Real World Applications of Machine Learning and Artificial Intelligence A Look at Retail Businesses

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1 Real World Applications of Machine Learning and Artificial Intelligence A Look at Retail Businesses Prof. Dr. Michael Feindt Professor of Physics, Karlsruhe Institute of Technology (KIT) Founder and Chief Scientist, Blue Yonder GmbH

2 Combination of Big Data with Data Science, Machine-Learning and Artificial Intelligence, to reinvent and revolutionalize business processes in retail. Basis: CERN

3 2 main branches in AI: brute force silicon Machines learn, what humans can do easily (see, understand, drive a car). Moore s law + deep neural networks intelligent algorithms Construction applying domain knowledge for concrete problem settings, optimized and adapted by Machine Learning on the basis of observed or simulated data. Machines become better than the best human experts 3

4 Start Idee NeuroBayes Idee Hintergrund Ziele NeuroBayes f(t x) Beispiele Historie Anwendung Prinzip Funktion Beispiel Konkurrenz Projekt l Forschung Projekt ll Ablauf Spiel Summary A AB Very clear signal enrichment of the X(3872) (discovered 2004) by NeuroBayes 4 Prof. Dr. M. Feindt NeuroBayes / Anwendungen bei Gewährleistungen 22

5 NeuroBayes B s to J/ψ Φ selection without MC (2 stage background subtraction training process) soft cut on net 1, all data input to second NeuroBayes training soft preselection, input to first NeuroBayes training cut on net 2 5

6 Exploiting S/B information more efficiently : The splot-method CDF Run II preliminary 2 Fit data signal and background in one distribution (e.g. mass). Compute splot weights w s for signal (may be <0 or >1) as Candidates per 0.5 MeV/c S B function of mass from fit Train NeuroBayes network with each event treated both Mass(p K + ) [GeV/c 2 ] as signal with signal weight w S and as background with weight 1-w S. Soft cut on output enriches S/B considerably: Make sure network cannot learn mass! 6

7 Prescriptive analytics even automates the work of 400 world-class research physicists: improvement > +100% Meta-Analysis: What does a scientist do in the analysis of data from particle collider experiment? àautomatic hierarchical reconstruction system with 72 NeuroBayes-networks rekonstructs1100 different reactions with a factor 2 better efficiency relative to all analyses performed during 10 years by world-wide 400 physicists together! Ø Work by Artificial Intelligence and 3 PhD students Ø Corresponds to 500 normal PhD theses Ø Corresponds to another 10 years of data taking (costs 700 M ) Photo: CERN 7 Belle II (2017): factor4, full reconstruction directly at data taking

8 Predictive applications on a chip blue yonder & KIT Use case: Belle II pixel detector at KEK particle collider CERN: about computers in parallel reduce data by about 1/100 million Next generation particle collider in Japan: so much data that it cannot be read out and distributed on computers any more. Solution: Implement Blue Yonder NeuroBayes decision algorithm on a chip Implement one such chip per sensor hardware module Chip decides which part of the detector is read out. World record: 8 billion decisions per second achieved. Einführung 8 in die Datenanalyse Michael Feindt Maria Laach 2004

9 Blue Yonder = Digital innovation / disruption from Germany, originating from CERN Digitalisation: Value through data and (scientific) software

10 Important personal and professional decisions: gut feeling 0%

11 Operational decisions e.g. in retail: automatic and data driven by AI 99%

12 Repeated decisions Order? Price? Catalog? 12

13 do nothing business rules think

14 Human brain: (1) fast, intuitive (2) slow, rational

15 Human decision making: biases, biases

16 Predictive Analytics Prescriptive Analytics = Disciplines of machine learning ML / artificial intelligence AI

17 data predictions cost/utility optimisation automation 17

18 When When predictive Predictive analytics? Analytics? Pendulum Pendulum (deterministic) (completely deterministic) Predictive Analytics 100% Lottery Lottery (purely random) (purely random) individual predictability individual predictability 0%

19 When When predictive Predictive analytics? Analytics? Pendulum Pendulum (deterministic) (completely deterministic) Predictive Analytics 100% Lottery Lottery (purely random) (purely random) individual predictability individual predictability 0%

20 Many influencing factors Weather forecast Weather Individual customer promos Brand Price Day of the week Promotion rules Local holidays Local competitor Seasonality Competitor price Vendor/retailer promotions Print/online promotions Competitive articles Coupon promotions TV ads 20

