T H E O I L A N D G A S I N D U S T R Y E N T E R S A N E W D I G I TA L R E G I M E

Managing Assets Digitally

This supplement appeared in the January 6, 2020, issue of the Oil & Gas Journal. This online version includes Appendices with the survey questions and a detailed explanation of the normalization calculations used in the analysis.

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2 *Olivier Le Peuch presentation at Barclays CEO Energy-Power Conference, 4 September 2019

Oil and gas digital transformationWhether it be “digitalization,” “digitization” or just “going digital,” with the introduction of new technologies and analytical methods, the oil and gas industry is entering a new digital regime.

Over the past several years the Gulf Coast section of the Society of Petroleum Engineers has gone from a single Digital Energy Study group to three – Data Analytics, Digital Transformation, and Innovate. A search on the Oil and Gas Journal website yields 26,000 hits on the word ‘digital’ in just the last two years. In the words of Olivier Le Peuch, the new CEO of Schlumberger, “… the future of oil and gas is digital!.”*

This recent statement by Schlumberger is, on one level, simply stating the obvious. For years, the industry has been capturing ever-increasing amounts of data, gradually figuring out how to make effective use of it along the way. However, technologies, in the form of processing power, communication systems, and the analysis techniques to use them, are now coming together in ways that make a digital transformation achievable.

The digitalization opportunity capitalizes on new ways for data to be found, created, modeled, analyzed, and shared. In doing so, it offers new ways to manage and optimize business functions ranging from managing a single piece of equipment all the way up to an entire plant or an entire network of plants. This approach is expected to provide more accurate and timely information, enabling better decision making resulting in better financial results, safety, and environmental performance.

One of the most significant “going digital” use cases is the opportunity to leverage structured and unstructured information created, measured and modeled throughout an asset’s lifecycle to make better and faster decisions.

Structured data is data which is sufficiently annotated to be completely and easily readable by computerized methods. Digital models of an asset and the data that support them are mostly structured data. They are the “digital DNA” for the industry, and as more and more data becomes available in such easily accessible forms, companies can make better decisions in all business processes. Designing assets effectively; constructing them faster, with quality; and improving performance during operations and maintaining them better are all common objectives.

Managing Assets Digitally - 2019

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Growth of unstructured data is also robust and potentially valuable, yet it generally takes much more effort to use effectively. Unstructured data is data which is not easily interpreted by a machine. These include natural language documents (i.e., reports, presentations, etc.), photos, videos, and other data sources that would normally require human intervention to interpret. “Going digital” for this type of information can be a challenge.

At a minimum, such data sources need to be made searchable with metadata, key words and other descriptors. Preferably they would be integrated in such a way as to make the meaningful information it contains available for computerized analysis, essentially creating a process for turning them into structured data. Artificial intelligence and machine learning software have so far provided some promising, if less than ideal, methods for automating, making such unstructured data searchable. However, making it reliably available for computerized analysis remains elusive outside very specialized circumstances. Much of this still requires disciplined business processes to achieve the value potential.

Operators, service companies, manufacturers and contractors alike can contribute to and share in transformational benefits of designing, building and operating an asset in a digital data environment. Firms have been improving data collection, both in quantity and quality, for decades. Integrating data within processes and between organizations inside a company has always been challenging. However, in the last several years there have been technologies developed that create new opportunities to facilitate data sharing and its conversion into useful information.

• Cloud computing has increased the availability of processing power by orders of magnitude, enabling data to be processed faster and different analyses carried out, especially with respect to 3D visualization. It also brings serious data and processing power to more and more users.

• Sensors have become ubiquitous, much less expensive, and can have their own processing and connectivity capability imbedded in them – the Industrial Internet of Things (IIOT).

• Communication networks have become more globally ubiquitous and their speeds have increased dramatically.

• Data-driven analytical approaches such as artificial intelligence and other machine learning algorithms have matured, opening up previously intractable problems to analysis.

These technological advances provide opportunities for industry to develop new business models and optimize complex physical and human systems in entirely new ways. Powerful processing devices and methods combined with organized and expanding troves of data provide opportunities to improve the design, construction and operation of oil and gas facilities through the integration and seamless use of engineering and technical data. Companies are leveraging these capabilities into cost reductions, productivity improvements, and safer work environments.

