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Demystifying Machine Learning for Manufacturing: Data Science for all


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The presentation looks into the application of machine learning in manufacturing

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Demystifying Machine Learning for Manufacturing: Data Science for all

  1. 1. Infosys Confidential1 |1 Internet of Manufacturing Midwest 2018 DemystifyingMachine Learningfor Manufacturing: Data ScienceforAll Jeff Kavanaugh June 7, 2018
  2. 2. Infosys Confidential2 |2 Internet of Manufacturing Midwest 2018 Today’s discussion Research Industry 4.0 maturity index and framework Future proof: learning and communication through data science and critical thinking Machine Learning and Analytics Background IIoT and AI in Practice Industrial IoT through facilities energy management Water Treatment Plant Automotive OEM predictive churn
  3. 3. Infosys Confidential3 |3 Internet of Manufacturing Midwest 2018
  4. 4. Infosys Confidential4 |4 Internet of Manufacturing Midwest 2018 Industry 4.0: Beyond the POC… the time to scale is upon us Industry 4.0 integrates the physical and virtual worlds through technology enablers, which brings the fungibility and speed of software to manufacturing operations. The potential value created by Industry 4.0 vastly exceeds the low- single-digit cost savings that many manufacturers pursue today (acatech, Infosys, BCG, McKinsey, et al). Disruptive technology enablers for Industry 4.0 are at a tipping point *McKinsey, acatech, Infosys, BCG research 100xdisruptive digitalinnovation is 100x faster than physical disruption* 25Bconnected things forecasted to ship by 2020.* 250Mconnected vehicles are forecasted to have some form of wireless network connection by 2020.* $421Bin cost and efficiency gains per annum $907Bin annual digitalinvestments $493Bin digital revenue gains per annum Industry 4.0 is changing manufacturing But are we ready?....
  5. 5. Infosys Confidential5 |5 Internet of Manufacturing Midwest 2018 • Industry 4.0 announced atHannover Messe 2011, butsystematic implementation still only 18% • Current speed of implementation places the 2022 goalof 46% at risk • Reason: data hurdles and piecemeal POCapproach – unclear path Approach toovercome barriers: 1. Evaluate your digital maturity 2. Proof of concepts to demonstrate business value, then scaledaction 3. Set clear targets 4. Prioritize measures thatwill bring the mostvalue to business 5. Demonstrate courage, persistence Industry 4.0: Global study conducted on operations efficiency as a driver for competitiveness Dimensions Maintenance Efficiency Information Efficiency Energy Efficiency Service Efficiency • Vast majority (82%) of companies areaware of the high potential in implementingIndustry4.0 concepts • 46% want to implement Industry 4.0 solutions systematically for enhanced asset efficiency by 2022 • Only 30% have implemented data-driven or intelligent services Potential recognizedSystematically implemented Partly implemented No awareness 2017 2022 Directional findings Source: Infosys and Institute for Industrial Management (FIR) at RWTHAachen study conducted in 2015 and updated in 2017. Sample size: 433 executives across industrial manufacturing sectors from China, France, Germany, UK and USA OPERATIONSEFFICIENCY The opportunity Performance Efficiency Engineering Efficiency 15% 4% 32% 18% 35% 32% 18% 46%
  6. 6. Infosys Confidential6 |6 Internet of Manufacturing Midwest 2018 Humans still matter! Industry 4.0 maturity is about more than the technology, and poor reasoning skills are constraining progress Their No. 1 complaint? Poor critical-reasoning skills A survey by PayScale Inc., an online pay and benefits researcher, showed 50% of employers complain that college graduates they hire aren’t ready for the workplace. Source: UTD research study December 2017 and PayScale Inc., 2016
