Information Scattered Across Systems: Why Work Waits While People Search.
The information people need to do their job usually exists. It is just in five different places, and none of them is where the work happens.
Ask a maintenance technician what they need before starting a repair. The asset history is in SAP. The manual is on a shared drive. The latest sensor readings are in another tool. Whether the spare part is in stock means a call to the warehouse. What went wrong last time is in an email, or in the head of the colleague on the night shift.
That is what SAP data silos look like on the ground. Not an architecture diagram, but a person with a phone in one hand and a clipboard in the other, piecing together the picture before they can act.
This is the third post in our series on how workers experience the execution gap. The first two looked at friction inside a single task. From here, we look at what happens between teams and systems.
In the first post of this series, we looked at too many screens and clicks. This post looks at what happens when the screen is so far from the work that people stop using it in the moment and write things down instead.
The Problem: Every System Holds Part of the Answer.
SAP is the System of Record for the processes your business runs on. But work rarely stops at the edge of SAP. A repair touches a sensor platform, a document system and a contractor. A customer question touches SAP, a CRM and a logistics portal.
Each system does its own job well. The trouble starts when one person needs a little of each, at the same moment, to make one decision. No system shows them the full picture, so they build it themselves: logging in, searching, exporting, asking around.
The more experienced the person, the faster they do it. That is the hidden risk. The knowledge of where to look becomes a skill that lives in people, not in the process.
A Practical Example: Starting an Unplanned Repair.
A conveyor stops on the morning shift. A technician gets the call.
Before they can fix anything, they need answers. Has this fault happened before? What did the last technician do? Which spare part fits, and is one in stock? Is the machine still under warranty with the supplier? What do the sensor readings show from the last hour?
So the search begins. The order history in one SAP transaction. The stock level in another. The manual on a shared drive that doesn’t open on the handheld. The sensor data on a dashboard in the control room. The warranty terms in an email thread with purchasing. Thirty minutes later, the technician has most of the picture, and the line has been down for thirty minutes.
Now picture the same call answered in one place. The technician scans the asset and sees its history, open orders, the right spare part with live stock, the manual and the latest sensor readings on one screen. The repair starts while the information is still fresh.
The Impact: Time Lost Before the Work Starts.
Scattered information costs more than search time:
- Longer downtime and delays. Work can’t start until the picture is complete. Every minute spent searching is a minute the asset, the order or the customer waits.
- Decisions on partial information. When finding everything takes too long, people act on what they have. Wrong parts get ordered, and known fixes get missed.
- Dependence on a few experts. The colleagues who know where everything is become the bottleneck, and their knowledge leaves with them.
- Calls and messages between teams. Maintenance calls the warehouse, the warehouse emails purchasing, purchasing checks with the supplier. Each handoff adds waiting time.
- Blind spots for AI. An AI agent can’t recommend the right action if the context it needs sits in six systems it can’t see. Connected information is what lets an agent help instead of guess.
How Neptune Closes the Gap.
The fix is not a faster typist. It is capturing data once, where the work happens, straight into SAP.
With Neptune, you build Business Applications that bring the SAP process to the point of work:
- One view per task. SAP data, documents, sensor readings and data from other systems on one screen, shaped around the decision the person has to make.
- Context from the start. Scan an asset, an order or a material, and the relevant history, stock and documents are already there.
- Native on SAP, connected across the enterprise. SAP data comes in through native SAP integration, and other systems connect through APIs, so SAP stays the System of Record.
- AI Agents with the full picture. Built into the application, agents can summarize the history, suggest the likely cause or prepare the next step, with the same context the person sees and under existing SAP governance.
In Asset Maintenance, this is how customers work toward less unplanned downtime: the repair starts with the answer, not the search.
The Metrics It Moves.
Bringing information together shows up in three of the six outcome areas from the series introduction:
| Outcome area | KPIs to baseline |
|---|---|
| Increase operational responsiveness | Exception resolution time · Time from event to action · On-time task completion |
| Increase workforce productivity | Task completion time · Manual administration time |
| Improve accuracy and process quality | First-time-right rate · Rework rate |
How to Build the Business Case.
Start with one decision people make often, and follow the search behind it.
- Pick the moment. Choose a recurring decision: starting a repair, answering a delivery question, releasing a batch, approving a change.
- Map the sources. Sit with the people who make it. List every system, file, inbox and person they check before they act.
- Time the search. Measure the time from “I need to act” to “I have what I need”, and count how often the work waits on someone else.
- Do the math. Search time per decision, multiplied by decisions per day, people and working days. Then add the cost of what waits: downtime, delayed orders, rework from decisions made on partial information.
An illustrative example: 20 technicians each spend 15 minutes gathering information before 4 repairs a day. That is 20 hours a day, or about 4,400 hours over 220 working days, before you count a single minute of downtime.An illustrative example: a site receives 80 deliveries a day, and each one takes three minutes to type into SAP from paper. That is four hours a day, or about 880 hours over 220 working days, before you count a single correction.
The Question to Ask Your Teams.
Where does work wait longest between teams, and who chases it?
The answer shows you where your information lives in silos, and where your execution gap is costing the most time. Next in this series: applications that don’t fit the job, and why one transaction can’t serve every role.
