Lean Six Sigma
Reducing Street Light Inventory
Rick Orr, Finance Manager Public Works
Project Objectives
Work Towards Achieving Mayor Richard’s City Goals
-Safe City
-Quality jobs
-Improved customer service - B.E.S.T.
Demonstrate how Lean Six Sigma Improves Customer
Service and Saves Resources
Improve Customer Service by Reducing Capital
Investment in Street Light Inventory
What Is Lean Six Sigma?
Systematic approach to reducing process defects that
produce undesired outcomes - in our case, improving the
decision making regarding inventory purchases
DMAIC – Define, Measure, Analyze, Improve, Control
Team focus to problem solving - each of us are experts in
certain areas of the inventory process and each have
specialized knowledge of portions of the process
Project Description
Problem
Statement:
Objective:
Street light inventory seems
excessive relative to usage
Reduce inventory to optimum level
Cost of Poor Quality
External Customers
Citizens
Internal Customers
City Staff
Carrying excessive
inventory ties up
capital that can be
used elsewhere
Uncertain ordering
schedules makes it
difficult to anticipate
ordering needs
Lost capital
opportunities cause
unnecessary high tax
rates
Inaccurate inventory
records
Inaccurate damage
recoveries
Inaccurate materials
billing
Benefits
Frees capital funds to be redirected
towards other use and helps maintain low
taxes
The “Y”
The Y: the total value of street light inventory, measured
monthly
Y = f(x1,x2,x3,…,xk)
Why Minimize Inventory?
Minimizing Inventory:
Increases flexibility in asset management
Makes it easier to control
Reduces the need for space
Makes it easier to count
Reduces aged inventory
Inventory is an asset, but it is a non-productive asset.
It earns no interest but costs City in handling, shrinkage, and space.
Definition of the Y
The Defect: excessive street light inventory
The Y: the total value of street light inventory, measured monthly
Y = f(x1,x2,x3,…,xk)
The Project Plan: examine the factors that drive inventory levels
on various items and appropriately reduce the level of individual
street light items
The Goal: Reach optimal levels of inventory to reduce the
invested capital
Project Team
Champion:
Greg Meszaros
Assisting:
Michele Hill, Roger Hirt
Team Members:
Rick Orr, Project Leader/Black Belt
Dave Pepper, St Light Warehouse
Nate Parker, St Light Warehouse
Lori Dekoninck, St Light Warehouse
Phyllis Davis, St Light Engineering Admin
Steve Davis, Assistant Traffic Engineer
Tracy Neumeier, Internal Audit/Black Belt
Project Schedule
Define
March – April 2003
Measure
May – Sept 2003
Analyze
Oct – March 2004
Improve
Apr – Jun 2004
Control
Jun 2004 +
Street Lighting System
Number of Street Lights (Approx)
27,500
Number of Alley Lights (Approx)
3,100
Energy Expense, 2003
$453,367
Department Expense, 2003
$2,743,285
Estimated Value of Network
$8,500,000
Process Map
Material Needs Determined
Materials Ordered
Materials Delivered
Materials Stored
Materials Depleted
1
2
needs
determined
materials
delivered
3
materials
depleted
4
materials
depleted
5
6
needs
determined
needs
determined
budget
availability
time, order to
delivery
materials
requested by
maintenance
crews
materials
requested by
construction
contractors
past usage
considerations
staff inventory
experience
6
responsiveness
(maintenance,
construction,
lights out)
8
cost
effective
purchases
Process Step Process Inputs
10
minimize
total
inventory
investment level
Rating of
Importance to
Customer
Effect Matrix
4
pleasing
aesthetics
Cause and effect
matrix:
Cause
and
Total
5
10
5
10
195
10
5
10
0
190
10
5
5
5
185
5
10
5
1
159
10
5
1
0
145
10
5
1
0
145
Important Factors: demand, lead time, order interval, level of safety stock
How Can Our Processes Fail?
How can our process fail?
As ranked with FMEA, failures can result if:
• historical usage data is not maintained and monitored
• inventory usage is not recorded by maintenance crews
• material usage is not recorded on work order tickets
• expensive in-stock items are substituted for out of
stock items
• vendor states inaccurate delivery time on bid
• poor analysis done in budgeting cycle
Budget
BudgetVs.
vs Actual
actual: Costs
2000 2000-2003
- 2003
$900,000
$800,000
$700,000
$600,000
$500,000
$400,000
$300,000
$200,000
$100,000
$0
budget
expenditure
2000
2001
2002
2003
2004
In May of 2003, the inventory budget was reduced by $100,000 in
anticipation of project success. Approximately $80k less was spent on
materials than modified budget would have allowed for ’03.
Estimated savings to date (March ’04), $180,000.
Has
All
the
Data
Been
Captured?
Has all data been captured?
