2. Company INFORM
INFORM Institute for Operations Research and Management
INFORM Institute for Operations Research and Management
Aachen, Germany
Aachen, Germany
Advanced Decision Support
Optimization
based on Operations Research, Fuzzy Logic
Administrative IT Systems e.g. SAP and other ERP-Systems,
Order Management, etc.
IT Infrastructure e.g. DB/2, Oracle, e.g. TCP/IP,
Databases / Connectivity Informix, Sybase UNIX, Windows, etc.
First company ever to receive Enterprise Award
First company ever to receive Enterprise Award
by German Operations Research Society (GOR)
by German Operations Research Society (GOR)
3. Company INFORM
250
Company Headquaters in Aachen, Germany
Since 1986, more than 24%
Since 1986, more than 24%
200
annual growth each year
annual growth each year
150
1h
100
1.5h
2h
50
0
1969 4
1 2 3 2005
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Seattle
Tokyo
Monterrey
Johannesburg
Brisbane
4. Inventory Optimisation – Objectives
Item = applicable to
Finished Goods
Maximising Item Availability Production Parts & Materials
= Maximising Customer Service Aftermarket Spare Parts
Plant Maitenance Spare Parts
100 %
-20 %
Return On
Minimising Inventories
Investment
0%
Stock reduction financing IT investment by 100% ...500%
Significant ROI without affecting any labour issues
Storage & Capital Costs
Discount Pricing
Minimising Logistics Costs
Transport & Receiving
Planning & Administration
6. Demand Forecasting Matters!
Close to 100 % service level
Stock the required stock value
Value tends to 'explode‘
High quality forecasting enables
High quality forecasting enables
to meet any stock availability target
asting to meet any stock availability target
poo r forc
•• with less inventories,
with less inventories,
sting •• and lower logistics costs!
and lower logistics costs!
good foreca
Target Service Level
7. Factor 1: Forecasting Models
Models & Methods
Models & Methods
Simple Moving Averages
Simple Moving Averages
Exponential Smoothing, e.g.
Exponential Smoothing, e.g. No Additive Multiplicative
Stationary series (SES)
Stationary series (SES) Season Season Season
Linear trend (DES)
Linear trend (DES) No
Trend
Seasonal trend (Holt-Winters)
Seasonal trend (Holt-Winters)
Dampened trend
Dampened trend Additive
Trend
Irregular demand (Croston)
Irregular demand (Croston)
Multiplicative
Regression
Regression Trend
ARIMA / /Box-Jenkins
ARIMA Box-Jenkins
Strong theoeretical
Strong theoeretical Patterns based on Pegels’ classification
background but empirical for some exponential smoothing methods
background but empirical
results not as impressive
results not as impressive
Parameter selection not easy
Parameter selection not easy
Combinations
Combinations
8. Forecasting Model Competitions
Competitions
Competitions
Long history
Long history
M3-Competition
M3-Competition
[Makridakis, Hibon 2000, IJOF]
[Makridakis, Hibon 2000, IJOF]
Total of 3,000 different test data sets
Total of 3,000 different test data sets
24 methods from many areas
24 methods from many areas
Explicit trend
Explicit trend
ARIMA
ARIMA
Expert systems
Expert systems
Neural networks
Neural networks
Results in aanutshell
Results in nutshell
Combinations of models do well !!
Combinations of models do well
Some M3-Competition results:
Parameters matter a lot !!
Parameters matter a lot Average symmetric MAPE for all data
Source: [Makridakis and Hibon 2000]
9. Factor 2: Forecasting Parameters
Parameter Adaptation
Parameter Adaptation
All methods require attention to
All methods require attention to
'good' parameters
'good' parameters
Hence, forecast quality strongly
Hence, forecast quality strongly
depends on parameter selection
depends on parameter selection
Some Examples:
Some Examples:
Original data series with noise
Simple moving average forecast with different parameters DES forecast with better parameters
10. Auto Adaptive, Dynamic Forecasting
State-of-the-Art Forecasting Models
State-of-the-Art Forecasting Models
•• coping with seasonal factors, trends,
coping with seasonal factors, trends,
stochastic outliers, and structural breaks
stochastic outliers, and structural breaks
Auto Adaptive Forecasting
Auto Adaptive Forecasting
•• Automatic adaptation of model &
Automatic adaptation of model &
parameters daily / /weekly
parameters daily weekly
(depending on data volume)
(depending on data volume)
Dynamic Forecasting
Dynamic Forecasting
•• Updating the demand profile every time
Updating the demand profile every time
new data becomes known
new data becomes known
Manual Intervention
Manual Intervention Future Demand Profile
Future Demand Profile
•• Manual modification of forecast where
Manual modification of forecast where
applicable (e.g. sales promotions)
applicable (e.g. sales promotions)
•• Item substitution rules
Item substitution rules
12. Demand Planning I
key-account structure
r e warehouse
ctu
s tru ..
n al
regio
..