  7. 7. Infosys Confidential7 |7 Internet of Manufacturing Midwest 2018 Industry 4.0 maturity drives significant efficiency improvement, and analytics is a fundamental requirement Near-Term Long-Term Computerization E.g. CNC milling machinebutnot connected Business applications connected to each other Up to date digitalmodel (Digital Shadow) to showwhat’s happening now BigData analyticsto understand rootcauses Advanced analyticsfor simulation& identification of mostlikely scenarios Automated decision makingand actions Source: Industrie 4.0 Maturity Index, acatechstudy supported by Infosys, 2017
  8. 8. Infosys Confidential8 |8 Internet of Manufacturing Midwest 2018 Machine Learning is an important component in Industry 4.0 analytics Applied Machine Learning Computer Vision Unstructured Text Analytics Other AI Offerings Deep Neural Networks Data Analytics Cognitive Time&AIInfrastructure Predict Categories? Labeled Data ? Classification Clustering / Anomaly Detection Predicting values ? Regression Dimension Reduction Y N Y N Y N High-Fidelity Speech Synthesis Video Analysis Image Insights / Comparison Names Entity Extraction Chat Bots Knowledge Management Language Translation Text Extraction
  9. 9. Infosys Confidential 9 Infosys Confidential|9 Internet of Manufacturing Midwest 2018 Machine Learning involves solving business problems using 25+ algorithms segmented into 4 groups Solving a Machine Learning (ML) problem depends on finding the right algorithms for the business problem Different algorithms are better suited for different types of data and different problems We have found the Python scikit-learn flowchart useful for selecting ML algorithms specific to business problem and available data Yes/No; quality pass/fail Addresses too much sensor data! Old Faithful (Describes relationships) Groups into similar characteristics
  10. 10. Infosys Confidential 10 Infosys Confidential|10 Internet of Manufacturing Midwest 2018 Machine Learning platforms (tools) have a large library of algorithms, designed to address different types of business problems Classification Regression Clustering and Anomaly Detection Dimensionality Reduction Identify category to which an object belongs Applications:spam detection, image recognition, quality P/F Support Vector Machine (SVM) Stochastic Gradient Descent (SGD) Classifier k-Neighbors Classification Random Forest / Decision Trees Predicts a continuous-valued attribute associated with an object Applications:forecasting,pricing determination Stochastic Gradient Descent (SGD) Regressor Lasso Ridge Regression Elastic Net Automatic groupingof similar objects into sets Applications: visualization, sensor feeds, efficiency improvement K-Means Clustering Gaussian Mixture Models Mean Shift Spectral Clustering Reduce the number of random variables to consider Applications: customer feed (Twitter) segmentation, groupingdata Randomized Principal Component Analysis (PCA) Kernel Approximation Locally Linear Embedding Spectral Embedding
  11. 11. Infosys Confidential 11 Infosys Confidential|11 Internet of Manufacturing Midwest 2018 Machine learning techniques are organized by ability to learn Supervised Machine Learning Raw Data Features / Labels Train and Evaluate Trained Model Deploy / Improve Unsupervised Machine Learning Raw Data Algorithms Cluster / Anomalies Use in supervised learning Reinforcement Machine Learning Goal Initialize Agent Environment Action Reward / Penalty Traditional Data Analysis Raw Data Use Model/ Improve Analyze and Write Rules  “If” and “Else” decisions designed by humans, coupled with functions ( e.g. Excel functions), to process data or adjust to user input  Changing the task might require a rewrite of the model  The training data one feeds to the algorithm includes the desired solutions, called labels  Based on learning, algorithm provides outputs for new real time inputs  Examples: Classification, Regression  Only the input data is known, and no known output data is given to the algorithm. They are usually harder to understand and evaluate  Examples: Clustering (Identifying topics in a set of blog posts, segmenting customers into groups with similar preferences, detecting abnormal access patterns to a website)  The learning system, called an Agent, can observe the Environment, select and perform Actions, and get Rewards in return, or Penalties in the form of negative rewards  Examples : DeepMind’s AlphaGo, Walking Robot, Automated Trader