Actual material expense 2001
Actual material expense 2002
Actual material expense 2003 thru 9-30
Total
$636,865
$584,287
$320,199
$1,541,351
Historical usage captured
Jan ‘01 – Sept ’03, valued at
$966,547
Current inventory value as of
Sept 30, 2003
$630,806
*Note that recorded usage does not total the amount expended
Has
All
the
Data
Been
Captured?
Has all data been captured?
All recorded historical usage was collected
•
•
•
•
Work orders
Re-lamping lists
Proactive maintenance files
Capital project files
Historical inventory values were not kept. It can not be
determined if some usage was not recorded or if the
differences shown on the previous slide are attributable to
changes in the value of inventory on January 1, 2001 as
compared to the value of inventory on September 30, 2003.
What can be done to insure data integrity, going forward?
Lowhanging
Hanging
Fruit-Data
Source
Low
fruit
– data source
Implementation of an inventory tracking database
 Material usage recorded as it leaves warehouse
 Information readily available to all staff
 Facilitates data collection going forward
 Improves accuracy of recorded usage
Accomplished without adding any additional tasks not
already being performed by warehouse personnel
Data base implementation should help address 2 factors
identified in the C&E matrix: availability of historical data
and reliance on staff experience
Key
problem
– poor record keeping
Key
Problem-Poor
Record
Keeping
Modified Microsoft Office Template: In-house expertise without
added cost
Inventory Turn-Annual Inventory Use
Inventory Turn: A common method of measuring inventory
management
Calculated by dividing the average inventory level ($) into the annual
inventory usage ($)
2003 material usage
$450,539
2003 average inventory value $682,441
*For 2003, Street light inventory turned only .66 times
*For 2004, Street light inventory turned 1.124 times
Inventory Records-Inventory Accuracy
At the start of this project, 165 items were identified with
specific item numbers
Shortly after implementation of database, an additional 88
inventory numbers were assigned to materials not previously
carried on “the books”
4%
inventory 10/20 ($)
"new" items ($)
*Value of items not
previously accounted for
totaled $26,581 or 4% of
inventory on hand as of Oct
21, 2003
96%
Inventory accuracy
Inventory
Accuracy
Accuracy Benefits
Enhance Customer Service
Reduce Stock Outs
Production is not jeopardized
Inventory Accuracy
Past: Historically, a physical inventory count was conducted once per
year. Accuracy statistics were not maintained, and the existing stock
record was over-written with updated counts.
Effective 2004, implemented Cycle Counting
Current: Inventory items are now differentiated and counted multiple
times per year, depending on usage-value (inventory classification)
Class A items, count 6 times/year – 80% of $ spent over 33 months
Class B items, count 2 times/year – 15% of $ spent over 33 months
Class C items, count 1 time /year – 5% of $ spent over 33 months
Inventory Accuracy Rates
Inventory accuracy rates
After annual 2003 inventory count, error rates were established.
An error occurs whenever an item count differs from the
inventory record, while considering +/- 5% as an acceptable
tolerance.
Class A items – 27.3% error rate
Class B items – 35.7% error rate
Class C items – 26.1% error rate
All items – 27.3% error rate, 12-31-03
Error rates will be tracked with control charts, going forward. If
the use of the inventory data base and the implementation of cycle
counting fail to improve this error rate, this problem could be
investigated further as a Green Belt project.
Defective rate: Class A inventory items
0.5
Proportion
0.4
UCL=0.3992
0.3
_
P=0.1860
0.2
0.1
0.0
dec03
LCL=0
feb04
apr
jun
Sample
5% allowable tolerance
Tests performed with unequal sample sizes
aug
oct
dec04
Defective rate: Class B inventory items
0.5
UCL=0.4271
Proportion
0.4
0.3
_
P=0.2353
0.2
0.1
LCL=0.0435
0.0
dec'03
jun'04
Sample
5% allowable tolerance
Tests performed with unequal sample sizes
dec'04
Defective rate: Class C inventory items
0.35
UCL=0.3369
Proportion
0.30
_
P=0.245
0.25
0.20
LCL=0.1531
0.15
dec03
dec04
Sample
5% allowable tolerance
Tests performed with unequal sample sizes
Show Me the Money!
3 yrs of expense, 165 item numbers
material usage expense by item, jan '01 - sep '03
100
150
100
60
40
Percent
Count
80
50
20
0
0
143 item numbers
22 item numbers
19.18 % of dollars expended
80.82 % of dollars expended
($185,430)
($781,117)
Most of the project effort and analysis will be directed at the 22 items comprising 80% of the
expenditures. These top 22 items are designated as class A items.
