Did we hit target, and where did we lose it?
Actual against target by line and shift, with the gap attributed to downtime, rate loss, or quality loss rather than left as a single variance number.
Throughput, downtime, yield, and OEE against target — by line, by shift, and by crew
Production Performance Dashboards for Plants That Run on Shift Data
Most plants already collect enough data to explain a bad week. It just lives in a SCADA historian, a shift log, a maintenance system, and a spreadsheet that the production manager assembles on Monday morning. We build the dashboard that joins them, so the production meeting starts with the causes rather than the collection.
Downtime reason codes cleaned and grouped before the build
Target, actual, and variance shown at line and shift level
Handover notes attached to the shift they belong to
Service command deck
Production Performance Dashboards for Plants That Run on Shift Data
Insights changing live
Starter range
$400-$1k
Downtime reason codes cleaned and grouped before the build
Designed for faster decisions and cleaner execution from week one.
Production performance
The weekly operating view, not another chart wall.
A production dashboard is only useful if it survives contact with the shift meeting. This is the shape we build: the number, the exception that explains it, the person who owns it, and what changes before the next run.
Book the Reporting Friction AuditWeekly operator pack
Monday action rhythm
Throughput vs target
94%
+3 pts
Unplanned downtime
11.4h
2 causes
First-pass yield
97.1%
stable
Shift data entry
3.2h
removed / week
Exceptions with owners
3 require follow-upLine 2 changeover
Due this weekOwner: Production Supervisor
Trial the revised changeover sequence on night shift
Packaging downtime
At riskOwner: Maintenance Lead
Confirm root cause on the recurring infeed stoppage
Scrap on grade change
In progressOwner: Quality Coordinator
Review the hold-and-release rule with the shift leads
Weekly meeting flow
- 1.Check trusted KPI movement
- 2.Review exceptions and owner actions
- 3.Approve the weekly summary pack
- 4.Carry actions into next week’s rhythm
What a production dashboard should settle before the meeting starts
Production reviews lose most of their time to reconstructing what happened. These views remove that step.
What is actually causing the downtime?
Downtime ranked by cause and by cost, not by frequency alone. The long tail of short stops often outweighs the one incident everybody remembers.
Is the problem the line, the shift, or the product?
The same performance data cut three ways. Most persistent production problems only become obvious when you can hold two of those constant.
Where is yield being lost?
Scrap, rework, and off-spec by stage and by reason, tied back to the run and the crew, so quality actions land where the loss occurs.
How is OEE actually made up?
Availability, performance, and quality shown separately. A single OEE figure hides which of the three moved, which is the only part that tells you what to do.
What did the last shift hand over?
Handover notes, holds, and open issues attached to the shift record rather than living in a paper book or a group chat.
What we build the reporting from
Plant data is rarely tidy and almost never in one place. The first sprint works with what is already captured, including the parts that are still manual.
No system replacement is required to start. The first sprint works from the access you already have — exports, extracts, or a read-only connection — and integration is scoped only once the reporting has proved worth automating.
- SCADA, historian, or PLC tag exports
- MES or production recording systems
- Shift logs and handover books, including spreadsheets
- Downtime reason codes and event logs
- Quality, laboratory, and grade-check records
- Maintenance work orders for breakdown correlation
- Production schedules and target rates
- Weighbridge, packing, and dispatch records
Scope, outcomes, and pricing
Decision framework built for confident buying
Clear outcomes, practical scope, and pricing you can plan around.
Outcome framing
Causes, not just totals
Downtime and loss are attributed to a cause and a line, so the review discusses fixes instead of establishing facts.
Shift data entry reduced
The manual daily assembly step is replaced by a scheduled refresh, and supervisors record exceptions rather than retyping numbers.
Comparable shifts
The same definitions apply across crews and lines, which is what makes shift-to-shift comparison fair enough to act on.
Actions that carry over
Trials, root-cause investigations, and process changes stay visible across weeks instead of resetting each Monday.