.. distribution
.. center
a customer
are
storage
ion
reg .. variant
product
y ..
ntr
cou
product group
rld total product range
wo
product structure
13. Demand Planning II
Multiple Hierarchical Dimensions
Multiple Hierarchical Dimensions
Permanent Planning Consistency
Permanent Planning Consistency: :
All changes will be automatically aggregated and
All changes will be automatically aggregated and
disaggregated within the entire planning hierarchy
disaggregated within the entire planning hierarchy
Alternative use of quantities or values
Alternative use of quantities or values
Efficient editing of distribution keys
Efficient editing of distribution keys
16. Line Item Planning I
order quantity X
Annual
Costs
maximum stock
cumulative costs
reorder point
storage costs
lead time
total cost minimum
ordering + handling costs
Avg. Order Quantity
Fixed Rythm / /Order Point Procedures
Fixed Rythm Order Point Procedures Economic Order Quantity Procedures
Economic Order Quantity Procedures
•• Traditional method
Traditional method •• Various heuristical algorithms
Various heuristical algorithms
•• Simple control mechanism (Kanban)
Simple control mechanism (Kanban) •• Optimisation: Wagner Within Algorithm
Optimisation: Wagner Within Algorithm
•• Extended to cover discount schemes
Extended to cover discount schemes
17. Line Item Planning II
Ordering / /Setup Costs
Ordering Setup Costs
Storage Costs
Storage Costs
Discounts / /Price Changes
Discounts Price Changes
Supply Calenders
Supply Calenders
Planned Service Level
Planned Service Level
Dynamic Safety Stock
Dynamic Safety Stock
Supplier Contract Specifics
Supplier Contract Specifics
Minimum Ordering Quantities
Minimum Ordering Quantities
Packaging / /Shipment Units
Packaging Shipment Units
Planning Focus Matrix
etc.
etc.
19. Supplier & Shipment Consolidation
Order A
Cost optimised
ordering schedules
Planning Planning
by Line Item by Supplier
Planning
Order B
by Shipment
21. Capacity Planning
Capacity compliant scheduling of production orders
No setup time constraints, no order splitting, no sub-task planning
Priority based capacity scheduling
Identification of bottlenecks, and long-term capacity usage
What-if scenario simulation capabilities
22. Multiple Tier Network Planning
•• The planning and supply relationship between different plants / /warehouses is modelled as
The planning and supply relationship between different plants warehouses is modelled as
aa'network' for inventory planning purposes.
'network' for inventory planning purposes.
DC
DC
DC
Plant Global DC
Regio DC Regio DC
Plant
DC
DC
DC
DC
DC
26. IT Support for Inventory Optimisation
SCM
SCM
ERP // WMS
ERP WMS Supply Chain
Supply Chain
Management
Management
Focus on data management Numerical Complexity suitable
and information infrastructure only for key items
very limited math. decision support (100 .. 1,000 <> 10,000 .. 500,000)
(e.g. no auto-adapive parameters) Decision Making
is often de-centralised
(even within one company)
Analytical
Analytical APS
APS
Advanced
Advanced
Systems
Systems Planning Systems
Planning Systems
Statistics, Data Warehouse, Math. Models right decision level
Controlling, BI, etc. Coping with large data volumes
Focus on 'insight' Day-to-day Operations Support
no direct operations support Synchronised with ERP / WMS
27. Synchronised APS System add*ONE
Download
add*ONE add*ONE
Automatically released Items
Server Night
Processing
Manually released items
ERP
System add*ONE Clients
Capacity/
Capacity/
Demand Line Item Item Group
Item Group Inventory
Demand Line Item Network Inventory
Forecasting Planning Planning Network Controlling
Forecasting Planning Planning Planning Controlling
Planning
... add-on complementing rather than replacing existing IT functions
... add-on complementing rather than replacing existing IT functions
28. Benefits I
Example: Electrical Tools Manufacturer, Spare Parts Inventory
High service levels despite reduced stock levels.
High service levels despite reduced stock levels.
[%] 100
Availability
Parts
99
98 €1,900,000.- Inventory Reduction (18%)
€1,900,000.- Inventory Reduction (18%)
[Mio €] 11 within 44months after go-live
within months after go-live
Inventory
10
9
10. 17. 28. 06. 13. 20. 27. 04. 12. 19. 26.
April May June
29. Benefits II
Example: Energy Producer, Plant Maintenance Inventory
Return-on-Investment:
Return-on-Investment:
€€20,000,000.- just 77months after add*ONE go-live
20,000,000.- just months after add*ONE go-live
110,0
105,4
100,0
- 20 Mio €
90,0
85,5
80,0
70,0
Start add*One
Start Zentrale Disposition
60,0
00
1
2
07
08
09
10
11
12
01
02
03
04
05
06
07
08
09
10
11
12
01
02
03
04
05
06
07
08
09
10
11
12
Q
Q
el
itt
M
30. Benefits III
Return-on-Investment, Example: Small manufacturing company,
Return-on-Investment, Example: Small manufacturing company,
30,000 line items spare parts inventory of ca. 77Mio. value
30,000 line items spare parts inventory of ca. Mio. value
3,25
Aufwand (Personen)
3
2,1 2,1
Planning Workload
Staff Hours Reduced by 35%
88
85 85
Servicegrad (%)
0 1
Jahr
2 3 Service Level 80
(Materials & Parts Availability)
Improved by 10%
6,91 0 1 2 3
Jahr
Bestand (Mio. DM)
5,39
Inventory Level
4,91
4,82
Reduced by 1.52 Mio. = (22%) in first year;
yielding pay-back of invested software within 3 months!
0 1 2 3
Jahr
31. add*ONE Selected Reference Clients
Linde Gas
More than 90 companies
More than 90 companies
in Europe, Asia, South America
in Europe, Asia, South America
