Big Data for Supply Chain Optimization · The Matas Case CALCULATION EXAMPLE FROM MATAS Item 100059...
Transcript of Big Data for Supply Chain Optimization · The Matas Case CALCULATION EXAMPLE FROM MATAS Item 100059...
Copyright © 2015, SAS Institute Inc. All right reserved.
By: Anders Richter, SAS Institute, Denmark
Big Data for Supply Chain Optimization
Copyright © 2015, SAS Institute Inc. All right reserved.
Agenda
• Demand-Driven Planning & Optimization
and Big data
• Inventory Optimization (IO)
• The Matas case
• Results and takeaways from implementations
• Further readings
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Demand-Driven
Planning &
Optimization
EXPLOSION OF DEMAND-RELATED DATA
Volume
Velocity
Variety
Bulk of this “BIG Data”
is generated outside
the company
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Demand-Driven
Planning &
Optimization
THE PROCESS
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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
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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
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Inventory
Optimization
INDIVIDUAL REORDER LEVEL AND
ORDER UP TO LEVEL
ERP policies
IO policies
70% 10% 20%
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Inventory
Optimization
INDIVIDUAL REORDER LEVEL AND
ORDER UP TO LEVEL
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The Matas Case
About Matas
292 stores in Denmark
30,000 items
Own brands + Lancôme,
Clinique, etc.
2,100 employees in
stores and
administration
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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
saleVery time-costly
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The Matas Case THE IT SET-UP
Matas DWStore system
RCM
SpaceManERP system
Axapta
POS sales
Stock levels
Assortment
POS sales
Stock levels
Orders
Assortment
Order proposal
store + DC
Order
proposals
storeOrders
Target BI
Order
Assortment
SAS®
DC Suppliers
Orders
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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
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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
IT
EM
STORE
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Inventory
Optimization
LIMITATION OF IO
• Limitation of IO
• Cannot aggregate orders on supplier level
• So when to use OR?
• When there are constrains on supplier level (minimum order
amount/order size)
• Container optimization
• Push allocation
• Optimal distribution in case of shortages
• Displays
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The Matas Case RESULTS AND TAKEAWAYS
Total stock value reduced by 10%
Out-of-stock situations reduced by 2 percentage points
Able to control the out-of-stock on their ABC classification
Man-hours spent on replenishment reduced by 70%
Facts instead of gut feeling
Coherent replenishment flows
Do not forget change management
Matas’ case study is outlined in Chapter 8
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Results and
Takeaways
ARGUMENTS FOR STARTING WITH DDPO
The system is objective
• It uses historical information and master data when calculating min./max.
instead of being dependent on a person – both with regard to gut feeling
and skills
Automating the creation of order proposals ensures:
• Time spent on generating order proposals is reduced
• SKUs are not forgotten, and the risk of out-of-stock
situations is thus reduced
• Min. and max. values are always up-to-date
• Individual reorder level and order up to level, not
“one size fits all”
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Results and
Takeaways
MARKET-DRIVEN SUPPLY CHAIN BENEFITS
Sense market changes 5X faster
Align their supply 3X faster to fluctuations in demand
With better customer service with substantially less inventory, waste and working
capital (e.g., profitable supply chains)
Bottom-line: Market-Driven processes are designed from the
market-back -- based on sensing and shaping demand and
optimizing supply
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Results and
Takeaways
GETTING THEREVision
Phase one:
Limited scope and creating of
the data process, reap the
benefits
Phase two:
Increase scope and
automation in the process
Phase 3 …
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Lean Lean
Forecasting Management
(FVA)
Demand-Driven
Planning &
Optimization
FURTHER READING
Market Supplier
Demand-
Driven
Sales & Operations Planning
Market-
Driven
Selling through the channel (pull) Selling into the channel (push)
Supply-
Driven
Supply Sensing
Supply Shaping
Synchronized
Replenishment
Inside-out
Focused
Reactive
Process
Inventory
Optimization
Demand Sensing
Demand Shaping
Demand Shifting
Outside-in
Focused
Proactive
Process
Collaborative
Planning
DDPO
Solution
overview
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Anders Richter
Business Delivery Manager
Commercial & Life Sciences Division
SAS Institute Denmark
E-mail: [email protected]
Mobile: +45 27 21 28 21
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