Manufacturing Analytics in the Age of IoT

Transcription

1 Manufacturing Analytics in the Age of IoT Mike Alperin September 2016 Copyright TIBCO Software Inc.

2 Speaker Mike Alperin Principal Industry Consultant Data Science Team Copyright TIBCO Software Inc. 2

3 Agenda TIBCO Analytics Product Quality Manufacturing Operations Resource Optimization Customer Analytics Copyright TIBCO Software Inc.

4 TIBCO Analytics Copyright TIBCO Software Inc.

5 People & Processes Reporting & Dashboards APIs Data Visualizations Data & Systems Streaming Analytics

6 Visualizing Data in Spotfire

7 Smart Visual Analytics Recommendations Copyright TIBCO Software Inc.

8 Analytics Journey: Insights Action Business Case Assemble Data Explore Present Develop Model Decision, Action Value Theses Data Wrangling Signals Dashboards Model Prediction Action Supplier Quality Product Grow Revenue Efficient Factory Process Equipment Product Sales Visualize Clean Shape Merge Enrich Filter Yield Defects Downtime Warranty Claims Pressure Temperature Product Measurements Production Volumes Equipment Failure Product Quality Copyright TIBCO Software Inc.

9 Enterprise Data Access SAP R/3 Oracle E-Business SAP BW Siebel ebusiness SQL Server Oracle MySQL Teradata Netezza JDBC/ODBC Databases Local data sources Excel Access STDF Drag-and-drop Salesforce OBIEE Hadoop SFDC In Memory 100s of millions of rows Web Services XML PostgreSQL Etc. Flat Files Spreadsheets RDBMS RDBMS RDBMS RDBMS Databases Teradata MS SSAS Oracle TeradataAster Big Data: In-Database Billons of rows MySQL Netezza Hadoop Etc. Custom GUI-driven data access via SDK Direct connection ODBC OLE DB SqlClient

10 Spotfire Big Data Architecture Visualise Calculate Copyright TIBCO Software Inc.

11 Spotfire Data Connectors Amazon Redshift Apache Spark SQL (Databricks Cloud, Apache Hadoop, Cloudera, Hortonworks, IBM BigInsights, MapR) Cloudera Hive Cloudera Impala Cisco Information Server Google Analytics (inc Business Author) Hortonworks (Apache Hive, BigInsights Hive, MapR Hive) HP Vertica IBM DB2 IBM Netezza Microsoft SQL Server Microsoft SQL Server Analysis Services OData Oracle Oracle Essbase Oracle MySQL OSI PI Pivotal Greenplum Pivotal HAWQ PostgreSQL Salesforce.com (inc Spotfire Business Author) SAP HANA SAP BW (NetWeaver Business Warehouse) Teradata Teradata Aster TIBCO : OpenSpirit, AS, LDM, Copyright TIBCO Software Inc.

12 Advanced Analytics: Model and Predict SAS MATLAB TERR Aster Open Source R Spotfire Knime Predictive analytics for Analysts and Citizen Data Scientists Built- in TIBCO Enterprise Runtime for R (TERR) Big Data Ecosystem

13 TERR and Spotfire What does TERR do in Spotfire? Runs TERR Data Functions in Spotfire analyses Powers the Predictive Modeling Tools Powers the Forecast tool Can be used directly in Expressions TERR is embedded in Spotfire Analyst/Desktop No other software required, no connection to server required Copyright TIBCO Software Inc.

14 Advanced Analytics: TIBCO s Enterprise Runtime for R (TERR) TIBCO has rewritten R as a Commercial Compute Engine Latest statistics scripting engine: S a S-PLUS a R a TERR Runs R code including CRAN packages Engine internals rebuilt from scratch at low-level Redesigned data objects, memory management High performance + Big Data TERR is licensed from TIBCO TERR Installs (free) with Spotfire Analyst / Desktop and other TIBCO products (CEP, Stats) Spotfire Server can manage all TERR / R scripts, artifacts for reuse Standalone Developer Edition: Supported by TIBCO Copyright TIBCO Software Inc.

15 Copyright TIBCO Software Inc.

16 Selected Spotfire Manufacturing Customers

17 Manufacturing Use Cases Sales & Marketing Supply Chain Manufacturing Operations Product Quality & Reliability Field Service & Support Customer Needs Product Requirements Market Segmentation Propensity many others Demand Forecasting Order Management Inventory Optimization Supplier Performance Transportation Analytics Early Warning Systems Real-time Equipment & Process Monitor Process Capability Optimize Maintenance OEE & Factory Productivity Real-time Quality Monitor Root Cause Reliability Warranty Optimize Maintenance Repair & Customer Service Analytics Supplier Performance Order Management Inventory Optimization Transportation Analytics Copyright TIBCO Software Inc.