So how is the digital transformation progressing?Oil and Gas Research conducted a survey, “Managing Assets Digitally,” to answer that question. The survey was distributed via email to oil and gas professionals in August 2019. Responders represented multiple levels of management and a wide variety of companies in roles including: producers, midstream, refiners, service companies, consultants and many others. The survey was primarily designed to assess if there really is a transformation and how the industry is adopting to it.

The three major business segments in oil and gas are covered by the survey: 1. Finding and producing hydrocarbons [E&P business] 2. Processing and transporting oil and natural gas

[midstream] 3. Conversion of liquid hydrocarbons into products for sale

[refining and marketing]

Within each business segment, an asset’s lifecycle includes: 1. Investment decisions – market analysis and preliminary

design leading to final investment decision (FID) 2. Detail engineering, construction and start-up 3. On-going operation and maintenance 4. Supply chain – transportation optimization

and operations 5. Marketing – interface with customers

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Managing Assets Digitally – 2019

Here is a summary of what the survey has revealed

Is there a transformation underway?

Seventy-three percent (73%) of respondents said there is potential value in data-driven techniques, though only a third of those (23% of respondents) have made proven implementations. The other two-thirds (50% of respondents) have “high expectations” and are moving ahead.

Twenty-three percent (23%) feel they do not have enough information to make a good decision, and only five percent (5%) feel there is not a lot of potential for the techniques in O&G.

23% Confirmed positive: Have implemented data analytics techniques which have proven themselves to be valuable already

50% Positive: Have high expectations for data analytics techniques, but they have not yet proven themselves in a practical application

23% Neutral: Do not yet know enough about data analytics techniques and how they relate to our business to say whether they will prove useful

2% Negative: Do not feel that data analytics techniques are likely to prove valuable to our business

3% Confirmed negative: Have attempted data analytics techniques, and they have proven to be ineffective or unsuitable for our business

USEFULNESS FOR DATA-DRIVEN ANALYTIC TECHNIQUES

Table 1 Source Question 71

However, the proof is in the actual doing. Question 61 asked about company’s capability and level of deployment of data-driven solutions. Seventeen percent (17%) have completed multiple successful projects. The majority, fifty-six percent (56%), have done successful pilot projects. All told, more than seventy percent (70%) of respondents are actively engaged with some degree of success. Clearly, there is a transformation happening.

Despite this, a surprising twenty-seven percent (27%) of responders classified themselves as comfortable where they are but “… always trying to find incremental improvements.” At nearly a third of responders, this is a significant group indicating that they do not see a great deal of utility in data-driven analytics for their business models. This implies that there may be portions of the industry which are either not amenable to the digital transformation, or appropriate applications have not yet been found.

Bottom line, many in O&G believe there is real potential in the digital transformation. Early adopters are benefiting already; many are looking, but there is a non-trivial segment yet to be convinced or have situations where there is less opportunity.

Where is the industry with the key components of the transformation?

Data: Managing and SharingData is a critical part of the transformation, forming the foundation for everything else. The effective use and management of that data presents ongoing challenges, but

27% Basic: Our business operations are running well, but we are always trying to find incremental improvements

Level 1: We are aware of data-driven technologies and are actively evaluating their applicability in our business

56% Level 2: We have implemented data-driven technologies in pilot projects and achieved good results

17% Level 3: We have implemented such technologies, had great success and believe they have transformational potential in our business

Level 4: Advanced data-driven analytical techniques are part of our culture – all capabilities are adopted and used

CAPABILITY AND LEVEL OF DEPLOYMENT

Table 2 Source Question 61

5

technology is available to make a digital data environment – one in which data is stored and readily available for use as a seamless part of normal operations. Within such a system, data can be pushed to those who need it for normal operations, but it can also be found by those who want it for special circumstances, such as data-driven analyses for decision-making processes.

Such a digital data environment includes: information from all engineering disciplines (including geo-sciences, process, structural, electrical, control, etc.); models (including 1D, 2D, 3D and 4D); design assumptions; material specifications; sensor readings (direct and indirect); third-party information that may impact the asset performance (weather, ocean currents, power, etc.); and unstructured data such as video, audio, and reports about an asset.

