Predictive Decision-Making through the Power of AI Xavier Health AI Summit 2018

Transcription

1 Predictive Decision-Making through the Power of AI Xavier Health AI Summit 2018

2 Imagine this is a medical device

3 Under the case it s a

4 Now imagine the processes to develop it Design and Design Controls Simulation of Product

5 Now imagine the processes to develop it to make it Production Planning, Design, Simulation and Supply Chain Management Digitalization of the DHR

6 Now imagine the processes to develop it to make it to surveil it Post Market Surveillance Service History and Tracking

7 Now imagine the processes to develop it to make it to surveil it to change it Pre-Production Data Sustaining Engineering Production Data Predict what might fail based on GOOD HISTORICAL DATA? Post-Production Data

8 Now imagine the processes to develop it to make it to surveil it to change it to learn from it

9 Product Intelligence

10 Shipping & returns Shipping & returns Shipping & returns Shipping & returns Shipping & returns Decision Complexity in the Total Product Lifecycle DESIGN MANUFACTURING DISTRIBUTION - USERS - PATIENTS Product 1 Product 2 Product N

11 Answers Needed to Critical Questions Every Day Design Manufacturing Quality Ship and return What kind of experience do our customers have with our products? Customer feedback How much visibility do we have of performance of suppliers and distribution channels? When complaints occur how can we trace them back though manufacturing and design? What do we know about quality relative to plant or production line?

12 Answers are in the Data but Challenges Persist Legacy Systems Prolific data silos Poor data quality Massive data volumes Regulatory constraints Lack of in-house data expertise Increasing device & process complexity

13 The future with Product Intelligence Descriptive Diagnostic Predictive Prescriptive

14 Feeding Insights Back to all Stakeholders Product design Production planning Production engineering Production execution Product & Plant Performance Suppliers

15 Product Intelligence Functional View ALL DATA SOURCES Supplier Mfg Sites ODMs CMs Service Call Center Service Repair Depot OF ANY DATA TYPE Supplier Test & Ship Data As Built Data Dispatch & RMS Data Assembly Test Data Field Device Data IoT/Call Home Data Design Spec Data Failure Analysis Data Repair & Refund Data Service Data UNIFIED TOGETHER Product Intelligence FOR EVERY STAKEHOLDER DESIGN MANUFACTURING QUALITY PROCUREMENT ENGINEERING FIELD

16 Predicting the Patient Experience Dell at MDIC At recent launch of Dell XPS13 An engineer noticed the LCD was flickering on 2 of 6 demo Units. Michael Shepard Sr. Strategy Director Demonstrating the power of Analytics at the Speed of Thought Used Product Intelligence to identify and isolate the problem in 3 hours vs 3 days. Firmware issue contained the following day.

17 Decision Velocity / Engineering Cost Product Intelligence Discovery Michael Shepard Possible dimensions analyzed (per year per person) Sr. Strategy Director 192 thousand with traditional BI vs billion with Product Intelligence Discovery Product Intelligence Search Traditional BI Excel

18 Data Collaborative Model to Unite Business Stakeholders and IT Product Intelligence Users Predictions / Results Dashboards Unrestricted Data Scientists Extended Users

19 To Drive Real Value Close the loop between product design and performance Discover emerging product trends to prevent complaints and recalls Eliminate time and costs required to repeatedly consolidate and search big data Empower data-driven decision making across the organization Improve the patient experience

20 Case Studies

21 Case Study Field Failure Root Cause Analysis Cartridge Reliability is impacting bottom line and customer satisfaction Business Problem Cartridge failure rate in the field Bottom line impact annually Existing root cause analysis is time consuming and requires expert knowledge Expected outcome Automated Root Cause analysis Proactively mitigate the causes 20% failure reduction

