TRUSTED ADVISORS TO HEALTHCARE AND LIFE SCIENCE EXECUTIVES

2015 Axendia, Inc. 1

INTRODUCTION

If you had a time machine, would you rather take a peek in to the future or change something in the past? If you could build a digital twin that enabled time travel, would you? The theme of this year’s Siemens Industry Analyst Conference was Realizing Innovation.

Virtualization and digitalization capabilities throughout the product lifecycle are giving industry

the opportunity to do something new – time travel.

The opening Keynote delivered by Dr. Horst Kayser, Siemens Chief Strategy Officer, stressed

that digitalization creates value by combining the physical and virtual words throughout the

value chain. The result of that combination is the creation of a digital twin of products,

processes or facilities. While some life sciences companies have embraced the digital twin

concept (see Medical Experiments on Your Digital Twin), other industries including automotive,

aerospace and consumer electronics have already gained the ability to travel back in time using

real world data to simulate a failure or conduct a root cause analysis with actual production

data; thereby shifting from CACA to PACA. Conversely, they can travel into the future to make

process and product adjustments that improve quality.

Chuck Grindstaff, President and CEO for Siemens PLM Software, noted that product innovation

has resulted in smarter products that are more complex today than even five years ago: a

smart watch requires 12,000,000 lines of code. Yet the market window for those products is

increasingly shrinking. This leaves companies with less time to develop new models or future

models of current products. To support this shift, Siemens has built a Smart Innovation

Portfolio that focuses are four key areas: Engaged Users, Intelligent Models, Realized Products

and Adaptive Systems.

Engaged Users – Right Information. Right Time. Right Context.

Intelligent Models - Knowing what they are and how they’re made.

Realized Products - Virtual products. Real Products.

Adaptive Systems – Easy deployment today. Flexibility for tomorrow.

SIEMENS INDUSTRY ANALYST CONFERENCE 2015 DIGITAL TWINS ENABLE TIME TRAVEL By Sandra K. Rodriguez, Research Associate

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WHEN TWO WORLDS COLLIDE: THE FORMATION OF THE DIGITIAL INDUSTRIAL

REVOLUTION

To combine the physical and virtual worlds, the Smart Innovation Portfolio needed to

leverage:

The right skill sets: 28,800 researchers; 220 data scientists; 17,500 software

engineers; 150 R&D locations.

Creativity: 30 patent first filings per day; 60,000 patents held; 1,000 research

partnerships launched annually.

Siemens executives pointed out that in roughly four years, there will be five times as many

connected devices as connected people. Machine generated data is estimated to hit 18

Zettabytes via 20 billion devices. Industry can benefit from this exponential increase in

areas such as service optimization and increased plant security, i.e., more things watching

more people.

Zvi Feuer’s presentation on Digital Manufacturing began with words of caution. Businesses

will either be disrupted or they can be the disruptors. Mr. Feuer referenced disruptive

technologies such as:

3D Printing / Additive

Manufacturing Technology

Advanced Robotics

Big Data Analytics

Cloud Technologies

Crowd Sourcing

Cyber Security

Internet of Things

Simulation and Digital Twins

Mr. Feuer put forth a model to enable time travel. First companies must accurately map

and represent their real world and then simulate it. Siemens PLM software aims to permit

time travel by delivering the digital twin of the production system lifecycle so customers

can achieve the next level of manufacturing innovation.

By building a digital twin of a physical production system, companies can simulate how

changes using actual OEE data affect the system’s future performance. In turn, the digital

twin enables time travel for predictive and preventative decision making.

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SYSTEM-BASED ENGINEERING AND QUALIFICATION – FROM CONCEPT TO REALITY

Dr. Jan Leuridan, SVP of Siemens STS (Simulation and Test Solutions) Business Segment

presented the thought-provoking concept of doing virtual commissioning using the

example of systems, sub-systems and components. First companies must adopt system

driven product development. General Motors has already standardized on model-based

systems engineering. This approach has led to shorter development times and the ability to

explore how different control algorithms or a particular calibration might work on multiple

architectures without having those architectures available. This opens the door to virtual

qualification at the system, sub-system or component level. Information gathered and

tested at each level can be reused during the qualification process.

