ENABLING ON-DEMAND

DISTRIBUTED MANUFACTURING

PLATFORM

OFFICES: BERKELEY, CHICAGO, CHENNAI, VICTORIA

William Sobel

Chief Strategy Officer/Co-Founder, VIMANAwill.sobel@govimana.com

Mr. William Sobel is Chief Strategy Officer and Co-

Founder of VIMANA – the leading analytics platform for

discrete manufacturing process analytics – and the

Principle Architect and Chair of the Technical Steering

Committee for the MTConnect Standard – an international

standard for semantic IIOT data for manufacturing. In

addition, Mr. Sobel is the Co-Chair of the Industrial AI

Task Group at the Industrial Internet Consortium.

Athulan Vijayaraghavan

Chief Technology Officer/Co-Founder, VIMANAathulan@govimana.com

Dr. Athulan Vijayaraghavan ….

WHERE ARE WE GOING?

4

Present Operational Efficiency, Productivity

Emergent Digital Economies, Industrial AI, 3DP

Near Future Customer Centric, On-Demand

Future Automated, Agile

• Islands of Automation

• Analog Processes

• Highly Labor Intensive

• No Feedback

• Long Design and Engineering Cycles

TODAY

5

PROCESSES ARE STATIC AND BRITTLE

6

CUTTING THROUGH THE BUZZ

some reality?

Intelligent Automation

Industrial AI and Analytics

On Demand Manufacturing

Distributed Manufacturing

Additive Manufacturing

Predictive & Prescriptive

EXAMPLE: MAINTENANCE, REPAIR, AND OVERHAUL (MRO)

CURRENT MRO PROCESS

Inventory of spare parts and equipmentPurchase an extra piece of equipment to sit idle

most of the time & keep parts in stock

Equipment has minimal diagnostics and alerts PLCs and control systems have minimal intelligence

Equipment is often disconnected form the network My printer can order ink

Manual and labor intensiveTest and diagnostics are manual and discovery is

often disruptive

Where to repair?Local repair locations have less transport costs, but

may not have necessary equipment

In stock, special order, or remanufacture?To complicate more, the decision about availability

of parts can cause significant delays

FUTURE MRO

10

Sensor Data

From Product

CBM Analytics

0 1

0 2

0 3

0 40

50 60 70 80 9

0 1

00

Repair

Parts

Stock Manufacture

Materials Equipment

Capabilities Capacity

Service

On-Site Local Remote

Predict Component Failure Schedule Repair

Remanufacture

AUTOMATION

Route

Plan

R1

R2

IN

OUT

B1

B2

MILL

LATHE

CMM

Factory in a Box

Verify

Machine

OUTCOMES: POWER BY THE HOUR

EXAMPLE: ADDITIVE MANUFACTURING FOR ON-DEMAND DISTRIBUTED

DISCRETE WORKFLOW

• Part

• Machine

• Tool

• Processes

• Part Program

• Setup Sheet

• Work Instructions

• Tool Breakage

• Poor Surface Finish

• Productivity Gains

Design

CAD

Operator

• Process Actuals

– Feed/Speed

– Cycle Time

– Operator Feedback

• Geometry

• Tolerance

• Assembly

Engineering

CAM

• Time intensive engineering process

• Outcome takes many iterations

• Cost of single part is prohibitive

• Can only remove material

• Hard to accurately quote

ADDITIVE WORKFLOW

• Mesh Healing

• Support Optimizing

• Orientation

• Toolpath Generation

• Simulation

Design

CAD

• Geometry

• Tolerance

• Assembly

• Material

• Material Handling

• Layer Analysis

• Build Quality

• Surface Finish

Automated

Engineering

• We can print more complex geometries than

we can design

• Incremental cost of each part is small – can be

combined with others on same build

• Engineering is automated

• 3D Printing Makes Batch-Size-One Work

• Additive has achieved art-to-part

• Data analytics from 3D printing processes have exceeded

analytics for discrete manufacturing

• Digital Twins for 3D Processes exist today

• Quoting can be accurately automated

• Distributed printing services have been successful

• 3D Printing is now 100x faster with technologies like SPJ,

new technologies in the lab will get to 1000x of current processes

• Hybrid – think about the needs for MRO…

DISTRIBUTED MANUFACTURING

Ca

pa

bili

tie

s

Re

qu

irem

en

ts

17

ENABLING TECH FOR ON-DEMAND DISTRIBUTED MANUFACTURING

CAPABILITIES THAT YOU NEED

18

Device ConnectivitySupport wide variety of your shop floor devices and sensors. Automated gateway

for data streaming and connectivity

Data Transformation

Realtime rules engine, machine learning, and AI capabilities to transform shop

floor data into predictive and prescriptive insights, providing the data foundation

for your advaned solutions.

Integration and ExtensibilityAbility to integrate via APIs, SDKs etc., with your own applications. Extend the

data transformation with custom algorithms and analytics.

Applications

Out-of-the-box applications to drive decision-making and realize value, including.

For example: Dashboards, Operator Panel, Analytics, Alerts, Ticketing, Work

Instructions.