With this survey we have sought to understand the state of adoption of such technologies in the industry and the current progress towards producing a digital data environment across the asset lifecycle.

We asked about companies’ capability to manage structured and unstructured data. The objective was to understand data capture, accessibility through the asset lifecycle (design through operations) and sharing for collaboration.

Forty percent (40%), say their companies capture data (structured and unstructured) but it is not indexed in a way to make it easily accessible beyond the point of capture. Notably, analytical results are stored in unstructured document formats and not indexed for searchability.

The balance of responders, sixty percent (60%) were not only capturing data digitally but were also indexing it to make it more easily discoverable within their companies. This is important because knowing what data is available is a prerequisite for using that data effectively.

In question 21 we explored respondents’ capacity to make use of unstructured data. Using unstructured data includes, at a minimum, adding metadata and indexing of documents to make them easily searchable.

40% Basic: Information is generally paper-based, and digital data is siloed

Level 1: Selected design and operations data (structured) are captured, validated and stored digitally. Most analytical results using that data are stored in documents (Word, PDF, etc.) as unstructured data

60% Level 2: Major asset design and operations data are captured, validated and stored digitally. Certain analytical results are digitized and made searchable (using, metadata, key words, etc.) so they are more easily available to asset teams

Level 3: Most design and operations data are captured, validated and stored digitally. Most analytical results are digitized and searchable (using, metadata, key words, etc.) so they are more easily available to asset teams

Level 4: Managing a digital data environment is part of our culture – all capabilities are adopted and used

CAPABILITY FOR MANAGING DATA IN A DIGITAL ENVIRONMENT

Table 3 Source Question 11

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Managing Assets Digitally – 2019

A significant majority, sixty-three percent (63%), of responders were at a basic level with unstructured data made available, but with metadata and indexing only added by individual initiative. Another sixteen percent (16%) made extensive use of unstructured data only in selective circumstances. The remaining twenty-one percent (21%) are the early adopters, claiming to be using it effectively throughout the organization. This is a likely area where more benefits are possible for the industry.

Question 31 investigated data sharing. Companies appear to be making efforts to effectively share data, successful ones at that. Thirty-one percent (31%) of responders claim the highest levels (#3 and #4) of sharing, including corporate-wide adoption and

selective sharing outside the company. These companies also share quantitative modeling results. Relaxing the requirement for selective outside-company sharing raises the number to sixty-four percent (64%) of respondents. Most O&G companies have the capability to share data effectively, and they are using it. Only fourteen percent (14%) still rely mostly on point-to-point sharing methods.

A common pitfall of data systems is the formation of what are commonly known as data silos. Data silos are stores of information which are kept isolated and almost secret by the business group responsible for their creation and maintenance. The reasons for this secrecy vary, often involving concerns over data security and quality, and they are often used quite effectively by the groups responsible for them, but the fact remains that such things are common obstacles to shared data projects.

63% Basic: Unstructured data is filed on servers which are searchable. Some unstructured data includes metadata which provides context about the content

Level 1: Unstructured data on major assets is handled by a process which adds metadata for context and substance, they are filed with all other information about an asset. The data is available to be found and used for decision making, but adoption is selective based on the individual behavior

16% Level 2: Unstructured data on major assets has metadata added for context, and substance. It is used by operators on a selective basis to help manage an asset, e.g., cameras providing visibility to a portion of a plant

21% Level 3: Unstructured data is identified with metadata for context and processes are in place for individuals to include it in their analysis processes. On selective assets it can be a key source of information, contributing to better asset performance, e.g., video/audio technologies can be selected to provide unique data streams for safer operations

Level 4: Unstructured data are part of our culture – all capabilities are adopted and used, e.g., video of plants are made on a regular basis to identify emissions and are integrated into day-to-day operations

CAPABILITY WITH UNSTRUCTURED DATA

Table 4 Source Question 21

14% Basic: Information is shared mostly between individuals using point-to-point methods (e.g., email, texting or file sharing service)

22% Level 1: Information is shared mostly using multi-person collaborative work systems for each organizational group (e.g., collaborative workspaces, group list-serves or forums, etc.)

