputs received.

In practical terms, many companies achieve full data visibility by using different sources of inputs: EDI connections, excel reports and input gathered manually from websites (commonly known as screen scraping). But when complex systems are designed, aspects such as accuracy and timeliness of data exchanged are not always a top consideration. In a survey about supply chain visibility published by MIT, nearly half of the respondents were less than moderately satisfied with their current visibility solutions. Timeliness is an aspect of attention with only 40 percent of respondents reporting to receive updates within 12 hours across all ocean shipping milestones.

Working with different files, formats and without a single source of truth can be problematic for several reasons. It primarily impacts the efficiency of daily operations as these can only be performed based on the information available. On a strategic level, it decreases the reporting and analytical capabilities for better decisions making.

Consider the scenario in which a shipper needs access to information to properly handle an exception; their ability to make the best decision will often be constrained by not having real time information. The dependency on information gathered manually or received in batches can result in either incomplete data, late date, or both resulting in a potentially suboptimal outcome for the shipper or their end client.

Timeliness: Is the data received on time?

The ability of conducting an effective day to day logistics operation is highly dependent on receiving critical data on time. Operational risks are reduced if transportation milestones are received in near real-time, especially when managing exceptions. Using the common industry standards for communication, such as EDI messages or updates shared via reports, the information is often shared in batches. Waiting hours to receive important milestones on the system, especially if they require immediate action, are costly. The cost is even higher if the information is being gathered by manual work — screen-scraping different websites and platforms, making several phone calls, and sending multiple emails.

Today, using traditional platforms, when shippers or consignees search for shipping milestones, they most likely will have access only to information showing “actuals”. This information highlights when the milestone occurred. However, to assess the real value of the data there is another key element that needs to be included:  when was this milestone sent or made available to you?

This is an important aspect to assess data quality as there is often a delay between milestone occurrence and information received. This difference can be massive, taking hours or in some cases even days. This can have a significant impact on the decision-making process, as well as, understanding the reliability and comparative performance of your data sources and logistics partners.

TradeLens conducted a study involving a side-by-side comparison of milestones for 50 containers with an inland destination in Europe. The comparison involved data from the current system used by the 4PL provider and the milestones received by TradeLens. We studied the timeliness and accuracy of the milestones received. The current setup used by the 4PL did not have the functionality to measure data timeliness. When all the milestones at the destination were tracked by TradeLens, the average time between occurrence and submission time was 31 minutes. This means that the milestones submitted by carriers, terminals and inland depots in this study were available and visible in around half an hour after they actually occurred.   

Accuracy: Is the data received accurate?

Understanding the accuracy of the data received allows companies to have better conversations with their business partners about the quality of their input. To fight against a common industry problem, popularly known as GIGO — garbage in, garbage out, it’s key to consider new ways of exchanging data that are readily available today. A collaborative operating model cannot be achieved if the means of collecting data are not being tracked and the data itself is not easily available to companies. The strategic use of supply chain data is only realistic with a change to the traditional model, where data is often overwritten in systems by new updates not allowing history to be tracked.

Even the most sophisticated and complex systems, involving EDI connections across different providers and manual reports, can be subject to inaccuracy. Having as a basis the same study mentioned above, the 4PL’s current system, which includes reports produced by EDI connections and manual updates, did not fully track the source of the update and therefore was unable to identify the root cause of inaccurate data. The same milestones on TradeLens are easily auditable. Information about data providers and submission time are a mandatory part of every milestone and new updates to the system are visible but in the back end do not overwrite existing information. 

The first step for this change is working with a system that allows data errors to be located and addressed. Strong feedback and collaboration with the data providers needs to be backed by evidence and numbers. Keeping track of errors and discrepancies supports companies to identify possible risks to their supply chain related to inconsistencies on the information received on all stages of the logistics operations. As described by the previous post on data quality, It’s time to trust your data — and more importantly, to truly use your data:

“When our airline app tells us the flight has landed, we are confident our friend will soon be ready for us to pick them up at arrival. Not so in international shipping, where data for everything from a cargo’s departure, gate-in at a terminal or release from customs is far less dependable than we would like.”Marvin Erdly, TradeLens Collaboration Leader

Only when data is properly tracked, shippers and consignees will be able to have conversations with their partners to identify and correct the issues that put our industry behind others in data accuracy.  Inaccuracy is often overlooked with the assumption that this is the norm. The good news is that already today, tools exist that can already improve data accuracy within the logistics industry.

How are accuracy and timeliness issues being addressed?

Understanding data quality and building accountability can strengthen partnerships and trust across the logistics industry. The traditional model has often impeded companies from being able to monitor and track aspects such as accuracy and timeliness in their data. Working through different systems, connections and providers translates to a lack of ownership when it comes to acknowledging, and ultimately fixing data qualities issues.  

Solving this fundamental industry issue, generated by a fragmented landscape and multiple non-interoperable systems, is a massive task. When setting up your own operational system or working with data providers, it is important to keep in mind the fundamental aspects to track data quality and the process in place to flag discrepancies and create feedback loops to address concerns. Further, adding data quality to selection criteria for the right partners will become even more critical in the future.

TradeLens is a platform that enables you to build your business on true end-to-end visibility, simplifying the connection and information exchange between you and your partners. TradeLens is driving data validation through standards, proactive data monitoring and issue-detection, and by facilitating a feedback loop across all parties.

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