lhp engineering solutions self service iot - microsoft€¦ · • cummins uses the microsoft...

LHP Engineering Solutions

Self Service IoTBusiness Empowerment at the Edge

Michael King, President, Data Analytics & IoT

LHP Engineering Solutions

http://LHPES.com

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

Self Service BI at CumminsSelf Service BI at CumminsSelf Service BI at CumminsSelf Service BI at Cummins

4

Build a sustainable future for all stakeholdersCummins - Profitable Growth

Strong Shareholder ReturnProfits Grow Faster Than Revenues

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5.0%

10.0%

15.0%

20.0%

25.0%

$0

$5

$10

$15

$20

2007 2008 2009 2010 2011 2012 2013 2014

Revenue (

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Revenue and EBIT

Revenue

EBIT %

1 EBIT excludes restructuring charges in 2009 and 2014 (in Power Generation), and the gains from the divestiture of two businesses and flood insurance recovery are excluded from 2011. 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.

$20 BSales in 2014

Record Level Revenues

Power Generation

Components DistributionEngine

Global Power LeaderCummins Complementary Businesses

Cummins Market ApplicationsGlobal Power Leader

countries and territories employees worldwide

Develop, design and manufacture products on continents

Business Units

Regional Organizations

Corporate Functions

Engine Applications

Market Segments

Why Self-Service BI?

The Most Effective Way to Deploy Analytics to a Global Organization

Why Self Service BI? Why Self Service BI? Why Self Service BI? Why Self Service BI? –––– The Lego ExampleThe Lego ExampleThe Lego ExampleThe Lego Example

24 combinations

6/13/2018 8

2 x

3 x

6 x

1,060 combinations

915,103,765 combinations

=

=

=

Why Self-Service Business Intelligence?

Our current business intelligence and analytical platforms are

not delivering on our business needs

Current State Microsoft Self-Service BI Platform

Business requests for new data analysis require

analyst engagement and IT development to

source and deliver data

Existing data sources are directly accessible by

business users; minimal to no analyst and IT

work needed

Sourcing of new data sources into the data

warehouse requires modeling, coding, and

testing

New data sources can be rapidly sourced into ad

hoc data area for quick access; formal modeling

into warehouse if needed

Multiple end user tools for analytics requires

substantial licensing, maintenance, and training

Common set of analytics tools focuses

investments in training and increases reusability

across Cummins

Limited capacity for analyzing of ad hoc and what

if scenarios for exploring business data

Full featured suite of data analysis tools that

build on and around a widely-understood Excel

platform

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

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 3rd party developers

• Grass-Roots deployment, word of mouth communication

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.)

Why Self-Service BI Benefits the Business

• 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

Business

Effective data driven

decisions

Faster time-to-market

Quality data on which to make

decisions

Decision

DecisionTime to Data Analysis

AnalysisTime to Data

Why Self-Service BI Benefits IT

• 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

IT

Security

Governance

Planning for the future

Timeline to Self-Service

15

Initial SSBI Proof of Concepts

6/13/2018 16

Self-Service BI: Program Status: First 18 months

17

Self-Service BI: Training Status

18

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

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2015 Self-Service Cost Avoidance

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Self-Service BI: Success Stories

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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

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

HighlightedHighlightedHighlightedHighlightedSuccess StoriesSuccess StoriesSuccess StoriesSuccess Stories

Aftermarket Parts: Pricing and Analytics Category: Large entity, high visibility

What: Expensive, inflexible 3rd 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

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

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

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


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

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).


Efficiencies Gained 46 Support Personnel 1 Person Part-Time

Process Compliance Chronic Weakness Automated

Data QualityManual Entry - Worse

than SourceSame as Source

DBU Business Systems FootprintDBU Business Systems FootprintDBU Business Systems FootprintDBU Business Systems Footprint

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


Availability of 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

Enterprise Remedy Analytics – Geospatial View

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

9

7

Self-Service BI solutions at Cummins: Engine Volumes

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.

6/13/2018 36


37


How Self Service Works

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CLT Sponsor

BU’s / ABO’s

COS Functions

Priorities

Common, Flexible Tools-------------------------------------------------------------------------------

Analytics Platform as a Service

Program Leadership Team

MRG

Program Leader

EBI Project Teams

Enterprise Business IntelligenceGovernance Structure

Functional group in BI program

• Subject Matter Expert (SME), Tool Experts

Technical Group in BI program

• Data-warehouse maintenance, Environment maintenance, ownership of tools

IT Enablers

Create Analysis,

Modelling, Automatic Reports….F

unctional G

roup B

usin

ess U

nit

BI program

Users

Analysts

42

Self Service BI Program Framework

43

Enterprise Business IntelligenceProgram Structure

Program Leadership

BusinessUnit Teams

(Business & IT)

FunctionalTeams

(Business & IT)

EnterpriseBusinessAnalytics

EnterpriseData

Management

GlobalInfrastructure

•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 Whse build, maintenance, consolidation•Integration Tools•Servers, storage, network•Infrastructure Monitoring•Cloud/OnPremManagement

•Data Modeling•Data Architecture•Standard Subject Areas•Data Source Strategy•Data Warehouse Strategy, Roadmap•Master Data Mgt integration

•Program Management•Governance, Change Management•Vendor Management•Planning, Budgeting

Self Service BI Governance Model

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6/13/2018 45

Intake ProcessIntake ProcessIntake ProcessIntake Process

Initiate, Assess, Assign, Train, Build Environment

Build Data Models

Read / Use Data, Initiate Analysis

Visualize Data,

Data-Driven Decisions

Operating Model Operating Model Operating Model Operating Model –––– Self Service BISelf Service BISelf Service BISelf Service BI

46

Self Service BI Self Service BI Self Service BI Self Service BI –––– Lifecycle ModelLifecycle ModelLifecycle ModelLifecycle Model

47

Self Service BI Self Service BI Self Service BI Self Service BI –––– Lifecycle ModelLifecycle ModelLifecycle ModelLifecycle Model

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Self Service BI Self Service BI Self Service BI Self Service BI –––– Intake Process Work FlowIntake Process Work FlowIntake Process Work FlowIntake Process Work Flow

LHP Self Service IoT

Onboarding Process

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

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

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

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

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 scope-

creep.• 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.

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.

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 REAL value 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.

Self Service Analytics 18 Month Roadmap

Self Service Analytics: Governance Matrix

Self Service Analytics: Responsibility Matrix

Self Service Analytics: SSBI Owner Checklist

Self Service Analytics: BI4BI Platform Monitoring

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 more potential value under this paradigm, as it has the potential to create entirely new revenue streams, etc.

LHP DATA ANALYTICS SOLUTIONSContact Information

• Technical and Analytics

• James Roberts• Vice President, Data Analytics Solutions

• [email protected]

• 812.314.7921

• Michael King• President, Data Analytics Solutions


• 812.341.8460 • Account Management

• Paul Wright• Director, Business Development


• 812.314.7920

lhp engineering solutions self service iot - microsoft€¦ · • cummins uses the microsoft...

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