By applying engineering analytics across the business, manufacturers can reimagine how they design, produce and deliver new products and services that resonate with customer needs and preferences.
'Applying System Science and System Thinking Techniques to BIM Management' Alan Martin Redmond, PhD
Redmond, A. and Alshawi, M. (2017) 'Applying System Science and System Thinking Techniques to BIM Management' Developments in eSystems Engineering, IEEE CELEBRATING 10 YEARS OF ADVANCING E-SYSTEMS ENGINEERING RESEARCH AND DEVELOPMENT, Paris, France, 14th – 16th June 2017,
Business Process Modeling: An Example of Re-engineering the EnterpriseMassimo Talia
How the Software Engineering and Electrical and Electronic System Engineering walk together. Software Engineering is more related to the software, System Engineering is related to the Physical Systems.
INFORMATION MODELING AND KNOWLEDGE MANAGEMENT APPROACH TO RECONFIGURING MANUF...ijait
This research aims to gain a detailed understanding of data producers, data consumers and format/flow of the data within automotive industry for defining and using Bill of Process (BoP) for engine assembly lines. The focus remained on the real industrial challenge of rapid constraint evaluation for designing
and/or reconfiguration of Powertrain assembly lines to cater for a new/changed product. A methodology is developed to facilitate Ford Company to quickly model and re-configure new/changed assembly line for building new/changed engine. This is made possible with the help of modular approach and
developing relationships among products, processes and resources. The data and information of the PPR is made available to all the stake holders of the organisation independent of the platform or specific application being used at the department/facility.
Current Trends in Product Development during COVID-19vivatechijri
This paper will summarize the authors´ experience over the last decades, from new methods developed
and used within Product Development, as well as current trends. Hence, a general and broad overview is
presented, rather than recent research results. Driving forces in PD are: Technology, Market and Society.
Ecological, economic and social sustainability require recycling, reuse, energy conservation and new business
concepts. Customization is carried out by modular architecture, combining customer specific products with
volume production of components and sub-systems. PD integrates “hard” properties (engineering), with “soft”
properties (industrial design). Fundamental PD characteristics are: Iteration, Integration (technical and
organizational), and Innovation. Globally distributed industrial partners co-operate using Internet. Iteration:
modeling/simulation, virtual prototyping and additive manufacturing speed up process loops. Structured PD:
Initial specification of “what” – functional requirements, then “how” - generation of design solutions.
Interdependencies analysis is important to simplify the product´s structure. The V-model for specification and
verification is commonly used. A 3-stage industrial process separates strategy, core technology development, and
product design for market introduction.
'Applying System Science and System Thinking Techniques to BIM Management' Alan Martin Redmond, PhD
Redmond, A. and Alshawi, M. (2017) 'Applying System Science and System Thinking Techniques to BIM Management' Developments in eSystems Engineering, IEEE CELEBRATING 10 YEARS OF ADVANCING E-SYSTEMS ENGINEERING RESEARCH AND DEVELOPMENT, Paris, France, 14th – 16th June 2017,
Business Process Modeling: An Example of Re-engineering the EnterpriseMassimo Talia
How the Software Engineering and Electrical and Electronic System Engineering walk together. Software Engineering is more related to the software, System Engineering is related to the Physical Systems.
INFORMATION MODELING AND KNOWLEDGE MANAGEMENT APPROACH TO RECONFIGURING MANUF...ijait
This research aims to gain a detailed understanding of data producers, data consumers and format/flow of the data within automotive industry for defining and using Bill of Process (BoP) for engine assembly lines. The focus remained on the real industrial challenge of rapid constraint evaluation for designing
and/or reconfiguration of Powertrain assembly lines to cater for a new/changed product. A methodology is developed to facilitate Ford Company to quickly model and re-configure new/changed assembly line for building new/changed engine. This is made possible with the help of modular approach and
developing relationships among products, processes and resources. The data and information of the PPR is made available to all the stake holders of the organisation independent of the platform or specific application being used at the department/facility.