  12. 12. Infosys Confidential 12 Infosys Confidential|12 Internet of Manufacturing Midwest 2018 Common machine learning use cases in a manufacturing context Order to Cash Core Manufacturing Procure to Pay Record to Report Demand estimation - order quantities Predictive maintenance Contracts analysis for named entity FP&A Forecasting Anomaly detection: credit risk Tech support / knowledge base Commodity price forecasting Real-time monitoring of foreign exchange Order entry automation In-line quality inspection Consistent supplier terms Automated inventory stocking for service truck Defect root cause and corrective action Long tail spend analytics Simplification and automation of manual services billing Production planning and scheduling Demand forecasting using sales pipeline
  13. 13. Infosys Confidential13 |13 Internet of Manufacturing Midwest 2018 Industry 4.0: Illustrative Case Studies 13 Industrial Examples
  14. 14. Infosys Confidential14 |14 Internet of Manufacturing Midwest 2018 Energy matters! Industrial IoT aids energy optimization in Infosys campuses 46% reduction in per-capita energy consumption over 8 years $100 million savings over 3 years • Chillers • HVAC • Generators • Elevators • Sewage Treatment plants • Solar power plants Large Campuses 80 million+ square feet Assets Managed Central Command Center Demand Management Digital Twin and Optimum Operating Conditions Predictive Maintenance Solution Approach Business Benefit Business Need Sustainability initiative at Infosys and implementation using IIoT solution
  15. 15. Infosys Confidential15 |15 Internet of Manufacturing Midwest 2018 Transparency Predictability AdaptabilityVisibility Path of development MaturityLevel/ BusinessValue Visibility Centralized command center for real-time visibility Real-time data acquisition Visibility to key operating parameters
  16. 16. Infosys Confidential16 |16 Internet of Manufacturing Midwest 2018 Transparency and the Digital Twin Analyzing performance – As Designed vs As Installed vs As Operated As Designed As Installed As Operated Transparency Predictability AdaptabilityVisibility Path of development MaturityLevel/ BusinessValue Plot of Critical Performance Parameters • Condenser Water Delta (leaving temp – entering temp) • Chiller Water Delta (leaving temp – entering temp) • Evaporator Small Temp Diff (Ref. Sat temp– Chiller Water leaving temp) • Condenser Small Temp Diff (Ref. Sat temp– Condenser Water leaving temp) • Chiller Working Hours Digital Model complements physical assets Study operating conditions, trends and performance
  17. 17. Infosys Confidential17 |17 Internet of Manufacturing Midwest 2018 Predictability Data Collection Data Cleansing Correlation Analysis Exploratory Data Analysis Event Detection Prognostics • Identificationof key performance indicators • Exploratoryanalysis and visualization of data • Event detection – Hotelling’sT-squared and quartile- based method • Prognostics– ARIMA model with xreg variable • Knowledge model development Implemented advanced analytics on chiller data for event detection and prognostics Transparency Predictability AdaptabilityVisibility Path of development MaturityLevel/ BusinessValue
  18. 18. Infosys Confidential18 |18 Internet of Manufacturing Midwest 2018 Example: greenfield waste water treatment plant’s pumping station • Plant: State-of-the-art waste water treatment plant in Europe • Three operating scenarios: 1) Average flow 2) Average + Industry peak flow 3) Peak flow (heavy rain, flood) • Three different design solutions for the pumping station to be analyzed Case 1: 4 big pumps and 3 small pumps (original design requirement from the Client) Case 2: 3 big pumps and 3 small pumps Case 3: 2 big pumps and 3 small pumps FOCUS The strict environmental permit must be fulfilled which means that the effluent from the pumping station to environment is not acceptable TARGET To find optimal design solution to fulfil the required availability and safety with minimum lifecycle cost EVALUATE To create a RAMS simulation model of the different design alternatives with different operation and maintenance scenarios RESULTS To find out design solution to fulfil the required availability and safety with minimum lifecycle cost
