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

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© 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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© 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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© 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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© 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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© 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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© 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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© 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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© 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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© 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 10