WorldLink Applies AI and Data Engineering to Supply Chain and Tariff Problems SAP Shops Know Well

WorldLink, a technology services firm working in artificial intelligence, cloud computing, and data engineering, has delivered two engagements that treat clean, unified data as the precondition for AI rather than an afterthought.

The company describes more than 25 years of work and a technology-agnostic stance, running a delivery lifecycle that moves from strategy and use-case definition through solution design, development, analytics, deployment, and managed operations.

One engagement rebuilt shipment visibility for a global logistics operator. The other applied analytics to tariff exposure for an equipment manufacturer. Both sit close to problems SAP customers manage in transportation, trade, and finance.

Rebuilding Shipment Visibility on a Common Data Layer

The logistics client ran a large network and could not see across it. Operating from more than 335 locations in over 100 countries with more than 20,000 employees, the company moved freight by ocean, truck, air, rail, port, and local delivery, yet its shipment data sat scattered among carriers, third-party logistics providers, aggregators, brokers, customers, and internal systems. Staff pieced together status from spreadsheets and manual follow-up, and word of a disruption often arrived after it had already hit a shipment.

WorldLink attacked the data problem first. The team ingested feeds from the many outside parties that touch a shipment, then mapped each type of record, from orders and items through advance shipping notices, milestones, events, and documents, into shared models that hold their meaning as goods pass between companies, transport modes, and regions. Harmonizing records this way gives a shipment one identity across the journey, which is the same reconciliation challenge SAP shops face when Transportation Management has to align with external carrier feeds and non-SAP partner systems.

Only with that layer in place did the visible tools become useful. A central portal let logistics staff monitor shipments, coordinate with partners, and pull their own reports without waiting on transmitted updates, and embedded predictive alerts flagged emerging problems early enough to act before cost or service degraded. The reported effect was near-real-time visibility and earlier warning, with the harmonized data positioned to carry later automation and machine learning.

Turning Tariff Data into Recovered and Avoided Cost

A global equipment manufacturer engaged WorldLink to find duty it should not have paid and exemptions it had not claimed. The work recovered $10 million in tariff overpayments and secured $88 million in tariff exemptions. Those numbers describe different kinds of value and do not add into one savings figure. Recovery returns money already paid on entries that carried too much duty, while an exemption lowers or removes an obligation going forward.

Tariff exposure lives in data. Duty owed depends on how a product is classified, how it is valued, where it originated, and whether a trade agreement applies, and those attributes sit in the material master, procurement, and import records that SAP customers already maintain. Running analytics across that data lets a manufacturer surface misclassified goods and unclaimed trade preferences at a volume manual review rarely reaches.

What This Means for SAPinsiders

• Test the data layer before the dashboard. Visibility tools stand or fall on how a vendor unifies shipment records across carriers and partners. SAP logistics teams should probe that harmonization directly, because inconsistent shipment identity across TM and external feeds is what breaks exception handling.
• Tariff work is a finance and compliance control. Duty recovery and exemption capture depend on classification, valuation, and origin data held in ERP. Finance and trade leaders should judge whether that data is clean and governed enough to support automated review before scoping any analytics.
• Scope data engineering ahead of AI. Both engagements put analytics on harmonized data, not raw feeds. SAP teams weighing similar work should fund governance and integration first, since predictive alerts and tariff models inherit the quality of the records beneath them.