Current Trends in Product Development during COVID-19vivatechijri
This paper will summarize the authors´ experience over the last decades, from new methods developed
and used within Product Development, as well as current trends. Hence, a general and broad overview is
presented, rather than recent research results. Driving forces in PD are: Technology, Market and Society.
Ecological, economic and social sustainability require recycling, reuse, energy conservation and new business
concepts. Customization is carried out by modular architecture, combining customer specific products with
volume production of components and sub-systems. PD integrates “hard” properties (engineering), with “soft”
properties (industrial design). Fundamental PD characteristics are: Iteration, Integration (technical and
organizational), and Innovation. Globally distributed industrial partners co-operate using Internet. Iteration:
modeling/simulation, virtual prototyping and additive manufacturing speed up process loops. Structured PD:
Initial specification of “what” – functional requirements, then “how” - generation of design solutions.
Interdependencies analysis is important to simplify the product´s structure. The V-model for specification and
verification is commonly used. A 3-stage industrial process separates strategy, core technology development, and
product design for market introduction.
WHEN DOES PRECISION ENGINEERING STARTS?
Precision engineering was first published in January 1979; since 1986 it has also been known to many of its readers as the Journal of the American Society of Precision Engineering. Now with effect from 2000, it assumes a new look, proudly proclaiming itself the Journal of the International Societies of Precision Engineering and nanotechnology.
Our Industrial Modeling Service (IMS) involves several important (but rarely implemented) methods to significantly improve and advance your existing models and data. Since it is well-known that good decision-making requires good models and data, IMS is ideally suited to support this continuous-improvement endeavour. IMS is specifically designed to either co-exist with your existing design, planning, scheduling, etc. applications or these same models and data can be used seamlessly into our Industrial Modeling and Programming Language (IMPL) to create new value-added applications. The following techniques form the basis of our IMS offering.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Which benchmarks for wich level of information on your porftfolio?
• office, retail, labs, multinational, environment, green performance, Human Experience, HX working from Home
INVESTIGATING HUMAN-MACHINE INTERFACES’ EFFICIENCY IN INDUSTRIAL MACHINERY AN...IJITCA Journal
The twenty-first century has seen a vast technological revolution characterized by the development of
cyber-physical systems, integration of things, and new and computationally improved machines and
systems. However, there have been seemingly little strides in the development of user interfaces,
specifically for industrial machines and equipment. The aim of this study was to assess the efficiency of the
human-machine interfaces in the Kenyan context in providing a consistent and reliable working
environment for industrial machine operators. The researcher employed a convenient purposive sampling
to select 15 participants who had at least two years of hands-on experience in machines operation, control,
or instrumentation. The results of the study are herein presented, including the recommendations to
enhance workforce productivity and efficiency.
Decisions Optimization Related to the Production Within Refining and Petroche...ijtsrd
The paper underlines the use of quantitative analyses and mathematical models to optimize the decision within companies from oil and gas industry. It will be presented a case study from a refinery that use RPMS Refinery and Petrochemical Modeling System software for optimizing LPG blends. Catalin Popescu "Decisions Optimization Related to the Production Within Refining and Petrochemical Industry" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: http://www.ijtsrd.com/papers/ijtsrd20189.pdf
http://www.ijtsrd.com/management/operations-management/20189/decisions-optimization-related-to-the-production-within-refining-and-petrochemical-industry/catalin-popescu
Measurement and Comparison of Productivity Performance Under Fuzzy Imprecise ...Waqas Tariq
The creation of goods and services requires changing the expended resources into the output goods and services. How efficiently we transform these input resources into goods and services depends on the productivity of the transformation process. However, it has been observed there is always a vagueness or imprecision associated with the values of inputs and outputs. Therefore, it becomes hard for a productivity measurement expert to specify the amount of resources and the outputs as exact scalar numbers. The present paper, applies fuzzy set theory to measure and compare productivity performance of transformation processes when numerical data cannot be specified in exact terms. The approach makes it possible to measure and compare productivity of organizational units (including non-government and non-profit entities) when the expert inputs can not be specified as exact scalar quantities. The model has been applied to compare productivity of different branches of a company.