18 Data Access and Mashup enable Analytics Supplier - Incoming Materials and Components: measured electrical, chemical, physical characteristics Batch ID Manufacturing Process Product, Revision, Unit / Batch ID Physical, chemical or electrical measurements WIP / MES: track-in / track-out date, equipment id, recipe, Process equipment sensor data Equipment Maintenance logs Defect Inspections Product Quality and Reliability Product test results Accelerated life test results Failure mode Failure analysis root cause results Field Service Product usage patterns Call Center: Warranty / Repair claim / call center structured & unstructured Social Media - Customer sentiment Finance Costs Sales External Sources Customer Demographics Weather Copyright TIBCO Software Inc.

19 Product Quality & Reliability Copyright TIBCO Software Inc.

20 Product Quality and Reliability Problem Producing defective, unreliable or poor performing product Shorter lifecycles faster new product ramps Value Increase Yield / % Good product Reduce Defects & Rework Improve Reliability Use Cases Quality Monitors and Dashboards Root Cause Analyses Root Cause: Equipment Commonality - Machine Effects Copyright TIBCO Software Inc.

21 Machine Learning to Predict Equipment or Product Fails Problem Value Method Product & Equipment problems difficult to accurately diagnose for complex manufacturing processes Big Data problem millions of units, hundreds / thousands of predictors Response: Product, Process or Equipment Fail data Predictors: in-process equipment, process and product measurements or attributes Being used by customers to find previously undetected problems. Reduces time-tomarket and increases profit. GBM analysis template to identify significant predictors, interactions and nonlinearities For large datasets, hybrid data access used to perform variable reduction step in-db Simple interface easy for business analyst to run and interpret results GBM results for semiconductor yield as a function of in-process equipment & product measurements

22 Semiconductor and Electronic Component Yield Example: Hard Drive Manufacturing Problem in week 17 Yield drops from 96% to 55% Production reduced from 70K to 3K drives Machine Learning Model Parameter linked to head is primary culprit Publish Model to Event Server to monitor Copyright TIBCO Software Inc.

23 Real-time Predictive Analytics for Process Cost reduction Goal: Scrap parts as early as possible to reduce costs in a manufacturing process. Question: When to scrap a part in Station 1 instead of sending it to Station 2? Station 1 Station 2 Cost Before Total Cost 29 (or more) Scrap? Scrap?

24 Spotfire with H2O Integration: Big Data Machine Learning Advanced Analytics ( Scrap parts as early as possible! )

25 Deploy real-time model: TIBCO Live Datamart & Streambase Operational Intelligence ( Monitor the manufacturing process and change rules in real time! ) Live Dartmart Desktop Client

26 Process Capability & Control Use Case Areas Assess Process Capability Process Control: Detect changes from baseline Root Cause Analysis Spotfire Solutions Classic: Shewhart charts Advanced: multivariate, Predictive models Root Cause: Basic to Advanced Automated Alerting: Periodic or Real-time Process Capability Summary with drill-down Copyright TIBCO Software Inc.

27 Solar Cell Manufacturing Business Problem Solar Cell Manufacturer 600K Wafers / day Immediately Troubleshoot Process when producing bad product TIBCO Solution Real-time Monitoring of Equipment and Product critical metrics: temperature, pressure, resistivity Alert when metrics beyond limits Root Cause Analysis to improve process Results Improved solar cell efficiencies Reduced equipment downtime For every 1% increase in shipped product, we make $11MM in profit. The demand is there, we just need to fulfill it. - Head of Quality, Solar Panel Manufacturer Copyright TIBCO Software Inc.

28 Real-time Operational Visibility & Alerts: Tibco Solution Real-time operational visibility - Detect problems in a timely manner Automated Alerts for Critical Equipment & Product Alarms Diagnostic Analysis Real-time Monitor ( ) Alert Drill-down

29 Examples of Notifications and Alerts Dashboards Drill-down to Root Cause Analysis Copyright TIBCO Software Inc. Mobile Devices

30 Automotive Warranty Early Warning System Business Problem Major Automotive Manufacturer Costs of producing unreliable product TIBCO Solution Rapidly detect and respond to warranty problems For every component: Model Fail rate Use model to predict Field Fail rates Alert when Field Fail rates exceed predictions Model: Fail rate = f(production date, days in service, etc) Control Chart showing actual and predicted fail rates for a component Problems / 100 Vehicles (PP100) by Production Month UCL Copyright TIBCO Software Inc.

31 Manufacturing Operations Copyright TIBCO Software Inc.

32 Manufacturing Operations Problem Value Max value from expensive, complex automated equipment Lowest cycle times Effective resource utilization Quality Product Minimize manufacturing costs Use Cases Real-time Operations Monitoring Optimize Maintenance Factory Productivity 1950 s Ford Assembly Line Tesla Factory Copyright TIBCO Software Inc.

