Field guide
Continuous improvement in manufacturing
Build continuous improvement into the way your factory operates.
A practical system for finding, testing, verifying, and sustaining manufacturing improvements with evidence.
Vrolen blog
Ideas for better operational decisions.
Practical field guides for teams connecting operational data, diagnosing system behavior, and choosing what to improve next.
Read article
Field guide
Lean manufacturing
Use lean manufacturing to improve customer flow—not local efficiency.
Understand lean principles, flow, pull, standard work, problem solving, and the conditions required for lasting results.
Field guide
Operational excellence in manufacturing
Turn operational excellence from a slogan into a managed system.
Align purpose, people, processes, technology, and measures so better results survive beyond individual projects.
Field guide
Continuous improvement software
Choose continuous improvement software around the work—not the feature list.
Evaluate CI software for evidence capture, prioritization, experiments, governance, integrations, security, and adoption.
Field guide
Manufacturing software guide
Understand where ERP, MES, MOM, QMS, CMMS, and improvement platforms fit.
Use ISA-95 boundaries and real operating decisions to design a manufacturing software architecture without duplicate systems.
Field guide
The eight wastes of lean manufacturing
See the eight wastes without turning them into a scavenger hunt.
Recognize defects, overproduction, waiting, non-utilized talent, transportation, inventory, motion, and extra processing in system context.
Field guide
Lean manufacturing vs Six Sigma
Choose Lean, Six Sigma, or a combined approach from the problem evidence.
Compare goals, methods, data needs, strengths, and failure modes before selecting an improvement approach.
Field guide
Continuous improvement project portfolio
Fund the improvement portfolio your operation can actually finish.
Select, sequence, stop, and verify Kaizen and capital projects using system impact, evidence, capacity, risk, and learning.
Field guide
Operational excellence metrics
Measure operational excellence without rewarding local optimization.
Design a connected metric system spanning safety, quality, delivery, flow, cost, people, resilience, and verified improvement.
Field guide
Real-time production monitoring
Turn live production signals into timely, owned decisions.
Connect machines, sensors, orders, quality, and people while controlling latency, context, alarms, security, and response.
Field guide
Value-stream mapping
Move from a static VSM to a testable future state.
Map the flow, add assumptions, and test changes before rollout.
Field guide
Root-cause analysis
Trace the loss through the system—not just to the red KPI.
Use modeled interactions and evidence to focus RCA on plausible causes.
Field guide
OEE loss analysis
See which OEE loss actually constrains the system.
Connect availability, performance, and quality loss to total flow.
Field guide
Digital twin in manufacturing
Build a manufacturing digital twin that improves decisions—not just visualization.
Connect live operating data to a validated model, test scenarios, and turn results into controlled improvements.
Field guide
Predictive maintenance
Turn machine signals into maintenance decisions the production system can trust.
Use sensors, maintenance history, AI, and simulation to detect degradation and choose the right intervention window.
Field guide
5S methodology
Make 5S a measurable operating system—not a cleanup campaign.
Use sort, set in order, shine, standardize, and sustain to expose abnormal conditions and protect flow.
Field guide
Kaizen event
Run a Kaizen event that produces a verified improvement.
Frame the problem, observe the real process, test countermeasures, and sustain the result after the workshop.
Field guide
Lean Six Sigma
Choose Lean, Six Sigma, or both for the problem in front of you.
Separate flow and waste problems from variation and defect problems, then use evidence to select the right method.
Field guide
PDCA cycle
Turn PDCA into a learning loop—not four boxes on a slide.
Define a prediction, test a controlled change, study the result, and standardize only what the evidence supports.
Field guide
SMED changeover reduction
Reduce changeover time without moving risk into startup.
Separate internal and external setup, simplify adjustments, and verify quality, safety, and total-system flow.
Field guide
Theory of Constraints
Improve the system by managing its current constraint.
Identify, exploit, subordinate, elevate, and repeat while measuring throughput, inventory, and operating impact.
Field guide
Manufacturing bottleneck analysis
Find the bottleneck that actually limits customer output.
Combine cycle time, queues, blocking, starvation, product mix, and simulation instead of relying on utilization alone.
Field guide
Process mining in manufacturing
Turn event logs into an evidence-based view of how work really flows.
Reconstruct process variants from timestamps, compare conformance, and connect discovered delays to physical operations.
Field guide
Industrial AI in manufacturing
Deploy industrial AI around a decision that people can verify.
Connect production data, domain knowledge, machine learning, and simulation with clear confidence and human control.
Field guide
Industrial IoT and smart manufacturing
Connect industrial data without creating a larger blind spot.
Design an IIoT path from sensors and PLCs to interoperable context, secure analytics, and operational action.
Field guide
Manufacturing data analytics
Turn factory data into decisions—not another dashboard.
Build trustworthy manufacturing analytics from PLC, sensor, MES, ERP, quality, maintenance, and manual data, then connect every insight to an operating decision.
Field guide
Production downtime tracking
Track downtime precisely enough to prevent the next loss.
Define stop boundaries, capture microstops and context, validate reason codes, and rank downtime by lost system output rather than minutes alone.
Field guide
Production scheduling optimization
Build production schedules that survive the real factory.
Combine orders, constraints, changeovers, labor, material, downtime, and uncertainty to test schedules before releasing them to the floor.
Field guide
Predictive quality in manufacturing
Predict quality risk early—without automating bad decisions.
Connect process conditions, product context, inspection results, and machine learning to intervene before defects become scrap or customer escapes.
Field guide
Andon system in manufacturing
Design an Andon system that triggers response and learning.
Use clear abnormality signals, owned escalation, response-time measures, and root-cause follow-up so alerts improve flow instead of becoming background noise.
Private pilot waitlist