Solutions
Equipment Effectiveness Improvement
Move from 'equipment looks busy' to 'where are the losses, who owns the response, and did the action work?'
Centered on OEE and andon, this solution combines equipment connectivity and necessary MES data to create a closed loop from state acquisition and loss attribution to improvement verification. The focus is not a single metric but identifying constraints, reducing recurring abnormalities, and stabilizing team reviews. It can support TPM activities without promising a complete TPM system as part of the package.
Typical challenges
- When output is insufficient, teams cannot readily distinguish equipment faults, changeovers, material shortages, waiting, micro-stops, reduced speed, and quality losses, so improvement goals become too broad.
- A line is judged only by individual-machine utilization, ignoring blocking, starvation, and shifting bottlenecks; speeding up a local machine may not improve line output.
- Downtime causes are entered after the event, with classifications that are too coarse or influenced by performance pressure, so the data do not reflect the real process.
- Exception notifications and response ownership are unclear, allowing similar faults, waiting, and short stops to recur without escalation or recurrence reviews.
- Equipment, production, and quality teams use different definitions, making before-and-after data incomparable and improvement conclusions difficult to verify.
Solution approach
Standardize OEE and Calendar Definitions
Define planned production time, ideal cycle time, good parts, and downtime boundaries, retaining raw events so a composite metric does not conceal calculation assumptions.
Build a Line-Level Loss Tree
Drill availability, performance, and quality losses down to equipment, products, shifts, and causes while analyzing blocking, starvation, and bottleneck-station relationships.
Calibrate Micro-Stop and State Logic
Use raw signals, shop-floor observations, and process intervals to calibrate debouncing, minimum durations, and cause-confirmation rules, reducing false micro-stops and misattribution.
Close the Andon Response Loop
Configure calls, responses, escalation, recovery, and reviews by exception type, bringing frequent short stops and major long stops into appropriate handling mechanisms.
Validate Measures Through Improvement Experiments
Set like-for-like observation windows for priority losses and record actions, ownership, and review conclusions. Standardize effective practices in checks, standard work, or maintenance processes.
Expected operational outcomes
- ✓ Equipment losses are broken down from generic downtime into analyzable causes, periods, and accountability categories, providing a stronger basis for improvement priorities.
- ✓ Line and machine data can be interpreted together, reducing optimization of non-bottleneck equipment at the expense of system constraints.
- ✓ Andon events include response, escalation, and review records, making recurring abnormalities easier to bring into cross-functional improvement.
- ✓ Daily team meetings gain more stable loss lists and trends, reducing manual spreadsheet assembly and disputes over definitions.
- ✓ Data inputs support autonomous maintenance, planned maintenance, and changeover improvement, helping verify whether measures remain effective.
Implementation
- 1.
Current-Loss and Data Diagnosis
Examine production calendars, cycle times, equipment signals, downtime records, and quality data to determine which losses can currently be identified automatically.
- 2.
Demonstration-Line Model and Definition Alignment
Select a representative line, define line constraints, equipment states, cause trees, and metric interpretation, and review them jointly with production and equipment teams.
- 3.
Acquisition, OEE, and Andon Integration
Connect states and counts, configure OEE, andon, and dashboards, and test scenarios including micro-stops, changeovers, material shortages, and exception recovery.
- 4.
Parallel Observation and Team Pilot
Verify data against shop-floor observations and raw records, using them first in reviews rather than applying unstable definitions directly to personnel appraisal.
- 5.
Improvement Verification and Line-by-Line Replication
Track actions around priority losses and review trends. Replicate stable templates, recalibrating states and cycle times for each new line.
Frequently asked questions
Should we start by raising OEE to one common target?
A common target is not recommended without considering the process, changeover structure, and equipment constraints. First ensure credible definitions and identify major losses, then set targets around bottlenecks and improvement opportunities. OEE values from different lines are not directly comparable merely because they are numerically close.
Why emphasize bottlenecks rather than the utilization of every machine?
Line output is governed by system constraints, and speeding up a non-bottleneck machine may only increase waiting or work in process. The solution examines blocking, starvation, buffers, and cycle times together, directing improvement resources toward factors that affect line flow.
Can every downtime cause be identified automatically?
Usually not. Some equipment-fault signals can be captured automatically, but material shortages, waiting, planned changeovers, and organizational causes often require work-order context or manual confirmation. The design focus is reducing supplemental entry, standardizing classifications, and retaining raw events for review.
Is this solution equivalent to complete TPM or AI predictive maintenance?
No. OEE and andon can support TPM activities and integration with maintenance workflows, but the complete TPM organization, spare-parts management, and asset management must be built separately. AI predictive maintenance is not a ready-made standard capability of this solution.
Related products
OEE and Andon Visualization
Go beyond a single metric by breaking losses into actionable causes and accountability loops
Machine Data Acquisition and Production-Line Connectivity
Capture data from mixed-vendor equipment accurately and reliably before reusing it for monitoring, traceability, and analysis
Production-Line MES and Quality Traceability
Ground work-order execution in evidence, record process exceptions, and make product genealogy traceable
Start by identifying the major losses on one line
Reconstruct equipment time using consistent definitions and raw events to select actionable improvements instead of chasing an OEE number first.