Freight Forwarder’s Guide to Efficient Data Quality Assurance

How can you build better resiliency for your operations? It all starts with data quality.

When we were first building Expedock, we met with hundreds of freight forwarders in North America. Unsurprisingly, we found that over 65% of business owners were facing data quality issues that were directly linked to lower customer satisfaction and higher disputes.

Why You Need Better Data Quality Control

Data quality and integrity issues have always plagued the logistics industry, but this shouldn’t be the norm. It’s gotten so bad that your average freight forwarder handles hundreds of documents and has zero visibility on what’s actually going on with your shipment.

Freight Forwarders today have so many systems in play but have a legitimate challenge with trusting their own data. Larger freight companies save millions of dollars from manual errors by having tools that can show the exact shipping costs in real-time. But did you know that smaller companies benefit from these solutions as well?

Here are some of our top recommendations in quality and data efficacy before executing solutions that fit your company’s needs. Here are 3 essential steps to making that happen:

1. Prioritize Data Entry

The worst decisions are made with bad data. A lot of you may already know the importance of data-driven decision-making. Maybe your company already uses analytics but isn’t properly ensuring data accuracy. In that case, you might face an even bigger blow-up: making costly decisions because of inaccurate data.

In most cases, bad data starts from how you extract and enter your data. Making sure that your data is accurate from the very beginning during entry eases most if not all pains with siloed data. In fact, 70% of invoices are overpaid simply because of manual errors during data entry. This is a problem that will probably never go away, because people mistype things regularly, and this must be accounted for. In order to avoid these problems, it’s best to make forms that are easy to fill out and establish guard rails that ensure accuracy. This won’t prevent user error entirely, but it will at least mitigate it.

There are opportunities in improving your data quality assurance using AI. With tech playing an increasingly important role in all aspects of business, forwarders need to understand the need to speed up the adoption of digital solutions in order to stay resilient.

2. Standardize your Systems

The data quality of your documents is outside of your control. Having no standard format for your freight documents is exactly why solutions like RPA and OCR need to be reprogrammed frequently. Even the most advanced OCRs can’t catch errors on their own. Whenever a data issue arises, it becomes difficult and time-consuming to trace the root cause, not to mention fixing it. Tools such as these are only helpful to the extent to which you instruct them to be.

Instead of tirelessly begging your IT to troubleshoot your problems, why consider automating data profiling and data quality alerts? This guarantees that the quality of incoming data is consistent and managed correctly by your team. A rigorous data quality control of incoming data is the most important aspect of creating better data integrity. As we connect to more systems, each system will require a distinct set of fields leading to an overall unique standard. The key here is to find a solution that allows you to manage this robust set of standards instantly.

3. Find Real-time Visibility Solutions

Real-time visibility is the borderline between “getting by” and being a contender. Achieving end-to-end data visibility in the supply chain is no simple task. Most companies need to compile data from disparate systems and spreadsheets just to build one report.

Currently, 3PLs rely on their vendors to provide the status of their shipments. While in transit, there’s little to no visibility into the location of shipments, the condition they’re in, or what issues might hold them up. A large part of logistical issues is that we encounter them only when it’s too late. This creates endless and tedious backfilling, which they could have used to generate productive tasks.

Data quality can be maintained in real-time, if you can sync it to how you operate in real-time. Paired with extensive reporting, having full visibility of all your shipments will allow for informed decisions, and better interactions with your clients. For example, after extracting data from documents, you can run an audit through Expedock to cross-check if the supposed rate in your invoice matches your vendors’ quotation. We also provide dashboards and reports which can show and pinpoint information in the shipment life-cycle where you do not have visibility, allowing you to improve the quality of data that you want to capture and present.

Conclusion

Even while real-time visibility is critical for the supply chain’s future, implementing it on your own is difficult. The biggest obstacles that businesses encounter in doing so are the silos in their IT departments and the planning done on simple platforms like spreadsheets.

For supply chain managers hoping to capitalize on this technology, implementing the solutions is requires the right solution. It’s recommended to invest in platforms that can merge disparate data streams into actionable insights. By building a single source of truth across the supply chain, managers will have the information and tools to make the right decisions at the right time.