SAP BTP Business Scenarios for Analytics

Specialty pharma company needs advanced clinical trial analytics. Standard dashboards don't match the domain-specific workflows required by researchers.

Your pharmaceutical company runs multiple clinical trials. Each trial requires specialized analytics that standard SAC dashboards can't provide. Researchers need to view enrollment progress, adverse event trends, and protocol deviation analysis together—with the ability to slice by patient cohort, trial site, and time period. They need to generate compliance reports for regulators, with specific narrative requirements that don't fit standard dashboard templates. Currently, researchers use a combination of SAC dashboards and manual Excel work, because the custom workflows don't exist in analytics. They're limited by what standard dashboards can express. Trial monitoring is slower and more error-prone than it could be.

Custom React application built on SAP CAP. Embeds SAC analytics. Custom UX for trial-specific workflows. Rapid development, enterprise-grade backend.

Using SAP Cloud Application Programming (CAP), you build a custom clinical trial analytics application. The React frontend is tailored to researcher workflows: enrollment dashboard, adverse event timeline, protocol deviation analysis, all on one screen in the order researchers actually use them. Data comes from governed Datasphere views through the Datasphere consumption APIs (or, for data that lives only in SAC models, the SAC Data Export Service API), and SAC stories are embedded for exploration, with filters passed through the SAC URL API. Custom reports are generated with regulatory-compliant templates. Researchers can configure analyses on-the-fly: filter by protocol version, by site region, by patient age cohort. The application is deployed on BTP as a multi-tenant SaaS offering (eventually you could sell this to other pharma companies). Plan a phased delivery: a first trial-monitoring release, then reporting templates. ROI: trial monitoring cycles accelerate, compliance reporting is faster, and you have a scalable platform for future trials.

SAP CAP + React + Datasphere APIs + embedded SAC + BTP deployment

• SAP Cloud Application Programming (CAP)
• React (custom UX)
• Embedded SAC stories + SAC URL API
• SAP BTP (application platform)
• Datasphere (governed data)

Trial monitoring cycles shorten. Custom reporting becomes self-service. Platform is reusable for future trials.

Speed: Automating compliance report generation shortens the monitoring cycle, and researchers stop rebuilding Excel reports. Accuracy: Automated enrollment tracking and adverse-event aggregation remove recurring manual data-entry errors. Scale: The platform built for one trial can be configured for later trials.

Domain-specific analytics need is clear. SAC is deployed with trial data. BTP and CAP expertise available.

• Standard SAC dashboards don't meet domain-specific workflow requirements
• Datasphere (or SAC) holds the governed trial data the application needs
• Custom UX can be specified and designed (not overly complex, iterative design is possible)
• BTP is available and development environment is set up
• CAP and React development expertise is available (or can be trained/hired)
• Application deployment strategy is agreed (SaaS, internal, multi-tenant, single-tenant)

Supply chain analytics identify at-risk inventory weekly. Exception list is sent to procurement, who manually evaluates and decides on action. Response time: 5 business days. Stockouts still occur.

Your supply chain analytics team has built excellent inventory risk dashboards in SAC. Every Friday, they pull exception reports: items at risk of stockout in the next 30 days, items with excess inventory, items with slow-moving trends. The list goes to procurement. Procurement reviews it, manually researches each item (checking supplier lead times, current orders, upcoming demand forecasts), makes decisions (order more, or hold), and sends emails. Response time is 5 business days. By then, demand has changed, and some exceptions that were identified Friday are no longer relevant Monday. Stockouts still occur because the decision-to-action cycle is too slow. Excess inventory is being carried because hold decisions aren't acted on quickly enough.

A scheduled job turns each exception into an SAP Build Process Automation workflow. Automated scoring and recommendations. Procurement approves, the system executes.

A scheduled job (for example a small CAP service on BTP) reads the at-risk items from the governed exception view in Datasphere that also feeds the SAC dashboard, and publishes one event per exception to Event Mesh; SAP Build Process Automation starts a workflow for each event (or the job starts the workflows through the process API). The workflow automatically gathers additional context: current orders in S/4HANA, forecasted demand from SAC Planning (through the SAC Data Export Service API), current on-hand inventory. An AI model (for example one deployed on SAP AI Core, or SAP-RPT-1.6 for tabular predictions) scores the exception (probability of stockout, financial impact) and recommends action (order quantity, if any). Procurement receives a structured task: "Exceeds risk score, recommend order 500 units by Tuesday, current lead time is 10 days." They approve or adjust. System executes: PO is created in S/4HANA, supplier notification is sent. The cycle shrinks from days to hours, which cuts stockouts and excess inventory.