CUSTOMER PRESENTATIONS

KONECRANES

Konecranes, a company with maintenance contracts covering more than 450,000 pieces of equipment of

different makes, realized the benefits of time travel through its Industrial Internet Initiative. Juha

Pankakoski, Chief Digital Office and CIO at Konecranes, described how intelligent machines are not only

aware of their condition, but are networked for real-time visibility for enhanced safety and productivity.

Pankakoski also pointed out the industrial internet is not actually the Internet of Things but the IoD

(Internet of Data) and data is the new oil. However on its own, data is just a four letter word that

requires an adjective -- it needs to be Meaningful Data. By investing early and heavily in the industrial -

internet, the meaningful type of data Konecranes is able to share with its customers includes:

Your crane did 28 starts and stops today, moved 427 meters, and lifted 87.3 tons.

You consumed wearable parts in your crane for 201 minutes.

Your hoisting brake has less than 10% of its expected life remaining.

Based on present usage, you have less than 2 weeks before the brake needs to be changed.

Konecranes service technician will change your brake next week Friday, when you have a

scheduled production break.

By training your operators on 2nd shift not to use emergency brake for operations, you could

extend your crane life by 30%.

To develop this type of feedback, the company combined big data with reliability analysis and

simulations. Time travel into the future paid off as equipment predictability was enhanced further than

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previously thought possible. This resulted in carrying less spare parts for routine maintenance and

making data driven decisions when scheduling maintenance service calls.

DELL

The customer presentation from Dell focused on its biggest challenge: Time. With the support of big

data analytics, Dell was able to take billions of data points, discover key relationships and extract

predictive insights. Dell’s unified data model starts with the following sources: manufacturers, assembly

operations, supply hubs, customer information and repair centers. The type of data input includes:

customer data and call logs, failure analysis, returns and repair, part replacements and product

assembly. The resulting output is: fewer dispatches by linking commonly dispatched parts, close

problems detected across the supply chain faster, optimize test process through data mining and push

solutions to the customer before the problem happens.

Through the current use of predictive analytics in Dell’s support and customer environment, problems

are identified, researched, solutions tested and implemented. This is not the only scenario where Dell

saved time. To demonstrate “analytics at the speed of thought” an issue was identified and isolated

within 3 hours rather than 3 days, when 2 out of 6 demo unit LCDs began flickering at the recent launch

of Dell XPS13. Through the use of its technology, Dell was able to compress the time it takes to address

root causes of issues from days to hours, improving customer satisfaction while improving product

quality and reducing costs.

AMWAY

Amway’s presentation revolved around a not-so-unique business challenge that companies are still

facing: unconnected, paper-based systems. As a result, Amway lacked a data structure to realize the

benefits of functioning as a digital enterprise. Life science and consumer products companies face

similar challenges: supply chain disruptions, regulatory pain points and risks associated with events,

recalls or contaminations. The initial focus of the project was on 5 select PLM capabilities: compliance,

formulation, specification, systems integration and product data. By weaving a digital thread through

its product content, the end result was what it refers to as a “single source of truth” for its employees.

What Amway employees also saved was time.

CONCLUSION

Einstein’s “twin paradox” states that a twin traveling close to the speed of light will age

more slowly that the twin remaining stationary. Digital twins are enabling companies to

achieve time travel to improve product quality, increase reliability and lower costs.

From understanding the overall impact of a design change to a product, to real-time

visualization of equipment condition monitoring, companies that have already begun

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weaving a digital thread through their lines of business will continue to gain a competitive

advantage. There is no denying the digital revolution is knocking on factory doors around

the globe.

Siemens’s virtualization and digitalization capabilities provide the tools industry needs to

create digital twins which can enable time travel leading to predictive and preventative

decision making. This technology will allow for issues to be addressed in both the future

and past digital realms thereby driving improved quality in the real world.

Companies must remember the best time to plant a tree was twenty years ago. The next best time is today.

The opinions and analysis expressed in this research reflect the judgment of Axendia at the time of publication and are subject to change without notice. Information contained in this document is current as of publication date. Information cited is not warranted by Axendia but has been obtained through a valid research methodology. This document is not intended to endorse any company or product and should not be attributed as such.

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Published OCTOBER 2015