Path to Digitally Transform
New Product Innovation
Improving Competitiveness and Managing Risk in a
Compute-Driven WorldRSDXEB-2023-1.0

Introduction
In the last two decades, enterprises have seen dramatic changes inbusiness processes enabled by virtualization, cloud services, and machinelearning. But most engineering and R&D-driven companies have yet tobring cloud-enabled digital transformation to product development.
Today, leading engineering-driven organizations are digitally transforming
new product development processes using cloud high performancecomputing (HPC), and adopting new digital engineering practices andartiﬁcial intelligence to improve their competitiveness. Rescale serves
many of these leaders, including the majority of top aerospace, automotive,
and life sciences companies.
This report describes the patterns that Rescale has identiﬁed while working
with our customers to transform their R&D capabilities. This journey
includes three stages:

Digitaltransformation
is a top priority for74%of organizations
Source:Flexera, Tech Spend Pulse 2022

1. Cloud-enabled R&D

35% of

2. Uniﬁed Management

Only

3. Digital Transformation

digitaltransformation
efforts are
successful

In this report, you will learn the key challenges organizations are addressing
at each stage, what outcomes they are looking to achieve, as well as
Rescale’s approach in helping our customers get to these outcomes.

Source:Boston Consulting Group,
Performance and Innovation are theRewards of Digital Transformation

2

Path to Digitally Transform New Product Innovation

Computing Powers New Product Commercialization
New product innovation is the lifeblood of today’s leading engineering-driven companies. Acrossindustries, innovative leaders now integrate advanced computing into nearly every stage of new
product development, from creating personalized medicines to autonomous driving systems.
Today, approaches like digital twin, artiﬁcial intelligence, and cloud-based collaboration are driving a
new wave of product innovations by making better use of data and empowering engineers with
data-driven and AI-enabled breakthroughs. Commercializing new engineering breakthroughs is morediﬃcult than ever, according to 90% of R&D leaders surveyed by Gartner (Source: R&D LeadershipCouncil Survey 2021). Today’s R&D technology stacks span broad sets of computational tools,
including simulation software, specialized computing architectures, and supporting middleware data
management and workﬂow tools.
While most organizations have adopted digital methods of product development like computer-aided
design (CAD) and engineering (CAE), for many, legacy systems, data silos, and the increasing
complexity of engineering challenges often inhibit further R&D process improvements.

Exhibit 1 - Landscape of Computational Science and Engineering

Simulations
Fluid
Dynamics

Aerospace & Defense
Propulsion, Aerodynamics,
Acoustics, & Structural

Structural
Analysis

Chip Design, Veriﬁcation, &Classiﬁcation

Core Elements
Specialized
HardwareAutomotive

Specialized
Softwareary
imin
Prel rationoExplAI, ML,& DL

Multibody
Dynamics

F
Opti inal
miza
tion

Molecular
Dynamics

Crash Safety, Advanced
Driver Assistance Systems,
Battery, & Combustion

Semiconductor & EDA

Design Cycle

Energy & Earth Sciences

DomainExpertise(Physics,
Chemistry)

Pharmaco
Kinetics

Particle
Fluidics

Validation& TestingElectroMagnetics

HydroDynamics

Climate & Weather Modeling,
Drill & Pipeline Safety, Seismic &Reservoir

ThermoDynamics

Computational
Chemistry

3Life Sciences

Manufacturing

Genomics,
Drug Discovery, &Bioinformatics

Materials Testing, Waste
Reduction, & Factory
Optimization

Path to Digitally Transform New Product Innovation

Market Trends Impact Engineering and R&D Decisions
Democratization of HPC and AI Has Increased Competitive Pressure
Growth in cloud HPC, open-source applications and container portability has democratized access totechnologies once reserved for only the largest R&D organizations. This opens the door for startups tocompete and commercialize disruptive new products. A wave of “digital native” new entrants iscreating urgency for incumbent innovators to transform their R&D capabilities and remove theconstraints of legacy systems and processes.
Companies that can become more digitally mature and data-driven will achieve new levels of
operational eﬃciency and reduce repetitive, non-value-adding engineering tasks, which frees engineers
to focus on innovation and achieve industry breakthroughs faster.