  19. 19. Infosys Confidential19 |19 Internet of Manufacturing Midwest 2018 Moving from a traditional RAMS* to AI-based RAMS design enabled faster decision-making with more accuracy Integrated Operation Defined design solutios: - Design basis, specification, objectives & requirements Supplier-specific Work Packages Selected WP- suppliers Technical Performance & Availability Operability & Maintainability Safety & O&M Cost Supply management Anomaly? Identify parameter Healty Baseline In-Situ MonitoringAlarm Parameter Isolation Failure DefinitionData-Driven Models Physics of Failures Model RAMS database Remaining Useful Life Estimation Yes Continue monitoring No Instrumented process Automated identification and data capture Application of Prognostics and Healty Management Drishti 4.0 Operational Excellence AI Platform RAMS Design Process System Design and Realization Processes Identification of critical RCM positions Definition of Maintenance Categories for RCM- positions Development and Planning of CBM, TBM and CM task Optimization of the Plant specific maintenance service program to achieve required availability and safety with minimum costs * RAMS = Reliability, Availability, Maintainability, Safety
  20. 20. Infosys Confidential20 |20 Internet of Manufacturing Midwest 2018 Visualization was an important tool to take decisions and interventions based on analytics recommendations Plant performance KPIs: RAMS and Risk Index Downtime insights: Troubled asset, reason for failures, cost savings
  21. 21. Infosys Confidential21 |21 Internet of Manufacturing Midwest 2018 Condition-based monitoring used AI to recommend pump maintenance and proactive interventions Results of RAMSanalyses of three design solution cases with currentmaintenance service program(without CBM= Condition Based Maintenance)and with Drishti 4.0* operational excellence AI platform (=with CBM) Pumping station operational time 30 a Case 1: 4 big and 3 small pumps Case 2: 3 big and 3 small pumps Case 3: 2 big and 3 small pumps Maintenance service program Without CBM With CBM Without CBM With CBM Without CBM With CBM Reliability and Availability requirementsare fulfilled YES YES YES YES NO NO Total Life Cycle costs (€) 2,307,358 2,107,910 1,743,175 1,568,232 1,486,811 1,308,584 Case 3: Not acceptable because of violation of environmental permit Case 2 with CBM: recommended designsolution 1)No environmental risks 2)LCC cost are 740k€less than the original design solution RAMS designsavings 565k€ With CBMLCC savings 175k€ Case 1 without CBM: The current design solution * Dhristri 4.0:
  22. 22. Infosys Confidential22 |22 Internet of Manufacturing Midwest 2018 Machine Learning in action: Churn Prediction MajorAuto Manufacturer
  23. 23. Infosys Confidential23 |23 Internet of Manufacturing Midwest 2018 • Purchased vehicle • Enrolled in trial (1 year) • Converted to paid subscription • Renewed paid subscription The customer digital services cycle can be defined in the shape of a funnel, and at each stage, there is churn. How do we reduce churn? • At each stage of the funnel, we lose customers – What can we do to reduce churn at each stage? • By using customer and vehicle data across each stage, we can use machine learning to predict a customer’s likelihood to churn (i.e. customer does not progress to the next stage of the funnel) Find my car Remote Climate Start Remote Lock/Unlock Typical cloud connected features Are there usage trends or customer behaviors thatcan predict a customer’s likelihood of churning?
