Demystifying Engineering AnalyticsBy 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 SummaryA 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 EAEA 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

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

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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.

Oil & Gas Energy & Utilities Farming Automotive

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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 ChallengesEach 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.

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 AheadWhile 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.

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Category I: A Phased Approach to Problem-Solving

Validation Phase Solution 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

• 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.

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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

About CognizantCognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process outsourcing services, dedicated to helping the world’s leading companies build stronger business-es. Headquartered in Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industry and business process expertise, and a global, collaborative work-force that embodies the future of work. With over 75 development and delivery centers worldwide and approximately 211,500 employees as of December 31, 2014, Cognizant is a member of the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: Cognizant.

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About the AuthorsVivek Diwanji is a Chief Architect with Cognizant’s Engineering and Manufacturing Solutions Business Unit. He has 18-plus years of experience in applied research and innovative solutions and has worked in various domains, such as medical devices, automotive, process control and defense. Vivek has written for several technical publications, and his research interests include intelligent systems, AI applications, advanced controls and optimization. Vivek can be reached at Vivek.Diwanji@cognizant.com.

Nishant Verma is a Senior Business Consultant with Cognizant’s Engineering and Manufacturing Solutions Business Unit. Nishant has nearly nine years of experience in consulting, project management and business development in diverse industries, such as FMCG, heavy machinery, automotive, tire, textile, F&B and pharmaceuticals. Nishant holds an M.B.A. from S.P. Jain institute of Management & Research, Mumbai, and is a Black Belt (Lean Six Sigma). Nishant can be reached at Nishant.Verma@cognizant.com.

Nitesh Waghode is a Consulting Systems Engineer with Cognizant’s Engineering and Manufacturing Solutions Business Unit. He has 14-plus years of diverse experience in applied research and innovative solutions. He has worked in various domains, such as automotive, oil and gas, marine, defense, transpor-tation, medical, railway, process control and management. Nitesh has written for several technical pub-lications, and his research interests include advanced controls, systems analysis, modeling, analytics, cyber physical systems, and optimization. Nitesh can be reached at Nitesh.Waghode@cognizant.com.

Jessy Smith is a Senior Architect with Cognizant’s Engineering and Manufacturing Solutions Business Unit. She has 14-plus years of experience in consulting, research and delivery across multiple domains, including automotive, manufacturing, oil and gas and life sciences. Her area of expertise covers mod-eling-simulation, statistical programming, reliability analytics, machine learning, controller synthesis, data analytics and fault diagnosis. Jessy can be reached at Jessy.Smith@cognizant.com.

Dr. Phanibhushan Sistu (Phani) heads the consulting team within Cognizant’s Engineering and Manufacturing Solutions Business Unit. He holds a doctorate in chemical engineering and has over 20 years of experience in the control systems domain, software product development, manufactur-ing IT solutions, optimization, strategy, innovation and leadership roles. Phani can be reached at Phanibhsuhan.Sistu@cognizant.com.