21 Blue Yonder Predictive Analytics STANDARD / LEGACY APPROACH BLUE YONDER APPROACH f(t x) < N > σ N max < N > N 0.05 N 0.95 t Prediction is a number (no uncertainty measure) Prediction is complete conditional probability density function pdf allows risk management contains all information

22 Prescriptive Analytics 37, ,5 15 7, optimisation Recipe 37,5 30 Decision Decision order 8 22,5 15 7, decide optimally, given KPI

23 on many individual events Items Stores Days

24 Many predictions / optimisations / recipes > automate!

25 low high You can only improve one KPI - by hurting the other Write-Off low Out-of-Stock high

26 low high You can only improve one KPI - by hurting the other low OOS many write-offs Write-Off low Out-of-Stock high

27 low high You can only improve one KPI - by hurting the other Write-Off few write-offs high OOS low Out-of-Stock high

28 low high Global strategy will stay a human decision Write-Off low Out-of-Stock high

29 low high operational decisions automatically aligned to strategy working point -> huge improvement Write-Off Status quo New optimum low Out-of-Stock 29 high

30 Influence of automation 7% 7,5% out-of-stock items (original situation) 6% 5% 5% out-of-stock 4% 3% 2% 0.5% out-of-stock 1% 0% OOS rate in German supermarket chain at constant overall stock level and waste rate

31 AI-Supply Chain in Retail 99% Automation Write-offs / waste Freshness Capital Out-of-stock Turnover Efficiency

32 Morrisons Supply Chain

33 Make our customers work smarter: Store of the week: the new system has made a big difference not just in terms of,maintaining great availability but on the amount of stock we need to keep Morale is really high in the store 33

34 Stock price development 2 years Start using Blue Yonder AI MRW (Morrisons) FTSE 100 (UK stock index) FTSE NMX5370 (UK retail stock index)

35 Results. From Morrison HY result press conference Sept 2017 David Potts, CEO our customer satisfaction had improved year-onyear by 5%. This independent survey score scores Morrison s especially high on service availability and the quality of our fresh food. Our new automated stock ordering system is also improving availability and freeing up time for our colleagues to serve customers better. We re continuing to refine and improve the ordering system which will now be in all stores and most categories in time for Christmas. Trevor Strain, CFO In the half we made significant cost savings across four areas. Automated ordering, which is eliminating wasted effort, saving labor hours, improving availability, and reducing stock holding. We expect the full year benefit from mid-18, longer term we have the opportunity to utilize some of the benefits from more automation in our manufacturing business and in our warehouses.

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37 AI-fresh meat SC automation First mover: Kaufland (Lidl-Schwarz group) Medium term predictions of BY orders strongly improve efficiency of supply chain up until production plants. Made possible Europe s most modern meat factory with reduced storage and improved effective shelf life in POS

38 Predictive Shipping for online market place >99% Automation Delivery 5-7 days > 1-2 days Turnover Customer satisfaction Returns Storage Deliveries per order 38

39 The effect of price on demand Price Customer Weather forecast Demand Local competitor Weather Day of the week Seasonality Competitor price Promotion rules Local holidays Brand Competitive articles Vendor/retailer promotions Coupon promotions Print/online promotions TV ads Individual customer promos 39 #RAAIS2017

40 Training target may be a functional: price elasticity Usually given by demand at reference price slope at reference price

41 AI-pricing in retail 99% Automation market share turnover raw profit customers / new customers returns 0 complaints rests at end of season

42 »For every complex problem there is an answer that is clear, simple, and wrong.«h.l. Mencken CAUSALITY and CORRELATION 42 Einführung in die Datenanalyse Michael Feindt Maria Laach 2004

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45 Do customers buy because they received a catalog? or Do customers get a catalog because they usually buy? Correlation is not enough: need extraction of causal effect of action on customer behavior. Possible with A/B testing and modern causal AI algorithms. Will improve customer targeting greatly. Can achieve higher turnover at lower marketing costs.

46 Vertical end-to-end AI solutions Specialized providers combine expertise and experience in solving complicated business problems by narrow AI/ML Combine expert domain knowledge with AI/ML and enterprise software expertise to package AI into consumable, robust solutions for specific tasks ready to deliver proven value and competitive advantage fast. Hide complexity. Provide as a service in the cloud. As Intelligence Layer on top of existing ERP system. Make people work smarter. Blue Yonder is a first mover in this area.

47 Prof. Dr. Michael