Ranked listing of high expense items
(class A) Jan 01-Sept 03
33 month expense
$206,919.72
$85,283.37
$77,538.34
$49,688.52
$47,504.40
$42,803.20
$40,236.56
$31,433.04
$27,884.22
$19,740.00
$16,926.90
$16,213.00
$15,702.57
$12,751.83
$12,324.00
$12,060.00
$12,056.65
$11,711.10
$11,546.00
$10,873.50
$10,831.59
$9,088.00
item #
14-120
14-105
16-200
13-503
16-400
14-151
17-205
13-504
16-209
16-210
18-116
14-122
16-100
14-203
14-106
16-410
14-131
16-291
14-205
14-107
16-213
14-500
description
100w HPS Town & Country fixture
150w cobrahead fixture
30' embedded aluminum pole
100w HPS bulb
16' black metal pole
100w alley fixture
#6 3 conductor uf 600v tray cable
150w HPS bulb
30' aluminum bolt down pole
35' aluminum pole single bracket
1 1/2" pe tubing
250w HPS Town & Country fixture
35' wood pole
250w HPS power door
250w cobrahead fixture
Fort Wayne standard post
100w PMA fixture
transformer base, small
750w power door
400w HPS fixture with photo cell
35' bronze painted aluminum pole
300v photo cell
% total cost cumulative %
21.41%
8.82%
8.02%
5.14%
4.91%
4.43%
4.16%
3.25%
2.88%
2.04%
1.75%
1.68%
1.62%
1.32%
1.28%
1.25%
1.25%
1.21%
1.19%
1.12%
1.12%
0.94%
21.41%
30.23%
38.25%
43.39%
48.31%
52.74%
56.90%
60.15%
63.04%
65.08%
66.83%
68.51%
70.13%
71.45%
72.73%
73.98%
75.22%
76.43%
77.63%
78.75%
79.87%
80.82%
Poles Jan
Used:
Jan
2001-Sept
Poles used:
2001
– Sept
2003
$ ranking
33
months
3
5
9
10
13
16
18
21
23
24
29
39
41
47
52
56
61
74
75
76
82
98
100
33 month
expense
$77,538.34
$47,504.40
$27,884.22
$19,740.00
$15,702.57
$12,060.00
$11,711.10
$10,831.59
$8,531.00
$8,246.00
$5,964.00
$4,213.82
$3,858.00
$2,588.75
$1,922.20
$1,445.00
$1,247.34
$836.00
$776.00
$720.00
$613.00
$305.37
$286.60
$0.00
2003
% of 33
33 month
33 month quantity
month
usage
usage
on hand
expense maintenance
capital
10-1-03
description
30' embedded aluminum pole
8.02%
132
94
152
16' black metal pole
4.91%
43
425
444
30' aluminum bolt down pole
2.88%
33
45
13
35' aluminum pole single bracket
2.04%
28
19
18
35' wood pole
1.62%
45
72
77
Fort Wayne standard post
1.25%
10
8
27
transformer base, small
1.21%
9
43
6
35' bronze painted aluminum pole
1.12%
21
0
29
30' big top pole
0.88%
20
0
11
Broadway post
0.85%
7
0
5
35' aluminium pole double bracket
0.62%
12
0
18
20' aluminum bolt down (Tower Heights)
0.44%
13
0
13
S Calhoun pole
0.40%
3
0
7
12' aluminum bolt down
0.27%
19
0
7
22' fiberglass embedded
0.20%
14
0
56
50' aluminum 2-piece
0.15%
1
0
2
8' arm 4'upsweep wood pole
0.13%
6
0
19
35' alum box fixture
0.09%
2
0
48
24' alum bolt down
0.08%
2
0
5
casing for FW standard
0.07%
8
0
0
T base large
0.06%
1
0
6
16' fiberglass silver
0.03%
3
0
27
40' wood pole
0.03%
1
0
6
35' alum big top
0.00%
0
0
3
Fixturesused:
Used:
Jan
2001-Sept
2003
Fixtures
Jan
2001
– Sept 2003
$ ranking
33
months
1
2
6
12
15
17
20
26
34
51
55
64
66
67
71
77
78
84
93
115
33 month
expense
$206,919.72
$85,283.37
$42,803.20
$16,213.00
$12,324.00
$12,056.65
$10,873.50
$7,573.53
$5,202.00
$1,938.00
$1,677.95
$1,135.40
$1,048.00
$1,021.93
$980.00
$710.22
$695.00
$574.00
$380.00
$108.89
$0.00
$0.00
$0.00
$0.00
description
100w HPS Town & Country fixture
150w cobrahead fixture
100w alley fixture
250w HPS T/C (discontinue)
250w cobrahead fixture
100w PMA fixture
400w HPS fixture with photo cell
400w HPS box fixture
400w HPS cutoff fixture
150w cutoff
250w T/C
100w HPS downtown fixture
250 W MH fixture S Calhoun
250w cutoff fixture
150w HPS wallmount fixture
250w HPS box fixture
