Challenge

Maersk was facing a highly manual and error-prone process in their handling of commercial invoice (CIV) data. The team was manually extracting line items from PDFs and mapping HS codes using a VLOOKUP-based database. While this worked in some cases, it became a bottleneck when no matches were found in the master data. This required additional manual work and introduced a high risk of inaccuracies, especially critical in logistics, where incorrect HS codes can lead to delays or financial penalties.
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Another significant challenge was the lack of predictability and efficiency in HS code mapping. Maersk needed a more intelligent system that could not only reference internal master data but also incorporate external sources to accurately map HS codes, even in complex or ambiguous cases. Their existing system lacked the sophistication to handle this, leading to inconsistent results and increasing the burden on their teams.
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As Maersk continued to scale operations and expand into additional branches, the limitations of their current process became more pronounced. The manual workload was not sustainable at scale, and the risk of compounding inefficiencies threatened to slow down growth and customer responsiveness.
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To address these challenges, Maersk required a solution that could accurately automate the extraction of CIV lines from PDFs, integrate seamlessly with existing data sources, and improve the accuracy of HS code mapping up to 8-digit precision. Their ideal system would deliver at least 99.97% extraction accuracy, reduce manual intervention, and enable their team to focus on higher-value strategic tasks rather than repetitive data processing.

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Why They Chose Expedock

Maersk chose Expedock because of:

• HS Code Mapping Advancements: Expedock’s development on HS code mapping, which integrates external sources and the ability to map 8-digit codes, was a key selling point.
• Proven Accuracy: Expedock’s track record with Maersk Indonesia’s CIV line extraction, which had received positive feedback, reassured Maersk of the solution’s reliability.
• Cost Savings: Expedock’s solution offered a cost-effective alternative to their existing provider. By automating these processes, Maersk expected to reduce per-line costs and overall operational expenses.

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Solution

To address Maersk’s operational challenges, Expedock implemented a targeted set of automation solutions. First, the extraction of commercial invoice (CIV) lines from PDFs was fully automated, significantly reducing manual workload and minimizing the risk of human error. This allowed Maersk's team to process documents faster and with greater confidence in the accuracy of the data.
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Additionally, Expedock introduced an automated HS code mapping system that intelligently leveraged both internal master data and external sources. This not only improved the accuracy of classifications, especially in cases where codes were missing or ambiguous, but also reduced the need for manual intervention, enabling the team to focus on higher-value tasks. Together, these solutions laid the foundation for scalable, reliable, and efficient customs operations.

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Implementation/Onboarding Steps

Each automation solution follows this onboarding flow.

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Results

The implementation of Expedock’s automation solutions delivered measurable improvements across Maersk’s operations. Key outcomes included:

• Improved Efficiency: Maersk significantly reduced the time spent on manual data entry and HS code mapping, allowing their team to focus on more value-added tasks.
• Enhanced Accuracy: Expedock’s solution maintained a 98% accuracy rate on the first attempt, reducing the risk of penalties and improving data integrity.
• Scalability: As Maersk expanded its operations, the solution was able to handle increased volumes and seamlessly scale to meet growing demands.
• Cost Savings: By automating manual processes and reducing per-line costs, Maersk realized significant cost savings compared to their previous solution.
• Positive ROI: Maersk saw a measurable return on investment by automating manual processes, improving speed, and reducing operational costs related to data entry and mapping.