Philips - BDE

Transcript

1. Alexei Cherenkov
   How a boring accounting topic
   became the exciting data science / AI
   project
   

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2. © Koninklijke Philips N.V.
   Preface: Warranty and business
   2
   • Warranty? Of course, if my product is broken, I can repair it for free!
   • Warranty is the obligation of a manufacturer (or vendor) to repair items
   sold to a client during limited time since passing of the ownership (it is the
   term of sales contract)
   • Company must make a financial provision in its ledger to be able fulfilling
   the warranty, this is the law in almost all countries
   

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3. © Koninklijke Philips N.V.
   Some Philips products must visit repair workshop despite of
   lockdown…
   ―I need to bring you to the
   workshop, my little Ironny!
   ―No, mom, I don’t want to go!...
   ―We should! Cause it’s still
   warranty period. Philips pays. 57
   million Euro provisioned, don’t
   spoil this budget, let them spend
   it exactly!
   At the end, each little Iron must be counted…
   

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4. © Koninklijke Philips N.V.
   The story begins…
   4
   •CoNQ (Cost of Non-Quality) for B2C business unit
   •~50M / year
   •Estimated as linear trend with known expected corrections
   •So, what’s the alternative?
   •Bottom Up:
   –Total Cash-Out forecast to be estimated as the aggregation of
   forecasts for each product in each country.
   •Kaplan-Meier estimator
   –Wallace R. Blischke, M. Rezaul Karim, D. N. Prabhakar Murthy.
   Warranty Data Collection and Analysis (Springer-Verlag London
   Limited 2011)
   

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5. © Koninklijke Philips N.V.
   Kaplan and Meier present the estimator
   • The black curve (Fig. above) shows the share of products surviving during
   a time (horizontal axis, weeks) passed after purchase. Line is ended at
   106th week (length of warranty). In this example, approximately 96% of
   sold items survive over warranty period.
   • Red curve is Weibull distribution function extrapolation over empirical
   stepwise distribution.
   5
   All products of a category and
   country look different
   

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6. © Koninklijke Philips N.V.
   Is our data ready for Kaplan-Meier?
   6
   • How the claim process work?
   • Sell-in vs. Sell-out
   • Some products are not included in data
   –Spare parts
   –Bundles
   There is always some space for data science…
   

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7. © Koninklijke Philips N.V.
   Predict
   Train
   14-Sep-22
   Flying in a helicopter
   7
   Sell-In
   Projected
   claims
   Sell-In forecast
   Sell-Out
   forecast per
   product,
   2014-now
   Cash Out
   forecast
   Sell-Out
   (GfK)
   Transfor
   -mation
   rule
   K-M
   Survival
   model
   Claims
   Sell-In Actual
   Avg
   Cash
   Out per
   product
   QI
   Bundles
   Country aggregation rules
   Sell-Out 2014-2023
   

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8. © Koninklijke Philips N.V.
   Data is the new oil, but oil quality differs a lot…
   • Actual Sell In from 5 different databases (legacy+current+very new)
   • Sales plan (till end of the year, requires artificial 24 prolongation based
   on strategic plan)
   • Warranty Claims (Invoices) – World, China, USA. Note: 3 warranty
   business models
   • Product hierarchy (master file)
   • Bundle hierarchy (master file)
   • Quality improvement schedule
   • All databases have different country granularity (grouping)
   8
   Huge volume
   of data Not enough
   data
   

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9. © Koninklijke Philips N.V.
   Better than perfect
   • Poor products are passing improvements at factory
   • This drives to the appearance on the market of product
   modifications / generations
   14-Sep-22 2019, Philips confidential
   9
   

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10. © Koninklijke Philips N.V.
    Time granularity
    • Sell-in and sell-out is monthly data
    • GCS delivers exact claim date, with strong weekly pattern
    • One of the requirements: early forecast of bad behavior of NPIs, so
    the model must be built on weekly level
    • COVID-19 impact
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