bollards for mall
150w HPS downtown fixture
150w HPS ornamental fixture
special fixture type 5 t/c
250w Hadco W Central
250w wall mount
175w MH Allen Co fixture
welcome marker fixtures
% of 33
33 month
33 month quantity
month
usage
usage
on hand
expense maintenance capital
10-1-03
21.41%
988
425
240
8.82%
633
240
119
4.43%
593
15
108
1.68%
25
0
2
1.28%
59
20
96
1.25%
67
0
3
1.12%
36
30
84
0.78%
37
0
23
0.54%
15
19
11
0.20%
19
0
17
0.17%
5
0
24
0.12%
2
0
5
0.11%
2
0
4
0.11%
7
0
27
0.10%
7
0
11
0.07%
3
0
18
0.07%
1
0
3
0.06%
1
0
10
0.04%
1
0
12
0.01%
1
0
0
0.00%
0
0
0
0.00%
0
0
9
0.00%
0
0
1
0.00%
0
0
7
Jan– 2001-Sept
BulbsBulbs
used: Used:
Jan 2001
Sept 2003
$ ranking
33
months
4
8
27
37
63
69
73
81
91
102
122
126
143
33 month
expense
$49,688.52
$31,433.04
$6,687.12
$4,925.94
$1,185.45
$1,000.58
$864.82
$614.79
$411.60
$268.78
$88.40
$75.00
$32.05
$0.00
$0.00
$0.00
description
100w HPS bulb
150w HPS bulb
250w HPS bulb
400w HPS lamp
250w MH
175w MH
750w HPS lamp
1000w HPS lamp
400w MH
189w Edison base
69w Edison bulb
special bulb 310
150w MH lamp
1000w MH lamp
70w MH lamp
100w MH lamp
2003
% of 33
33 month
33 month quantity
month
usage
usage
on hand
expense maintenance
capital
10-1-03
5.14%
5012
333
2173
3.25%
3516
0
2028
0.69%
748
0
1378
0.51%
551
0
645
0.12%
103
0
137
0.10%
94
0
8
0.09%
22
0
22
0.06%
23
0
27
0.04%
42
0
36
0.03%
129
0
80
0.01%
121
0
67
0.01%
1
0
18
0.00%
4
0
31
0.00%
0
0
11
0.00%
0
0
13
0.00%
0
0
24
In early October 2003, 48 250w bulbs and 48 400w bulbs were ordered!
Why? “Because we need them!”
Purchase Decisions Made On Usage
Differences in usage values and dollars spent each month could mean
that not all material usage was recorded or more inventory is being
purchased than is being used.
material usage
$180,000
invoices paid
$160,000
$140,000
$120,000
$100,000
$80,000
$60,000
$40,000
$20,000
Total $ value of materials used = $1,034,998
Total $ expended = $1,577,055
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m
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$0
*34 months examined
Correlation of Funds and Usage
Regression Plot
If R-Sq > 80%,
then correlation
is significant
mat used (y) = 32355.5 - 0.0412731 spending (x)
S = 13907.1
R-Sq = 1.2 %
R-Sq(adj) = 0.0 %
material usage ($)
70000
60000
R-Sq = 1.2%
50000
40000
30000
Regression
20000
95% CI
10000
0
50000
100000
150000
monthly expenditures ($)
* With monthly measurements, there does not appear to be a
significant linear correlation between material usage and the
amount of funds spent for inventory acquisition.
Changes to Bidding Specifications
Additional bidding expectations were requested of
vendors bidding on poles, mast arms, and fixtures
Informed all bidders of our goal to minimize inventory
carrying costs
Required bidders to list best price at minimum quantity
levels, price at lesser quantity order levels, and worst
price if only 1 unit ordered
Required vendors to list the length of time between
order placement and order delivery (lead time)
*This information will be critical in determining
optimal inventory levels and reorder points
Purchase Decision: What Bulb is the
Most Cost Effective to Purchase?
Beginning in 2000, Street Light Engineering began testing the
longevity of various bulb manufacturers
ORIGINAL
COST
1 YR
2 YR
3 YR
4 YR
G. E.
$
8.97 $ 8.97 $ 9.72 $ 10.84 $ 16.07
PHILIPS
$
8.95 $ 9.18 $ 10.10 $ 11.70 $ 12.62
SYLVANIA $ 10.15 $ 10.15 $ 10.67 $ 10.93 $ 11.19
FAILURES AND RATES
MFGR
G.E.
