Classic lean methods — delivered with AI-assisted measurement, reporting, and sustainment. We phase tools based on your losses, not a generic checklist.
5S Implementation
5S (Sort, Set in order, Shine, Standardise, Sustain) creates a workplace where waste is visible and standards stick — the foundation most lean programmes skip or do as a one-off tidy-up.
AI role: AI assists audit scoring from photos and checklists, tracks sustainment trends by area, and generates shift-ready summaries so leaders see slippage before the board walk.
- ✓Current-state 5S assessment by zone or line
- ✓Red-tag and standardisation plan with ownership
- ✓Visual management and audit checklist design
- ✓AI-assisted audit tracking and sustainment reporting
7 Wastes (Muda)
The seven wastes — transport, inventory, motion, waiting, overproduction, over-processing, and defects — give your team a shared language for spotting loss on the floor.
AI role: AI categorises loss data from shift reports, flags recurring waste patterns, and prioritises which waste types to attack first based on volume and ease of fix.
- ✓Waste mapping workshop with operations teams
- ✓Loss categorisation aligned to your data sources
- ✓Prioritised waste-reduction backlog
- ✓AI-assisted trend review in weekly improvement cycles
Production Preparation Process (PPS)
Production Preparation Process ensures new products, layouts, and line changes are ready before they hit the floor — reducing startup scrap, downtime, and firefighting.
AI role: AI accelerates readiness checks, consolidates preparation tasks from multiple departments, and surfaces gaps before go-live so nothing is discovered on first run.
- ✓PPS readiness framework for your change types
- ✓Cross-functional preparation checklist and gates
- ✓Pre-production trial plan and sign-off criteria
- ✓AI-assisted status dashboards for preparation milestones
Value Stream Mapping
Value Stream Mapping shows where value is added, where it waits, and where information breaks down — from order to shipment, not just inside one department.
AI role: AI helps compile cycle-time and inventory data from existing systems, drafts current-state maps faster, and compares future-state scenarios with less manual spreadsheet work.
- ✓Current-state value stream map (product family or line)
- ✓Data-backed lead time and WIP analysis
- ✓Future-state design with prioritised kaizen actions
- ✓Implementation roadmap with ownership and targets
SMED (Quick Changeover)
Single Minute Exchange of Die (SMED) cuts changeover time so batches shrink, flexibility rises, and waiting waste falls — without buying new equipment first.
AI role: AI analyses changeover video or step logs, separates internal vs external work, and tracks improvement trials so gains are measured not guessed.
- ✓Changeover time study and baseline
- ✓Internal/external work separation plan
- ✓Standard work for optimised changeover
- ✓Before/after measurement and sustainment checks
Standard Work
Standard work documents the best known method today — takt time, work sequence, and standard WIP — so improvement has a baseline and training is repeatable.
AI role: AI drafts standard work instructions from expert interviews and video, keeps version control visible, and flags when actual cycle times drift from standard.
- ✓Standard work documentation for priority operations
- ✓Takt time and line balance analysis
- ✓Training and visual job aid design
- ✓Review rhythm to update standards after kaizen
Kanban & Pull Systems
Kanban replaces push scheduling with pull — material and work move when the next step signals need, cutting overproduction and inventory waste.
AI role: AI forecasts consumption patterns, recommends kanban loop sizes, and alerts when pull signals break down before stock-outs or piles appear.
- ✓Pull system design for chosen value streams
- ✓Kanban loop sizing and signal design
- ✓Supermarket and replenishment rules
- ✓Performance tracking for pull system health
TPM (Total Productive Maintenance)
TPM engages operators and maintenance in autonomous care, planned maintenance, and focused improvement — so breakdowns fall and OEE rises together.
AI role: AI links TPM tasks to OEE loss data, predicts recurring failure modes, and summarises autonomous maintenance compliance by asset or line.
- ✓TPM pillar assessment and maturity baseline
- ✓Autonomous maintenance standards and schedules
- ✓Planned maintenance alignment with top losses
- ✓Integration with OEE measurement where applicable
Poka-Yoke (Error Proofing)
Poka-yoke makes mistakes impossible or immediately visible — before defects reach the customer or the next operation.
AI role: AI helps analyse defect Pareto data, suggests error-proofing points, and documents verification methods for new and existing failure modes.
- ✓Defect and near-miss analysis by operation
- ✓Error-proofing concepts (physical, visual, or digital)
- ✓Verification and audit method for each device
- ✓Link to quality metrics and OEE where relevant
Gemba Walks & Kaizen Events
Gemba walks keep leaders at the real place work happens; kaizen events concentrate cross-functional effort on one high-impact problem in days, not months.
AI role: AI captures gemba notes, themes recurring issues across walks, and produces kaizen event reports with actions, owners, and follow-up dates — fast.
- ✓Gemba walk structure and question sets for leaders
- ✓Kaizen event scoping and charter template
- ✓Facilitation support for focused improvement events
- ✓AI-assisted action tracking and close-out reporting