22 Cartridge Reliability Data Sets Factory Season IoT / Machine Specs Test Event Shipping Event Build Event Cartridge S/N 1234 Service Event Part Lot # 567 Part Lot # abc Reagent Valve Region Date Type Supplier Event Supplier Event OOB Failure Region Plant Lot Version Carrier Transport Region Cartridge Is a P/N ABC Part s/n xyz Is a Sensor Test Event Parametric DateTime

23 Monitor Find issues as they emerge Built here Failed here

24 Discovery - Root Cause Analysis Discovery automatically find the Component Lot 1701 accounts for 28% of field failures in 2017

25 Identify affected cartridge population Early Identification Recall or Replace Prevent further lot usage Layout the entire population distribution through few clicks: Unused components in stock In process product In stock product In transit product Delivered product Failed/returned product

26 Rate of Cartridge Failures Mitigate the Issue Earlier Containment Reduced Exposure and Patient Risk Time in Field

27 Case Study A Transformative Journey To improve operational excellence, predict plant performance and global quality process standardization

28 Diverse Enterprise data flow drives KPI s US UK China Brazil India MES Data Work Order Header: Site ID, WO Number, WO issue date, Product code, Product Desc, Product family, Machine ID Operation Code, Operation Name, Op Date time, Machine List, Employee ID, Quantity Work Order Scrap: Site ID, WO Number, WO issue date, Product code, Scrap Qty, Scrap Date time, Scrap Reason, Scrap Operation Code Common Process Semantic Layer Machine Data Operation History: Site ID, MachineID, Datetime, TagID, TagName, TagType, TagValue, UOM Machine Status History: Site ID, MachineID, Datetime, Status, Duration Employee Time & Attendance Site ID, EmployeeID, FirstName, LastName, WorkGroup, Superviser, Clock DateTime Master Data Operation Code master & lookup Product Code master& lookup Operation Standard Cycle time New Data Sources To be defined Product Intelligence Analytics Standard Time OEE Scrap Non- Conformance Machine Availability

29 Plant to Plant Comparison Taipei generally has the lowest OEE values but at the same time shows the most improvement Charlotte starts with the best OEE values but loses ground Plants 3 and 4 show modest increases in OEE Why is Charlotte trending down? OEE = (Good Count Ideal Cycle Time) / Planned Production Time

30 Issue Isolation Drill down to decompose the OEE KPI back into its components to look for correlations: Availability Performance Quality The drop in Charlotte plant OEE closely correlates to a corresponding drop in Availability. How do we diagnose Availability?

31 Product Intelligence Discovery Single Plant Availability parameters: Months Resin Work Center Eng Series Product Line 7 Values 158 Values 160 Values 261 Values 9 Values Possible Combinations > 400 Million! Discovery reveals the outliers in real time hidden data combinations that are the root cause drivers of poor availability

32 The Journey Reveals Data Gaps Joins Processing Scrap Machine Event Attendance Machine IoT Processing N/A Work Order Number Machine Name 1 Scrap N/A Machine Name 1 Employee Number 2 Employee Number 2 Machine Name 3 Machine Name 3 Machine Event N/A None Machine Name 3 Attendance N/A None Machine IoT N/A Green Able to join events and analyze multiple fields Yellow Able to join events and analyze limited fields Red Not able to join on key fields required for analysis

33 Providing a Solution Roadmap to automate Financial KPI s Joins Processing Scrap Machine Event Attendance Machine IoT Processing N/A Quality (Yield) OEE Employee Utilization Scrap N/A Machine Name 1 Employee Number 2 Machine Performance Machine Name 3 Machine Event N/A None Machine Name 3 Attendance N/A Machine IoT N/A Green Able to join events and analyze multiple fields Yellow Able to join events and analyze limited fields Red Not able to join on key fields required for analysis

34 Learnings

35 Learnings Increasing product and process complexity leads to quality failure modes that are often unknown Integration of GxP and non GxP systems radically improves ability to problem solve with big data Automated discovery of root cause leads to faster response and better baseline for Predictive Learning

36 Nick Ranly Jeff Spencer DJ Deng Realize innovation.