PHILIPS
SYLVANIA
TEST
SAMPLE
24
39
39
FAILED
BY YR 1
0
0%
1 2.60%
0
0%
FAILED
BY YR 2
2
8.3%
5 12.8%
2
5.1%
FAILED
BY YR 3
5 20.8%
12 30.8%
3
7.7%
FAILED
BY YR 4
19 79.2%
16 41.0%
4 10.3%
Low Price ≠ Best Price
Sylvania bulbs are the
most cost effective for
the City
$18.00
$16.00
$12.00
G. E.
PHILIPS
SYLVANIA
$10.00
$8.00
$6.00
$4.00
$2.00
Test Time
4 YR
3 YR
2 YR
1 YR
$-
ORIGINAL COST
Cost per Lamp
$14.00
Without the
cost/lifespan analysis,
former procedures
would have directed us
to purchase Phillips
bulbs
The addition of bulb
replacement labor costs
to the analysis, would
further expand the cost
differences
Changes to Ordering Procedures
Material ordering procedures were tightened
for all inventory purchases
• order form initiated by warehouse personnel or engineers
• order requires sign-off by department director
• order requires sign-off by finance manager
First time the procedure was used, an order of
photo cells was reduced from 500 (4-5 month
supply) originally requested to 200 ordered
Purchase/Replenish Pull System
Purchase/Replenish Pull System
Implemented a widely recognized
inventory system, developed by Toyota
Motor Corp, known as Kanban
Kanban is an empirically driven method
of both signaling the need for inventory
and controlling inventory levels
Kanban – Japanese word for “sign”
Purchase/Replenish Pull System
4 Variables for an Effective Purchase/Pull System
Demand – the average monthly usage amount
Lead Time – length of time expired between placing
order and receiving goods, measured in monthly
units
Order Interval – how often orders are anticipated, in
monthly units
Safety Stock – amount of inventory to be held to
compensate for demand variability and/or lead time
variability
Historical Demand
Historical Demand
Estimate Future Costs By Analyzing Past Material Usage
4 Uses of Materials
 Maintenance Repair to Damaged Facilities
 Re-lamping Activities Based on Light-Out Lists
 Proactive Replacement of Aged Facilities and/or Bulbs
 Capital Construction Project
Capital projects are known prior to construction. By meeting
minimum requirements, capital materials can be ordered on a
project by project basis. On appropriate projects, capital
needs will now be segregated from other material needs.
Recall that some of the historical data might be suspect…
Demand Analysis
Demand analysis
Demand Analysis = Compare means, standard deviations,
and medians for each item
• Pre data base implementation
• Post data base implementation
If similar, conclude historical usage was accurately collected
– use data collected since January 2001 for a specific item
If different, conclude historical usage was not accurately collected
– use data collected since October 2003 for a specific item
100 HPS Town & Country Fixture
21.41% of material expense
14-120: 100w Town & Country fixture
100
< oct 1 '03
> oct 1
1
80
Quantity used
+1SL=72.5
60
40
+1SL=49.4
_
X=44.4
X=29.9
20
-1SL=16.3
0
4
8
12
16
20
24
28
Observation, per month
32
36
100 HPS Town & Country Fixture
(Continued)
Should all data be
used to estimate
monthly demand?
Test for Equal Variances for 14-120nc
est StDevs=26.62
F-Test
< oct 1 '03
factor
Test Statistic
P-Value
Levene's Test
est StDevs=22.23
Test Statistic
P-Value
> oct 1
10
1.43
0.801
20
30
40
50
60
70
95% Bonferroni Confidence Intervals for StDevs
80
0.18
0.671
Difference in
means
Difference in
medians
Similarity in
Standard
Deviations
factor
< oct 1 '03
> oct 1
0
20
40
60
14-120nc
80
100
Inconclusive – to
not under estimate,
use data since Oct
1, 2003
150w Cobra Head Fixture
8.82% of material expense
14-105: 150w cobra head fixture
60
< oct 1 '03
> oct 1
+1SL=56.83
1
Quantity used
50
_
X=44.2
40
-1SL=31.57
30
X=19.18
20
10
0
4
8
12
16
20
24
28
Observation, per month
32
36
150w Cobra Head Fixture
Large difference
in means
(Continued)
Test for Equal Variances for 14-105nc
F-Test
est StDevs= 9.22
Test Statistic
P-Value
< oct 1 '03
1.00
0.849
factor
Levene's Test
Test Statistic
P-Value
est StDevs= 9.20
> oct 1
5
10
15
20
25
30
95% Bonferroni Confidence Intervals for StDevs
35
factor
< oct 1 '03
> oct 1
0
10
20
30
14-105nc
40
50
60
0.10
0.757
Large difference
in medians
Similar standard
deviations
Conclusion –
Including data
prior to Oct ’03
might result in
under estimation
of usage
100w Alley Fixture
Demand Analysis – Lots of Variability
14-151: 100w cobra head alley fixture
< oct 1 '03
> oct 1
40
Quantity used
30
+1SL=29.83
X=18.42
20
_
X=15.2
10
-1SL=0.57
0
4
8
12
16
20
24
28
Observation, per month
32
36
100w alley fixture (continued)
100w Alley Fixture
(Continued)
Similar Means