Exhibit 2 - Shift to Specialized Computing Architectures in the Cloud
Architectures Available in the Cloud

Performance of Computing Architectures

500

Specialized Computing
Architectures

400

1000x Growthin 10 Years

2x Per Year

300

Single-Threaded CPU
Architectures
1.1x Per Year200
1.5x Per Year
100

1980

20

13 014 015 016 017 018 019 020 021 022
2
2
2
2
2
2
2
2
2

1985

1990

1995

Homogeneous Architectures(CPUs in the Moore’s Law Era)

2000

2005

2010

2015

Heterogeneous Architectures(GPU, FPGA, TPU, ASICs, Quantum)

Explosion of Computing Architectures Complicates Planning
The end of Moore’s Law means that companies can no longer expect continued computing
performance gains without disproportionate increases in cost computing hardware or power
consumption. This has led to a Cambrian explosion of new specialized computing architectures
optimized to speciﬁc workloads. Examples include GPUs, ASICs, and RISC architectures like Arm, eachwith advantages for tasks like graphics rendering, AI training, and physics simulations.
But taking advantage of these specialized architectures comes with its own challenges. Organizations
can no longer sign multi-year commitments for homogenous hardware - and planning ahead for thetype and quantity of specialized hardware needed is highly impractical.
A just-in-time approach is needed, which is only possible with cloud computing. When organizations
continuously take advantage of the latest architectures as they become available, they can achievesigniﬁcantly better performance than with on-premises systems.

4

Path to Digitally Transform New Product Innovation2020

Mainstream Cloud Adoption for Engineering and R&D
Cloud HPC adoption is now mainstream, with growth of cloud HPC and AI projected
to continue outpacing on-premises investments: 85% YoY in the cloud,* versus
single-digit growth on premises. Today, 78% of organizations have used the cloudfor science and engineering workloads, and more than half, 53%, use the cloudconsistently.**

78% ofPublic cloud infrastructure offerings are increasingly cost competitive, with cloudinfrastructure now generally at ﬁnancial parity with full-loaded data center costs. In
addition to consumption-based pricing, cloud providers also provide reserved
instances consistent with CapEx budgets.
Migrating to cloud has been shown to deliver signiﬁcant ROI, including a 37%
decrease in operational expenses, a 391% return on investment within three years,
and an annual revenue increase of $139 million per organization.*** Additionally, byusing public cloud, companies can use the latest computing architectures as soon
as they become available, without having to wait for hardware refresh cycles.

Managing Growing Technical Complexity and Skills Gaps

organizations
have used thecloud for scienceand engineering
workloads,

53%
use the cloudconsistently

Business users expect on-demand access to an ever-growing technology stackfrom anywhere in the world. Often, IT teams are challenged to meet these needs.
Additionally, IT teams are dealing with new security challenges in a hybrid andmulti-cloud world. Doing this in today’s evolving ecosystem requires their IT
organizations to upskill their teams and take advantage of automation.
Sources: Intersect 360 2022 Market Data *Rescale 2022 State of Computational Engineering Report **IDC, The Business Value of Migrating and Modernizing to Microsoft Azure***160%
and achieve year
over year increase inperformance of30%
5Cost-Performance of New Cloud Hardware (HW) Across Cloud Service Providers (CSP)
Source: LS-Dyna Car2Car Crash Test Run on Rescale
30
CSP C HW3
CSP C HW4

Performance Per Dollar

Organizations using
the latest computing
architectures canincrease application
performance by

Exhibit 3 - Architectural Flexibility Delivers
Increased Cost-Performance

CSP B HW4

20
ON-PREM
REFRESHCSP A HW4CSP C HW1
CSP B HW4CSP C HW1

10

CSP B HW3

CSP A HW3
CSP B HW2

ON-PREM
REFRESH

2020

2021

CSP B HW1
CSP A HW2
CSP A HW1

2017

Path to Digitally Transform New Product Innovation

2018

Cloud Provider A
Cloud Provider C

2019

2022

Cloud Provider B
Typical On-Prem Infrastructure

The Need to Digitally Transform New Product Innovation
Digital transformation is made possible by digital technologies driving process improvements. For
example, CAD improved manufacturing collaboration and information management, and CAE reduced
the need for physical testing and improved design cycles.
Today, leading companies are taking the next step by adopting new levels of automation combinedwith cloud and AI to further empower the engineer or researcher. The goal is to accelerate innovation
velocity, manage risk, and improve business competitiveness.

