LHP Engineering Solutions. Self Service IoT. Business Empowerment at the Edge

Transcription

1 LHP Engineering Solutions Self Service IoT Business Empowerment at the Edge Michael King, President, Data Analytics & IoT LHP Engineering Solutions

2 Self Service IoT: Business Empowerment at the Edge Background: Self Service BI at Cummins Highlighted Success Stories How Self Service Works (the LHP way) Self Service IoT Onboarding Process Appendix

3 Self Service BI at Cummins

4 Cummins - Profitable Growth Build a sustainable future for all stakeholders Revenue and EBIT $20 B Sales in 2014 Revenue ($ Billion) $20 $15 $10 $5 $0 Revenue EBIT % % 20.0% 15.0% 10.0% 5.0% 0.0% Record Level Revenues Profits Grow Faster Than Revenues Strong Shareholder Return 1 EBIT excludes restructuring charges in 2009 and 2014 (in Power Generation), and the gains from the divestiture of two businessesand flood insurance recovery are excluded from Also, Q2 12 EBIT excludes $6 million pre-tax additional gain from the divestiture of two businesses in 2011, and Q4 12 EBIT excludes $52 million in restructuring charges. 4

5 Cummins Complementary Businesses Global Power Leader Engine Power Generation Components Distribution

6 Cummins Market Applications Global Power Leader

7 Why Self-Service BI? The Most Effective Way to Deploy Analytics to a Global Organization countries and territories employees worldwide Develop, design and manufacture products on continents Business Units Regional Organizations Corporate Functions Engine Applications Market Segments

8 Why Self Service BI? The Lego Example 2 x = 24 combinations 3 x = 1,060 combinations 6 x = 915,103,765 combinations 6/13/2018 8

9 Why Self-Service Business Intelligence? Our current business intelligence and analytical platforms are not delivering on our business needs Current State Businessrequests for new data analysis require analyst engagement and IT developmentto source and deliver data Sourcing of new data sources into the data warehouse requires modeling, coding, and testing Multiple end usertools for analytics requires substantial licensing, maintenance, and training Limitedcapacityfor analyzing of ad hoc and what if scenarios for exploring business data Microsoft Self-Service BI Platform Existingdata sources are directly accessible by business users; minimal to no analyst and IT work needed New data sources can be rapidly sourced into ad hoc data area for quick access; formal modeling into warehouse if needed Commonset of analytics tools focuses investments in training and increases reusability across Cummins Full featured suite of dataanalysis tools that build on and around a widely-understood Excel platform

10 Self-Service BI: Guiding Principles Focus on the end users, think like an analyst Build and foster an analytical organization Reduce IT complexity Business owns the data Work together to govern the data and process Drive BI technology innovation

11 Self-Service BI Distinctions Introduces a new analytical paradigm Rapid prototyping, agile deployment Connect to any data source, connect any data source Spreadsheets with unlimited columns, unlimited rows Eliminates costly ETLs, replaced by Extract, Load, then Transform Faster development, easier to reconcile data, easier to adapt to changes Leverage analysts and business community Reduce dependency on 3 rd party developers Grass-Roots deployment, word of mouth communication

12 What is Self-Service BI? Cummins uses the Microsoft Business Intelligence built in Azure to provide: Business friendly reporting, analytics, and modeling tools Web front end for scheduling data refreshes and migrating analytical models Flexible and scalable Analytical platform Ability to connect to any data set (Source Systems, Data Marts, Data Warehouse, Teradata, Hadoop, etc.)

13 Why Self-Service BI Benefits the Business Business Effective data driven decisions Faster time-tomarket Quality data on which to make decisions Business accountability and responsibility of the data they already own SSBI improves time to market (introducing agile processes) New engagements: empirically gives the business their data up to 27 weeks faster Change requests: empirically realized by the business up to 34 weeks faster SSBI allows data cleansing without impacting source data Time to Data Analysis Decision Time to Data Analysis Decision

14 Why Self-Service BI Benefits IT IT Security Governance Planning for the future Security and auditing is streamlined The infrastructure and environment isolation in place The business owns the responsibility for the user-level security and auditing of their data and access rules IT has governance visibility into the environment Complete records of who is using the system, what and when they have accessed, etc. Complete visibility on the creation of data models and all connections to source systems Gold standard Data Warehouse can be developed over time based on actual usage IT can focus on the infrastructure and providing a service to the business

