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

8

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?

15

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

E: romoberg@se.ibm.com

M: +46 707 93 12 52