Exhibit 4 - Impact of Computational Tools on Project Success
100%
85%
80%
67%
60%

54%
46%

40%

20%

33%

Organizations thatcannot provide
easy access tocomputing at
scale are

3X

15%

0%
I have easy access to all thecomputational resources I could
possibly needIt’s not easy, but I can accessthe computational resources I
need

We consistently or often achieve
our goals on budget

Lack of computational resources
is somewhat impacting what I
can do as a scientist or engineer

more likely to see
projects failWe often miss our goals or
run over budget

Source: Rescale 2022 State of Computational Engineering ReportUser-centric Automation Has Measurable Impact on Project SuccessEngineers and researchers rely on computing to evaluate new product designs, requiring a full R&D
technology stack from simulation software, to data analytics packages, to HPC hardware at scale.
Organizations that understand the critical role computing has on new product innovation haveadopted cloud computing for not only computing at scale, but also to maximize performance andlower costs. Additionally, these companies are using this opportunity to make computing resources
easily accessible, as this plays a critical role in a project’s success. Organizations where computing
resources are diﬃcult to use are twice as likely to see projects fail, and those that cannot provide
suﬃcient resources are three times more likely to see projects fail.

6

Path to Digitally Transform New Product Innovation

Computational Pipelines and AI Will Improve Business CompetitivenessEnterprises commonly use data pipelines to automate and streamline the processof data extraction, transformation, and aggregation. Computational pipelines are
nascent by comparison, but their impact is likely to be far more dramatic than data
pipelines in deﬁning how competitive an enterprise can be.
By using computational pipelines on a cloud-based control plane for hybrid andmulti-cloud operations, organizations can deﬁne, automate, and version control
entire product development processes. This has the effect of not only dramaticallyaccelerating new product development, but also supporting digital threads, toprovide a continuous and integrated ﬂow of data and information that connects all
aspects of a product life cycle, from concept and design, through manufacturing
and operation.

72% ofpharmaceuticalexecutives
believe that
digitaltransformationis critical toachieving R&D
imperatives

Artiﬁcial intelligence is increasingly used in data-driven businesses for predictivemodeling of customer interactions, but a more recent development is the use ofmachine learning (ML) in applied science & engineering. Typically this falls in two
use cases. The ﬁrst is using physics-informed neural networks in the place ofrunning actual simulations. The goal here is to reduce simulation costs and get toapproximate answers quickly. The second is to develop ML models on the process
by which different product designs perform across conditions, helping
organizations accelerate identiﬁcation of optimal designs.

Source: Accenture & BioIT

By adopting computational pipeline automation and machine learning,organizations are able to accelerate new product innovation, and improve businesscompetitiveness.

Exhibit 5 - Data Pipelines vs Computational Pipelines

Support businessprocesses & dataservices

Computational
Pipelines
Power new physicalproduct development
processes

7

Source 1…

Data
Pipelines

Extract

Load

Transform
Raw Data
Staging
Area

Source N

PreProcessing

Simulation A

Path to Digitally Transform New Product Innovation

Prepared
Data

Analytics &Processing

Analyze
Data
Warehouse

Simulation B

Analytics &Processing

Path to Digitally Transform New Product Innovation
Rescale’s work with leading global engineering and R&D-driven companies has enabled us to identify
three common patterns of how organizations are transforming R&D.
Cloud computing is a prerequisite for successful enterprise R&D transformation for three reasons.
First, being user and workload-centric requires on-demand access to software, hardware, andcomputing capacity. Second, supporting R&D process improvements requires establishing a loosely
coupled toolchain from a broad set of ecosystem technologies. Third, enabling worldwide R&D
collaboration requires connectivity. In some cases, data center resources can also be used to support
a hybrid cloud operating model.