15 Timeline to Self-Service 15

16 Initial SSBI Proof of Concepts 6/13/

17 Self-Service BI: Program Status: First 18 months 17

18 Self-Service BI: Training Status 18

19 2015 Self-Service Engagements Faster time to deliver Quicker to business value Higher satisfaction What could 6 months buy the business? Initial cost will be higher only for complex projects 100 s of small projects vs. 15 high complexity projects 19

20 2015 Self-Service Cost Avoidance 20

21 Self-Service BI: Success Stories 21

22 Self Service BI - Next Steps Continue Expanding Global Training Program Formal Communication? Expand Azure China, Europe, Singapore, others Integration and alignment with traditional BI programs Expand advanced analytics, Machine Learning, IoT Tools and processes Continue to Expand beyond BI: QSOL, Pricing Engines, etc

23 Self Service BI In Summary Increased Revenue Decreased IT Costs Millions in IT Cost Avoidance Increased User Satisfaction Global Deployment Business & IT Teams working together

24 Highlighted Success Stories

25 Aftermarket Parts: Pricing and Analytics Category: Large entity, high visibility What: Expensive, inflexible 3 rd party system delivered no result How: Designed a Self-Service solution that the Parts business teams (Pricing, Product Management, and Analytics) now support Value: Enabled the Parts business to analytically price 80,000 parts and begin building the history to achieve optimal pricing User Experience Third-Party Proprietary Self-Service Analytics Parts Pricing Parts BI $M s $XM $0* Parts Consulting Fees $M s $K s Implementation Time 3.5 Years 12 Weeks Parts Priced 0 80,000 Year 1 Revenue $0 $33M

26 Engine Warranty Analysis Category: New capability, cross-bu/cross-functional unsatisfied need What: Existing Big Data solution was limited and not delivering end-user data or linking with other CMI data sets How: Built a small Hadoop solution in Microsoft Azure within 3 weeks, including reprocessing all engine INSITE logs for 3 years Value: Extracted and delivered all engine information to Engineering, Reliability, and Six Sigma teams to use in their investigations. Data used in one Six Sigma project with projected $M s in savings. User Experience Past Experience Self-Service Analytics Linkage to CMI Data Not Designed Fully Capable Effort Realization Continual Churn 3 Weeks New Capability Cost Estimate >$270K $87K Savings * >$M s

27 Engine Warranty Analysis (con t) Phase I work completed in 3 weeks for under $60K Over 2600 parameters per engine combined into a single analytical model to enable correlation of engine faults and failure codes to specific components Engine service logs pulled from EDW 93M and from CloudOne Customer information loaded from multiple sources (ERP s, EDW, Customer Masters, and National Accounts) POLK / VIN data Reliability Warranty events Expert Diagnostic data Work order details/headers Warranty campaigns Vehicle registrations/oem info Plasma/Genealogy Part sales/product Coverage; some portions of the BOM/SBOM

28 Corporate HR Analytics Category: Large scope What: Manual process with continual churn and no analytical capabilities How: Designed a Self-Service solution that allowed for automated global business efficiencies Value: In my 4 years at Cummins, this is the first time that we have successfully moved forward from a system perspective on analytics. This is very exciting, the possibilities are huge! -Brian Hamilton, Director -HR Reporting & Analysis User Experience Past Experience Self-Service Analytics Efficiencies Gained Manual Reporting Workforce Analytics BI Engagement No Capability/Support Capable in 4 Weeks AOP Process Manual Automated Decision Cycle Time No Ad-Hoc Responses 5 Minutes Data Quality/Governance 1 Day 5 Minutes

29 Distribution BU Global Inventory Category: Do it yourself, never had BI AOP funding What: Manual inventory and cleansing collection process against disparate systems. Error prone, time consuming, and demanded user compliance. How: Designed a Self-Service solution that pulls and aggregates the data automatically from multiple ERP systems Value: Automatic data aggregation allowed for the resolution of many customer down incidents. Additionally, it provides for a secure data environment that reduces user error and enhances work satisfaction (Cummins employees can now work strategically to benefit the business rather than spend their time with data entry). User Experience Past Experience Self-Service Analytics Efficiencies Gained 46 Support Personnel 1 Person Part-Time Process Compliance Chronic Weakness Automated Data Quality Manual Entry - Worse than Source Same as Source

