From SAP Work Orders to Smart Maintenance Decisions

In most manufacturing factories in Sri Lanka and even around the world a similar maintenance workflow is being used. When a machine breaks down, someone raises a ticket in SAP, a work order is created, components are identified, stores are called, inventory is checked, a technician is assigned and the repair begins. This workflow is logical on paper. However, between the work order and the actual repair, there is a huge delay waiting for approvals, searching for the right documents, calling stores, deciphering maintenance logs from a while back etc. A significant downtime accumulates not from the failure itself, but from everything that happens before the repair could start.

The Workflow and Its Limits

The traditional maintenance workflow relies heavily on coordination. When production notifies maintenance, maintenance raises a work order in SAP, the required spare components are identified and confirmed with inventory and a technician is allocated. Each of these steps involves different people and different departments in the company and most often this communication happens through phone calls, WhatsApp messages, or verbal handoffs. If every involved person and department in this chain lines up perfectly during a breakdown, then the system would work without issues. But in reality, approvals could get delayed, stores might not have the correct component to replace, inventory might not be properly updated, the right technician for the job might be allocated elsewhere etc. The Siemens 2024 True Cost of Downtime study states that the collective annual cost of unplanned stoppages for the world's 500 largest companies is USD 1.4 trillion which is roughly 11% of their total revenues (Siemens, 2024). The ugly truth behind this is that a significant portion of this is coordination and information-gathering time, dressed up as downtime, rather than actual machine failure time.

What SAP Actually Does and Its Limits

SAP is one of the most widely used enterprise resource planning platforms in manufacturing. It records and tracks maintenance history, work orders, inventory levels, component usage, and repair logs. For compliance tasks, auditing, and long-term record-keeping, it is a reliable system. However, it is not an efficient system to accelerate real-time decision-making on the factory floor. Traditional SAP reports are typically refreshed at scheduled intervals either daily or weekly for more complex manufacturing operations (Delfoi, 2026). SAP is not designed for a maintenance engineer to decide what to do right now, on a specific breakdown on a specific machine. There is a visibility gap between the record and the decision. When a machine stops and an engineer needs to understand whether this failure has happened before, what was replaced last time, and whether the same component is in stock, the answers are technically in SAP, but accessing them requires a long process of exporting reports, filtering it in Excel and cross-referencing separate maintenance logs.

The Excel Layer Between SAP and a Decision

In today’s industry Excel runs most of the behind the scenes operational work that happens between SAP and the actual decision. Excel is widely used by engineers to export data to spreadsheets for downtime analysis, KPI calculations, breakdown summaries, and maintenance trend reviews. But this layer could result in many errors and end up costing a lot. Around 90% of spreadsheets contain at least one error from manual data entry, according to industry research on spreadsheet reliability (GOIS, 2025). In the manufacturing industry, a small error can propagate a long way. A mistyped component code or an incorrectly calculated downtime figure feeds into repair decisions, spare-parts orders, KPI reports etc. and eventually influences the analysis and decision making of engineers. Beyond accuracy, the time cost is another problem. A significant portion of an engineer's day is going toward finding information rather than using it.

A survey found that nearly half of engineers spend at least an hour every working day just searching for parts and technical information (CADENAS, via Z2Data, 2022). Manual spreadsheet preparation, inter-departmental follow-ups and a Industry benchmarks for "wrench time", the share of a technician's shift actually spent on hands-on maintenance sits between 25% and 35% for most organisations, against a world-class benchmark of around 55% (FTMaintenance, 2024). That gap is generally filled with information-gathering, departmental coordination, and waiting for data to be put together.

Why Scattered Information Slows Down Decisions

The real blocker today in a maintenance workflow is not the technology but the fact that the information is scattered. SAP holds the records, Excel holds the analysis and machine manuals might be in a binder on a shelf. The SOP for this failure type might be filed in a shared drive that has not been reorganised in years and the engineer who remembers what was done last time could be on a different shift.

Due to this fragmented data, during a machine breakdown, the engineer has to become a data collector before they can be a problem solver. They need to spend time pulling data from multiple disconnected sources such as SAP exports, maintenance logs, component lists, manuals, informal conversations and assemble a picture manually, under time pressure, while the line is down (SAP Business One, 2026; Delfoi, 2026). Even though they might be able to reach the right answer, the process is much slower and harder than it needs to be.

What Connected Maintenance Intelligence Changes

Platforms like Protonest Connect are built to address this operational gap. The solution is not to replace SAP or remove the workflow, but to connect what is currently scattered into something an engineer can actually use at the moment of a failure.

With the Protonest Connect backend, sensor data from the production floor such as power monitoring, temperature readings, real-time machine status etc. becomes visible as clear dashboards with configurable alerts. Abnormal behaviour can be detected early before a minor issue becomes a full breakdown.

Beyond monitoring, the intelligent analysis layer brings together maintenance records, machine manuals, SOPs, KPI data, and inventory history which are currently not available in a single place for the engineers to consult on. McKinsey estimates that predictive maintenance built on connected data can reduce unplanned downtime by up to 50% and cut maintenance costs by 18 to 25% (McKinsey, 2020). Deloitte's research adds that poor maintenance strategy alone can reduce a plant's productive capacity by 5 to 20 percent — capacity that better operational visibility directly recovers (Deloitte, 2024).

Decision Making Is Still Yours but with Connected Data

SAP is still required for recording, tracking, and compliance. The work order process is needed as well. But the distance to bridge is between raising a work order and making a good maintenance decision. It should not require an hour of spreadsheet work and a few phone calls. Connected maintenance intelligence will help close the gap between the work order and the decision. For factories still running on exports and paper, closing that gap will provide a significant improvement.

References

1. Siemens. True Cost of Downtime 2024. Reported via IndexBox: https://www.indexbox.io/blog/network-downtime-costs-manufacturers-billions-analysis-of-2024-siemens-report/
2. Delfoi, SAP report data latency and real-time visibility gap: https://delfoi.com/en/articles/real-time-visibility-in-sap-production-processes/
3. SAP Business One / Lake Technologies, manufacturing visibility and spreadsheet limitations: https://www.lake.co.uk/manufacturing-problem-3-lack-of-real-time-visibility/
4. GOIS (Good Orders Inventory System), spreadsheet error rate in manufacturing (~90% of spreadsheets contain errors): https://www.goodsorderinventory.com/blog/inventory-management-excel/
5. CADENAS survey (via Z2Data), engineers spending 1+ hour daily searching for information: https://www.z2data.com/insights/how-much-time-component-engineers-losing-searching-for-data
6. FTMaintenance, wrench time benchmarks (average 25–35%, world class 55%): https://ftmaintenance.com/maintenance-management/5-labor-kpis-measuring-maintenance-team-performance/
7. McKinsey & Company, predictive maintenance downtime and cost reductions. Via Com4: https://www.com4.no/en/blog/predictive-maintenance-how-to-use-iot-to-reduce-downtime-and-costs
8. Deloitte. Industry 4.0 and Predictive Technologies for Asset Maintenance: https://www.deloitte.com/us/en/insights/industry/manufacturing-industrial-products/industry-4-0/using-predictive-technologies-for-asset-maintenance.html
9. Protonest Connect: https://www.linkedin.com/company/protonest-connect/