Exhibit 6 - Path to Digital Transformation of New Product Development

Phase 3Phase 2Phase 1Cloud-Enabled R&D

Uniﬁed Management
Challenge
Challenge

Challenge
R&D velocity constrained by lack ofcomputing capacity or legacy hardware

IT challenged to onboard more teams &support growing portfolios ofsoftware, hardware, & tools

Accelerated product development withsecure access to computing at scale
enabled by full-stack cloud automation

8

Business competitiveness & R&D
innovation constrained by legacy
product development processes

Outcome
Outcome

Outcome

Digital Transformation

Increased R&D eﬃciency and reduced
ﬁnancial and compliance risk enabled by
uniﬁed visibility and policy-driven control

Path to Digitally Transform New Product Innovation

More competitive products brought tomarket faster enabled by seamlesscollaboration and data integration

1 Phase 1: Cloud-enabled R&D
Traditional data center approaches to HPC focus on multi-year hardware
procurements and driving high utilization, even as engineering teams arebottlenecked on the lack of computing resources.

Eliminating System Constraints to Innovation Velocity
Computing capacity and performance of legacy systems can constrain
R&D innovation velocity, compelling technology leaders to explore cloudcomputing. As product innovation becomes increasingly compute-driven,cloud offers instant access to advanced computing capabilities without
expensive, time-intensive upfront investment.
Cloud provides not only similar economics to data center infrastructure,
but also reliable access to new specialized architectures that may be
diﬃcult to procure due to supply chain disruptions and global shortages.
Supporting business continuity is an additional beneﬁt.

Phase 1Cloud-Enabled R&D
Challenge
R&D velocity constrained by lack ofcomputing capacity or legacy hardware

OutcomeAccelerated product development withsecure access to computing at scale
enabled by full-stack cloud automation

Given cloud’s advantages, the challenge for organizations is how to help
their engineering and R&D organizations to be cloud-enabled, which canbe diﬃcult given many HPC organizations are still new to cloudcomputing. HPC-as-a-Service provides a viable approach.

Accelerated Computing Through Cloud Automation
HPC-as-a-Service (HPCaaS) builds on the common Infrastructure-as-a-Service (IaaS) model of cloudcomputing. However, unlike IaaS, which provides virtual machines on demand to developers, HPCaaS
delivers the entire HPC stack to engineers or researchers. This stack may include HPC hardware
clusters including storage, simulation software, analytics packages, virtual desktops, and more.

Sensatek Accelerates Speed to Market with
Cloud-enabled Design and Simulation

Aerospace, Energy, Electronics

9

Path to Digitally Transform New Product Innovation

To compete in competitive and highly regulatedindustries, Sensatek evaluated ways to accelerate the
R&D of new sensor technologies for aerospace, energy,
and government applications. They needed a ﬂexible
solution that could be implemented quickly without
signiﬁcant IT resources. Sensatek selected Rescale as
its R&D platform to test new product designs under avariety of harsh conditions with greater ﬁdelity andperformance in the cloud. By automating complex HPC
application and hardware, Sensatek was able to meettight product development milestones and focus on
solving bigger R&D challenges without complexity.

1 Phase 1: Cloud-enabled R&D
Rescale’s Cloud-based Control Plane Approach
Rescale is a cloud-based platform for computational engineering and R&D, delivering the entire HPC
stack to users from a cloud-based control plane. With Rescale, engineers can run any application on
their cloud of their choice, with over 1,000 pre-installed applications and the largest global cloud
network of the latest computing architectures, fully supported by Rescale.
Unlike traditional datacenter or “lift-and-shift” approaches to cloud, there is no infrastructure orapplication to maintain, as these resources are instantiated only when users submit a request to run asimulation or analysis. To mitigate risk from broader tech stacks, data sources, and cybersecurity
threats, Rescale hardens public cloud environments to ensure full-stack security, extending coverage
beyond the common shared responsibility models of cloud providers.
Rescale’s cloud-based control plane delivers beneﬁts to both IT and business users, with SaaS-like
simplicity and self-service governed consistent with IT policies.

