IBM Smarter Business 2012 - Prediktion eller fakta?
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Prediktion eller fakta? Sortimentsplanering med prediktiv analys Robert Moberg Predictive Analytics Solutions Architect IBM
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Denna presentation är en fallstudie över hur sofistikerad prediktiv analys bistår dagligvaruhandeln med agerbara insikter för intelligent design av sortiment. En helt vanlig mellanstor livsmedelsaffär kan lätt presentera 30 – 50 000 artiklar i sitt sortiment. Vilka bland alla dessa artiklar förväntar sig kunderna finna i hyllorna? Och vilka inte? En optimerad sortimentsstrategi ger ökad försäljning och färre returer som resultat. Talare: Robert Moberg, Produktspecialist Prediktiv Analys, IBM Besök http://smarterbusiness.se för mer information.
Transcript of IBM Smarter Business 2012 - Prediktion eller fakta?
Prediktion eller fakta?Sortimentsplanering med prediktiv analys
Robert Moberg
Predictive Analytics Solutions Architect
IBM
Let me start with a few of qoutes...• "Not everything that can be counted counts, and
not everything that counts can be counted." – Albert Einstein– 1879 – 1955– Snille
• "Not everything that can be counted counts, and not everything that counts can be counted."
– Albert Einstein– 1879 – 1955– Snille
• ”Predictive Analytics – a prerequisite to drive a Smarter Business”
– Robert Moberg– 1969 – – PASA at IBM
A Sample of Data A Universe of Things That Generate Data
?
A Universe of Data
Attributes• Married, 2 kids• Home owner in Liseberg, Älvsjö• Has a house in Gotland• Owns a car• 43 years old• Enjoys fine wines and champagne• Plays golf
Predicted Attributes• Likes Beastie boys• Likes Gotland• Works long hours• Commutes• Middle Income
Predicted Behavior• Dines in descent restaurants• Consumes a lot of electricity• Buys green fees• Family vacations
A Predictive Model
A Universe of Data
Attributes• Kex• Söta• Små• Formade som djur• Färgglad kartong• Á 50 gram• Plays golf
Predicted Attributes• Passar bra till sylt• Riktar sig till barn• Bra mellanmål• Eller till utflykten• Middle Income
Predicted Behavior• Kommunicera till barnfamiljer• I områden där man inte är sockerfientliga• Placera bredvid sylten
A Predictive Model
PredictiveCustomer Analytics
PredictiveThreat and Risk
Analytics
PredictiveOperational
Analytics
Agile
Long term planning
Procurement
Development
AvailabilityDistribution
Product Lifecycle Management
Inventory Management
Service Management
Forecasting
Equipment Maintenance
Condition Monitoring
Assortment Planning
Energy Planning
Seasonal Decomposition
Returns Management
Inventory Allocation
Commodity PlanningFinished Goods Planning
Intelligent Logistics
Process ControlCost Forecasting
Quality Control
Warranty Analysis
Asset Management
Working Capital Forecasting
Capacity Planning
R&D Optimization
Inventory Planning
Claims Processing
Debt Management
Customer Research
Employee PerformanceProcurement
Development
DistributionSupportCall center Optimization
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Example: Predicting product sales by stores• Prediction• Sales model accuracy
is based on following inputs:•
1. POS data at SKU receipt level2. Customer information
• demographics• share of wallet• Geographical• Consumption Style• Primary/Secondary 1, 2• ...
Article nameTotUnits 2011 Prediction
A 9B 314C 95D 520. 224. 24. 302. 131. 4. 10. 374. 429. 6. 201. 123. 76. 103. 74. 87. 80. 187. 298. 122. 56Z 169
TotUnits 2011....
Error9 0313 095 0518 0224 024 0303 0131 04 010 0373 0429 06 0204 1124 175 1102 173 186 181 1184 2291 2125 255 2166 2
Is the model predicting or
providing facts?
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Store 1
Advance Auto Parts - Increases revenue by reducing both lost sales and non-working inventoryBackground
#2 auto parts retailer
3,300 stores in 40 states
Business goals
Stock the correct mix of SKUs in every store
Increase sales within “Do it for me” channel
Increase sales within slow turning part categories
Reduce handing costs by delivering the appropriate mix of SKUs to the correct point in the supply chain
Solution
Models created to predict SKU demand at the store level
Solution scales to handle 500K+ SKUs
Analytic assets are managed in one place and executed automatically every 120 days
Integrate analytics with existing merchandising systems
Results Reduce non working inventory (low
and slow turning SKUs over 13 periods) by $54.7M
Increase sales in the back of store segment by $109M per year
Predictive models for SKU demand have proven to be 70+% accurate
Significantly lowered resource cost through automation
Contact details
Robert Moberg
Predictive Analytics Solutions Architect
IBM
M: +46 707 93 12 52
