How Pharma and Manufacturing Warehouses Can Prepare for AI-Driven Operations: SAP EWM + QM + PP in Practice

Table of contents

• Why Warehouse AI Readiness Is End to End
• What the Practical Cases Show
• 1. Connect Inbound Receiving with Quality Inspection
• 2. Link Production Demand with Warehouse Staging
• 3. Strengthen Batch and Serial Traceability
• 4. Improve Picking Accuracy and Shipment Readiness
• Measure Readiness Through Operational KPIs
• Classify Decisions Before Automating Them
• A Practical Readiness Roadmap
• Build the Operational Foundation First

AI can help warehouse teams predict delays, prioritize work, identify shortages, and respond faster.

However, it cannot improve a warehouse built on inaccurate inventory, disconnected quality processes, weak traceability, or unclear ownership.

For pharma and manufacturing companies, readiness begins by connecting SAP EWM (Extended Warehouse Management), SAP QM(Quality Management), SAP PP (Production Planning), and SAP S/4HANA in one reliable operational flow.

The goal is not to automate every decision. It is to create trusted processes that AI can safely assist, analyze, and improve.

Why Warehouse AI Readiness Is End to End

Many warehouse problems begin earlier in the material flow.

A production shortage may start with a delayed inspection. A shipment delay may come from blocked stock. A picking error may happen because batch or serial data is incomplete.

AI therefore needs a connected process:

Inbound receipt → quality inspection → stock decision → putaway → production staging → consumption → finished goods → outbound shipment

SAP Quality Management supports quality-control processes in SAP S/4HANA. Quality Management in embedded EWM can also support inspections of delivered products and packaging.

SAP S/4HANA Manufacturing for planning and scheduling connects production plans with inventory and execution feedback while considering material and capacity constraints.

The value comes from how these processes work together, not from one module alone.

What the Practical Cases Show

The following cases support different parts of the AI-readiness journey.

Fonterra: A Warehouse-Execution Benchmark

The Fonterra case is directly relevant to SAP EWM and warehouse performance.

SAP reports that Fonterra achieved 99.7% inventory record accuracy, 250 case picks per hour, and 60,000 cartons dispatched daily after consolidating warehouse operations with SAP EWM and voice solutions.

These are customer-specific results, not universal benchmarks. However, they show what standardized processes, reliable inventory data, and connected warehouse execution can support. (Source: SAP Fonterra case study).

Adalvo: The Pharma Transformation Foundation

ITP’s Adalvo pharma case is not presented as a warehouse-performance case.

Instead, it shows the broader digital foundation required for automation in a regulated pharma environment.

ITP and Adalvo completed an SAP ECC-to-SAP S/4HANA migration on Microsoft Azure in 7.5 months, covering 23 SAP modules, including SAP Advanced Track and Trace for Pharmaceuticals.

The project strengthened process integration, data integrity, validation, traceability, and governance. These capabilities are essential before companies introduce AI into quality-sensitive or regulated processes.

Together, these examples show that AI readiness does not begin with an AI tool.

It begins with reliable data, integrated SAP processes, and clear operational controls.

For warehouse leaders, the preparation work can be organized around four practical areas.

1. Connect Inbound Receiving with Quality Inspection

Incoming materials may require packaging checks, sampling, laboratory testing, document reviews, or formal release.

Warehouse teams need correct storage instructions. QA teams need visibility into inspection workloads. Production planners need reliable material-availability dates.

SAP EWM and QM should provide clear visibility into unrestricted-use, quality-inspection, and blocked stock.

Once this foundation is stable, AI can:

• Flag inspections likely to delay production
• Prioritize urgent inspection lots
• Identify repeated supplier-quality issues
• Warn planners when blocked stock creates shortages
• Summarize inspection backlogs

AI may help identify risks and recommend priorities. However, material release, batch disposition, and other regulated quality decisions should follow defined approval rules.