Exhibit 7 - Traditional HPC vs HPC Built for the Cloud
Traditional Datacenter

Lift & Shift to Cloud

10 Path to Digitally Transform New Product Innovation

HPC Built for the Cloud Approach1 Phase 1: Cloud-enabled R&D
Secure and Accelerated New Product Development
Shifting R&D operations to the cloud can bring new levels of agility and ﬂexibility. Catalysts forcloud adoption can vary. In some cases, time-sensitive projects cannot wait for new hardware
to be acquired. In other cases, project requirements exceed the capacity of on-prem data
center infrastructure, requiring bursts to cloud. Lastly, some organizations have a declared
cloud strategy, developing a comprehensive approach to cloud security, compliance,
operations, and performance.
Rescale’s cloud-based control plane approach enables IT teams to deﬁne and operationalize
automated policies, without the complexity of rebuilding bespoke environments. This approach
dramatically increases the probability of successful cloud projects for those traditionallyoperating on prem. By automating steps and reducing the complexity to run workloads in thecloud, organizations can focus on their core competencies instead of cloud infrastructure
conﬁguration and management.
IT teams leverage Rescale to ﬂexibly scale and secure consistent computing environments
across any cloud for any R&D application with self-service to end users, removing the
obstacles to innovation. As technology portfolios continue to proliferate, automating
infrastructure, applications, and security end-to-end is a competitive advantage in
speed-to-market and de-risking mission-critical computing operations.

Common Patterns in Phase 1: Cloud-enabled R&D
Capabilities

Initiatives
●
●
●
●
●

High-priority R&D projects
Realize cloud-ﬁrst strategy
Shift from CAPEX to OPEXDisaster recovery and business
continuity
Eliminate vendor lock-in acrosscloud and software providers

●
●
●
●
●
●
●

HPCaaS for full-stack automation
GPU acceleration
Cloud bursting demand peaks
Hybrid, multi-cloud orchestration
Application lifecycle management
Infrastructure redundancy / failover
Self-service IT support

11 Path to Digitally Transform New Product Innovation

Outcomes
●
●
●
●
●
●

Accelerated innovation velocity
Financial ﬂexibility
Increased productivity of R&D teams
globally
Enhanced cybersecurity
De-risked computing operations
Increased IT responsivenessCloud-enabledManagement
R&D
2
1 Phase 2:1: Uniﬁed
As organizations adopt cloud HPC more broadly, needs around visibility,
control, and establishing a multi-cloud posture become a priority.

Managing Operational Complexity
IT teams are always challenged to manage ever-increasing portfolios oftechnologies and teams, while being under pressure to control spending.
Adding cloud can sometimes further complicate this challenge.
First, some organizations that have adopted cloud can ﬁnd it diﬃcult tounderstand where the spend is going and whether budgets are being
used effectively. Cloud providers have multiple regions, and instancetypes, each of which have their own price points.
Second, understanding which instance types to use for each application
can be quite complex requiring benchmark investigations. Third, R&D
teams are increasingly distributed, further complicating the necessary
infrastructure footprint. Fourth, IT organizations must manage data
sovereignty considerations and make sure data stays in speciﬁc
geographies. For some companies, quantifying their sustainability
posture is an additional consideration. Furthermore, adding multi-cloud
operations adds an additional dimension to these challenges.

Phase 2Uniﬁed Management
Challenge
IT challenged to onboard more teams &support growing portfolios ofsoftware, hardware, & tools

OutcomeIncreased R&D eﬃciency and reduced
ﬁnancial and compliance risk enabled by
uniﬁed visibility and policy-driven control

Policy-based IT Control with Intelligent Automation
For IT to be an effective partner to the business, and enable successful R&D teams, they needcomprehensive visibility and control with automation to deliver resources on demand.
Intelligent automation in this context means automating the provisioning of resources basedon pricing, application-speciﬁc hardware performance, capacity, and other intelligence. This
information enables IT to operationalize policies to improve security, manage risk, comply withdata locality requirements, and meet broader business objectives.

90% of R&D leaders say "accelerating speed to maturity

45%

for technologies in the R&D portfolio" is a top priority while only
are conﬁdent that they can solve this challenge within 12-18 months

Source: Gartner, Top R&D Priorities 2023

12 Path to Digitally Transform New Product Innovation

Cloud-enabledManagement
R&D
2
1 Phase 2:1: Uniﬁed
Rescale’s Intelligent Automation Approach
Rescale’s intelligent automation approach to managing computing
resources starts with providing transparency across software licensing
availability, hardware performance, costs from hardware and software,
global infrastructure capacity and maturity, and compliance.
Automation can also be used to extend on-premises environments via
schedulers for hybrid operations.
With a uniﬁed view of these resources, IT administrators can makeinformed decisions about the best conﬁguration of resources based on
the goals of the business. With granular and continuous visibility ofutilization, performance, and cost, business leaders can more accurately
forecast future resource needs and prevent budget overruns.
As new projects drive increased collaboration and resource demands
around the world, policy-based automation can enforce strict data
access and locality requirements while ensuring users can access only
approved hardware and software.

