This presentation was originally developed over 10 years ago to highlight ways to make use of data in manufacturing to improve operational results. Key points include:
Data is an important tool in reducing cost.
We often focus on less important data.
The things we measure for result improvement are the same as those we should measure for start-ups.
Engineering plays a key role in the design of processes, the acquisition of data, and the level of long-term costs
It takes a lot of data to tell the whole story.
Written by Eric Allen of Data Driven Manufacturing, this presentation is meant to give an overview to those starting down a path to use data for improving manufacturing results.
1. The Use of Operational Data
to Improve Results
Eric Allen
Data Driven Manufacturing LLC
DataDrivenManufacturing.com
2. Agenda
Background on use of data
Ranking data by importance
How data is used
Data, Design, and Start-ups
Recommendations
3. Introduction
Data is an important tool in reducing cost
We often focus on less important data
The things we measure for result
improvement are the same as those we
should measure for start-ups
Engineering plays a key role in the design
of processes, the acquisition of data, and the
level of long-term costs
It takes a lot of data to tell the whole story
6. Uptime and Downtime
Uptime is the total time the line is running
Downtime is the total time the line is down
A Stop is every event when the line stops
running, no matter how long it has been
running or why it stopped
Overall Equipment Effectiveness (OEE) is a
standard measure that quantifies the
production made as a percentage of what
was possible to have been made.
7. MTTR & MTBF
Mean time to repair
MTTR = downtime / stops
____________________
Mean time between failures
MTBF = uptime / stops
8. Availablity
Availability is the percent of time the line is
running.
Availability = uptime / scheduled time
Availability = MTBF / (MTBF + MTTR)
OEE = Availability - Uptime Losses
9. Overall Equipment Effectiveness
OEE = Availability x Rate Performance x
%Acceptable Quality, a holistic measure of
Efficiency or Reliability.
Rate Performance = Actual Rate / Planned Rate, a
measure of Rate Loss/Gain
% Acceptable Quality= Amount of Shippable
Product / All product produced, a measure of
Quality Loss or “Scrap”
Another way to calculate OEE is to divide quality
product made by the the ideal amount that could
have been made during the scheduled time.
10. Traditional OEE Improvement
Track Downtime for
R e lia b ilit y L o s s e s
P ro d u c t F e e d
each unit op
Loss = 2%
Pareto Losses
U n it O p 1 L in e B L in e C
Loss = 5%
Focus on biggest
U n it O p 2
Loss = 3%
Downtime unit op
U n it O p 3 A U n it O p 3 B
Loss = 4% Loss = 4% Go after chunks of
U n it O p 4
Loss = 6%
6% Unit Op 4
Unit Op 1
downtime
4% Unit Op 3
U n it O p 5
Unit Op 2 Get operators to fix it
Loss = 1% 2% Supply
faster (MTTR)
Unit Op 5
0%
12. Downtime Losses
Breakdowns
Minor Stops
Planned Maintenance
Changeovers
Lunches/Breaks/Meetings
Material Supply
13. Downtime Losses
Equipment
Breakdowns Specific Stops-
Minor The rest are
Stops
associated with the
Planned Maintenance whole line.
Changeovers
Lunches/Breaks/Meetings
Material Supply
14. Downtime Losses
Breakdowns Since Minor Stops are
shorter in duration than all
Minor Stops other stops, reducing the
Planned Maintenance number of minors stops
will increase MTTR.
Changeovers
Lunches/Breaks/Meetings
Material Supply
15. Downtime Losses
Breakdowns Eliminate with
Equipment
Minor Stops Design,
Prevention, and
Planned Maintenance
Planning
Changeovers
Lunches/Breaks/Meetings
Material Supply
16. Downtime Losses
Reduce with planning and
Breakdowns skills. Of all
Minor
downtime, only these
Stops two are truly speed
Planned Maintenance dependent. (With
Changeovers proper design, most of this
work can be done during
Lunches/Breaks/Meetings uptime anyway.)
Material Supply
21. Downtime Reduction, Stop
Elimination, What’s the difference?
Downtime Stops
Focus Focus on Events
on Time
Get it back up Stay down until fixed
Repair Skills Focus Root Cause
Elimination
22. The Goal of Data is…
to Reduce
Downtime
to Eliminate
Stops???