For more detail, read ITP’s guide to SAP S/4HANA for pharmaceutical quality management.

2. Link Production Demand with Warehouse Staging

SAP PP may determine what production needs and when. SAP EWM must stage the correct quantity, batch, handling unit, or component at the correct production area.

Weak integration can create:

• Materials staged too early
• Production-line shortages
• Incorrect batches supplied
• Poor warehouse-task priorities
• Limited visibility into staging completion
• Differences between physical and system stock

AI can use reliable production and warehouse data to predict staging delays, identify likely shortages, and recommend task priorities.

However, those recommendations are useful only when demand, stock status, and warehouse confirmations are accurate.

ITP’s SAP S/4HANA for manufacturing overview explains how EWM, QM, and PP support connected production operations.

3. Strengthen Batch and Serial Traceability

Traceability affects receiving, storage, quality release, production supply, picking, and shipment.

Pharma warehouses may control stock by batch, expiry date, quality status, and release status.

Manufacturers may use serial numbers to connect components and finished products with production, warranty, or service history.

Before applying AI, companies should ensure that:

• Batch and serial data is captured consistently
• Quality status is visible during execution
• Shelf-life and expiry rules are maintained
• Material movements are confirmed promptly
• Production consumption is recorded accurately
• Stock adjustments include reasons and approvals

SAP EWM can also integrate with SAP Global Batch Traceability.

AI can identify patterns and exceptions in complete traceability data. It cannot rebuild transaction history that was never captured.

For a broader view, read ITP’s article on warehouse management with SAP S/4HANA.

4. Improve Picking Accuracy and Shipment Readiness

Correct picking requires more than selecting the right material number.

The warehouse may also need to confirm the correct quantity, batch, serial number, stock type, storage location, and handling unit.

Shipment readiness should confirm that:

• Stock is available and released
• Picking and packing are complete
• Batch and serial details are correct
• Labels and documents are ready
• Warehouse exceptions are resolved
• The shipment can leave as planned

AI can identify at-risk orders, unusual task patterns, and changing priorities.

Measure Readiness Through Operational KPIs

The first AI use case should target one measurable problem with a clear owner, baseline, target, and approval model.

Classify Decisions Before Automating Them

Not every workflow needs the same level of autonomy.

Assist: AI summarizes information or highlights exceptions.

Recommend: AI suggests priorities or actions.

Execute with approval: AI prepares an action for an authorized person.

Do not automate yet: The process remains manual because its data, controls, ownership, or risk level is not ready.

This classification is especially important for stock releases, batch disposition, production-plan changes, and other quality-sensitive decisions.

A Practical Readiness Roadmap

Warehouse, manufacturing, QA, and supply-chain leaders can begin with five steps:

1. Map the material flow from receipt to shipment.
2. Assign owners for inspections, stock decisions, staging, picking, and exceptions.
3. Improve master data and transaction discipline.
4. Integrate EWM, QM, PP, and SAP S/4HANA.
5. Select an AI use case with a baseline, target KPI, approval model, and business owner.

A structured SAP implementation and migration assessment can identify process, data, integration, and control gaps before new AI tools are introduced.

Build the Operational Foundation First

An AI-driven warehouse can anticipate risks, recommend actions, and support faster decisions.

Success still depends on accurate inventory, integrated quality processes, reliable production staging, strong traceability, and clear ownership.

SAP EWM provides the warehouse-execution layer. SAP QM supports controlled quality processes. SAP PP connects material demand with production requirements. SAP S/4HANA brings these processes together.

From our experience, AI readiness is rarely about the technology alone. It depends on reliable data, connected processes, clear ownership, and the right SAP foundation.

At ITP, we support pharma and manufacturing companies with digital transformation consulting, AI implementation, and full SAP implementation – from initial assessment through delivery.

Contact us to discuss an EWM/QM process review and identify where your warehouse is ready for AI – and what should be improved first.