30 DBU Business Systems Footprint

31 Enterprise Remedy Analytics Category: Global IT Production Support group supporting multiple BI reporting applications, reporting requests, and data warehousing services What: Analytics capability on large volume of support requests (1k/mo) How: Pull daily tickets direct from source into the MSBI environment for visibility to SLA performance and all other KPIs Value: Significantly improved ability to focus improvement work, reduce support costs, improve customer satisfaction User Experience Past Experience Self-Service Analytics Availabilityof data Limited by vendor Full access to all relevant data Reporting interval Monthly per vendor Daily refresh in MSBI Ability to drill down None Full Visualizations on data Minimal Unlimited

32 Enterprise Remedy Analytics Geospatial View

33 Self-Service BI solutions at Cummins: On-Time-Delivery 9

34 Self-Service BI solutions at Cummins: Engine Volumes 7

35 Self-Service BI solutions at Cummins: Service Analytics

36 Self-Service BI solutions at Cummins: Service Analytics RADAR is a self service data interface.. Currently TSR s Engine birth info (Engine history) Campaigns, claims (most), policy, work orders (USA) Parts, WWSPS, VSS Next telematics, biography of an engine, EDS, EPFIRG, INSITE, Reliability, FITS, Promotion, Deviations, and who knows Used in awareness, early warning, issue understanding, fix effectiveness. Allows us to be prepared to answer today s questions as well as tomorrow s questions. 6/13/

37 Self-Service BI solutions at Cummins: Service Analytics 37

38 Self-Service BI solutions at Cummins: Service Analytics

39 Self-Service BI solutions at Cummins: Service Analytics

40 How Self Service Works

41 Enterprise Business Intelligence Governance Structure CLT Sponsor MRG Program Leader Priorities Program Leadership Team COS Functions BU s / ABO s EBI Project Teams Common, Flexible Tools Analytics Platform as a Service 41

42 Self Service BI Program Framework BI program Users Functional Group Analysts Create Analysis, Modelling, Automatic Reports. Business Unit IT Enablers Functional group in BI program Subject Matter Expert (SME), Tool Experts Technical Group in BI program Data-warehouse maintenance, Environment maintenance, ownership of tools 42

43 Enterprise Business Intelligence Program Structure Program Leadership Program Management Governance, Change Management Vendor Management Planning, Budgeting Business Unit Teams (Business & IT) Functional Teams (Business & IT) Enterprise Business Analytics Enterprise Data Management Global Infrastructure Align BU Requirements to Functional teams Align BU IT resources to EBI efforts EBI Integration with BU initiatives Manage legacy transition plan Ensure End User focus Common KPI s, Requirements, Priorities Business Analysis Project Management Project Delivery Functional Roadmap Enhancements Function specific Training EBI Strategy CMI EBI Roadmap EBI Technology Roadmap EBI Application Footprint, integration Cross-Functional Alignment EBI Tools Training First Level Support Data Modeling Data Architecture Standard Subject Areas Data Source Strategy Data Warehouse Strategy, Roadmap Master Data Mgt integration Data Whse build, maintenance, consolidation Integration Tools Servers, storage, network Infrastructure Monitoring Cloud/OnPrem Management 43

44 Self Service BI Governance Model 44

45 Operating Model Self Service BI Intake Process Initiate, Assess, Assign, Train, Build Environment Visualize Data, Data-Driven Decisions Build Data Models Read / Use Data, Initiate Analysis 6/13/

46 Self Service BI Lifecycle Model 46

47 Self Service BI Lifecycle Model 47

48 Self Service BI Intake Process Work Flow 48

49 LHP Self Service IoT Onboarding Process

50 Overview Process goals: Quickly assess, assimilate, and enable quick-win IoT projects. Leverage demand from the business/functional area(s) Just like IoT empowers products at the edge, this process enables users who are often furthest from centralized-support, but also often where the most potential value resides Process Steps: Introduction Sandbox/PoC Provisioning & Development Deployment Support Value-Capture