33% Level 2: Information is available through an organization-wide system (subject to security checks), but it is not fully indexed, and people may not know what there is to use

31% Level 3: Information is easily available to anyone in the organization (subject to security rules). It can be incorporated easily into quantitative analysis work (is machine-readable, where appropriate). It can also be shared selectively to selected partners outside the organization as needed

Level 4: Sharing data within a digital data environment is part of our culture – all capabilities are adopted and used

CAPABILITY FOR SHARING DATA

Table 5 Source Question 31

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In an effort to learn about such silos, we posed the question: “Information and data are often held in silos and not always available to all the disciplines that might benefit from them. Which data silos if shared with other disciplines, are the most likely to provide increased business value?”

The complete list of responses is shown in Figure 1, which depicts the normalized benefit ranking of each data silo. It is worth noting that, while process design and reservoir data clearly led the pack, the spread was not very wide. Sharing data across disciplines seems to have positive benefits across the board.

As has been mentioned before, data management is critical to the success of the digital data environment. Although companies are doing admirable work addressing the task, it remains a constant challenge. We asked respondents to rank the biggest challenges of data management, presenting 11 options, including an “other” category. Obtaining and maintaining datasets are the two greatest challenges by the numbers, though as before, the spread is not particularly wide, indicating significant variation among the sample group.

ModelingOver time, technological developments have greatly expanded the tool kit available for digital modeling. The industry has long relied heavily on first principles models, using physics and known mathematical relationships to design very effective facilities, e.g., platforms, pipelines and refineries. Yet no system performs exactly as designed, and imperfections in fabrication, variation in the environment, and unpredictable inputs all drive systems away from their designed operational specifications.

Modern analysis techniques, such as artificial intelligence, neural networks, and machine learning, offer methods to address the unexpected variations using data-driven analytics to handle such complexities. Such methods can add a welcome extra measure of predictability to systems whose dynamics cannot be practically described or whose state cannot be measured in enough detail to lend themselves to traditional physical models. Additionally, with modern data handling and processing capacity, they can do so in a time frame sufficiently short to aid decision making.

IMPORTANT DATA SILOS

Figure 1: Source Question 131: Normalized benefit ranking of various data silos, calculated by normalizing the average ranking over all responses. Note: A score of 0 corresponds to no votes for the silo, while 1 corresponds to every respondent ranking the silo as most beneficial.

0.1

0.3

0.2

0.4

0

Process design models

Control systems models

Materialspecifica-tions

Procurement information

Inventory of materials, feedstocks, etc.

Wellbore and surface equipment

Equipment specifica-tions

Maintenance records

Third-party data

Reservoir and geologic informa-tion

3D models / images of the plant

Electrical diagrams/specifica-tions

Operational sensor readings

BIGGEST CHALLENGES FACED IN DATA MANAGEMENT

Figure 2: Biggest challenges faced in data management, uses data from Question 91. Results are given as a normalized ranking, per the calculation in Normalized Ranking Calculation

0.1

0.3

0.2

0.4

0Finding and organizing the data required

Making sure the digital data environment is secure from unauthorized access

Securing adoption from engineering and operating staff

Developing metrics to measure the results

Securing acceptance of results from management results

Identifying an asset with potential benefits

Selecting the right service providers to support developing the solutions (internal and / or external)

OtherValidating the results of the analysis and maintaining the accuracy over time

Developing and maintaining the quality of the data and ensuring it is maintained over time

Determining the analytical tools which are appropriate to process the data to answer the questions posed

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Managing Assets Digitally – 2019

These new modeling methods promise to greatly expand the role of modeling in the business process. With that expansion, the ability to share not just the results of models, but also the logic and dynamic execution becomes much more important.

Traditionally, modeling has been a personal effort, managed by the engineer doing the work. An asset team might collaborate, but extensive collaboration has not been common. In the survey sixty-five percent (65%) of responders said that is where their companies are today. Systems for sharing with similar teams can be useful. Twenty-two percent (22%) of respondents have limited systems for sharing such models between disparate disciplines, while an additional thirteen percent (13%) have integrated such systems heavily throughout their organizations.

