Transcript BMW Project “Ship-to-Average“ by Matthias Pauli

BMW Project
“Ship-to-Average“
by
Matthias Pauli
Thomas Drtil
Claus Reeker
Stefan Lier
Christopher Vine
Fernando Cruz
Plant Spartanburg
• ~140,000 vehicles in 2004
• Over 6,000 part numbers for X5
– 70% option driven
• 40% of parts from Europe
Supply Chain
Challenges
Long Lead-Times
Demand Variability
Late Order Changes
Product Variety
What Impact ?
Built-to-order
Fixed Delivery Dates
100% Customer
Satisfaction
Demand Variability*
180
Standard Deviation:
42/day
Mean Demand:
78/day
160
140
demand
120
100
80
60
40
20
0
1
11
21
31
41
51
61
days
*) Data of engine #7781905-00, high runner
71
81
91
101
111
BMW policy: Ship-to-forecast
Forecast
Order
Placement
Shipping
Order Arrival
≠
Demand
Day 1
Demand
Day 10
Demand
Demand
Day 40
Inventory
• On-hand inventory* with ship-to-forecast:
OHI
– constant level?
1600
1400
1200
1000
800
600
400
200
0
1
11
21
31
41
51
61
days
*) Data of engine #7781905-00, high runner
71
81
91
101
111
Forecast error
• Why try to chase the daily forecast?
%
45.00%
40.00%
35.00%
30.00%
Use forecast accuracy
over longer period of
time!
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%
Day
Week
Month
Quarter
Different forecasts*
140
Forecast quantity
120
100
daily
weekly
monthly
quarterly
80
60
40
20
0
1
3
5
7
9
11
13
15
*) Data of engine #7781905-00 , high runner
17
19
21
23
25
27
29
31
Approach: Ship-to-average
• Don’t ship to daily forecast
• Consider a longer forecast period instead
• “Keep shipments constant, let the inventory
swing“
• Goals:
#1) Minimum impact on total avoidable costs
#2) More stability for the supply chain
Basic Implementation
• Always ship average quantity!
• What happens to the inventory*?
High inventory level:
3300 units
4000
On-hand inventory
3500
3000
2500
2000
1500
1000
500
0
1
22 43
64 inventory
85 106 127 148
169 190
211 232 253 274 295 316 337 358 379 400 421 442
Low
level:
600
units
*) Data of engine #7781905-00, high runner
How to control the inventory?
Deflate shipments:
Avg. forecast (x weeks)
* deflation factor
Inflate shipments:
Avg. forecast (x weeks)
* inflation factor
Inventory Position
Max. Inventory
Position
(almost) constant
shipment quantities !
Time
Which Part analyzed?
• Part
– Engine #7781905-00
– High runner
• Policy
– # of weeks for average: 3
– Max. Inventory Position: 2509
– Inflation/deflation: 1.8%
Performance Overview
• How does ship-to-average perform for this
engine:
COSTS
TRANSPORTATION
Total
avoidable
costs
Inventory
costs
Pipeline
costs
Air costs
Air count
Air volume
Reduction
shipment
changes
-0.56%
+4.9%
-0.37%
-57.71%
-50.55%
-57.71%
-51.46%
Goal #1 achieved!
Shipment Comparison
800
ship-to-forecast
shipments
700
(shipment adjustment: 66%)
600
500
400
300
1
ship-to-average
(shipment adjustment: 14%)
5
7
9
11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
weeks
800
700
shipments
= shipment quantity
changes more than
10% compared to
previous one
3
600
500
400
300
Shipment adjustments
happen in 14% of all
shipments
1
3
5
7
9
11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
weeks
Goal #2 achieved!
What’s next?
• Goals achieved! Optimized policy works.
• But how robust is the result?
• What are the trade-offs?
• How do the 3 parameter…
– # of weeks for average
– Max. inventory position
– Inflation/deflation factor
… influence the result?
Sensitivity Analysis
• # of weeks for average:
Total avoidable costs [$]
$1,046,000
Total avoidable costs
$1,045,000
$1,044,000
$1,043,000
$1,042,000
$1,041,000
$1,040,000
$1,039,000
$1,038,000
$1,037,000
1
2
3
4
5
6
$25,000
Air costs [$]
Air costs
$20,000
$15,000
$10,000
$5,000
$0
1
2
3
4
5
6
Sensitivity Analysis
• Max. Inventory Position:
Total avoidable costs [$]
$1,200,000
$1,150,000
$1,100,000
$1,050,000
$1,000,000
$950,000
$900,000
1909
Air costs [$]
Total Avoidable Costs
$1,250,000
2009
2109
2209
2309
2409
2509
2609
2709
2809
2909
3009
Sensitivity Analysis
• Inflation/deflation factor:
Total avoidable costs [$]
$1,050,000
$1,046,000
$1,044,000
$1,042,000
$1,040,000
$1,038,000
$1,036,000
$1,034,000
$1,032,000
$1,030,000
±0%
Air costs [$]
Total avoidable costs
$1,048,000
±0.3%
±0.6%
±0.9%
±1.2%
±1.5%
±1.8%
±2.1%
±2.4%
±2.7%
±3%
±3.3%
Summary Table
1092396-00
HIGH
6756673-00
HIGH
6762958-00
HIGH
7781905-00
HIGH
7783354-00
HIGH
1552166-00
LOW
6753862-00
LOW
7759119-00
LOW
7781903-00
LOW
-0.36%
-6.47%
-5.70%
-0.56%
-0.45%
-3.87%
-1.67%
-0.94%
-3.85%
Air cost
+25.25%
-77.14%
+461.28%
-57.71%
-5.36%
+15.22%
-14.00%
-60.45%
0.00%
Shipment
changes
-47.11%
-32.22%
-16.32%
-51.46%
-55.83%
-29.60%
-48.84%
-21.79%
-56.04%
Part #
Total
avoidable
cost
(incl. air cost)
Advantages
• Small cost reduction compared to current
ship-to-forecast policy
• Less variation in order quantities
– Less bullwhip effect
– Easier operations for
Spartanburg/ Wackersdorf/ upstream suppliers
– Facilitates negotiation with transportation partner
Limitations of the study
•
•
•
•
Simulation vs. reality
Restricted original data sets provided
Small number of parts considered
Constant shipment frequency assumed (once
per week)
Recommendations
• Run pilot to check performance:
– pick high runner with relatively stable demand
over time
• Analyze larger set of parts
• Evaluate cost savings upstream
• Evaluate trade-off between higher savings
and increasing expediting
Q&A