Подробности на www.ibs.ru Планирование и оптимизация запасов на основе спроса. Большие данные в управлении цепочек поставок. Андерс Рихтер (Anders Richter), ведущий эксперт центра компетенции SAS по решениям управления спросом и оптимизации товарных остатков. Бизнес-завтрак IBS и SAS «Прогнозирование спроса и оптимизация товарных запасов в розничном бизнесе. Примеры из практики» (12.03.2015)

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DEMAND-DRIVEN PLANNING AND OPTIMIZATION
BIG DATA AND SUPPLY CHAIN MANAGEMENT
BY BUSINESS DELIVERY MANAGER ANDERS RICHTER
SAS INSTITUTE DENMARK

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AGENDA BIG DATA AND SUPPLY CHAIN MANAGEMENT
• Big Data
• Demand synchronization
• Common challenges and demands
• Demand-Driven Planning and Optimization (DDPO)
• Forecasting
• Collaborative Planning
• Inventory optimization
• Results and take-aways
• Further readings

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BIG DATA WHAT IS DRIVING BIG DATA?

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Lean Lean
Forecasting Management
(FVA)
DEMAND SYNCHRONIZATION
Inventory
Optimization
Demand Sensing
Demand Shaping
Outside-in
Focused
Proactive
Process
Collaborative
Planning
Market Supplier
Demand-
Driven
Inside-out
Focused
Reactive
Process
Supply Sensing
Supply Shaping
Sales &
Operations
Planning
Market-
Driven
Selling through the channel (pull) Selling into the channel (push)
Supply-
Driven
15-30%
5-7%
Synchronize the demand and supply sides of the supply chain equation

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DEMAND-DRIVEN
FORECASTING
DEFINITION
• Demand-driven forecasting is the use of forecasting technologies along with demand
sensing, shaping, and translation techniques to improve supply chain processes.
Focuses on identifying the market signals and translating them into the drivers of
demand.
• The input signals from the market are:
• Trend
• Seasonality
• Sales promotions
• Marketing events
• Price
• Advertising
• In-store merchandising
• Competitive pressures
• Others
“Demand-planning has evolved from a shadowy concept to
a critical planning function.”
—Deborah Goldstein,
Vice President Demand Planning, McCormick

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COMMON
CHALLENGES
 Incoherent flows
 High stock values
 Many out-of-stock (OOS) situations
 Many manual processes
 Gut feeling instead of facts
 Many man-hours spent on
replenishment
COMMON
DEMANDS
 Coherent replenishment flow
 Forecasting based on POS data
 Automated orders
 Fewer man-hours
 Higher turnover rate
 Fewer OOS situations
 Especially on critical articles

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DEMAND
SYNCHRONIZATION
SOLUTION OVERVIEW
Inventory
Optimization
Demand Signal
Analytics
Executive
S&OP
Segmentation
& Clustering
Advanced
Statistical
Forecasting
New Product
Forecasting
Collaborative
Planning
Access Engines
SAS DEMAND-DRIVEN PLANNING & OPTIMIZATION
Data Integration
Demand
Signal
Repository
(DDPO Foundation)
Demand POS/Syndicated Scanner
Sales Promotions
Distribution
Price
Advertising
In-Store Merchandising (Display,
Feature, TPR, and others)
SupplySales Orders
Shipments
Trade Promotion
Wholesale Gross Price
Off Invoice Allowances
Retail Inventory
Forecasting
for
SAP/APO
Forecasting Collaborative
Planning

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FORECASTING THE LAWS OF FORECASTING
1. Forecasts are almost always wrong!
2. Forecasts for near future are more accurate
3. Forecasts on SKU level are usally less accurate than forecasts on
product group level
4. Forecasts cannot substitute calculated values

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FORECASTING RESULTS OF POOR FORECASTING
Forecast Error
Over Forecast Under Forecast
Excess Inventory
Holding Cost
Transshipment Cost
Obsolescence
Reduce Margin
Expediting Cost
Higher Product Cost
Lost Sales Cost
Lost Companion Sales
Customer Satisfaction

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FORECASTING LARGE-SCALE FORECASTING SCENARIO
Time Series Data
80% can be forecasted automatically
10% require extra effort
10% cannot be forecasted accurately

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EXAMPLE FROM A HIGH-PERFORMANCE
FORECASTING (HPF) INSTALLATION
• 2 million forecasts each week for SKU/store combinations
 (52 weeks on weekly level, based on up to 3 years of data)
• 26,500 forecasts each day on SKU level
 (52 weeks on daily level, based on up to 3 years of data)
• Forecast is reconciled each day
• Model types: ARIMAX, ESM and pre-made naive models
• Explaining variables
 Flyer, avis, smuk, jule, uann, x_kampagne, vareOvergang, forside,
familie_rabat, soendags_aabent, kamp_uge_1, kamp_uge_2 and uannon_periode
• Output is expected sales on SKU/store and SKU level, and the uncertainty
of the excepted sales