In the data-driven world, there is a need for broader approach to modeling including both these new modeling methods and the distribution of such models to many interested and often non-expert parties. This is a big change for an engineering-driven industry. It should be noted that first-principles modeling continues to have its place, a prominent one at that, but the

expanding toolkit provides methods to handle previously intractably complex, noisy systems efficiently, quickly, and reasonably accurately.

Digital Workflows While the data and analysis methods introduced in the digital data environment are valuable in their own right, there is potentially a great deal of value to be gained by incorporating them into fully integrated digital workflows that drive approvals, enable safe operation, and optimize performance of assets. Ideally, digital workflows should seamlessly facilitate the capture and processing of data, ensure its accuracy, and make it – its associated models and analysis – available when and where it is needed, whether those destinations are operators and engineers, or automated engineering and operating software tools. Such smooth workflows may help improve the timeliness and quality of information delivered to decision makers.

Question 51 in the survey revealed that many respondents – nearly eighty-five percent (85%) – are using digital workflows. However only twenty-two percent (22%) are using them in the operating segment of the business. This implies that most companies use digital workflows to drive major project approvals, but they do not extend them to support day-to-day operating business decisions.

As the digital transformation continues and businesses learn how to make better use of the new tools it brings with it, it is likely the benefit from digital workflows will grow. For instance, as they become better integrated and more reliable, data-driven tools might identify problems or potential problems in operations and initiate work orders to deal with them, streamlining the usual maintenance process. As will be shown in the next section, this scenario is expected to be one of the biggest opportunities as the digital transformation continues.

Results achieved / expected?

We asked responders to rank the business functions that are likely to have the most benefit from a digital data environment. The results are shown below in Figure 3.

As is apparent from the relatively narrow range, the respondents expect all the surveyed business functions to benefit, though

65% Basic: Digital modeling is currently handled by individuals and they manage most of the associated data which is used

Level 1: Digital models are used and shared among multiple people within an asset team and usually within the same discipline. Access does not extend to all stakeholders

22% Level 2: Digital models are used and shared among multiple disciplines within an asset team and other similar teams. Access extends to all stakeholders

13% Level 3: Our digital modeling environment is open and interoperable with other project information management systems. Our digital modeling environment collates and federates data throughout the project lifecycle

Level 4: Digital modeling is part of our culture - all capabilities are adopted and used

MODELING CAPABILITIES

Table 7 Source Question 41

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ongoing maintenance is expected to benefit the most by a slight margin, followed closely by detail engineering.

This emphasis on maintenance is likely due in part to the nature of data analytics and the problems it lends itself to, but it also reflects shifts in the industry. While midstream and refiners have always had a natural bias to running equipment as efficiently as possible, their responses would often be drowned out in a survey such as this one. A large percentage of the respondents are primarily E&P (finding and producing oriented) companies, and their historical focus was always on “finding” new assets without worrying too much about the efficiency of the process. The traditionally high rate of return on those assets often made efficiency a secondary concern at best. In today’s shale environment with its highly repetitive factory-drilling economics, that circumstance has changed, and there is greater focus on efficiently run field operations.

In an effort to fill in a bit more detail about what people expect to accomplish with the digital transformation, we asked respondents to rank the benefits – either expected benefits or those already achieved, depending on individual progress – of the transformation. The results are presented in Figure 4.

These results are consistent with those presented in Figure 3 with a narrow spread in rankings indicating expectations vary widely across the board. Despite the small margins, however, reductions in operations costs lead the pack in expected benefits (related to the ongoing operations and maintenance from the previous question), followed closely by improvements in uptime (related to both the ongoing operations topic and the detail engineering one). Health safety and environment, another ongoing performance topic, rounded out the top three.

In addition to ranking the business functions, we also asked respondents to rank the business value contributions of seven different aspects of the digital data environment. The results are presented in Figure 5.

Figure 3: Normalized ranking of business functions most likely to benefit from a digital data environment. Created based on the results of Question 81 of the survey.

MOST LIKELY TO BENEFIT FROM A DIGITAL DATA ENVIRONMENT

0.175

0.525

0.35

0.7

0

Investment decisions – market analysis and preliminary design leading to final investment decision (FID)

On-going operation and maintenance

Detail engineering, construction and start-up

Supply chain – transportation optimization and operations

Marketing – interface with customers

Figure 4: Benefits (either expected or already achieved) of the digital data environment. Rankings are normalized from the results of Question 141 of the survey.