24. Uptime Losses
Scrap (Destructive Quality Sampling &
Rework)
Rate Losses (speed ramp-ups at start-up and
running off target speeds at steady state)
Empty or missed products (could be rate or
scrap loss depending on situation)
31. Stops and Touches Tie
Operators to Equipment
Unit Op A
50 stops/shift
Unit Op B Unit Op A
75 stops/shift 30 stops/shift
Unit Op A
60 stops/shift
32. Eliminating Stops Improves
Productivity
Every stop requires
operator effort.
The more stops there
are, the closer the
operator is tied to the
line.
The closer the
operator is tied to each
unit operation, the
more operators are
required.
33. Touches
Operators often adjust and assist the line to
keep it from stopping
Often these assists are jam clears
Many adjustments can be automated
Find ways to detect and count
34. How Do You Eliminate?
Stops Adjustments
Touches Assists
Scrap
Rate Loss
35. How Do You Eliminate?
Stops Adjustments
Touches Assists
Scrap
Rate Loss
Stabilize the Process
36. All processes vary-
The challenge is to minimize
Steady State Variation- when the line is
running normally, how much does the
process vary and why?
Start-up Variation- during ramp-up of the
equipment, what is impacted and how can
the variation be reduced in magnitude and
time?
Process Upsets- How do sudden events
(splices,batch changes, etc.) affect stability?
37. What Varies?
Materials
Equipment
Utilities
Control Systems
Environment
Set Points
Operators
Cleanliness
38. Eliminating Variation
Use stops and touch
data to determine area
where variation is
impacting
Investigate process for
variation
Develop methods to
eliminate or control
the source
39. Stability gets Results
Quality is improved with lower Standard
Deviation and reduced defects
Touches are needed less as adjustments are
not needed
Most stops can be traced to instability in
part of the process
More stable processes need less sampling
40. Don’t forget Throughput
Know your rate limiter(s).
OEE List them.
Study them.
= Stabilize them.
Speed them up.
Throughput
44. 1. Quality
Without quality, there is no reliability
Get quality data easy to access and analyze
Automate quality data collection
Get in process data to replace destructive
finished product sampling
Ideally, incorporate quality data into same
system as Reliability measures
45. 2. Count Stops
Line Stops
Unit Op Stops
Eliminating Stops improves every aspect of OEE
Stops are the best in-process measure of progress of work
46. 3. Uptime Losses
Track Availability vs. OEE
Separate Rate from Scrap
Split Quality Sampling Scrap from Quality
Defect Scrap
47. 4. Process Stability Measures
More in-process data leads to faster
improvement capability and root cause
analysis
Track all variable data (pressures,
temperatures, tensions, weights, speeds,
amps, etc.)- Install transducers to get data
Utilize to discover sources of variation
Eliminate or use as feedback to other parts
of the process to reduce
48. 5. Causes
Stop Causes
Reject/Scrap Causes
Causes are hard to determine automatically
but valuable to know
50. Ranking of Data Importance
Quality
Stop Counts
Uptime Losses
Process Stability Measures
Causes
Touches and Downtime
51. Data can be collected and
used many ways
PLC programming is critical to capturing
events for operator display and long-term
storage.
Find effective ways to display data to
operator
Store data for long-term trending in
databases
52. Data has many sources
Counts (stops, starts, products, defects,
rejects, cases, touches)
Time (uptime, downtime)
Variables (pressures, tensions, temperature,
speeds, currents)
Causes (stops, rejects)
53. Stops
100
120
140
160
20
40
60
80
0
5/5-Nite
5/12-Day
No Data
5/25-Nite
to Operator
6/2-Day
6/8-Nite
6/21-Nite
6/28-Nite
7/6-Day
Date
7/13-Day
Turret Stops
7/19-Nite
7/27-Nite
8/3-Nite
Data Broken out
Customer is the Operator
8/24-Day
9/15-Day
9/28
Stops-Turret System
10/7
54. Data Helps Focus Efforts Daily
“You get what you
measure”
Results occur minute-by-
minute and are controlled
by operators
With updated data,
operators can make good
decisions
Use on-line data to
eliminate short-term data
variation
55. Use data averages and trends to
develop long term improvements
MTBF shows progress
and opportunities in
stop reduction
Scrap rates show
uptime losses
?
