1. IoT Total Solution Management Shannon Craft – J&J Global eCMMS Lead Matt Boehne - Banetti

2. Introduction While J&J is standardizing their global work and asset management under the eCMMS Maximo project led by Shannon Craft, there still remains a significant gap in the ability to see true overall global operations. In order to fill this gap, J&J is leveraging IBM Watson Analytics and IoT toolsets with the Banetti IoT TSM expertise to gain deep understanding and control over the breadth and depth of all Intelligent Assets deployed in the J&J Enterprise.

3. Historical Overview eCMMS Phase 1

4. Purpose: Develop a standard ME2 based solution to support J&J Enterprise best practices for asset management Vision: Develop a platform for growth, innovation and site maturation utilizing a phased approach Mission: Onboard all J&J Sites Worldwide to a single, standardized platform that allowed for a ROI to business 38 Target sites January 2012 - December 2013 Journey and Foundation

5. Rooted in ME2 (Manufacturing Equipment Excellence) Culture change 15 years in the making Goal: Build best practices into a CMMS Challenge: Select best product to build on (Maximo vs SAP) Method: Analysis, Voice of Customer, Demonstrations, Research 17 different customized CMMS’ were brought into the standard platform Managed organizational culture change across all sites Provided ROI back to site business Phase I Implementation

6. eCMMS Phase 2 Historical Overview

7. Continue success of Phase I which include: Phase I Site Maturation (while Site is Live) System Usage Process Understanding and Adoption Continuous Improvement Selected another 36 sites Prepare for onboarding to eCMMS (Before Go-Live) Includes Process Understanding and Adoption System Utilization Continuous Improvement Phase 2 Implementation

8. Development of 5-10 Year Site Plan to include Cloud AMR (provided by IoT) Mobile Suite EDAM Analytics (provided by IoT) Total Solution Management (TSM): A Self Sustaining Turnkey Solution for Device/Asset Lifecycle Management Phase 2 IoT Expansion

9. October 2014 J&J, Banetti and SCHAD Cordis production facility in Miami, FL POC Scope Automatic Meter Readings Mobile SCADA Alarm Management Mobile Work Management IoT / AMR POC

10. eCMMS Phase 3 Historical Overview

11. Sustainability & CIP Process for Phase 1 and 2 Site Maturation (100+ global sites live) Selection of 65 Sites Prepare for eCMMS Go-Live Same process that was utilized for Phase 2 Implementation of IoT TSM Plan Hybrid Internal/External Cloud IoT AMR Analytics Mobile Suite EDAM End Result is TSM Phase 3

12. March 2016 J&J, Banetti, SCHAD and IBM Lititz, PA production facility Pilot Scope Automatic Meter Readings Mobile SCADA Alarm Management Mobile Work Management IoT Production Monitoring Watson Analytics PM Optimization IoT Pilot

13. IoT TSM Objective With IoT TSM in place, J&J has an unprecedented window into the Enterprise technology landscape, both legacy and planned. The solution provides a single dashboard view of the Enterprise, enabling insight into the “dark” sectors of the manufacturing organization while providing intelligent decision support across the Enterprise from top to bottom. TSM is an “active listener” in the world of invisible IT communications and information, which allows J&J to react immediately and intelligently to the rapidly changing BYOD landscape.

14. Goals of IoT TSM Implementation Provide a turnkey solution for global sites Provide a single solution layer across all pre-existing systems Dashboard for visualization of production operations from top to bottom Leverage real time asset information within Maximo Provide capability for visualization and analysis of Maximo and sensor data within Watson Analytics Improved data quality in the short term and on-going data completeness

15. Goals of IoT TSM Implementation (Cont’d) Manufacturing operations trend analysis Increased asset reliability through PM optimization and predictive analytics Reduce unplanned downtime (not tracked everywhere) Decreased cost of asset maintenance per production unit Improved labor utilization Increased usable asset life Lower PM and Spare Parts Costs

16. Deploying TSM

17. TSM can … Be Cloud based or On-Premise Appliance Options Be Layered on top of existing systems and technology “dip down” to fill gaps in technology base TSM Delivery Levels … A Phased Approach Banetti IoT TSM Analytics Suite Banetti IoT TSM Predictive Suite Banetti IoT TSM Cognitive Suite TSM Delivery Model

18. TSM IoT is a single layer solution overlay Leverages existing devices, systems and applications Fills gaps where necessary for process continuity Enhances your current infrastructure capabilities Enables a single view across all technologies Mobilizes decision support for all systems TSM and Existing Systems

19. Summary of Benefits D IRECT B ENEFIT Operational Dashboard (What’s Happening Now) Operational Line of Sight Increased Operational Efficiency Streamlined Operational Planning Decreased Critical Response Time Threat Reduction – Internal Device Security Risk Reduction – Unplanned Operational Downtime S ECOND D EGREE B ENEFIT Operational Cost Reduction Operational Capacity Insight Optimized Maintenance Plans Increased Maintenance Efficiency T HIRD D EGREE B ENEFIT Predictive Operations Extended Asset & Device Lifespan (ROI) Optimized Inventory / Spares Production Cost Reduction ($/Widget) Increased Maintenance Capacity

20. 20 Source: Gartner Goal – Mature to Predictive

21. expensepotential improvements* annual cost of unplanned downtimereduce 60% to 90% excess capacity to compensate for unplanned downtime reduce up to 90% scrap or re-workreduce up 50% asset useful lifeextend life 5% to 15% cost of failureannual cost x probability of failure recall exposure# of recalls x cost of recall 21 * Based on Nucleus Research analysis Predictive Maintenance Impacts

22. 22 5%  in aircraft-on-ground events 1%  in overall maintenance costs 97% ability to predict delays & cancellations within 12 weeks 25%  in overall production line productivity 7 – 10%  in plant maintenance costs 10%  in paint yield 49%  of average inventory 97% fault recognition for specific assembly operation 60%  redundant service calls 87% accuracy within 48-hour warning about potential equipment failures 85% accuracy using analytical model to predict stuck pipe situations 23%  in operating expenses 10%-15%  in annual OPEX budget 1%-3%  in annual CAPEX budget 20-30%  in warranty claim processing times 45.5%  in unplanned maintenance 33%  anticipated in equipment and vehicle failures 34% accuracy predicting machine failures 2+ hours ahead of event maintenance finance supply chain/procurement customer service warranty production product design manufacturing quality control maintenance production data scientist/statistician maintenance Client Benefits Reported

23. 23 asset health score site health score maintenance schedule recommendations process & material quality top failure reasons KPIs

24. 24 Dashboards

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26. Q&A