Cutting First-Response Time from 38 Hours to 4 in a German Medtech Distributor

Background: A Mid-Size Medtech Distributor in Southern Germany

This case study is a composite. It draws on patterns Forfis has observed across multiple engagements in German healthcare and medtech distribution. No named customer is represented; the company, metrics, and timeline are representative of the work we deliver, not a single identifiable client.

The company in question is a mid-size medtech distributor in southern Germany, roughly 340 employees, operating across two regional warehouses and a central back office in Stuttgart. It handles order intake, shipment coordination, and after-sales support for orthopedic and diagnostic equipment. The ERP is SAP S/4HANA, the helpdesk is a legacy on-premises ticketing system, and the CRM is Microsoft Dynamics 365. The company had been running on a paper-and-email hybrid for inbound purchase orders and shipment confirmations for over a decade. No prior AI or automation project had been attempted; the operations team had flagged the bottleneck in internal reviews for three consecutive quarters without a funded solution.

Challenge: A 24-Hour SLA the Manual Process Could Not Meet

The trigger was a contractual deadline. A major hospital group, representing roughly 18 percent of the company’s annual revenue, issued a service-level agreement requiring order-status acknowledgments within 24 hours and shipment confirmations within 4 hours of dispatch. The existing process could not meet either threshold. Inbound purchase orders arrived as scanned PDFs, emailed attachments, and occasionally physical mail. A team of four operators manually transcribed each order into SAP, cross-referenced it against the shipment plan, and drafted a status email to the customer. The median first-response time was 38 hours. The 95th percentile was 72 hours. The error rate on transcribed fields was 6.2 percent, and each correction required a second pass through the approval chain.

The operational pressure was compounded by GDPR. The documents contained patient identifiers, billing addresses, and in some cases clinical context. The company’s data protection officer had flagged the manual process as a compliance risk: paper documents were stored in unsecured filing cabinets, and email attachments were not consistently encrypted. The deadline was not optional. The hospital group had indicated that non-compliance would trigger a contract review in the following quarter.

Approach: A Six-Week Integration Sprint on SAP and Claude

Forfis ran a six-week integration sprint. The first week was a process audit: mapping every document type, every handoff, every approval gate, and every data field that touched the ERP. The audit identified 14 distinct document formats across purchase orders, packing lists, customs declarations, and shipment confirmations. The team selected the three highest-volume formats for the pilot, covering roughly 70 percent of inbound documents.

The extraction pipeline used the Anthropic Claude API for document parsing and field classification. The model was prompted with structured output schemas matching the SAP data model. The orchestration layer, built on a workflow engine, routed each extracted record through a confidence check. Records above a 92 percent confidence threshold and containing no patient identifiers or payment amounts were auto-approved. Everything else went to a human approver in a queue built into the existing helpdesk. The SAP integration used the OData API to write order and shipment records directly into S/4HANA, bypassing the manual entry step entirely. The first-response template engine pulled the enriched record from SAP and generated a status email within 90 seconds of approval.

Outcome: 38 Hours to 4 Hours, 6.2 Percent to 0.9 Percent

The pilot went live in week seven on a subset of order types from two regional warehouses. The full rollout followed in weeks eight and nine, extending to all document types and both warehouses. The two-month stabilization phase that followed focused on reducing the human-review rate and tuning extraction thresholds per document type.

The measured outcomes, tracked against the pre-pilot baseline, were as follows:

• Median first-response time fell from 38 hours to 4 hours. The 95th percentile dropped from 72 hours to 11 hours.
• Error rate on extracted fields fell from 6.2 percent to 0.9 percent.
• Cycle time per document, from receipt to ERP entry, dropped from 4.5 hours to 22 minutes.
• Human-review rate settled at 15 to 20 percent of records in steady state, down from the initial 25 percent.
• Customer satisfaction for order-status inquiries rose by 11 points on a 100-point scale over the first quarter after go-live.

Two full-time operators were redirected from manual data entry to exception handling and quality review. The GDPR compliance work, including the DPIA under Article 35 and the pseudonymization pipeline, was completed before go-live and required no rework during the stabilization phase.

Lessons for Similar Teams in Healthcare and Medtech

Five lessons from this engagement generalize to similar teams in healthcare and medtech distribution:

• Start with the SLA, not the technology. The hospital group’s 24-hour acknowledgment requirement defined the success criterion. The technology choice followed from the constraint, not the other way around. Teams that start with a model demo and work backward to a business need tend to over-build and under-deliver.

• The process audit is not optional. The 14 document formats, the unsecured filing cabinets, the inconsistent email encryption — none of this was visible from a technology specification. The audit took one week and saved an estimated three weeks of rework later in the sprint.

• Human-in-the-loop is a design decision, not a fallback. The confidence threshold and the data-sensitivity routing were defined in week two, before any code was written. Teams that treat the human gate as an afterthought end up with either over-automation (errors in production) or under-automation (the human reviews everything, and the cycle time does not improve).

• Model-agnostic architecture protects the client’s future. The client’s data protection officer asked, in week four, whether the pipeline could run on an open-weight model if the hospital group’s contract was renegotiated. Because the orchestration layer was decoupled from the model API, the answer was yes, and the rework estimate was under two weeks. A hard-coded dependency on a single vendor API would have made that conversation much harder.

• The baseline is the deliverable. The before/after measurement on cycle time and error rate was agreed in the audit phase and tracked from day one of the pilot. Without that baseline, the 38-to-4-hour improvement would have been anecdotal. With it, the client could present the numbers to the hospital group’s procurement team with confidence.