WHEN DOES PRECISION ENGINEERING STARTS?
Precision engineering was first published in January 1979; since 1986 it has also been known to many of its readers as the Journal of the American Society of Precision Engineering. Now with effect from 2000, it assumes a new look, proudly proclaiming itself the Journal of the International Societies of Precision Engineering and nanotechnology.
Our Industrial Modeling Service (IMS) involves several important (but rarely implemented) methods to significantly improve and advance your existing models and data. Since it is well-known that good decision-making requires good models and data, IMS is ideally suited to support this continuous-improvement endeavour. IMS is specifically designed to either co-exist with your existing design, planning, scheduling, etc. applications or these same models and data can be used seamlessly into our Industrial Modeling and Programming Language (IMPL) to create new value-added applications. The following techniques form the basis of our IMS offering.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Which benchmarks for wich level of information on your porftfolio?
• office, retail, labs, multinational, environment, green performance, Human Experience, HX working from Home
INVESTIGATING HUMAN-MACHINE INTERFACES’ EFFICIENCY IN INDUSTRIAL MACHINERY AN...IJITCA Journal
The twenty-first century has seen a vast technological revolution characterized by the development of
cyber-physical systems, integration of things, and new and computationally improved machines and
systems. However, there have been seemingly little strides in the development of user interfaces,
specifically for industrial machines and equipment. The aim of this study was to assess the efficiency of the
human-machine interfaces in the Kenyan context in providing a consistent and reliable working
environment for industrial machine operators. The researcher employed a convenient purposive sampling
to select 15 participants who had at least two years of hands-on experience in machines operation, control,
or instrumentation. The results of the study are herein presented, including the recommendations to
enhance workforce productivity and efficiency.
Decisions Optimization Related to the Production Within Refining and Petroche...ijtsrd
The paper underlines the use of quantitative analyses and mathematical models to optimize the decision within companies from oil and gas industry. It will be presented a case study from a refinery that use RPMS Refinery and Petrochemical Modeling System software for optimizing LPG blends. Catalin Popescu "Decisions Optimization Related to the Production Within Refining and Petrochemical Industry" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: http://www.ijtsrd.com/papers/ijtsrd20189.pdf
http://www.ijtsrd.com/management/operations-management/20189/decisions-optimization-related-to-the-production-within-refining-and-petrochemical-industry/catalin-popescu
Measurement and Comparison of Productivity Performance Under Fuzzy Imprecise ...Waqas Tariq
The creation of goods and services requires changing the expended resources into the output goods and services. How efficiently we transform these input resources into goods and services depends on the productivity of the transformation process. However, it has been observed there is always a vagueness or imprecision associated with the values of inputs and outputs. Therefore, it becomes hard for a productivity measurement expert to specify the amount of resources and the outputs as exact scalar numbers. The present paper, applies fuzzy set theory to measure and compare productivity performance of transformation processes when numerical data cannot be specified in exact terms. The approach makes it possible to measure and compare productivity of organizational units (including non-government and non-profit entities) when the expert inputs can not be specified as exact scalar quantities. The model has been applied to compare productivity of different branches of a company.
IoT Startup State of The Union 2016--Wing Venture CapitalMartin Giles
At Wing, we have spent the past few months researching the state of the Internet of Things startup ecosystem. This presentation summarizes some of our initial high-level findings. An accompanying commentary can be found at www.wing.vc/blog
Tracxn Research — IoT Infrastructure Landscape, December 2016Tracxn
Twenty acquisition deals reported in the sector. Cisco’s acquisition of Jasper Networks for $1.4B is one of the biggest buyouts in the sector till date.
