By: Anders Richter, SAS Institute, Denmark
Agenda
• Demand-Driven Planning & Optimization
and Big data
• Inventory Optimization (IO)
• The Matas case
• Results and takeaways from implementations
• Further readings
Demand-Driven
Planning &
Optimization
EXPLOSION OF DEMAND-RELATED DATA
Volume
Velocity
Variety
Bulk of this “BIG Data”
is generated outside
Demand-Driven
Planning &
Optimization
Inventory
Optimization
TYPICAL NETWORK
DC
Store
Store
Customer
Store
Store
Supplier
Supplier
Supplier
Supplier
Store/ echelon
lvl 1
DC/ echelon
lvl 2
Customer
Customer
Customer
Inventory
Optimization
GOAL AND INPUT
Goal with IO
To find the most optimal reorder levels as to economy and which level should be ordered up to
– in other words finding minimum and maximum. This is done based on constrains and demand
expectations on SKU level
Model types
SS and BS, which are minimizing the cost given the demand and constrains information
Input variable
Costs
Ordering cost, holding cost and penalty cost
Demand
Expected sales in the total lead time, and the uncertainty of this expected demand
Constrains
Service level, service type (fill rate), batch size and minimum order quantity
Combining min./max. with inventory position gives the suggested
order for the SKU
Inventory
Optimization
INDIVIDUAL REORDER LEVEL AND
ORDER UP TO LEVEL
ERP policies
IO policies
Inventory
The Matas Case
About Matas
292 stores in Denmark
30,000 items
Own brands + Lancôme,
Clinique, etc.
2,100 employees in
stores and
The Matas Case
INCOHERENT FLOWS
Order proposals
based on DC sales
Manual process –
correcting proposals
No link to store
replenishment
DC replenishment
Store replenishment
Store manager
controls
replenishment
Based on gut feeling
and last 31 days of
sale
The Matas Case
THE IT SET-UP
Matas DW
Store system
RCM
SpaceMan
ERP system
Axapta
POS sales
Stock levels
Assortment
POS sales
Stock levels
Orders
Assortment
Order proposal
store + DC
Order
proposals
store
Orders
Target BI
Order
Assortment
SAS
®DC Suppliers
Orders
The Matas Case
REPLENISHMENT NOW – COHERENT FLOWS
SAS
®
forecasting
(POS data)
Order
proposals
to DC
(semi-automated)
Order
proposals
for stores
(locked for
editing)
Reporting
on SAS
quality
Adjust &
Improve
The Matas Case
CALCULATION EXAMPLE FROM MATAS
Item 100059 (Eye makeup remover)
Store 15288 (Greater Copenhagen)
FORECASTS Forecast 42 Std.dev. 26 Lead time 1 Service degree 0,99 Stock holding costs (n/a) Size of colli 12 Opening allowed N Store inventory 65 Min 113 Max 155 Order suggestions 96 RESULTS INVENTORY OPTIMIZATION SALES RECORDS, PROMOTIONS INVENTORY INFORMATION