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DEMAND
SYNCHRONIZATION
SOLUTION OVERVIEW
Inventory
Optimization
Demand Signal
Analytics
Executive
S&OP
Segmentation
& Clustering
Advanced
Statistical
Forecasting
New Product
Forecasting
Collaborative
Planning
Access Engines
SAS DEMAND-DRIVEN PLANNING & OPTIMIZATION
Data Integration
Demand
Signal
Repository
(DDPO Foundation)
Demand POS/Syndicated Scanner
Sales Promotions
Distribution
Price
Advertising
In-Store Merchandising (Display,
Feature, TPR, and others)
SupplySales Orders
Shipments
Trade Promotion
Wholesale Gross Price
Off Invoice Allowances
Retail Inventory
Forecasting
for
SAP/APO
Forecasting Collaborative
Planning

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FORECAST VALUE ADDED (FVA)
Focus on forecasting process efficiency
• Forecast accuracy is largely a function of the “forecastability” of the demand
• We may never be able to achieve the accuracy desired
• But we can control the process used and the resources we invest
COLLABORATIVE
PLANNING

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COLLABORATIVE
PLANNING
FORECAST VALUE ADDED (FVA)
The “naïve” forecast
• Performance must always be evaluated with respect to the alternatives
• The naïve forecast is a baseline of performance against which all forecasting efforts must be
compared
• Two commonly used naïve models are:
• Random Walk
• Seasonally Adjusted Random Walk
• If you can’t beat a naïve forecast, then why bother?

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COLLABORATIVE
PLANNING
FORECAST VALUE ADDED (FVA)
Forecasting performance evaluation
• Who is the best analyst?
Analyst Item Type
Item Life
Cycle
Seasonal Promos
New
Items
Demand
Volatility
MAPE
A Basic Long No None None Low 20%
B Basic Long Some Few Few Medium 30%
C Fashion Short Highly Many Many High 40%
Naïve MAPE FVA
10% -10%
30% 0%
50% 10%

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DEMAND
SYNCHRONIZATION
SOLUTION OVERVIEW
Demand Signal
Analytics
Executive
S&OP
Segmentation
& Clustering
Advanced
Statistical
Forecasting
New Product
Forecasting
Collaborative
Planning
Access Engines
SAS DEMAND-DRIVEN PLANNING & OPTIMIZATION
Data Integration
Demand
Signal
Repository
(DDPO Foundation)
Demand POS/Syndicated Scanner
Sales Promotions
Distribution
Price
Advertising
In-Store Merchandising (Display,
Feature, TPR, and others)
SupplySales Orders
Shipments
Trade Promotion
Wholesale Gross Price
Off Invoice Allowances
Retail Inventory
Forecasting
for
SAP/APO
Forecasting Collaborative
Planning
Inventory
Optimization

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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 what 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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RESULTS AND
TAKE-AWAYS
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
TAKE-AWAYS
• Total stock value reduced by 10% to 50%
• Out-of-stock situations reduced with up to 50%
• Man-hours spent on replenishment reduced by 70%
• Facts instead of gut feeling
• Coherent replenishment flows
• Don’t forget change management

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RESULTS AND
TAKE-AWAYS
COHERENT REPLENISHMENT 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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RESULTS AND
TAKE-AWAYS
GETTING THERE Vision
Phase one:
Limited scope and creating of
the data process, harvesting
the low-hanging fruits
Phase two:
Increase scope and
automation in the process
Phase 3 …

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FURTHER READING ABOUT THE TOPIC
Inventory
Optimization
Demand-Sensing
Demand-Shaping
Demand-Shifting
Outside-In
Focused
Proactive
Process
Consensus
Forecasting
FVA
Market Supplier
Demand
Driven
Outside-In
Focused
Proactive
Process
Synchronized
Replenishment
Supply Shaping
Sales &
Operations
Planning
Market
Driven
Selling through the channel (pull) Selling into the channel (push)
Supply
Driven
15-30%5-7%
Sales OrdersPOS
Lean
Manufacturing
Lean
Forecasting
FVA

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FOR MORE INFORMATION, PLEASE CONTACT:
Anders Richter
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Commercial & Life Sciences Division
SAS Institute Denmark
E-mail: Anders.richter@sas.com
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SAS Россия
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