Infrastructure and

Deployment

Support for flexible deployment options from on-premise, to private cloud, to

managed public cloud. Support automated provisioning and management

capabilities for highly scalable deployments.

CAPABILITY ARCHITECTURE

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Modular capabilities to enable flexible, customized, deployments and integrations

DEVICE CONNECTIVITY

20

‣ CNCs, PLCs, EDM, CMM, manual stations, additive machines

‣ MTConnect and OPC open standards compliance to ensure interoperability

DATA TRANSFORMATION BUILDING THE DATA FOUNDATION

21

DATA PREPARATION STANDARD ANALYTICS ADVANCED ANALYTICSDATA SOURCEVIMANA Data

Transformation

Extensive support for

standardized data collection

from a wide variety of sources

Prepare data for analysis using

Enrich rules engine, to capture

the true state of the

manufacturing process

Compute KPIs to track device,

operator, part, and tool

performance. Generate tickets

and alerts to respond to events.

Advanced analytics applies

proprietary domain-driven ML

and AI algorithms to identify

opportunities for productivity

improvement.

Transform shopfloor data to knowledge and insight for problem solving

Example CNC + Additive EquipmentProcess Health, Machine

PerformanceMachine Utilization, Part

Quality, Machine Health

Optimal Routings, Process

Transfer, Global Sourcing

APPLICATIONS TO DRIVE USAGE

22

Dashboards

Operator

Panels

Analytics Reports

Ticketing

INTEGRATION AND EXTENSIBILITY

23

ERP

BI

MES

EAM

Operator Input Operator Productivity

True OEE

• Bring data from your

enterprise systems

into VIMANA.

• Apply in calculating

custom KPIs to

measure device, part,

operator

performance.

• Integrate with open

APIs into your

enterprise tools.

Part Metrics

INFRASTRUCTURE AND DEPLOYMENT: RUN ANYWHERE

24

ON-PREMISEPRIVATE CLOUDMANAGED PUBLIC

CLOUD

Automated deployment, provisioning, and management across environments.

GETTING STARTED

25

26

1. DEFINE BUSINESS OBJECTIVES3. ASSESS NETWORK AND

CLOSE GAPS

2. PRIORITIZE ASSETS TO ANALYZE

6. DEPLOY AND TRAIN

4. CONNECT ASSETS

5. IMPLEMENT CONFIGURABLE RULES AND DASHBOARDS

27

7. BASELINE ASSESSMENT AND PRIORITIZE LARGEST PAYBACK

8. VALIDATE RESULTS WITHBUSINESS CASE

9. EXPAND ANALYTICS USE CASES

10. EXPAND TO MULTIPLE PLANTS WITH STANDARDIZED DEPLOYMENT

AND PROCESSES

11. EXPAND TO BUSINESS ENTERPRISE WITH

INTEGRATION ACROSS VALUE CHAIN

`• Identify and prioritize areas for process

improvement and cost reduction

• Improve machine and operator efficiency

• Optimize maintenance strategy while

reducing costs

• Drive productivity and staffing optimization

during volatile business demand cycles

SOLUTION

CHALLENGE BENEFITS

• 25 sites, 1000+ Devices Connected: legacy, CNCs,

EDM’s, PLC, CMM’s, Additive, welders, ovens,

automated repair processes, assembly stations

• Global footprint: NA, Mexico, Europe, Vietnam,

India, Singapore and China

• OT/IT Integration MES, APM/CMMS, and local

historian

• Architecture, standards and infrastructure Services

• Capacity/Throughput Analysis: Assess actual cycle time. Analyze std hour/costs relative to achievable throughput. Reset targets, adjust processes, validate improvement, realize reduced std cost. (30% increase in machine utilization across 6 months (double available capacity).

• Compare Sites: Compare operations across different sites and find opportunities to harmonize and optimize global workflows.

• Additive Tech: Connect and analyze data from AM processes that are replacing machining processes.

• Assembly workflow Optimization: Track “downtime” & waste in assembly operations. Adjust process to reduce NVA time. Implement, measure, control. [15% productivity increase]

• Automation Cell Cycle-Time Optimization:Understand actual VA time. Adjust process and automation to reduce NVA time. [ 62%—>82% utilization, 33% capacity increase, ~$236K product cost reduction for 2018]

• Process Parameter Alerting: Set threshold alerting for critical parameters. Avoid quality issues and rework.

EXAMPLE: MANUFACTURING + MRO FOR POWER EQUIPMENT MANUFACTURER

WE ARE VIMANA: THE LANGUAGE OF SMART MANUFACTURING

29

OUR PRODUCTS DELIVER

FASTEST TIME-TO-VALUE

TO OPTIMIZE

PERFORMANCE

WE ARE THE DATA

FOUNDATION FOR

ADVANCED

MANUFACTURING

PARADIGMS

OUR PROVEN SMART

MANUFACTURING

TRANSFORMATION LEADS

OUR CLIENTS FORWARD

WE HELP YOU REALIZE

YOUR VISION FOR SMART

MANUFACTURING

WE LEAD THE INDUSTRY

FORWARD WITH OPEN

STANDARDS

Manufacturing Analytics Platform for Operational Excellence

Meet us at Booth 133020.

THANK YOU

Questions?