Test for Equal Variances for 14-151
Similar Medians
F-Test
est StDevs = 9.78
Test Statistic
P-Value
< oct 1 '03
0.44
0.166
factor
Levene's Test
Test Statistic
P-Value
est StDevs = 14.74
> oct 1
10
20
30
40
95% Bonferroni Confidence Intervals for StDevs
50
factor
< oct 1 '03
> oct 1
0
10
20
14-151
30
0.01
0.934
Similar Standard
Deviations
Conclusion –
Including data
back to Jan ’01
should not result
in under estimated
demand
40
This methodology was used to analyze demand for all class A and class B items
lead time
Lead Time
Lead Time - Time Expired From Order
Initiation to Receipt of Goods
 stated in bid specifications for
poles, fixtures, bulbs
 include City staff time for
requisition preparation and sign-off
Lead Time Analysis
lead time analysis
lead time in days: vendor Graybar
A nderson-Darling N ormality Test
0
10
20
30
A -S quared
P -V alue <
5.73
0.005
M ean
S tDev
V ariance
S kew ness
Kurtosis
N
10.296
7.222
52.161
1.44571
2.68320
81
M inimum
1st Q uartile
M edian
3rd Q uartile
M aximum
40
1.000
6.000
7.000
16.000
41.000
95% C onfidence Interv al for M ean
8.699
11.893
95% C onfidence Interv al for M edian
95% Confidence Intervals
7.000
8.000
95% C onfidence Interv al for S tD ev
Mean
6.256
Median
7
8
9
10
11
12
8.545
14-120
14-500
14-502
14-505
14-520
14-557
14-700
14-701
17-112
17-113
17-114
17-116
17-118
17-120
17-122
17-123
17-300
17-301
17-306
17-309
17-331
17-332
17-333
17-340
17-399
17-503
17-505
17-506
18-103
18-112
18-113
18-114
18-201
18-202
18-203
18-205
18-210
18-706
18-707
19-603
20-221
20-222
20-242
20-244
20-246
There is
too much
variation in
lead time
between
different
items
Mean of days
lead time analysis
Lead Time Analysis
Lead Time on Graybar Items
Main Effects Plot: lead time (in days) for Graybar, by item number
40
30
20
10
0
item
Conclusion:
Lead Time Analysis must be done at the item level not the vendor level
Order Interval
Order Interval- Frequency of Placing Orders for Each part
Trade-off between the level of inventory quantities carried per item and
the frequency of ordering the item.
If ordering often, can order less quantities per order. But there are
overhead and administrative costs for
• initiating order, processing requisition, purchase order
• contacting the vendor and placing the order
• receiving the goods, re-stocking the shelves
• processing the payable
Pareto analysis used to establish order frequencies. Class A items are few
but are 80% of the dollars in inventory. Class C items are numerous, but
only a small part of total inventory value.
Order class A items frequently, and order class C items infrequently
Order Interval
Preferred Products: Poles, Mast Arms, Transformer Bases
Poles/Mast Arms: charged a 13 – 14 % premium for orders totaling less than
$11,000 / order, effective 2004. Various types of poles/mast arms can be mixed
per minimum $11,000 purchase.
Preferred Products purchases, October 2003 - February 2004 averaged $7,429
per month. To avoid paying an average premium of $1,003 per month (if the
interval is 1), the order interval should be at least 2 months.
This results in an inventory that is larger than would be necessary otherwise, for
items that are relatively expensive.
But in effect, the excess inventory carried is returning approximately 13.5% in
avoided expense.
Recall the Cause & Effect Matrix – the process output ‘cost effective purchases’
was ranked at 8 out of 10 in importance to the customer.
Order Interval
GE Supply Fixtures & Power Doors
Order Requirements: Lots of 25
Orders less than the per fixture price increases by 10%, or on
average, $9 more per item.
Again, the result is inventory that is larger than would be
necessary otherwise if cost effective purchasing is to be
achieved.
But some fixtures used infrequently,
anticipate paying premium charge.
Safety Stock
Safety Stock
Safety Stock: inventory stock required to guard against
• process variability
• demand variability
• lead time variability
• quality variability
Safety Stock Quantity: dependent on desired service level
• service level 1, on average no stock outs 84% of the time
• service level 2, on average no stock outs 98% of the time
• service level 1, 1 standard deviation of safety stock carried
• service level 2, 2 standard deviations of safety stock carried
High Service Levels Need More Inventory/Safety Stock
Safety Stock-Level of Service
Safety Stock= Standard Deviation * Service Level * (Lead Time ^ .7)
Materials for capital projects are known in advance and
ordered on a project by project basis. Capital projects are not
impacted by the service level choice.
