Wilson Sons

From 10 Weeks to 1: How Wilson Sons Scaled Scope 3 Reporting Without Growing the Team

Facing tight regulatory deadlines and half a million purchasing records to classify, Wilson Sons used SINAI's AI Emissions Match to cut Scope 3 cycle time by 90% — and finally expand emissions reporting across all nine business units.

The numbers that changed the operation

Wilson Sons, one of Brazil's largest port logistics and maritime services companies, needed to calculate Scope 3 emissions across five business units, processing roughly 500,000 purchasing records extracted from SAP. Under the previous manual methodology, that work took between five and ten weeks per inventory cycle, and expanding to all nine business units was not operationally viable.

After implementing SINAI's AI Emissions Match and Data Agents, part of SINAI's Measure module,  Wilson Sons completed the full classification process in approximately one week. The Scope 3 audit that followed took less than four hours. With classification time reduced from up to ten weeks to roughly one week, Wilson Sons can now realistically intend to expand Scope 3 reporting to all nine business units in the upcoming inventory cycles without adding headcount.

Key results:

• 80–90% reduction in Scope 3 cycle time
• From 5–10 weeks of manual work to 1 
• Up to 300 hours saved per inventory cycle
• Scope 3 audit completed in 3–4 hours
• Classification granularity expanded from ~45–50 manual categories to 700+ activity categories

Half a million records. Five business units. A deadline cut by three and a half months.

As sustainability disclosure requirements tightened, new reporting standards shortened Wilson Sons' usual Scope 3 timeline by approximately 3.5 months. This created a critical bottleneck in the company's classification of SAP purchasing data for Scope 3, Categories 1 and 2.

The emissions measurement process began with raw SAP data that contained short text, long text, purchase order numbers, and spend values. A custom mapping table matched materials and services to merchandise groups linked to emission factors. The problem: roughly 60% of records (around 300,000 lines) had no emissions factor match in the table and required individual manual review.

At that scale, item-by-item classification was simply not possible. The team applied broad generalizations: diverse items were grouped under "electrical equipment," and all maintenance services were placed in the same category regardless of scope. A single merchandise group might contain plastic parts, metal flanges, and vessel engine components; all forced into one classification.

Classification alone took one to two weeks per business unit. Across five units, that was five to ten weeks of manual work per cycle. Including the four remaining business units, a long-standing goal, would have added another 1 to 1.5 months; time the team simply did not have.

Speed Matters. But Audit-Ready Scope 3 Data Matters More.

With 500,000 purchasing records and an external audit ahead,  Wilson Sons’ team needed more than a faster process for Scope 3 emissions; they needed an output that could be independently audited. SINAI's platform with an AI-enabled workflow for

structuring data showed category-level spend, the applied emission factor, and the calculated result for each category. Auditors could validate the classification logic and check emission factor accuracy without reviewing individual line items. 

Two additional factors that shaped Wilson Sons decision to work with SINAI:

• Built for ERP-scale purchasing data. SINAI’s AI Emissions Match interprets raw SAP fields and maps them to granular emission activity categories without requiring the team to preprocess or normalize every description. The workflow moves from "clean, classify, summarize, upload" to uploading base data and letting SINAI handle the logic.
• Granularity that improves decisions. The previous process produced 45-50 categories per business unit. SINAI's classification produced more than 700 distinct activity categories across the same dataset, shifting from broad assumptions to precise, activity-level data that supports not just compliance, but credible decarbonization planning.‍

AI Emissions Match and Data Agents: from spreadsheet logic to automated classification

SINAI's AI Emissions Match automates the classification of large purchasing datasets against granular emission activity categories, all aligned with the GHG Protocol. Data Agents support more flexible ingestion of raw, unstructured purchasing data, reducing the preprocessing burden before data even enters the platform.

For Wilson Sons, the implementation changed the workflow at each stage:

• Data ingestion. The team previously maintained auxiliary spreadsheet tabs, ran VLOOKUP formulas against an internal mapping table, filtered unmatched records, and reformatted everything before uploading to SINAI, unit by unit. With AI Emissions Match, SAP extractions go in more directly, with SINAI handling classification and emission factor matching.
• Classification at scale. The AI-enabled workflow interpreted purchasing descriptions and merchandise group fields to classify records across 700+ emission activity categories, replacing the generic groupings imposed by manual review.
• Audit-ready output. SINAI's export provided category-level spend, emission factor, and calculated emissions per category. Giving auditors a clear, verifiable view of the classification logic without needing to trace 500,000 individual lines.
• Scalability by design. With the classification bottleneck removed, Wilson Sons can extend the same methodology to all nine business units, including units with entirely different purchasing profiles that the previous mapping table could not have covered.

Faster reporting. Stronger audits. A business ready to scale.

90% less time on classification

The full Scope 3 classification process, previously five to ten weeks across five business units, was completed in approximately one week, saving an estimated 160 to 360+ hours in a single cycle. Manual mapping tables, auxiliary spreadsheet tabs, and individual record review have been significantly reduced or eliminated from the workflow.

A 4-hour audit

The Scope 3 audit was completed in approximately three to four hours. Auditors worked from SINAI's structured export rather than reviewing individual records, and the purchased goods and services and capital goods categories — historically the most data-intensive — were the easiest to clear.

700+ categories instead of 45

Classification granularity increased from 45 to 50 manually assigned categories per business unit to more than 700 distinct emission activity categories. That precision reduces the risk of misclassification in future audits and strengthens the credibility of disclosures under the GHG Protocol and CDP reporting frameworks.

Regulatory resilience

Wilson Sons completed Scope 3 reporting under a 3.5-month deadline compression, without compromising auditability or data quality. As disclosure requirements continue to tighten, the ability to deliver audit-ready Scope 3 data under shortened timelines is a direct compliance advantage.

From 5 to 9 business units

For the first time, expanding Scope 3 coverage to all nine business units is operationally viable. Under the previous methodology, that expansion would have required an additional 1 to 1.5 months of work. With SINAI, Wilson Sons intends to expand Scope 3 to all businesses in the upcoming inventory cycles.

About Wilson Sons‍
‍With over 188 years in the market and nationwide operations, Wilson Sons is a port and maritime logistics operator. A reference in the sector, it provides comprehensive solutions to more than 5,000 clients, including shipowners, importers and exporters, the offshore energy industry, renewable energy projects, the agribusiness sector, as well as various other sectors of the economy.