March, 2015

• VP of Advanced Analytics – QueBIT Consulting, LLC
• IBM World Wide Predictive Analytics Lead – Retail
• Director of Merchandising Science – Advance Auto Parts
• Implemented Predictive Assortment Planning at 3
Automotive Parts Retailers (2 US/1 Canada) e: smutchler@quebit.com
P: 540-524-8380

• Experts in analytics strategy, implementation and training
• 700+ successful implementations in finance, sales, marketing and operations
• 350+ analytics customers in all types of industries

IBM Business Analytics Partner Excellence Awards:
• 2013-14: Worldwide Overall Business Analytics
Business Partner Excellence Award
• 2012: Business Analytics North America Business
Partner Excellence Award
• 2011: Worldwide Financial Business Analytics
Achievement Award
• 2010: Mid-Market Partner of the Year, and Sales Excellence

• Unique Challenge of Automotive Parts Retail
• High Level Overview of Predictive Assortment Planning
• Benefits of Predictive Assortment Planning
• Techniques
• Organizational Integration

• Long tail demand with high expectation of part availability and limited space
– 9,000+ make/model/year combinations at a single store
– 100’s of part types
• Commercial, retail and service channels often have competing requirements of assortments
• Stores [even in close proximity] can have completely different commercial customers and vehicle populations
• Clustered assortments are easier to manage but are too broad brush resulting in highly inaccurate assortments with high part duplication of low velocity parts (in-market)
• Store specific assortments result in higher assortment accuracy but require a very high number of assortment decisions
100 stores x 100,000 stockable SKUs = 10 million decisions!

• Data driven
– Assortment changes are driven by predictive models trained on your data
– Models are validated using historical hold-out data
– Not biased by vendor goals
– Merchants/vendors have input into what goes into the model
• Scalable
– The solution can process [score] billions of records in a few hours
• H ighly accurate
– Typical [first year] sell-through for predictive models on new inventory is 65-85%
(varies by part category) compared to 40-60% standard approaches
• Space efficient / availability maximizing
– Maximizes part availability for key categories based on demand elasticity
– Ensures all the parts required to complete the job are in the assortment
– Optionally: Maximizes market availability by splitting long-tail demand across stores
• Low risk
– Simulate changes before deploying – pilot in select stores

• All
– 10-30% increase in assortment accuracy
• US Retailer
– 400 bp improvement in inventory turn
– 7% reduction in inventory levels
– 11% increase in sales
– Highest customer satisfaction scores in part availability in company history
• Scores increased every month
– 3,000+ hours of merchant time was re-allocated to vendor management, etc.

• Train models to predict which parts will sell in the next 12 months using parts stocked for the past 12 months (and if they sold or not)
The data used to evaluate the models is NOT the same as the data used to train the model.

• Using historical sales/lookup data we model part failure rates for each part type/make/vehicle type (e.g. Rotors – Ford – Truck)
Rotors

1.
Construct a trade area using radius (based on population)
2.
Aggregate store/part VIO (using fitment)
3.
Weight VIO by lifecycle
– 800 VIO -> 73 vehicles that will need the part next year at this store

• X months stocked without sales = REMOVE
• Propensity to sell > Y% with incremental VIO coverage of Z = ADD
• ADD SKUs can be constrained to space freed + some % of growth
Expected gross revenue for assortment changes = probability of sale * gross margin

• We mine millions of transactions to find parts that are required to complete the job (e.g. complete brake jobs, water pumps/timing belts, tune-ups etc.)
• High lift associations between parts are then forced into new assortments (if having an oil pump screen drives more sales (lifts) pump sales significantly then we add the screen along with the pump)
SKU 372 is purchased 534x more often if SKUs
540 and 705 are also stocked.

• Customers will wait longer for certain part categories (in-elastic demand)
• Forward position (store stock) elastic part categories (e.g. brakes)
• Re-locate in-elastic part categories (e.g. radiators) to DC or hubs, etc.

• Modeling process: We work closely with Merchandising to
– Identify potential sales predictors
– Validate model outputs (do ADDs/REMOVEs make sense?)
– Tweak assortment rationalization rules to meet market/company-wide objectives
• On-going basis: The system will generate company-wide assortment recommendations on a weekly or monthly basis (100% automated)
– Merchant organization receives reporting that helps them validate any changes
• Impact reporting at SKU/line/vendor level (add to X stores, removed from Y stores)
– Inventory management receives impact reporting to understand supply chain impact
• Impact reporting at SKU/DC level (added to X stores/DCs, removed from Y stores/DCs)

2 FTEs
• Modeling lead
– Preferably an internal FMP analyst that QueBIT will help train/mentor to build/tune/evaluate models
– Analytical leaning
– Good communication skills (will work closely with Merchandising, etc.)
• Data lead
– Strong knowledge of corporate data
– Strong SQL skills
– Reporting skills