89%
of respondents agree
that security is themost critical
determinant of
success for a cloudstrategy, and this isexacerbated bymulti-cloudenvironment
complexity

31% ranked
complexity as themost signiﬁcantinternal challenge tocloud security
Source: Forrester, Unlocking Multi-cloud’s
Operational Potential

Exhibit 8 - Rescale Intelligent Resource Automation Simpliﬁes Multi-Cloud Operations
Resource Intelligence

Policy-Based Automation

Software & ISV Licensing

Software License Optimization

Architectural Performance

Multi-Architecture Prioritization

Infrastructure Costs
Hardware Capacity & Maturity
Security & Compliance

13 Path to Digitally Transform New Product Innovation

Multi-Cloud Prioritization
Multi-Service Level Prioritization
Data Locality and Access Management

Cloud-enabledManagement
R&D
2
1 Phase 2:1: Uniﬁed
Optimizing Performance and Managing Risk with Policy-based Controls
With comprehensive visibility control, IT leaders can balance business-level initiatives anduser-level eﬃciency gains. For example, Rescale customers have ﬂexibility to optimize for
reduced computing costs, increased computing performance, or a balance of the two.
Reducing or optimizing costs requires a full picture of workload economics, includingcommercial SW licensing costs combined with infrastructure, SLA, and architectural
conﬁgurations. By combining data from past workload performance and economics with
Rescale’s unique performance intelligence, customers can create standardized best practices
that control costs and maximize engineering performance across the organization.
Policies can also be standardized to enforce data locality requirements, which, when combinedwith enhanced cloud security, can reduce cybersecurity risk.

Common Patterns in Phase 2: Uniﬁed Management
Initiatives
●
●
●
●
●
●

Multi-cloud optimization
Cost management
Centralize and standardize
best-practices
Reduce emissions from business
operations
Acquisition integrations
Meet strict industry compliance

Capabilities
●
●
●
●
●
●
●

Intelligent resource automation
Granular budgeting and spend
traceability
Cost model ﬂexibility
Policy-based automation
Continuous performance optimization
Multi-team management
Sustainable infrastructure prioritization

Outcomes●●●●●

Reduced TCO in R&D computing
New business with strict compliancerequirements
Increased resource utilization
Increased engineering performance
Increased energy eﬃciency &sustainability

NOV Unlocks Engineering Productivity &Business Growth with Uniﬁed Cloud HPC
Global leader in oil and gas and renewable energy, NOV,
leverages cloud-enabled simulation to develop and deploy newenergy technologies across multiple regions and business units,from subsea oil and gas to offshore renewables.

Energy, Oil and Gas& Renewables
14 Path to Digitally Transform New Product Innovation

NOV relies on a variety of R&D applications to get accurate
predictions of safety, durability, and economic viability of new
products before they reach production. As the company
expanded, the increased complexity of IT management required
more visibility and control. This led NOV to select Rescale as tounify and optimize their broad portfolio of workloads andincrease their engineering agility.

3 Phase 3: Digital Transformation
Organizations that have successfully transitioned to cloud operations
are positioned to further drive transformational R&D process
improvements.
Phase 3

Constraints of Legacy R&D Processes

Digital Transformation

Legacy processes for product development, including manual steps forrunning simulations, and siloed data analysis and decision-making,constrain innovation velocity. Leading engineering companies recognizethat staying competitive requires R&D process modernization taking fulladvantage of cloud, automation, collaboration, and AI.

Challenge
Business competitiveness & R&D
innovation constrained by legacy
product development processes

Foundational Components to Successful Transformation
Successful digital transformation of engineering and R&D is predicatedon three prerequisites. First, is starting from the user, and providingthem instant access to the full technology stack from hardware, tosoftware, to data. Second is automating entire R&D processes, which
may include user or event-triggered tasks, and requires automating aloosely coupled toolchain from a broad set of ecosystem technologies.
Third, is supporting seamless user-to-user collaboration.