  24. 24. Infosys Confidential24 |24 Internet of Manufacturing Midwest 2018 Initial subscriptions present the biggest opportunity for improvement Current annual sales: ~300,000 Luxury Brand Annual sales (projected 2019): ~1,500,000 Mass Market Brand Select models Increasing initial paid subscriptions is largest improvementopportunity 13% 33,600 vehicles Enrolledintrial and converted to paid 64% 243,200 vehicles Enrolledintrial but did not convert to paid subscription 23% 64,400 vehicles Did not enroll in trial 9% 136,800 vehicles Enrolledintrial and convertedto paid 46% 699,200 vehicles Enrolledintrial but did not convert to paid subscription 45% 684,000 vehicles Did not enroll intrial
  25. 25. Infosys Confidential25 |25 Internet of Manufacturing Midwest 2018 We gathered all relative usage and subscription data • We gather data for new vehicles that were sold and enrolled in a trial of one month (in 2016) – These vehicles were up for renewal in the following year, total vehicles in sample: ~24,000 Sep Oct Nov Dec Jan Feb Mar Apt May Jun Jul Aug Sep Vehicle Sale Service Renewal • Next, we collected all usage and sales data for these vehicles for the month before the renewal (~35,000 total commands) – What specific commands were used by each vehicle? e.g. Remote Lock, Remote Start, Vehicle Finder, etc. – What year / model was the vehicle? – What was usage on the weekday vs the weekend for each vehicle? – What metropolitan area was the selling dealer in? – Did the customer subscribe to paid services? Usage Analysis Trial Period 20172016 After getting the right data, we can build a model to answer the overarching question: Which customers will subscribe?
  26. 26. Infosys Confidential26 |26 Internet of Manufacturing Midwest 2018 After a number of tuning iterations, the model enables churn prediction on an individual basis • The classification model was tuned over multiple iterations using Microsoft Azure, in order to find the ideal level of accuracy and resiliency measured with the Receiver Operating Characteristic (ROC) – Certain data was removed from the model to improve model quality The tuned model enables us to determine the churn probabilityfor each customer Infosys Churn Model POC, 2018 This curve would indicate we could predict every single customer perfectly (impossible!) This straight line would indicate we are guessing randomly This is the accuracy of our model using limited data – this can only improve as we add more data points, e.g. demographics VIN: SN0001 Model: SUV MODEL A Weekday uses: 48 Weekend uses: 18 Remote: 0 Status: 0 Finder: 31 Lock: 35 Renewal probability: 73.9% Over the course of 100+ iterations, the machine learning algorithm uses the training set to build a decision tree based on the input data. Sample decision branches: • Is weekend usage greater than weekday usage? • Was the car purchased in an area with extreme weather? repeatcustomers extraneousdata fields(e.g. VIN)
  27. 27. Infosys Confidential27 |27 Internet of Manufacturing Midwest 2018 Net Profit (in 000's) PerYear, 1 yearold vehicles Only CampaignConversion Rate ## 2% 3% 4% 5% Probabilityofrenewal 5 $ 16 $ 38 $ 59 $ 81 10 $ 12 $ 49 $ 87 $ 125 15 $ (9) $ 44 $ 96 $ 149 20 $ (36) $ 30 $ 96 $ 161 25 $ (59) $ 13 $ 85 $ 158 30 $ (80) $ (3) $ 74 $ 150 35 $ (95) $ (15) $ 65 $ 145 40 $ (109) $ (26) $ 57 $ 141 We can now choose customers to address, to maximize profitability • Incentives are 4% effective • Customers < 20% likely to renew • Profit in first year: $96k • This is maximum cumulative profit for the scenario How effectivecustomer incentives are (“change their minds”) We choose the retention(renewal) probabilityof customers to address Additional net profit per year (000s) Based on incentive effectiveness, we can maximize value by choosing the targets This is relevant for many manufacturing scenarios involving diminishing returns 0% likely to renew 100% likely to renew Less than 10% likelyto renew ~22,000 customers (VINs) Less than 25% likelyto renew • We can choose which customers to reach out to • This enables better efficiency of resources VIN Scored Probabilities SN00001 0.121978149 SN00002 0.48944521 SN00003 0.054602593 SN00004 0.196847379 SN00005 0.128807783 Actual model output (VIN data isanonymized) Sample implementation using machine learning output* * Using Azure Machine Learning Studio
  28. 28. Infosys Confidential28 |28 Internet of Manufacturing Midwest 2018 Continuing the conversation…. Jeff Kavanaugh Partner, Manufacturing Infosys Consulting Adjunct Professor University of Texas at Dallas @jeffkav Foundational skills for learning in the age of AI (Amazon,B&N) /age-of-ai/