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

4. The Goal of Data is… ???

5. Vocabulary
 Uptime/Downtime
 Stop
 MTTR/MTBF
 Availability
 OEE

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%

11. The Goal of Data is…
to Reduce
Downtime???

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

17. OEE and STOPS
OEE SHIFT IN-PROCESS
COMPONENTS COMPONENTS MEASURES SENSITIVITY
Runtime
MTBF (Variable)
Availability Stops
MTTR (Constant
Downtime w/in Range)
% OEE
% Scrap (Constant ~ 1% )
Rate Loss (Constant
except start-up)

18. OEE and STOPS
OEE SHIFT IN-PROCESS
COMPONENTS COMPONENTS MEASURES SENSITIVITY
Runtime
MTBF (Variable)
Availability Stops
MTTR (Constant
Downtime w/in Range)
% OEE
% Scrap (Constant ~ 1% )
Rate Loss (Constant
except start-up)
Downtime Focus only addresses part of Reliability

19. OEE and STOPS
OVERALL EQUIPMENT EFFECTIVENESS & STOPS
OEE SHIFT IN-PROCESS
COMPONENTS COMPONENTS MEASURES SENSITIVITY LEVER
Runtime
MTBF (Variable) Stop Elimination
Availability Stops
MTTR (Constant Stop Elimination
Downtime w/in Range)
% OEE
% Scrap (Constant ~ 1% ) Stop Elimination
Rate Loss (Constant Stop Elimination
except start-up)

20. OEE and STOPS
OVERALL EQUIPMENT EFFECTIVENESS & STOPS
OEE SHIFT IN-PROCESS
COMPONENTS COMPONENTS MEASURES SENSITIVITY LEVER
Runtime
MTBF (Variable) Stop Elimination
Availability Stops
MTTR (Constant Stop Elimination
Downtime w/in Range)
% OEE
% Scrap (Constant ~ 1% ) Stop Elimination
Rate Loss (Constant Stop Elimination
except start-up)
Stop Elimination addresses all
components of Reliability

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???

23. OEE and STOPS
OVERALL EQUIPMENT EFFECTIVENESS & STOPS
OEE SHIFT IN-PROCESS
COMPONENTS COMPONENTS MEASURES SENSITIVITY LEVER
Runtime
MTBF (Variable) Stop Elimination
Availability Stops
MTTR (Constant Stop Elimination
Downtime w/in Range)
% OEE
% Scrap (Constant ~ 1% ) Stop Elimination
Rate Loss (Constant Stop Elimination
except start-up)
Stop Elimination won’t fix uptime losses

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)

25. Quality Samples, Defective
Product, and Rate Can Be
Hidden Losses

26. Uptime Losses Can be
Significant 10.2%
11.5%
Dow ntime
Source: Case
Study- Oct ‘99 Uptime Losses
78.3%
Making Good Product
(%OEE)

27. Eliminate Stops & Uptime
Losses to Increase PR
Uptime Losses, Stops,
What Else?

28. The Goal of Data is…
to Reduce to Eliminate OEE
Downtime Losses???
to Eliminate
Stops???

29. Show Me the Money!
We are in business to make money, not OEE

30. Our Biggest On-going Cost
is...
People!

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

41. Cost =
Throughput x Productivity
 Rate  Material Handling
 Stops  Quality Sampling
 uptime Losses  Touches
 Equipment Geography

42. The Goal of Data is…
to Eliminate OEE
to Reduce Losses???
Downtime
to Eliminate to Reduce
Stops??? Cost!

43. Data Overload!
What Data is Most Important?

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

49. 6. Other Data
 Touches
 Downtime

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

56. For Stable Operations
You need good Design plus
good Process Management
vs.

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.

66. Review

67. Cost =
Throughput x Productivity
 Rate  Material Handling
 Stops  Quality Sampling
 uptime Losses  Touches
 Equipment Geography

68. OEE and STOPS
OVERALL EQUIPMENT EFFECTIVENESS & STOPS
OEE SHIFT IN-PROCESS
COMPONENTS COMPONENTS MEASURES SENSITIVITY LEVER
Runtime
MTBF (Variable) Stop Elimination
Availability Stops
MTTR (Constant Stop Elimination
Downtime w/in Range)
% OEE
% Scrap (Constant ~ 1% ) Stop Elimination
Rate Loss (Constant Stop Elimination
except start-up)
Stop Elimination addresses all
components of Reliability

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

74. Feedback?
Send Email to Eric.Allen@DataDrivenMfg.com