For the cause & effect matrix, process outcomes were ranked
by the Division Director
• minimizing total inventory carried ranked at 10 (high)
• responsiveness to calls, light outs ranked at 6 (medium)
Street lights are not a critical service, so a service level of 1
will be used to establish inventory re-ordering points and
optimal inventory levels.
Inventory Level/Order Triggering Formulas
Kanban System
Establish inventory levels and calculate reorder points for each
carried stock item.
Kmax = Max on-hand quantity for an item
(lead time * demand) + (order interval * demand) + safety stock
Kmin = Re-ordering trigger point for an item
(lead time * demand) + safety stock
Order more stock when (balance on hand + items on order) is less
than the trigger point
Order Quantity = Kmax – (balance on hand + items on order)
Controlling the X’s (Demand)
Inventory fills demand (after considering the acceptable
level of risk of running out, i.e., safety stock).
Demand is monitored not controlled. Demand affects
inventory level, inventory level does not affect demand.
Modified data base- demand transactions and values are
monthly calculated and updated with changes.
Materials for capital projects are bid and supplied by the
successful bidder, not by the City’s inventoried stock
Controlling the X’s (Lead Time)
The database was modified to better capture lead time
changes. As orders are filled and the database updated,
the received date is recorded and compared to other order
dates. The difference in dates is converted to monthly
units. The database prints lead time reports that list the
average lead time value by item and by vendor to update
lead time fields.
Control Plan Summary
Control Plan
Process
optimizing
inventory
levels
Process Step
eliminate need
for capital
project
inventory
Output
lower optimal
inventory level,
reduced demand
for stocked
inventory
Input
Process Specification (LSL,
USL, Target)
changed project
bidding specs
(contractor to
supply
materials)
project bid specs require
material acquisition by
successful contractor for
100% of bid street light
projects
posted
transactions
100% of transactions posted
daily for accurate kmax, kmin
value re-calculations monthly.
Demand calculations done for
all 'a items' and 'b items'
every month. Usage
updated daily.
Finance Manager to review
lead time summary report and
compare new values with
values listed on product
summary report. Update dtb
with any changes. Perform
task on a semi-annual basis.
optimizing
inventory
levels
calculate
demand for
stock items
optimizing
inventory
levels
lead time reanalysis and
determination,
re-calculate
necessary for
lead-times and valid kman, kmin
update dtb
calculations
lead time dtb
summary report
optimizing
inventory
levels
optimal order
interval
determine
established for
order intervals, each inventory
item by item
item
comparison of
the cost of order
processing to
the cost of
carrying
inventory
not yet established
accurate kmax,
kmin values
Cpk /Date
In 2004,
materials for
all bid
projects
supplied
from
inventory.
For 2005,
no materials
from
inventory for
bid projects
Summary
Measurement Technique
%R&R
P/T
Sample
Size
Sample
Frequency
1st project
bid each
construction
season until
procedures
are
embedded
examine project bid
specifications for inclusion of
materials as pay items in
project bids
review 1
project for
compliance
with
objectives
demand means and standard
deviations automatically
calculated and kmax, kmin
values automatically adjusted
based on new metrics. Dtb
auto runs on 1st of month
all usage
examined for
all items
monthly
comparision of database values
to lead time summary report
examine
100% of 'a
items' and 'b semiitems'
annually
not yet determined
Control
Plan
Summary
(cont)
Control
Plan
Summary
Process
optimizing
inventory
levels
material
acquisition
material
acquisition
receive
materials
Process Step
service level
determination
Output
service level
determined
Input
analyze stock
out reports for
service level
decision
Process Specification (LSL,
USL, Target)
stock out frequency rept
reviewed by Division Director
who makes determination if
the costs of stock-outs
exceed the costs of carrying
more inventory
Cpk /Date
(Continued)
Measurement Technique
Stock out reports sent to PW
Finance Manager. Finance
Manager compiles information
and reports to Director. Stock
out occurance rate tracked
with control charts (see also
2004 service material requistion process
level = 1
step)
updated material
order materials req sheet
At least weekly, run 'Materials
Needed?' rept and 'Product
Materials
Summary' rept to spot any
Needed?' report, items below kmin values.
as of dec
'Product
Process material requisition
'04, not yet
Summary' report sheet
initiated
compare stock out occurances
to total number of items
ordered on a monthly basis,
with stock out event classified
as defective occurance. Chart
quarterly performance
materials order
order materials processed
submit material req list to
supervisor for review. Upon
return, check dtb for each
item, noting price, vendor, and
any special ordering
considerations. Confirm
prices, and order goods. Input
material
order information into the dtb
requisition sheet by creating a purchase order
compare material req sheet to
dtb and ascertain
completeness of form.
