CONTINUOUS IMPROVEMENT · DATA · ENGINEERING
Continuous improvement supported by plant data.
Connect losses, interventions and results using comparable production, process, quality and energy data.
KERN IoP helps establish a baseline, record what changed, track behaviour after the intervention and retain the context needed to determine whether the result persists when the shift, product, mould, material or operating conditions change.
ENGINEERING PRINCIPLE
A substantiated improvement needs a baseline, an intervention and a comparable verification.
An isolated trial may deliver a promising result. Continuous improvement turns that result into reproducible knowledge by retaining the conditions surrounding the trial and observing what happens when normal production resumes.
Baseline
How the process currently operates, with enough context to identify equivalent products, states, shifts and conditions.
Documented intervention
What changed, when, on which asset or recipe, and what result was expected.
Follow-up
What happened afterwards, how much the result varied and whether the achieved condition is sustained in actual production.
KAIZEN · IMPROVEMENT CYCLE
Data should accompany the entire improvement cycle.
01 · OBSERVE
Isolate the loss
Downtime, cycle time, rejects, consumption, process variation or instability.
02 · UNDERSTAND
Formulate a hypothesis
Relate the loss to machine, process, peripheral, material or method conditions.
03 · INTERVENE
Change a condition
Document the adjustment, date and scope so the change can be related to the results.
04 · COMPARE
Measure the effect
Use comparable periods and conditions, accounting for product mix and shifts that could distort the comparison.
05 · STABILISE
Control variation
Observe dispersion, trends and condition changes to establish whether performance remains within the expected range.
06 · SUSTAIN
Continue monitoring after the trial
Verify that the condition is sustained and detect when a new cause starts to degrade the result.
Improvement measures become more targeted when losses are separated by mechanism.
An aggregate indicator helps reveal a performance gap. To choose an effective intervention, break the indicator down and examine which component is changing.
Availability and productivity
Downtime, microstops, speed, cycle time, counts and causes. OEE can summarise losses; meaningful analysis requires its components to remain available.
Quality and process
Rejects, weight, dimensions, inspections and critical process variables can be related to investigate stability and the causes of variation.
Energy and resources
kWh, power, specific consumption and other utilities, where instrumented, help assess changes under comparable conditions.
MEANINGFUL COMPARISONS
Operational context helps attribute improvements to the right cause.
Comparing averages from two weeks may be insufficient if the product, mould, material, shift or mix of operating states changed. KERN IoP retains variables and context to support more meaningful comparisons.
Equivalent conditions
Filter by machine, product, mould, material, recipe, shift or state when these variables affect the result.
Sufficient observation period
Extend monitoring beyond the initial trial to observe variation between cycles, shifts and normal production events.
Consistent calculation rules
Maintain the same definitions of states, losses, units and denominators so that before-and-after results remain comparable.
An improvement may be technically valid even if the overall average does not change immediately; the reverse is also possible. Retaining sufficient detail makes it possible to revisit the cause and interpret the aggregate KPI accurately.
INVESTIGATING THE CAUSE BEHIND THE KPI
Engineering analysis can investigate the mechanism behind the symptom.
When a loss depends on several variables, PM Tec Engineering can complement KERN IoP data with root-cause analysis, design of experiments, simulation and physical or hybrid modelling.
- Identify variables that change before the loss appears.
- Design trials that separate the effects of correlated variables.
- Relate process measurements to quality, energy or productivity.
- Build models to estimate states or compare scenarios.
- Define indicators that track the underlying mechanism as well as the aggregate result.
This use of information is directly linked to Industry 4.0 as support for the people working in production: making evidence visible so operators, technicians and engineers can diagnose problems and sustain improvements.
Follow-up helps turn a one-off improvement into an established operating condition.
After an intervention, KERN IoP can retain historical data and context to assess stability, detect regressions and compare performance with the defined baseline.
Technical guide
How to establish a baseline, record the intervention and verify that an improvement is sustained in production.
KERN IoP | MONITOR
Access historical data, states, variables and indicators from the sources integrated into the installation.
Implementation
If the variable is not yet available, we can assess how to acquire it, add context and validate it on the plant floor.
CONTINUOUS IMPROVEMENT · FREQUENTLY ASKED QUESTIONS
Useful data for determining whether an improvement works and lasts.
What should be measured before making an improvement?
How long should monitoring continue?
Is OEE sufficient for managing continuous improvement?
How does KERN IoP support comparisons of equivalent conditions?
Does KERN IoP guarantee savings after an improvement is implemented?
Applications
Engineering
Resources
PM Tec Engineering, through its Industry 4.0 division, KERN IoP.
Cota, Cundinamarca · Colombia.
Debe estar conectado para enviar un comentario.