Transform your Business with AI, Deep Learning and Machine LearningSri Ambati
Video: https://www.youtube.com/watch?v=R3IXd1iwqjc
Meetup: http://www.meetup.com/SF-Bay-ACM/events/231709894/
In this talk, Arno Candel presents a brief history of AI and how Deep Learning and Machine Learning techniques are transforming our everyday lives. Arno will introduce H2O, a scalable open-source machine learning platform, and show live demos on how to train sophisticated machine learning models on large distributed datasets. He will show how data scientists and application developers can use the Flow GUI, R, Python, Java, Scala, JavaScript and JSON to build smarter applications, and how to take them to production. He will present customer use cases from verticals including insurance, fraud, churn, fintech, and marketing.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Feature by Feature comparison of IoT Platform is no sufficient. This presentation talks about why leveraging IOT Platforms is important to accelerate innovation and focus on creating true differentiation. This presentation also provides a reference architecture and technical and business evaluation criteria.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
Streaming Analytics with Spark, Kafka, Cassandra and AkkaHelena Edelson
This talk will address how a new architecture is emerging for analytics, based on Spark, Mesos, Akka, Cassandra and Kafka (SMACK). Popular architecture like Lambda separate layers of computation and delivery and require many technologies which have overlapping functionality. Some of this results in duplicated code, untyped processes, or high operational overhead, let alone the cost (i.e. ETL). I will discuss the problem domain and what is needed in terms of strategies, architecture and application design and code to begin leveraging simpler data flows. We will cover how the particular set of technologies addresses common requirements and how collaboratively they work together to enrich and reinforce each other.
Fintech and Transformation of the Financial Services IndustryRobin Teigland
Slides from our FinTech day as part of the Entrepreneurship & Innovation Concentration in the Stockholm School of Economics Exec MBA program in Stockholm, Sweden.
162 flashcards covering all of the formulas, concepts and strategies needed for the quantitative section of the GMAT. If, at any time, you need more information or instruction, each flashcard is linked to a video lesson (from GMAT Prep Now’s GMAT course)
This presentation was given by Prof. Chiara Francalanci from Politecnico di Milano during the second Virtual BenchLearning organised by the H2020 DataBench project.
Using Predictive Analytics to Optimize Asset Maintenance in the Utilities Ind...Cognizant
Predictive analytics is a process of using statistical and data mining techniques to analyze historic and current data sets, create rules and predict future events. This paper outlines a game plan for effective implementation of predictive analytics.
Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Pr...Cognizant
Organizations rely on analytics to make intelligent decisions and improve business performance, which sometimes requires reproducing business processes from a legacy application to a digital-native state to reduce the functional, technical and operational debts. Adaptive Scrum can reduce the complexity of the reproduction process iteratively as well as provide transparency in data analytics porojects.
A Practical Approach for Power Utilities Seeking to Create Sustaining Busines...Cognizant
For power utilities, analytics are a key to enhanced operational performance and competitive standing. We offer a roadmap for determining and prioritizing relevant analytics, assessing analytics maturity, and implementing an effective analytics process encompassing smart meters, phasor measurement units and other useful sources.
Industrializing Zero Application MaintenanceCognizant
By applying a rigorous and automated approach to supporting applications, IT organizations can reduce spending, increase repair accuracy, minimize application debt across the portfolio, free up resources for more strategic business imperatives and improve application yield to deliver enhanced business outcomes.
A Holistic Approach to Yield Improvement in the Semiconductor Manufacturing I...yieldWerx Semiconductor
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It is an analytic solution for an oil and gas industry. This will provide the business intelligence solution for the industry business for management and deep insights.
It Takes an Ecosystem: How Technology Companies Deliver Exceptional ExperiencesCognizant
Experience is evolving into a strategy that reaches across technology companies. We offer guidance on the rise of experience and its role in business modernization, with details on how orgnizations can build the ecosystem to support it.
The Work Ahead: Transportation and Logistics Delivering on the Digital-Physic...Cognizant
The T&L industry appears poised to accelerate its long-overdue modernization drive, as the pandemic spurs an increased need for agility and resilience, according to our study.
Enhancing Desirability: Five Considerations for Winning Digital InitiativesCognizant
To be a modern digital business in the post-COVID era, organizations must be fanatical about the experiences they deliver to an increasingly savvy and expectant user community. Getting there requires a mastery of human-design thinking, compelling user interface and interaction design, and a focus on functional and nonfunctional capabilities that drive business differentiation and results.