Determine if request seems
reasonable.
receiving
goods
restocked
shelves,
confirmed receipt
of materials
order arrival
oversee deliverly and
unloading of materials. Verify
reciept of all goods with
packing slip. Update dtb.
payable clerk to monitor for
stock person's signature on
packing slip
%R&R
P/T
Sample
Size
Sample
Frequency
100% of
stock out
reports
annually
100% of
orders
placed
quarterly
random, as
determined
by
supervisor
whenever
payments
100% of
are
packing slips processed
Control Plan Summary (cont)
Control Plan Summary (Continued)
Process
inventory
record
accuracy
inventory
record
accuracy
monitor
inventory
process
output (monitoring
the project
Y)
monitor
inventory
process
efficiency inventory
turn rate
Process Step
Output
Input
cycle count
worksheet,
reconcile actual
physical
inventory to record counting of
of inventory,
selected stock
cycle counting control charts
items
item
classifications
determined for
cycle counting cycle counting
comparison of
dollars spent on
each item as
compared to
total dollars
spent for all
items
updated I chart of
monitor
dollar value of
monthly vale of month end
monthly usage
inventory
inventory
report
monitor long
term changes
in system
efficiencies
Process Specification (LSL,
USL, Target)
Cpk /Date
print cycle counting
worksheet, physically count
stock, update dtb with correct
counts, turn in completed
worksheet to office staff for
defective computations.
Investigate causes for
defectives. Update control
charts and post to network
drive
through Nov
'04, ave
defective
rate a items
= 19.7%, b
items =
27.6%, c
items not
yet
computed
Measurement Technique
%R&R
P/T
Sample
Size
Sample
Frequency
differences between actual
count and recorded count
considered defective only if
difference exceeds 5%
a items'
count every
other month,
'b items'
count 2
100% of
times/year,
each item
'c items'
classification count
category
once/year
run usage report, calculate
item classifications, compare
new classifications to old
classifications and update dtb
accordingly (pareto analysis)
Pareto items by spending level.
Sort and order items according
to dollars spent, highest to
lowest. Add highest items
until reaaching 80% of total
dollars spent and designate
these items as 'a items'. The
next set of items totalling 15%
of total dollar
total spent
for all items
in given time
period.
Minimum
period is 1 yr annually
statistical control limits as
designated on I-chart
Product summary report run on
last work day of month, and
total inventory value reported to
PW Finance Manager.
Finance Manager adds
monthly data to minitab file and
produces I-chart. I chart
pasted to file on shared drive.
aggregate
value of all
items in
inventory
5-15-03
$772,000.
12-31-03
$568,250.
12-31-04
$411,796
At year end, a usage report is
run to determine the total value
of inventory used in the past
year. The average monthly
total value of
realistic target not yet known
inventory value is computed
inventory used in because many items still
from data used to build I-chart.
past year,
overstocked (dec 2004). Goal
The total value of inventory
average monthly is to continue to increase turn 2003 = .69 used in the past year is then
inventory turn rate value of inventory rate going forward
2004 = 1.12 divided by th
monthly
100% annual
usage
spending/ave
month end
value of
inventory
carried over 1
yr
annually
Controlling the Process
1. Prior to this project, procedures were not standardized or
documented. As part of the control plan, inventory procedures
were spelled out, documented and distributed.
2. The “Street Light Inventory Procedures” manual will facilitate
implementation of the control plan, project understanding for all
personnel and staff training in inventory control.
3. Inventory control still needs more work. Cycle counting will be
examined in detail and order intervals will be further analyzed.
Methodology
is
Working
Methodology is Working!
To date,
$400,000
of funds
released
can be
redirected
towards
better use
Inventory Levels, March 2005
I Chart: St Light inventory values at month end
800000
baseline
improvements
Inventory values, $
700000
control
1
1
1
600000
500000
1
1
_
+1SL=410481
X=399026
-1SL=387570
400000
1
300000
jun'03
6
9
12
jun'04
18
21
mar'05
Methodology
is
Working
Methodology is Working!
I Chart: St Light inventory monthly values, all items
800000
2003
2004
2005
2006
Individual Value ($)
700000
600000
1
1
1
500000
1
400000
11
1
1
300000
11
1
1
_
+1SL=251156
X=244336
-1SL=237516
200000
1
jul
nov
mar
jul
nov mar
jul
Observation
nov
mar
jul
nov
Since project inception, $400,000 of funds have been made
available for use elsewhere. Without this project, inventory
values would likely be at the level they were in early 2003.
Street Light Maintenance Contract
ACTUAL VS 3% ANNUAL INCREASE
Annual Budget
$1,400,000
$1,300,000
$1,341,000 of
Accumulated Savings
$1,200,000
$1,100,000
$1,000,000
$900,000
2000
2001
BID YEAR
2002
2003
2004
2005
2006
2007
BID YEAR