How we automated Atlas's predictive maintenance alerting, supplier purchase order pipeline, quality control data processing, and production reporting — reducing unplanned downtime by 71% and saving £1.8M annually across three manufacturing plants.
Atlas Manufacturing operated three precision engineering plants with 14.2% unplanned downtime costing £4,800 per hour. Supplier POs took 3.2 days manually. Quality defects were found after full production runs. Reporting consumed 11 hours per plant manager weekly.
We deployed five automation pipelines across all three plants in 22 days. Unplanned downtime fell from 14.2% to 4.1%, OEE improved from 68% to 84%, scrap rate dropped 63%, and £1.8M in annual savings was verified at day 90.
Atlas had built a strong reputation in precision engineering — AS9100 certified for aerospace, IATF 16949 for automotive, and long-standing supply relationships with tier-1 OEMs. Their operational challenge was common to manufacturers their size: legacy plant equipment with rich sensor data that was never fully analysed, purchasing processes built for a company half their current volume, and management teams spending too much time on reporting rather than production. The Operations Director had tried to address downtime through a traditional maintenance scheduling overhaul. We showed him that the sensor data already present in the plant could predict failures before they happened.
Each failure was independently quantifiable. Together, they were suppressing OEE (Overall Equipment Effectiveness) below 68% when industry best practice for precision engineering is 85%+.
CNC machines, grinding equipment, and coordinate measuring machines were maintained on a fixed schedule — regardless of actual equipment condition. Bearing wear, coolant degradation, and spindle imbalance were only detected when machines failed. Each unplanned downtime event on a primary line averaged 6.2 hours and cost £29,760 in lost production. Atlas experienced 21 unplanned downtime events in the 12 months before engagement. Result: £625,000 in unplanned downtime costs annually.
Raw material and consumable purchase orders were raised manually by production planners, routed through a paper-based approval chain involving the procurement manager, plant manager, and finance director depending on value. Material shortages caused by late POs had halted production lines 8 times in the previous 12 months. Result: 8 production stoppages from material shortage · est. £180k in delay costs.
Inline sensors on CNC machines generated dimensional measurement data throughout each production run. This data was batch-processed at end of shift. When a tooling deviation caused dimensional drift, the defective parts weren't identified until the next morning — potentially after a full 8-hour shift of out-of-spec components. Scrap rate: 3.8%. Result: 3.8% scrap rate · £420k in scrap material costs annually.
Each of three plant managers spent 11 hours per week assembling production reports: OEE, output vs target, quality data, shift performance, and maintenance records — pulled from the MES, quality system, and maintenance logs, formatted in Excel, and emailed to the Operations Director. Data was 24–48 hours stale on delivery. Result: 1,716h/yr of plant manager time on report assembly · equivalent to 1 FTE.
All pipelines integrated with Atlas's existing Siemens MES, SAP ERP, and MQTT sensor network — no equipment replacement, no MES migration, no disruption to production.
MQTT sensor data from 47 machines streamed continuously. ML model trained on 2 years of maintenance history detects early warning signatures: vibration anomaly, temperature deviation, power consumption spike. Alert sent to maintenance team 48–72 hours before predicted failure. Maintenance scheduled during planned downtime window.
SAP inventory levels trigger automatic PO generation when stock crosses reorder point. PO pre-populated with preferred supplier, pricing from last order, and delivery schedule. Routed digitally by value band — <£5k auto-approved by system, £5k–£25k approved by procurement manager via Slack in one click. Zero paper.
CMM and inline measurement data processed continuously. Statistical process control algorithms detect dimensional drift mid-run. Alert sent to machine operator and quality engineer within 90 seconds of anomaly detection — before defective parts continue to accumulate. Tooling change or process correction triggered immediately.
Live OEE dashboard updated every 15 minutes from Siemens MES data. Daily digest delivered to plant managers at shift end with performance vs target, top 3 downtime causes, and quality summary. Weekly executive report generated by Claude AI with cross-plant benchmarking and anomaly narrative. Zero manual assembly.
Real-time stock level monitoring across all three plants. Demand signal from production schedule used to calculate forward-looking reorder points dynamically. Cross-plant stock transfer suggested before external reorder if surplus exists at another site. Zero stockouts in 60 days post-launch vs 8 in the previous quarter.
All figures extracted from Siemens MES, SAP ERP, and maintenance records at 90 days. Reviewed with the Operations Director and Plant Managers before publication.
On-site visits to all three plants. Siemens MES and SAP ERP access granted. MQTT sensor network mapped — 47 machines across 3 sites. Maintenance history analysed. Production planner and quality manager interviews.
2 years of sensor and maintenance data used to train predictive models per machine class. SPC thresholds calibrated per component tolerance. SAP integration architecture designed. All technical designs reviewed by IT Director and Operations Director.
Predictive maintenance alerting live day 15 — first alert fired day 16 (bearing wear on Plant 2 CNC, confirmed and replaced before failure). Real-time quality monitoring live day 18.
SAP PO automation live day 20. Inventory pipeline day 21. Live OEE dashboard and automated reporting day 22. Plant managers trained in 90-minute session per site.
Predictive model retrained monthly with new failure event data. SPC thresholds reviewed quarterly with quality team. 12-month retainer includes monthly ML calibration sprint and quarterly OEE review with Operations Director.
45-minute strategy call. We'll audit your ops, identify your top automation wins, and give you the ROI estimate — specific to your stack.
Book Free Strategy Call View PricingThe predictive maintenance system paid for the entire engagement in the first month. We avoided a primary line failure on Plant 2 on day 16 — a £29,760 event that would have taken 6 hours to fix. The model flagged it 52 hours before it would have failed. That was the moment I knew this was not a cost, it was an investment.
I used to spend Monday morning assembling a production report that told me what happened last week. Now I have a live dashboard on my phone that tells me what's happening right now. The shift in management visibility alone changed how we run the plant.
Whether your problem is onboarding, churn, reporting, or all three — the pattern is the same. Let's audit your ops and build the roadmap.