The Work Ahead in Manufacturing: Fulfilling the Agility MandateCognizant
According to our research, manufacturers are well ahead of other industries in their IoT deployments but need to marshal the investment required to meet today’s intensified demands for business resilience.
The Work Ahead in Higher Education: Repaving the Road for the Employees of To...Cognizant
Higher-ed institutions expect pandemic-driven disruption to continue, especially as hyperconnectivity, analytics and AI drive personalized education models over the lifetime of the learner, according to our recent research.
Engineering the Next-Gen Digital Claims Organisation for Australian General I...Cognizant
In recent years, insurers have invested in technology platforms and process improvements to improve
claims outcomes. Leaders will build on this foundation across the claims landscape, spanning experience,
operations, customer service and the overall supply chain with market-differentiating capabilities to
achieve sustainable results.
Profitability in the Direct-to-Consumer Marketplace: A Playbook for Media and...Cognizant
Amid constant change, industry leaders need an upgraded IT infrastructure capable of adapting to audience expectations while proactively anticipating ever-evolving business requirements.
Green Rush: The Economic Imperative for SustainabilityCognizant
Green business is good business, according to our recent research, whether for companies monetizing tech tools used for sustainability or for those that see the impact of these initiatives on business goals.
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1. Demystifying Engineering Analytics
By applying engineering analytics across the business,
manufacturers can reimagine how they design, produce and deliver
new products and services that resonate with customer needs and
preferences.
Executive Summary
A growing focus on operational efficiencies
and financial performance is causing manufac-
turers across industries to sharpen and refine
their engineering and manufacturing discipline.
Many are looking at analytical techniques across
stages — from design, to delivery and service — to
reimagine and revamp how they work.
Industrial equipment manufacturers, for example,
are seeking ways to detect component failures
and predict the likelihood of failure by monitoring
field data, usage patterns and environmental
conditions. In the automotive industry, many car
manufacturers are looking to boost revenues and
achieve market differentiation by offering new
digital features and services informed by data
generated by the vehicle, and combined with
an understanding of customer preferences and
lifestyles.
As businesses move from one-size-fits-all to more
customized products, personalization is becoming
critical, especially in new product design, feature
enhancements and the connected products space.
As these trends accelerate, utilities, infrastructure
and transportation companies face heightened
challenges to simultaneously improve margins
and future-proof their businesses by embracing
sustainability measures that enhance energy
efficiency and reduce their carbon footprint.
As if these challenges weren’t big enough, many
businesses are dealing with the proliferation of
data volume, variety and velocity across their
ecosystems — from raw material and suppliers,
through finished products and customer usability
patterns. Many are sitting on huge repositories of
structured and unstructured data, seeking ways
to make more informed, fact-based decisions on
business strategy and market direction.
This white paper introduces the concept of engi-
neering analytics (EA) and how it can be applied
to solve various industrial problems by leveraging
a structured approach that binds principles
of domain engineering, system thinking and
analytical techniques.
Defining EA
EA is a discipline that helps organizations derive
meaningful insights from information provided
by physical devices, machines and equipment to
develop a knowledge base for actionable intel-
cognizant 20-20 insights | march 2015
• Cognizant 20-20 Insights
2. Quick Take
DE encompasses engineering principles from elec-
trical, chemical, mechatronics, heat mechanics
and computer science. Most industry segment
problems can be easily broken down using these
disciplines.
Here’s an example: Directional drilling in the oil
and gas patch requires characterization of drilling
operations in the form of differential pressures
in the borehole, gravitational, torsional and
hydraulic forces, as well as their impact on the life
of drill-bit and drilling dysfunctions.
ST involves the identification of system functions
and sub-systems, their logical and functional rela-
tionships and their impact on failure, reliability,
performance and total cost of ownership (TCO). It
handles software, hardware interfaces and system
interaction with the environment under different
operating conditions.
