• Energy & Utilities

The need

Operations and finance teams across the group were running critical decisions on overnight BW extracts. Production, maintenance, and cost KPIs were already out of date before the working day began. Different teams relied on different reporting models, creating multiple versions of the same truth and increasing the effort required to reconcile numbers before action could be taken.

The business impact extended beyond reporting. Every failure introduced delays, uncertainty, and additional manual work for both users and support teams. The organisation needed more than new dashboards. It needed a reliable analytics foundation that could deliver trusted KPIs in near-real-time, reduce operational risk, and scale without creating a growing support burden.

Our solution

S5 delivered a live analytics foundation that gives operations, maintenance, and finance teams a single trusted source of truth while protecting the integrity of the SAP core. Reporting became automated, monitored, and easier to scale across the organisation. The solution combines SAP S/4HANA data with Azure analytics services and Power BI, creating a platform that supports today's reporting needs while keeping future SAP analytics and AI options open.

1. Scan, what does the business actually read?

Before any model was built, we mapped the KPIs that operations, maintenance, and finance teams open every morning. Cost overrun, downtime, carbon intensity, production yield, the handful of numbers that drive real decisions. Everything else got cut. That decision alone collapsed weeks of avoidable model design later.

2. Simplify, one model, not five spreadsheets

We deployed Azure Analysis Services with star-schema models fed by S/4HANA, BW, and historian data through Azure Data Factory pipelines. One model, one version of the truth, row-level security for cross-functional users. Excel joins stopped being a job.

3. Stack, self-service reporting that scales

Cost overrun, downtime, and carbon intensity landed as reusable reporting templates the business can extend on its own. Row-level security in place from day one, power users add new measures without raising IT tickets. Self-service stopped being a slogan.

4. Ship, DevOps-grade automation, not artisan refreshes

PowerShell runbooks handle pipeline restarts and refresh schedules. Everything is version-controlled in GitHub with pull-request gates. Azure Log Analytics stitches pipeline logs into live health dashboards for the DevOps team. Failures are visible before users feel them, many of them now self-heal.

5. Steward, enabling power users, not training tickets

The engagement closed with brown-bag sessions for business power users, the people who actually know what the next measure should look like. The platform is now extended from inside the business, not from outside it. IT stays in the architecture, not in the request queue.

Technologies:

• S/4Hana

Results

• From overnight to live
  Operations, maintenance, and finance teams now open the day with KPIs refreshed every fifteen minutes, not last night's extract. Decisions land on data the business can act on today.

• One trusted view across functions.
  Production, maintenance, and cost KPIs sit in one model with row-level security, not five spreadsheets. Reconciliation stopped being a job before the day started.

• Reliable reporting at scale
  DevOps-grade automation, version control, and live pipeline health mean failures surface before users feel them. The support burden dropped, the confidence went up.

• Future options stay open
  The S/4HANA core stayed clean throughout, no custom code, no shortcuts. The foundation is ready when the business chooses to move on SAP Business AI or extend into new domains.