Variation measures
show stability
opportunities
57. Built in Impacts of Design on
Manufacturing Cost
Simplicity of Equipment (# of unit ops)
Geography- Position of Touch Points
Designed in Stops/Touches (material
changes, etc.)
Data Systems- How much information does
the operator have?
Ease of Changeover
Maintainability- resistance to Breakdowns
58. Impacts of Process
Management
Outage Resolution
If-Down-Do / Planned Interventions
Run to Target
59. What does this have to do with
Engineering and Vertical Start-ups?
Design is a critical
component of long-
term costs
Data is essential to
make wise decisions
Vertical start-up tools
and targets lead to
right methodology if
used correctly
60. Use of Data and Results
in Case Study
A multiple unit-operation line used these principles in a
rigorous method to make substantial improvement. The
following slides show results as measured by the site.
61. MTBF GOOD
Uptim e Results
40.0 39.4
MTBF
Goal
35.0 34.2
MTTR
30.7
30.0
26.4
24.9
25.0
m inute s
20.0 18.8
17.3
15.4
15.0
13.0
11.0
10.1
10.0 10.4 10.2
9.1 9.5
8.1 8.1 7.8 7.7
6.2
5.0
4.4
3.6
This Month
Dec-98
May-99
Sep-99
Aug-99
Feb-99
Mar-99
Jan-99
Apr-99
Jun-99
Jul-99
-
m onth
62. Scrap Results
45.0%
41.0%
40.0% Scrap GOOD
Goal
35.0%
30.0% 28.7%
24.8%
pe rce nt
25.0%
21.9%
20.9%
20.0%
18.2%
16.7%
15.0%
12.9%
11.0% 11.2%
10.0% 8.9%
5.0%
This Month
Dec-98
May-99
Sep-99
Aug-99
Feb-99
Mar-99
Apr-99
Jan-99
Jun-99
Jul-99
0.0%
m onth
63. num ber of stops
10
20
30
40
50
60
70
0
5/10-Day
5/24-Nite
6/2-Day
6/9-Nite
6/23-Nite
7/6-Day
Date
7/14-Day
FFS Stops
7/26-Nite
8/3-Nite
8/25-Day
Stops
9/22
6 per. Mov.
10/7
Avg. (Stops)
64. Month to Date Results Averages
Turret 1 Turret 2 Turret 3 Turret 4 Turret 5
System MTBF 87.0 49.8 35.4 57.9 45.9
Scrap % 0.4% 1.3% 2.4% 0.8% 1.3%
Turret Stops/Day 6.1 12.4 17.8 10.4 13.0
Bag Stops/Day 2.2 2.0 2.5 2.1 2.7
Total Turret Scrap 1.9% This type of data was
MD Phasing Scrap 1.1% posted and reviewed
No Poly Cut Scrap 2.7% daily with operators
Start-up/Manual Scrap 2.5% to focus their efforts.
Sampling/Quality Scrap 4.0%
Oct 12, ‘99
65. Later Results
Results on this line continued to improve in
over time after this case study was
completed, and the line became a
benchmark for re-application.
OEE routinely exceeded 90%
Downtime for unplanned stops generally
was less than 2% of scheduled time.
69. Stops and Touches Tie
Operators to Equipment
Unit Op A
50 stops/shift
Unit Op B Unit Op A
75 stops/shift 30 stops/shift
Unit Op A
60 stops/shift
70. Ranking of Data Importance
Quality
Stop Counts
Uptime Losses
Process Stability Measures
Causes
Touches and Downtime
71. Engineering and Vertical Start-ups
Design is a critical
component of long-
term costs
Data is essential to
make wise decisions
Vertical Start-up tools
and targets lead to
right methodology if
used correctly
72. Summary
Downtime data is not nearly as important as
many other data types
Focus data systems to reduce costs
Get real-time data to operators
Process Stability reduces all losses
Design and Process Management combine
to produce results at start-up and long-term
73. Specific Recommendations
Focus on Quality data to reduce variation
and sampling losses
Focus on Stops (especially in unit ops) to
improve OEE and productivity
Include productivity considerations and
data capture ability in design efforts
Get easy to use data to operators