Scope clarity
- Reason-code cleanup and downtime taxonomy agreed with supervisors
- Target rates and OEE definitions confirmed before the build
- Connection to the historian or production system plus one manual source
- Throughput, downtime, yield, and OEE views at line and shift level
- Daily production pack and weekly review dashboard
- Exception list with owners, plus refresh and access setup
Pricing signals
Indicative ranges help planning. Final pricing is based on complexity, integrations, and delivery pace.
Starter
$400-$1k
1-2 weeks
One line or one question — usually downtime by cause — built from existing exports.
Scope this sprintMost chosen
Business Core
$1k-$5k
3-6 weeks
Plant-wide throughput, downtime, yield, and OEE with the daily pack and weekly review built in.
Scope this sprintConnected
$5k+
6 weeks+
Automated feed from the historian or MES, governed roles, and a maintained model across sites.
Scope this sprintTrust modules
Delivery model
Sprint first
One line proved before plant-wide rollout is scoped.
Plant access
Read-only
Reporting reads from exports or a read-only connection. No control-system changes.
Built with supervisors
Not around them
Reason codes and targets are agreed with the people who record them, or the data stays wrong.
Closest proof routes
FAQ support
Frequently asked questions
Clear answers on timeline, investment, and delivery scope.
Do you need to connect to our SCADA or control system directly?
No, and we usually would not start there. Most first sprints run from historian exports, MES reports, or the spreadsheets supervisors already maintain. Reporting is read-only by nature, and a direct historian connection is scoped later once the views have proved worth automating. Nothing we build writes to a control system.
Our downtime reason codes are a mess. Is that a blocker?
It is the most common starting condition and it is part of the work, not a prerequisite. The first sprint includes cleaning and grouping the reason codes with the supervisors who record them. Reporting built on codes nobody trusts will be ignored, so this step happens before the dashboard, not after.
Can you report OEE if we have never calculated it?
Yes, provided the underlying data exists — run time, planned time, actual output, target rate, and reject counts. The definition workshop is where the arguments get settled: what counts as planned downtime, whether changeover is availability or performance loss, and which rate is the target. Those choices are then published on the dashboard so the number stays comparable.
Will this work across multiple lines or multiple sites?
Yes. Multi-line and multi-site reporting is common, and it is where the comparability work pays off most. The constraint is usually that different sites record downtime differently — so the taxonomy has to be agreed once, centrally, before cross-site comparison means anything.
How do we handle shifts that still record data on paper?
Two options, and the choice is usually operational rather than technical. Either the paper record is replaced by a simple structured entry form that feeds the dashboard, or the paper stays and a single daily entry captures the exceptions. We would rather start with the second and change it later than stall the reporting on a data-capture project.
How long does the first production dashboard take?
One to two weeks for a single line or a single question in the Starter range. Three to six weeks for plant-wide throughput, downtime, yield, and OEE with the daily pack — the variable is almost always how quickly reason codes and target rates get agreed, not the build.
Other reporting areas
Most sprints start in one area and extend into the next once the weekly rhythm holds.
Financial reporting dashboardsCashflow, margin, debtors, and budget-versus-actual in one monthly pack leadership can sign off.Maintenance and reliability reportingBacklog ageing, PM compliance, breakdown history, and the jobs actually constraining the plan.Revenue and margin by jobWhich jobs, clients, and crews make money once labour, materials, and rework are counted.Compliance and training reportingTickets, VOCs, inductions, and statutory routines with expiry visible before the audit.SAP work management dashboardsSAP PM/MM work orders, notifications, shutdown readiness, and contractor execution.
Proof, industries, and delivery
Where this reporting has been built before, and the industries it fits most directly.
Dashboard / Reporting SprintThe parent sprint: audit, build, then the weekly operating rhythm.Transit Couriers dashboard case studyPublic-safe proof of operational reporting across disconnected sources.Manufacturing industry fitWhere production reporting sits alongside maintenance and quality.Mining and resources fitProduction reporting for asset-heavy operations and FIFO crews.
Send last month's production pack. We will map where it loses the causes.
The Reporting Friction Audit starts from the shift log, downtime report, or production spreadsheet you use today, and returns a friction map plus a 30-day fix path before any dashboard is scoped.
No commitment required. We will help you pick the right first sprint before any build commitment.