SAC + SAP Build Process Automation + S/4HANA + messaging + AI scoring

• SAP Datasphere + SAP Analytics Cloud (exception detection)
• SAP Build Process Automation (workflow engine)
• SAP S/4HANA (PO creation)
• Event Mesh capability or advanced event mesh (messaging)
• SAP AI Core model or SAP-RPT-1.6 (scoring)

Decision-to-action cycle drops from days to hours. Fewer stockouts. Lower inventory carrying cost.

Inventory efficiency: Compressing the decision cycle lets planners run with less safety stock and fewer stockouts. Working capital: Automated hold decisions reduce excess inventory and its carrying cost. Procurement efficiency: Procurement team time freed from manual exception research to strategic supplier management and contract negotiation.

SAC exception detection is working. Workflow requirements are clear. S/4HANA is accessible for PO creation.

• SAC analytics identifies inventory exceptions reliably (at-risk items, excess items, slow movers)
• Procurement workflows and decision criteria are documented
• S/4HANA PO creation can be triggered via API or messaging
• SAP Build Process Automation is available and development team is trained
• Eventing infrastructure (Event Mesh capability or advanced event mesh) is available
• Exception scoring and recommendation logic can be defined (or sourced from existing models)

Channel partners need to monitor their sales, commissions, and customer health. Currently they have read-only SAC dashboard access. Need to embed analytics in their own applications.

Your company sells through 400 channel partners. Partners need visibility into: their sales performance (YTD vs. quota), customer health (renewal risk, expansion opportunity), commission calculations (quarterly accrual), forecasting (pipeline, probability). Currently you publish SAC dashboards to partners (read-only access). Partners want to embed this data in their own CRM systems and business intelligence tools. They want REST APIs, not dashboards. You want to ensure partners only see their own data, not competitors' data. You want to track who accessed what (audit trail for disputes). Standard SAC dashboard sharing doesn't provide this.

API Management exposes governed Datasphere data to external partners. Row-level security per partner. Every call logged. Partners integrate via REST or OData.

SAP Datasphere exposes views and analytic models through its OData consumption APIs, and API Management in SAP Integration Suite publishes a partner-facing API product on top, listed in the Developer Hub. The product exposes sales performance, customer health, and commission data. Row-level security is applied per partner (with Datasphere data access controls or filtering in the API layer): Partner A's API calls return only Partner A's data. API Management records which partner called which API and when. Partners integrate this API into their CRM systems: they use it to build partner scorecards, embed sales performance in rep dashboards, calculate real-time commission accruals. Your analytics data becomes a service that partners consume. Partners reduce their dependency on your dashboards; they embed the data in their own workflows. Disputes about sales credit or commission calculations are resolved by audit logs.

Datasphere + SAC + BTP APIs + partner applications

• SAP Datasphere (data and API layer)
• SAP Analytics Cloud (internal dashboards on the same models)
• API Management in SAP Integration Suite (partner access)
• Row-level security (per-partner data isolation)
• Audit logging

Partner self-service improves. Dispute resolution becomes data-driven. Partner experience is tailored to their systems.

Partner satisfaction: Partners can integrate the data into their own CRM, which they value more than another dashboard login. Dispute reduction: Call logs and governed source data make it clear what each partner saw, so sales-credit disputes are settled faster. Efficiency: Partners query their own data, so fewer commission and sales-credit questions reach your support team.

Partner data requirements are clear. Datasphere is deployed. API security/governance model defined.

• Partner analytics requirements are documented (what data do they need)
• Datasphere is live with partner-facing views or analytic models
• Row-level security can be implemented per partner (via partner ID or organization)
• BTP is available and API development is supported
• API authentication and rate limiting requirements are defined
• Audit logging and compliance requirements are understood

Manufacturing facility has 500 IoT sensors (temperature, pressure, utilization). Cloud latency makes real-time local decisions impossible. Sending all data to cloud is prohibitively expensive.

Your manufacturing facility has 500 IoT sensors monitoring equipment temperature, pressure, utilization, and energy consumption. Real-time alerts need to be generated locally (if temperature exceeds threshold, alert operator immediately). But all data collection is currently cloud-based: sensors send data to cloud, cloud processes it, alerts come back down. Latency is 3-5 seconds. For some equipment, that's too slow—a 3-second delay in alerting an operator about excess pressure could be catastrophic. Additionally, sending all 500 sensors to cloud at 1-second granularity creates massive bandwidth costs. You need real-time, local intelligence that only syncs summarized, important data to cloud for historical analytics and dashboards.

Local connectivity at the facility evaluates alert rules in real time. Summarized data syncs to the cloud. SAC dashboards consume cloud data for historical analysis and planning.