51 Proven In-Use Approach successful at a Fortune 150 manufacturing company. ~300+ Groups served in ~2.5 Year period Every function Every business unit Every continent (except Antarctica) Process recognized by Microsoft for innovative, effective approach Millions $ Saved or Created ~$33m in top line profit on one project alone 10+ headcount redeployed to value-added processes on another single project

52 Introduction/Intake (1) Pull-based approach Request/Interest comes in from anywhere in business Entirely word-of-mouth, organic process, no internal marketing required Initial meeting Introduction of both teams and system Light-weight requirement gathering process Connect with existing groups/projects if applicable Quickly determine if project feasible If feasible, requesting team fills out Intake Form IoT Team reviews to ensure no redundancy, etc. and determines resources needed depending on situation

53 Sandbox/PoC (2) Large, shared test-bed environment Sample/Scrubbed data only Prove potential value and seek buy-in from all stakeholders to proceed Depending on potential value and specific needs, group may be assigned a Player-Coach at this stage This role can be either the player role where resource does most of the technical work that end users will then consume (given a fish), or resource serves more as coach who trains users to become self-sufficient (teach to fish). Virtually all teams granted access to Sandbox environment Restriction would stifle potential projects where value proposition may not be fully developed, understood, or identified yet

54 Provisioning & Development (3) Once project has identified needed resources (technical requirements and personnel), initial value-proposition is identified, and estimated timeline developed, resources are provisioned. Once a large enough ecosystem is created, it is possible and prudent to roll new projects into existing environments/initiatives Can be cross-charged or subsidized, depending on situation. Player-coach will be assigned at this time if not previously. Development/Production instances co-provisioned for rapid/agile capability ETL & Modeling at this stage Most critical stage Project management is needed to maintain rigor/discipline, avoid scopecreep. Team receiving services ultimately responsible for project management, but centralized guidance is available (and recommended) to remove road-blocks and supervise progress. Central team maintains right to pull resources if not being used effectively.

55 Deployment & Support (4 & 5) Simple governance check to ensure compliance Responsibility of data ownership lies with project owners, not central body. BI4BI meta-data tools available for both central team and end-users (security applied appropriately) for compliance checks and usage tracking. Production/Development instances (usually identical in spec) can allow agile development and iteration. Hourly refresh meant production migration can happen almost immediately. Player-Coach will train or assist in training end-users. Super-users & project owners should have already been trained at this point. Documentation should be completed and shared. Player-Coach remains on standby for pre-determined amount of time, but will shift off project post-migration.

56 Value Capture (6) After a reasonable amount of time, follow-up would be conducted with stake holders. This may have included management, project owners, or even customers Interview sought to identify and quantify REALvalue provided to project Reduced Product Cost, Reduced Warranty Contingency, etc. Improved profitability (Better pricing, reduced overhead) Redeployed Headcount, etc. Could also include qualitative improvements better customer service, better knowledge, reduced time-to-insight, etc. Feedback for program and kick-off Phase II discussions if warranted.

57 Self Service Analytics 18 Month Roadmap

58 Self Service Analytics: Governance Matrix

59 Self Service Analytics: Responsibility Matrix

60 Self Service Analytics: SSBI Owner Checklist

61 Self Service Analytics: BI4BI Platform Monitoring

62 Notes: This paradigm can easily be over-formalized and over-processed. Central team has to have solid grasp of business need and technical capability. Certain level of autonomy and latitude (within prudent guidelines) must remain for success. Stages of process can occur simultaneously. Some projects are fast-tracked and able to deploy in matter of days/weeks. Some projects can take months depending on workload, need, etc. Organizational Change Management is CRITICAL for sustainability In some cases, dedicated outside resources were devoted to needed behavior change Centralized trainings were also held to quickly train large cohorts of people Also served as a good incubator for cross-collaboration, ideation (reduce functional silos) Value capture identified exponential value (saved or created) compared to program investment IoT holds even morepotential value under this paradigm, as it has the potential to create entirely new revenue streams, etc.

63 LHP DATA ANALYTICS SOLUTIONS Contact Information Technical and Analytics President, Michael King Data Analytics Solutions Account Management James Roberts Paul Wright Vice President, Data Analytics Solutions Director, Business Development