A Fortune 500 chemicals company experienced surges in its tariff classification requests and predicted future volume would be even greater. Without support, the risk of misclassifying items was extremely high. Procuring an automated global trade system helped alleviate the strain on resources and mitigate the risk of delays and penalties. It also allowed the company to cut outsourced services, which yielded meaningful P&L savings and helped the organization manage its growth projections efficiently.

It is a timely case study as enterprises that engage in international trade continue to experience increases in tariff classification requests as their import and export shipments surge. With global merchandise volume forecast to grow 7.5% this year and 4.1% in 2022¹, organizations still using manual processes for product classifications — researching and applying HTS codes — may be misclassifying a variety of their products, including anything from direct materials to back-office supplies.

Misclassifications not only cost organizations shipping delays — sometimes from two to 14 days — increasing the likelihood of an audit, but they also lead to steep penalties. In fact, some companies have had more than 80% of their classifications incorrect for products and have incurred U.S. Customs and Border Protection fines of up to four times the lawful duties, taxes and fees.²

However, there is an overlooked solution. Today’s global trade management systems come equipped with automation and machine learning capabilities to streamline classification requests. They cut classification errors and the cycle time, improve a team’s productivity, and help prevent fines and border delays.

Here are the keys to success for organizations using trade systems to overhaul their tariff classification process:

1. Automate the consistent, repetitive classification requests that take up more than 60% of a resource’s time. Organizations can immediately alleviate the workload for classifiers by leveraging automation and machine learning for repetitive product classifications that have slight deviations. Those items can take hours of a resource’s time, leaving little to no bandwidth for other categories that may require more research. As the system learns more about the minor deviations in product types, it can provide accuracy of close to > 95%. Taking manual processes out of the equation helps guarantee supply assurance to an organization’s customer base while mitigating penalties from errors.

2. Eliminate third parties or outsourced contracts involved in classification overflow assistance. Implementing automation for tariff classifications allows an organization to remove outside brokerage services, equating to an immediate P&L savings impact. Some organizations have seen upwards of 10% savings captured by eliminating these obligations. That, in turn, helps positively impact the overall trade governance budget. Not only are the short-term effects instant, but for the long-term, global trade systems can help identify discounts for various classification codes based on trade agreements between importing and exporting countries. These discounts usually go overlooked by internal resources because of how busy they are with other tasks.

3. Use machine learning to help realize a cycle-time reduction for classification requests. Enterprises should leverage global trade services to automate customs rulings updates, ensuring compliance is current for all import/export nations. That leads to a reduction in the time spent by internal resources on researching the data each time a regulatory change occurs. Also, organizations should integrate databases with their global trade management systems to classify past and new unique classifications. Machine learning can leverage past classification mistakes for the future, but for new items, linking information flows from databases can help automate requests as they appear for the first time.

Organizations experiencing growth in their imports and exports must pay attention to global trade systems with automation and machine learning now more than ever to ensure business continuity and future scalability. While digitizing classification processes results in crucial P&L and cost savings, it’s also critical to mitigating the risk of future border delays and steep fines.

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Alex Hayes is a consulting manager at GEP, a leading provider of procurement and supply chain solutions to Fortune 500 companies.

1. https://ihsmarkit.com/research-analysis/global-trade-outlook-for-2021.html#:~:text=We%20forecast%20the%20volume%20of,2021%20and%204.1%25%20in%202022

2. https://www.govinfo.gov/content/pkg/USCODE-2011-title19/pdf/USCODE-2011-title19-chap4-subtitleIII-partV-sec1592.pdf