thingsgosocialThingsgosocial

Real-time manufacturing automation, built for clarity and speed

Unify IoT, vision, and production data. Catch issues early, automate corrective actions, and document everything for audit-ready traceability.

OEE92.4%Downtime (Δ)-18%Data Delivery99.8%
Factory Data Hub

Realize any use case with a universal data infrastructure

Connect machines, sensors, and apps into one operational model. Build once — power quality, throughput, maintenance, and compliance together.

Factory Overview
Factory Overview — Utilization & Downtime ParetoUtilization64% nowDowntime (mins) by cause64mSetup42mChange28mBreak18mStarve12mBlock

Live, plant→line→cell visibility for utilization, OEE, and shift attainment. Standardized downtime coding with minutes/root cause, plus drill-downs to active alerts, WIP aging, and work orders for fast triage.

OEE: 92.4%Downtime: 87mThroughput: 12,430
What you’ll get
  • Real-time utilization & attainment
  • Labeled downtime Pareto
  • Line→cell drilldowns
Inline Quality & SPC
FPY target bandFPY: 98.8%Cpk 1.47

FPY by part/tool/shift with SPC bands and auto-rule checks. Top defect modes linked to station & lot with visual evidence. CAR/CAPA workflows (owner, SLA, verification) tied to digital genealogy for audit.

FPY: 98.8%Defect Rate: 1.2%Cpk: 1.47
What you’ll get
  • SPC limits & auto-rules
  • Defect Pareto by station
  • CAR/CAPA with SLA
Production & Throughput
Plan12,430 / 13,000

Plan vs actual with takt-variance markers and end-of-shift forecasts. WIP heat by station highlights flow imbalance, while cycle-time histograms expose bottlenecks and changeover impact for proactive balancing.

Output: 12,430Target: 13,000WIP: 2,140
What you’ll get
  • Takt variance markers
  • WIP heatmap by station
  • EoS attainment forecast
Predictive Maintenance
Vib RMS (mm/s)Alert thresholdRUL: 36h

Condition monitoring (vibration, temperature, current) with Remaining Useful Life estimates. Alerts auto-create CMMS work orders; spares coverage & lead-time checks prevent unplanned stops.

Assets: 126Pred. alerts: 3Avoided stops: 5
What you’ll get
  • Condition triggers & RUL
  • Auto CMMS tickets
  • Spares coverage view
Downtime & Maintenance
MTBF (h)MTTR (m)Top downtime causes (mins)48mChangeover36mBreakdown22mStarved18mBlocked14mSetup

Never be caught off guard: ranked downtime causes with minutes and trendlines, MTBF/MTTR tracking, and backlog/SLA visibility—integrated with predictive alerts to keep machines running.

MTBF: 42hMTTR: 18mOpen WOs: 12
What you’ll get
  • Top causes with minutes
  • MTBF/MTTR trendlines
  • Maintenance backlog view
Workforce & Safety
Line A45%
Line B35%
Maint.20%

Shift allocation by line/role with skills & certification coverage. Incident and near-miss logs capture severity, root cause, and CAPA—so teams stay safe and production stays on plan.

Incidents (12w): 10Training: 96%Staffed lines: 100%
What you’ll get
  • Shift & skills matrix
  • Incident/near-miss log
  • Training & cert alerts

From discovery to value — fast

Discovery

Scope lines, goals, KPIs, and success criteria.

Data hookup

Connect PLCs, sensors, and cameras securely.

Model

Normalize tags; map parts, stations, and events.

Dashboards

Build role-based views and alerts.

Go-live

Validate on-shift, train ops, iterate quickly.

Review

Measure impact; lock in next wins.

Why manufacturers choose us

Real-time by design

Millisec-level ingest and stream processing power continuous decisions.

Quality without compromise

Inline QC, corrective actions, and audit-ready records.

Clarity for every role

Operators, engineers, and leaders share the same live truth.

Vendor-neutral

Bring any PLC, sensor, or camera — we normalize and fuse data.

Scales with you

Start with one line, expand to the entire network without rework.

Compliance built-in

Digital traceability and reporting aligned to ISO/industry standards.

Case studies

W1W2W3W4W5W6W7W8Downtime Δ: −18%Minutes/day

Assembly Line Rollout

Inline QC and automated alerts across multiple lines decreased unplanned downtime while stabilizing shift output.

Downtime Δ: −18%OEE: 92%+Payback: < 4 mo
What you’ll get
  • Standard alert playbooks
  • Shift report templates
  • Changeover best practices
FPY target bandFPY: 98.8%Cpk 1.47

Discrete Assembly

Lot/unit genealogy from raw to pack-out made audits a search-and-export task with clear CAR ownership.

FPY: → 99%Prep time: −80%Find time: <1s
What you’ll get
  • Audit-ready exports
  • Genealogy search patterns
  • CAPA workflow tips
kWh / unitPeakPeakBaselineNow: 66 kWh/u

Process Metals

Energy monitoring and load shifting reduced peaks; visibility aligned maintenance to cut rework.

kWh/unit: −14%Peak: −11%Rework: −9%
What you’ll get
  • Load-shift recipes
  • Peak window alerts
  • Cost impact model

Start your automation pilot

Share your lines, target KPIs, and current systems. We’ll propose a 6-week pilot with clear success metrics.

Uptimenear 99.7%Data delivery~99.8%Alert → actionminutes