For example, in cold chain logistics, several
factors — such as ambient conditions, product
metabolism and driving behavior (door opening/
closing patterns, harsh driving), controller tuning,
loading conditions and vehicle health index —
have an impact on operational expenditures (e.g.,
fuel and maintenance), quality (e.g., tempera-
ture variance) and service (e.g., SLA, quality on
arrival). To solve such problems, various systems
must be studied, including diesel engines, refrig-
eration units, container and regional climate, etc.
With analytics, data in different forms is consumed
to derive specific insights in the form of patterns,
correlations and models that capture cause-
effect relationships and behavioral/functional
representation to address business scenarios.
This includes well-known techniques of data pre-
processing, filtering, data mining, modeling and
visualization.
For example, asset performance management (in
utilities) requires time-series processing of data
emanating from geographical distributed assets
(e.g., transformers) to detect failure signature
and build a case-based library to diagnose faults
based on derived multivariate statistical patterns.
Domain Engineering and Systems Thinking Are Integral to
Engineering Analytics
cognizant 20-20 insights 2
ligence. It uses engineering/scientific principles
and mathematical representations of the
functional behavior of devices and machines —
coupled with deep domain understanding and
analytical tools — to build models that can address
specific business problems. The problem canvas
covers issues such as process efficiency improve-
ment, asset assurance, customer experience
enhancement, new product features introduction,
cost reduction, time-to-market reduction and ser-
vitization.1
Through all of this, a natural question arises:
What’s the difference between analytics and engi-
neering analytics?
Engineering analytics is a multi-disciplinary
approach for formulating problems using systems
thinking (ST) and domain engineering (DE) when
applying analytics techniques to solve business
challenges. Problem formulation is an involved
activity comprised of identifying influencing
variables and understanding in-depth principles
of physics, mechatronics, fluid mechanics, ener-
gy-mass balances, thermodynamics and specific
engineering laws. The main challenges include
sensor enablement, sensor diagnostics and
management, identifying secondary variables,
reconciling data and reducing the dimensional-
ity of the parameter space for building real-time
implementation models, while preserving the
engineering sanctity.
3. Oil & Gas Energy & Utilities Farming Automotive
cognizant 20-20 insights 3
Figure 1 illustrates market segments in which we
have leveraged EA to address specific industry
problems.
For example, we worked with an independent
major U.S. upstream company in the oil and gas
space with international operations in exploration
and production. This company sought to leverage
EA for asset optimization and tool downtime
management, using predictive analytics and a
proprietary application that was being rolled out
to different rigs to improve operator visualization
and decision-making.
The solution involves a domain-driven signal
processing and specific energy formulation for
identifying and predicting the drilling dysfunc-
tions and equipment failure based on limited
measurements at the surface and without any
down-hole measurement or data samples. The
algorithms enable drilling operators to visualize
and choose the sweet spots for drilling.
For every 5% reduction in drilling time, this
customer expects to realize savings of $1 million
annually, per rig. Along with downtime reduction,
its life of down-hole tools will now be extended by
reducing destructive vibrations.
Contending with Unique Challenges
Each EA problem is unique and requires indepen-
dent scrutiny; however, EA-based challenges can
be broadly divided into two categories. Problems
in both categories can be addressed via a four-
phased approach.
• Category I: A specific problem is known, and
a large amount of associated data is available
(see Figure 2, next page).
For example, in heavy-duty engines, valve
failures result in degraded engine performance.
In order to predict the likelihood of failure, thus
minimizing risk, various parameters must be
analyzed together, including engine param-
eters, operating conditions, control variables,
command triggers and quality metrics.
>> Phase A: Information-seeking. Relevant
information is gathered about the problem
and the associated system and sub-systems.
This requires a high level of domain engi-
neering and system thinking to arrive at data
requirements and evaluate data gaps.
>> Phase B: Problem formulation/hypothesis.
The initial hypothesis is defined, and the
problem formulation is performed. Phases A
and B are iterated, with multiple hypotheses
Applying EA to Solve Industry Challenges
Figure 1
• Predictive analytics
for improving drilling
efficiency.