OutcomeMore competitive products brought tomarket faster, enabled by seamlesscollaboration and data integration

These foundational components enable transformational improvements in the product development
process, including the adoption of AI/ML in R&D. The combination of these technologies isrevolutionizing the way research and development is conducted, increasing eﬃciency and accuracy
while reducing time and costs. Lastly, because R&D projects often involve cross-functional teams,
external collaborators, and regulators, organizations need to implement automated governance ofdata ﬂows and permissions consistent with IT policies.

Doosan Enerbility Deploys AI for Increased
Quality Control and Optimized Operations

Enerbility
Thermal, Nuclear, and Renewable Energy
15 Path to Digitally Transform New Product Innovation

Doosan Enerbility, a global leader of energy anddesalination plants, needed to accelerate thedevelopment of more sustainable and eﬃcientsolutions. This required new technologies to design,
test, and monitor products. Doosan partnered with
Rescale to transform their R&D capabilities and deploy
AI-enabled simulation to detect defects and predictmaintenance. This resulted in being able to operatecritical energy systems and infrastructure more
effectively, safely, and sustainably.

3 Phase 3: Digital Transformation
Rescale’s Computational Pipeline Approach
Rescale enables the digital transformation of engineering and R&D organizations
by providing an intelligently automated and extensible R&D computing backbone
with several key capabilities.
First is a user-centric experience for running simulations, analysis, andvisualizations, including cloud-based virtual desktops. Second is a cloud-based
control plane that automates hardware and software across hybrid andmulti-cloud. Third is full-stack performance intelligence, enabling organizations to
optimize costs, time-to-solve, ensure business continuity, and meet objectives
around data locality, compliance or security policies. And lastly, an event-driven
and automated computational pipeline that provides seamless integration
between each of the steps and tools used in the product development process.
A computational pipeline is a series of sequential or parallel processing steps thatare connected to form a complete workﬂow for solving a particular computational
problem. The steps in a pipeline can include tasks such as data preprocessing,feature extraction, model training, and evaluation. Each step in the pipeline
produces an output that is fed as input into the next step, allowing for the
automation and eﬃcient execution of the entire workﬂow. Computational
pipelines are commonly used in ﬁelds such as scientiﬁc computing, machinelearning, and bioinformatics to manage and simplify complex data analysis tasks.
These capabilities facilitate the implementation of modern digital R&D use cases,
including digital twin, digital thread, AI-assisted engineering, real-time
collaboration, and workﬂow automation.

Digital threadincreases the
supply chain
eﬃciency by

16%,and enablesdelivery of new
products to themarket20%
faster for
manufacturing
companies
Source: RGBSI & LNS
Research

Exhibit 9 - Computational Pipelines
Automotive
Data
Collection

VirtualModelsScenario
Creation

SensorModel

Score &
Performance
Evaluation

PhysicalValidation

Bioinformatics
SamplePreparation &Sequencing

Quality Check& Trimming

Alignment toOrganism’s
GenomeTargetPrediction

TargetValidation

Functional
Veriﬁcation

PhysicalDesignPhysicalVeriﬁcation &Sign OffElectronic Design Automation
Logic Design

Synthesis

16 Path to Digitally Transform New Product Innovation

Computational
pipelines power
new physicalproduct
development
processes

3 Phase 3: Digital Transformation
AI-assisted Engineering Can Deliver More Competitive Products
After many years of running simulations, many R&D-driven companies are
realizing that simulation data can not only help product development, but canalso help deﬁne the entire process by which different designs may interact with
their environments. By applying machine learning to a broad set of design andsimulation data across a range of conditions, organizations can develop an
institutional understanding of how variants of the product may perform. This
helps organizations move from traditional “intuition-led engineering” toward“AI-assisted engineering.”●

Intuition-led Engineering - Engineers use their experience to hypothesize
potential designs, and further reﬁne them with simulation and physicaltesting●

AI-assisted Engineering - Engineers leverage ML models to identify
potential design candidates that are likely to meet desired performance
characteristics, and further reﬁne those candidates with more detailed
simulation and physical validation

With AI-assisted
engineering, the“product” is themodel itself;
what’s being
manufactured
and shipped are
instances of thismodel

In this scheme, the “product” of the company is the model itself, an intellectual
property of the organization. And what’s being manufactured and shipped are
instances of this model. Organizations seeking to leverage AI in this way believe
they are able to respond more quickly to their customers, and provide guidance tocustomers on what’s possible from upcoming product designs.