33 Manufacturing Operations OEE Management Dashboard Copyright TIBCO Software Inc.

34 Manufacturing Operations Real-time Operator Dashboard Copyright TIBCO Software Inc.

35 Manufacturing Operations Availability Losses Copyright TIBCO Software Inc.

36 Evolution of Equipment Maintenance Strategies Reactive - Run to failure Preventive - Scheduled service Condition-based - Monitor to assess condition Predictive - Predict failures Proactive - Root cause analysis Copyright TIBCO Software Inc.

37 Preventive / Scheduled Maintenance Business Problem Identify equipment with high failure rates Determine expected lifetimes Determine optimal maintenance interval TIBCO Solution Model Fraction Failing vs. Time in Service Determine optimal maintenance interval Equipment with high failure rates Failure Analytics Optimal Maintenance Interval Copyright TIBCO Software Inc.

38 Building and Deploying Predictive Maintenance Models TIBCO Spotfire DATA PREDICT TIBCO Streambase WRANGLE DECIDE Insights Actions ANALYZE ACT MODEL MONITOR Copyright TIBCO Software Inc.

39 41 Condition-based / Predictive Maintenance Objective Predict: Machine outage Defective product Predictors: Machine sensor data Process measurement data Environmental data Predictive Maintenance Model from Sensor Data Build Predictive Model

40 Streambase for Equipment Monitoring Load Reference Data to be used in Rules/Alerts, Cleaning, or Anywhere else Publish and Expose Clean Data to Whole Organization Continuously Build Features and Publish Output to BPM Systems, Web Services, Databases, and anywhere else! Continuously Publish Summary Statistics for Analysis Publish Raw Events to another StreamBase Workflow or other Applications Copyright TIBCO Software Inc.

41 Data to Insight - Real-Time Analytics on Pumps Well names redacted Copyright TIBCO Software Inc.

42 Resource Optimization Copyright TIBCO Software Inc.

43 Production Demand

44 Production Optimization

45 Constrained Route Optimization Copyright TIBCO Software Inc.

46 Warehouse Shipment / Order Analysis Visualize bottlenecks and current status of orders on facility maps. Analyze task time and forecast ability to meet SLAs; orders at risk Prioritize issues with visualization and root cause analysis Identify slack resources to reallocate

47 Warehouse: Pick, Pack, Ship & Waves

48 Customer Analytics Copyright TIBCO Software Inc.

49 Customer & Marketing Analytics Market Analytics Pricing Promotion Campaign Effectiveness Forecasting Market Mix Media Attribution Market (Syndicated) Data Store & Distribution Analytics Store Clustering; geospatial modeling Store Performance Forecasting Effects: Price, Promotion Distribution: Pick, Pack, Ship Store and DC Data Customer Acquisition Relationship Growth Customer Lifecycle Customer Retention Consumer Analytics Segmentation Propensity Affinity & Association Social: Sentiment & Intent Churn Loyalty Cross-sell / Up-sell Test & Learn (A B testing) Online Analytics (Path, Cart Abandonment, ) PoS, Panel Loyalty Data Copyright TIBCO Software Inc.

50 Customer Segmentation Hardware Store Top Shopper 27% of customers & 35% of revenues Broad purchase behavior Budget Minded 34% of customers & 29% of revenues Highly focused on core building categories Outdoor Plus 15% of customers & 16% of revenues Mainly outdoor, but other spending Gardener 10% of customers & 5% of revenues Primarily garden Seasonal Shopper 11% of customers & 12% of revenues Very event oriented Pool Customer 3% of customers & 4% of revenues Very focused on pool and patio categories Copyright TIBCO Software Inc.

51 Segmentation - Cluster Analysis using historical purchase patterns Variables driving segments - Random Forest Customer segments - Cluster Analysis

52 Propensity to Buy Customer Success Story Objectives: Select most important Response Products to highlight in 2015 Holiday season direct marketing Identify and quantify predictive significance of Driver Products based on historical data from 2014 sales Build campaigns for as many people as possible that are relevant

53 Propensity to buy models

54 Results Same year repeat visits are 3x higher for customers targeted in the campaign Average order value is much higher Year over Year repeat visitors is double

55 Telco Machine Learning Churn Model

56 Telco Offer Design Maximize Profit

57 Attrition and Value Models Predicted Prob(attrition) = f (X, b) Y variable Attrition (Y/N over time period) X variables How long a member Website interactions - section Prior spend Time since last interaction Experian: demog, f function Additive Model Random Forest, Gradient Boosting Variable Names Redacted

58 Call Center Real-time Alert Actions

59 Real-Time Web Interactions / Offers No Login = No Customer History => Offer based on Product Association Sarah Login = Sarah s Customer History => Offer based on Propensity Model scored for Sarah Copyright TIBCO Software Inc.

60 TIBCO Community Wiki Copyright TIBCO Software Inc.

61 Thank You! 64