Connected ManufacturingCreating value through connected manufacturing

Wolfgang Lucny, Manufacturing Industry Innovation Leader EMEAJacques Spee, Manufacturing Industry Senior Advisor EMEA

HPE Point of View

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Age of Digitization

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1990 2016

Age of Digitization

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2018 2030

Challenges and Recommendations

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Challenges

– Lack of available, qualified personnel

– Concerns regarding data security

– Investment needs

– Too many technology options—no standards

– Focus on internal manufacturing leads to missed external opportunities

Recommendations

– Define a comprehensive program and a strategic roadmap with a clear end in mind

– Start small, simple, and – ultimately –achievable

– Remove barriers and align business, IT, and OT

Connected Manufacturing in Practice—Use Cases

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Real-time status monitoring

Covering both Legacy as well as Smart Components

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Converged Plant Infrastructure (CPI)An integrated solution for connected digital factories

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Business Software

Ubiquitous

Communication

IT & OT

Operation & Analytics

Compute for

IT, OT, Analytics

I40 Integration

Automation &

Hybridization

Workforce

Enterprises /

Business

Service

Collaboration

Support

IoT / Smart Object

Integration

Security

• Reduced material and parts excess inventory

due to improved planning accuracy

• First real-time integration of production data into

production planning

• Real-time order track & trace of machines,

equipment, and production orders

• Revision of order process for parts and

components even within forecast plan

Connected Manufacturing: Digital Factory Platform

HPE Solution

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Business Outcomes

• Use of HPE Converged Plant Infrastructure

(CPI) as secure digital factory platform

• Use CPI to integrate production planning system

with

• real-time status updates of shop floor

components within production process

• up-to-date legacy production system

components & messages

• overall ERP/ MES data, workflows, and

systems in defined revisions

OT & IT convergence

Value through additional insights

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HPE Real-time QM Analytics & Rules Engine

Production Tracking & Tracing (PCS)

Raw Glass Coating ShippingFormingPolishing and

engravingDe-blocking and cleaning

Coloring Quality

Real-time Quality Management: Reduction of Re-WorkEyeglass lens production–high level process flow

Measurement Measurement Measurement Measurement Measurement Measurement

Machine Data Sensors

Tool & Equipment

Database

Indirect Material

Fluids, Cleanings,

Supplies

Labor Data, Shift Team,

Person, Skills

Product Characteristics

Plant,

TemperaSensorsture,

Humidity

Quality Protocol

Raw Material

Machine Asset Data

(Type, Capability)

Quality Checkpoints

Production

Production Order

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15

8 weeks

Workforce

Sensors-Actors

Analytics

Rule Engine

Pattern recognition

Alarming

Incidentinitialization

New product

& problemresolution

KPI/SLAReport

Implementdiagnostic

Executediagnostic

check

Dash-boarding

Impact Analysis

I4.0 end2end

100%

22%

0%

43%

2,2%

Before pilot

Resolution time

Detection time

Business Case Achieved with I4.0 Life Cycle Management

• Decrease overall product costs

(increased margin, target is zero-fault)

• Real-time visibility of order costs, broken down

to machine and equipment level

• Automation and real-time decision making allow

for optimal use of staff

• Improved customer experience with track &

trace of product —from order, through

production, to final delivery

• Increased efficiency of machinery, equipment,

and labor

• Fusion of new information sources with current

systems improves manufacturing intelligence &

analytics.

• Extends the analytical baseline of available ERP

and Business Warehouse systems.

• Additional sensors, mobile devices, and M2M

communication enable a consolidated view of all

machines and equipment.

Connected Manufacturing: Integrated External I4.0 Services

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HPE Solution Business Outcomes

Connected Manufacturing

Power of partners in an Ecosystem

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Smart Robot Machine Data AnalyticsAutomated Guided Vehicle* connected to Navigation Services in the cloud

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* each AGV is generating several GB of data per day

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I4.0 Composite Services for Cyber Physical SystemLife Cycle Management for Automated Guided Vehicle

CPS

Industry 4.0 Digital Services Platform

Vehicle

Live DataNAV Algorithm

Development

Predictive

MaintenanceIntra-

Logistics

Navigation

Control

AGV

Dashboard

Orchestration, Service Management, Service Workflow

I4.0 Federation:

Central Regional Local

Smart Robot AGVLife Cycle Management

Manufacturing Service Bus: Integration, Workflow, Security

AGV Navigation ServicesMachine Analytics Services

AGV

AGV#1 AGV#2 AGV#n

Hewlett Packard

Enterprise

Fraunhofer IPA

AGV Manufacturer

Multi-tenant, federated database CPS CPSAGVAGV

• AGV manufacturer differentiates in market by

offering value-add services (navigation

optimization, equipment performance analytics)

• Equipment simplification and ease of

maintenance / enhancements for the AGV OEM

• Improved Intralogistics performance / availability

for the factory owner

Connected Manufacturing: Business Ecosystem

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HPE Solution Business Outcomes

• Integrating manufacturing equipment services

through an Industrial Private Cloud

• Provision of machine analytics services

(technology + data scientists)

• Industry 4.0 Manufacturing Service Bus offers

services on a demand basis and ensures

interoperability across partners

Connected Manufacturing

Growth through new Business Models

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Changing Business Models in Product Life Cycle

Traditional Model

• Traditional model

• OEM focused on maximising Spare Parts Revenue

• Customer may do own Maintenance

Extended Warranty

• Customer incentivised to buy extended warranty

• Dealer network may rely on revenue from warranty

Total Service

• Customer Pays to use equipment – OEM is responsible for all service costs

• Lifetime service costs directly impact OEM P&L

• Downtime = Zero revenue for OEM

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Value & Risk

“Air as-a-service” – Creating new business models Enabled by IOT connectivity and predictive analytics

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Plant Control Center Condition Monitoring

Predictive MaintenanceEnergy Saving

• Competitive advantage via increased customer

care and uptime

• Estimated annual spare parts inventory savings

of $10M via predictive analytics

• 60% reduction in downtime, reduced

maintenance cost

• New Product introduction / New Business Model

(as-a-service)

• Future product improvement

• Platform to develop additional services for other

product segments

Connected Manufacturing: New Business Model

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HPE Solution Business Outcomes

• Real-time analytics of sensor data from

distributed equipment

• Business Model development based on the new

capabilities

• SAP HANA implementation with Predictive

Maintenance components

• Large memory and processing capability to meet

global workload performance needs

Summary

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Summary

– Establish a strategic roadmap with a clear end in mind

– Check your readiness—get your house in order

– Start small, simple, and – ultimately – achievable

– Identify ecosystem players, define your role, and engage with partners

– Focus internally (Festo, Zeiss) first but don’t miss external opportunities (AGV, KAESER)

– Establish shared Digital Competence Center which includes business, IT, and OT

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Pilots

Pilots

POCIT

SupportCorporate Strategy

Pilots

Pilots

POCBusiness Strategies

IT

New Technologies

Digitization

Strategy

Thank you

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Wolfgang Lucny wolfgang.lucny@hpe.com +43 664 3844582

Jacques Spee jacques.spee@hpe.com +31 651 826 539