Exhibit 10 - Milestones Toward Computational Science & Engineering
Data and AI-assisted
Engineering

Intuition-led Engineering
1960
Drawn Wireframe

1980
Computer-Aided Design
(CAD)

2015
Computer-Aided Engineering(CAE)

Computational Science & Engineering(CSE)
Compute-led

Airfoil 1:
AoA, M, m, p,t

Automate

Optimize

Empower
Optimizers

Source: Big Compute 2022 Keynote

17 Path to Digitally Transform New Product Innovation

3 Phase 3: Digital Transformation
Accelerated Commercialization of More Competitive Products
Digital transformation improves the speed, eﬃciency, and quality of new
product innovation by empowering R&D end users, while enabling IT to bea strategic partner to the business.

Engineers spend as

50%

little asof
their time on actual
development due toadministrative andmanual work

This transformation is only possible by connecting the latest technologies
and systems of insight across the digital thread to reduce the time it takes
to bring ideas to market. By reducing manual steps in the productdevelopment process, R&D teams become more eﬃcient and effective.
As competition for talent heats up, organizations that create environments
and cultures geared for innovation will attract, retain, and empower theirmost valuable asset: their people. By freeing up their time and delivering
the necessary resources, digital transformation enables R&D teams tofocus more on innovation and stay ahead of the competition.

Source: Harvard Business
Review, Where Tech is Headed in
2023

Phase 3: Digital Transformation
Initiatives Common Patterns in
Capabilities
Outcomes
Initiatives
●
●
●
●
●
●

Adopt AI/ML for accelerated new
product development
Integrate platforms and data
sources
Distributed & remote workforce
Talent retention / engagement
Process automation
Industry 4.0, digital twin, andindustrial metaverse, etc.

Capabilities
●
●
●
●
●
●
●
●

Interactive simulation visualization
Real-time collaboration
Automated computational pipelines
Composable & templated workﬂows
Multidisciplinary design optimization
Design of Experiments
Ecosystem integration (e.g., AI/ML ops,
PLM, CAD, CAE)
Data warehouse & data lake

18 Path to Digitally Transform New Product Innovation

Outcomes●●●●●

Accelerated commercialization of new
products and business models
Increased IT and R&D productivity
New product insights
Process innovation
Technical talent attraction andretention

Conclusion:
Planning Your Digital Transformation Journey
As product-driven companies race to deliver innovation, successful digital transformation initiatives
will accelerate how they commercialize new product innovations.
This ebook provides practical steps to R&D transformation that organizations can use to identify theirown stage of digital maturity and plan further steps they can take to remove constraints to innovationand eﬃciency.
Key recommendations for business leaders to digitally transform new product innovation include:● Identify desired outcomes and technical approach to digital transformation● Improve user experience and invest in training to maximize talent & productivity● Automate complexity out of tech stack conﬁguration, maintenance, and security● Establish visibility and controls to increase eﬃciency and reduce risk
● Unify silos of data and analysis for seamless collaboration between R&D teams● Integrate AI/ML, digital twins, and additional tools to further drive business impact
As trends such as cloud, AI, and digital twins impact how engineering and IT teams work with eachother, organizations that work together to build a holistic transformation strategy will increase theirchances of success.
Rescale empowers the majority of industry-leading companies to accelerate new productbreakthroughs and achieve their digital transformation goals with cloud-enabled R&D, uniﬁed
management for IT, and connected digital thread. Our team of multi-disciplinary technology andindustry experts is ready to help you achieve your most ambitious engineering goals.

19 Path to Digitally Transform New Product Innovation

High Performance Computing Built for the Cloud
DigitalEngineering

Workload
Optimization

Intelligent
Automation

Security &Compliance

About Rescale
Rescale is high performance computing built for the cloud to empowerengineers and scientists with AI-enabled simulation and modeling, while
giving IT security and control.
From supersonic jets to personalized medicine, industry leaders arebringing new product innovations to market with unprecedented speed andeﬃciency with Rescale, a cloud platform delivering intelligent full-stackautomation and performance optimization.
IT leaders use Rescale to deliver HPC-as-a-Service with a secure control
plane to deliver any application, on any architecture, at any scale on theircloud of choice.

20 Path to Digitally Transform New Product Innovation

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