SAP Plant Connectivity is installed at the facility and connects to the sensors and controllers (for example through OPC UA). Its tag-based notifications evaluate trigger rules locally, such as "temperature above 85 °C", and can write alerts straight to local systems such as an OPC UA server or SCADA, so operator alerts don't wait for a cloud round trip; SAP Digital Manufacturing receives the events it needs for production. SAP Edge Integration Cell, running on a Kubernetes cluster in the plant network, aggregates the readings in an integration flow. Summarized data (5-minute aggregates: max temp, avg pressure, utilization %) is sent every 5 minutes to SAP HANA Cloud or Datasphere, where SAC reads it. Cloud SAC dashboards show historical trends: equipment wear over time, efficiency patterns, predictive maintenance needs. Local real-time protection is fast. Cloud analytics still work for optimization, planning, and predictive maintenance. Bandwidth costs drop sharply because raw data is processed locally and only summaries are sent to the cloud.

SAP Plant Connectivity + SAP Digital Manufacturing + Edge Integration Cell + cloud SAC

• SAP Plant Connectivity + SAP Digital Manufacturing
• SAP Edge Integration Cell (in the plant network)
• Local notification rules (alerting)
• SAP Analytics Cloud (cloud analytics)
• Datasphere (for summarized data)

Local alerting no longer waits on the cloud. Bandwidth costs drop sharply. Equipment downtime is reduced.

Safety: Local alerts reach operators without the cloud round trip, which matters for temperature- and pressure-critical equipment. Efficiency: Predictive-maintenance analysis on the summarized cloud data catches developing issues earlier, so unplanned downtime falls. Cost: Processing locally and syncing only summaries cuts data transmission costs substantially.

IoT sensors are installed. Edge network connectivity is available. Analytics requirements are clear.

• Manufacturing facility or field location has IoT sensors deployed
• Local network connectivity is available at the edge (WiFi or wired)
• Real-time alerting rules can be defined as local notification rules
• SAC and Datasphere are deployed for cloud analytics
• SAP Plant Connectivity, SAP Digital Manufacturing and an Edge Integration Cell (on a supported Kubernetes platform) are available and set up
• Bandwidth and latency requirements are documented

Supply chain team needs an AI advisor that understands their domain: lead times, geopolitics, supplier reliability, inventory optimization rules. Standard Joule doesn't know their policies.

Your supply chain team uses Joule (analytical insights on SAC models) to ask questions about inventory and supplier data. But Joule doesn't understand your industry-specific context: your suppliers' lead times, geopolitical risks affecting Asia-Pacific sourcing, your inventory optimization thresholds, your preferred sourcing regions. When you ask "should we order more of SKU-X?", Joule gives a generic answer. It should give a domain-aware answer: "Yes, order 500 units by Friday. Thai supplier lead time is 10 days due to recent factory issues. We have 8 days of inventory left. German backup supplier is 3x cost but available in 5 days. Recommend Thai supplier for cost savings, German as backup if needed."

A custom agent built in Joule Studio uses your lead times, supplier reliability, geopolitical risk assessments, and inventory policies as instructions, grounding, and tools. Deployed in Joule as a copilot for the supply chain team.

Using Joule Studio, you build a domain-specific agent: its instructions encode your inventory optimization rules, document grounding indexes your sourcing guidelines and geopolitical risk assessments, and its tools query lead-time history and stock levels in Datasphere and open purchase orders in S/4HANA. Deployed on the Joule Studio runtime, it appears in Joule next to SAP's own agents. When you ask "should we order SKU-X?", the agent considers: current inventory, lead times, geopolitical risks, and your policies. It recommends: order quantity, supplier selection, backup options. Overrides are logged with the reason, and the team reviews them to refine the agent's instructions and grounding documents, rather than retraining a model. The supply chain team gets a personal AI advisor that reflects their domain and their experience.

Joule + Joule Studio + document grounding + Datasphere + S/4HANA APIs

• Joule with SAC analytical insights (baseline)
• Joule Studio (agent, instructions, tools)
• Domain knowledge base (supply chain rules, policies)
• Decision log (overrides and outcomes, for review)
• Datasphere (data for agent queries)

Decision time drops. Decisions improve (better supplier selection, lower costs). Team productivity increases.

Speed: Research that took hours shrinks to a short conversation with the agent. Accuracy: Recommendations weigh cost against lead-time and geopolitical risk consistently, which improves supplier selection and lowers sourcing cost. Learning: Reviewing overrides and their reasons keeps the agent's instructions and grounding current.

Domain knowledge is documented. Training data is available. BTP AI Core is accessible.

• Domain-specific decision rules and policies are documented (not just tribal knowledge)
• Historical decisions and outcomes are available to test the agent against
• Joule is deployed and understood by the business team
• Joule Studio is available (a Joule entitlement plus BTPEA, CPEA, or pay-as-you-go) and agent development support is in place
• Datasphere contains the analytical data the agent will query
• Team has committed to iterative improvement (the agent improves as instructions and grounding are refined)