• Reduction in non-
productive time and
savings on sensors.
• Estimated saving of
$1M per year per rig for
every 5% reduction.
• Predictive asset
analytics.
• Reduction in
failure rates and
maintenance.
• Crop disease diagnostics
solution for integrated
farming.
• Savings on pesticide use
and loss of crop; guidance
on preserving soil fertility.
• Reduce crop losses for
cash crops up to 5%.
• Applications in safety
and performance.
• Total cost of
ownership for fleet
performance.
• New revenue
opportunity in urban
mobility.
4. and problems defined and modified based
on the information gathered and analyzed.
System thinking and domain engineering
continue to play a dominant role. Supple-
mentary data analysis and mining provide
the necessary insights.
>> Phase C: Solution. Analytics plays a crucial
role in this stage, as different tools and tech-
niques are leveraged to fulfill the objectives.
>> Phase D: Validation. The developed solu-
tion is tested and validated against the do-
main and business requirements. The effi-
cacy of the analytics component is verified
against the performance specifications for
the intended business scenario.
• Category II: A specific problem is unknown;
however, value is perceived in the huge amount
of data that is available (see Figure 3, next
page).
For example, service engineers in the
automotive industry have large numbers of
diagnostics codes from different electronic
control units (ECUs), as well as parameters
related to framing failures, at their disposal.
Businesses are looking at ways to exploit such
data to better understand the sequence of
events that cause failures and correlate them
to improve product design.
Given that the problem is unknown, an iterative
effort is required to generate and validate
candidate hypotheses before full-scale solution
development. Here, the solution phase offers
an intermediate rapid solutioning exercise to
solve formulated problems using analytics tools
before the hypothesis validation phase. After
a hypothesis is established, the problem gets
converted into a Category I problem.
Hurdles to EA and the Road Ahead
While EA offers a substantial upside to problem-
solving, organizations looking to embrace it will
need to overcome the following challenges:
• Return on investment: Given the nature of
EA problems, tangible benefits and ROI are not
always quantifiable upfront. The costs are sig-
nificant and involve establishing infrastructure
both at a lab scale and production scale. While
precise ROI is difficult to establish, big data/
cloud-based analytics can provide flexibility
and economy to test and build EA solutions.
cognizant 20-20 insights 4
Category I: A Phased Approach to Problem-Solving
Validation PhaseSolution Phase
A B C D
Analytics
Defining Loop
Sense-making and Contextualization Loop
Information-seeking
Phase
Problem Formulation/
Hypothesis Phase
System
Thinking
Domain
Engineering
Figure 2
5. • Talent: It can be expensive and challenging
to build a team with a cross-section of skills
that span domains, as well as systems and
analytics to work on newer types of problems.
This is especially true in light of ongoing skills
shortages across the analytics realm.
• Tools ecosystem: Multiple analytical and
statistical tools, such as R, Matlab, SPSS and
SAS, are commonly used for different types of
problem-solving, but there is no single tool that
can address the breadth and depth of EA-based
problems. The solution lies in creating a tools
ecosystem with common data access and inte-
gration layers. Such solutions are evolving.
Organizations are now setting up dedicated data
labs to generate insights into their business
ecosystems — ranging from product design and
manufacturing, to after-sales services. Addition-
ally, these insights are enabling new business
models based on servitization of the offerings.
In parallel, EA ecosystem partners, including
systems integrators, big data and cloud players,
as well as analytical software providers, are col-
laborating to create synergistic offerings to help
companies across the engineering and manufac-
turing spectrum to address their EA challenges.
cognizant 20-20 insights 5
Category II: Rapid Solutioning and Iteration
Quick & Dirty
Solution Phase
A B C D
Analytics
Defining, Sense-making and Contextualization Loop
Information-seeking &
Hypothesizing Phase
Problem Formulation
Phase
Hypothesis
Validation Phase
System
Thinking
Domain
Engineering
Figure 3