BENEFITS OF THE DIGITAL DATA ENVIRONMENT

0.125

0.375

0.25

0.5

0Capital investment reduced

Operations cost reduced

Improved safety, better environ-mental impact and reduced health risks

Volume/revenue increased

Time saved

Business competitiveness

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Managing Assets Digitally – 2019

Structured data was consistently ranked as the strongest contributor to business value. Structured data is the easiest aspect to make use of, so this stands to reason. Advanced modeling techniques, information sharing, and digital

to two criteria: first, rank them according to how much they were currently using them, and second, to rank the top three they would be using in the next five years.

It is readily apparent that all of these technologies are in use today to one degree or another, though the more general technologies (cloud computing, wireless broadband and security software) are used much more consistently than the

workflows were almost equally ranked, taking a collective second place, which again makes sense given their broad applicability. Logistics data fell significantly behind, and unsurprisingly, unstructured data – potentially highly useful, but generally the most difficult form to make use of – ranks last.

The results reflect basic business sense: The contribution of any change to the bottom line is a function of both its intrinsic usefulness and the difficulty involved in implementation.

Future – what is next?

Technology is always advancing and seems to be accelerating all the time. The survey provided responders with list of technologies, and asked respondents to rank them according

others, as should be expected given their broad applicability across many fields.

The future ranking is a top three ranking of technologies that will most improve your business. Note that even cloud computing, one of the most broadly applicable technologies on the list, does not rate that much higher than the rest (0.36 out of a possible 1.0). No single technology is likely to dominate the rest, everything has its niche. In fact, every technology listed had at least three votes for the top rank and at least one for 2nd and 3rd.

Also, of interest are the methods people use to discover new technologies (Figure 7). Perhaps unsurprisingly for traditionally close-knit oil and gas industry, the top method is networking with other companies that have been used in the past.

BIGGEST CHALLENGES FACED IN DATA MANAGEMENT

Figure 5: Normalized ranking of business value contributions of selected aspects of the digital data environment. Source: Question 101 of the survey.

0.2

0.6

0.4

Digital workflows - ensuring information to do work is delivered in the appropriate form, to the people and machines who need it, when and where it is required for decision making or further processing

Information sharing - delivering data, analysis and models to the locations and devices where they are required

Unstructured data - e.g., video, audio, images, reports, etc.

Structured data - engineering data, e.g., design assumptions, equipment specifications, sensor readings, etc.

Logistics data - information from the supply chain of feedstocks, materials, etc.

Modeling and analysis – physics-based, data-driven statistical; 1D & 2D diagrams; 3D visualization, etc.

WHAT TECHNOLOGY RESPONDANTS ARE USING

Figure 6: Fraction of respondents who use each type of technology listed (Used Now) and the normalized ranking of the technologies expected to be used in the next five years (5 Years). Compiled from Questions 11 and 121 of the survey.

0

0.175

0.35

0.525

0.7Next 5 Years (Ranking)

Used Now (Fraction of respondents)

Edge Computing

Wireless Broadband

Block Chain

Robotic Devices for Observation

Video w/infrared, multispectral, or other special features

Cloud Computing

Security Software

Robotic Devices to do Physical Work

Audio Sensors

Drone

Satellite imagery

Lasers for measurement in a plant

11

However, online research came in at a very close second followed by conferences.

The “other” category had one comment that was particularly worthy of mention, training. Some of the skills required for the data analytics aspects of the data-driven transformation are not part of the engineering or business skills courses many in the industry have taken.

Conclusion

The oil and gas industry is in the midst of a digital transformation. With the availability of new tools and processes, it stands poised for great improvements in technical and business performance. The necessary data is available but putting it into proper form and making it accessible to all who need it remains the biggest challenge. Companies seem confident that the transformation in progress will benefit the industry.

We welcome participation in the dialog on this important topic. To contribute to the discussion please join the OGJ Community (https://oilandgascollaboration.ning.com/).

Authors

Dr. Anthony Strathman – Has more than 15 years of experience with data analytics and complex systems. Anthony has done research for both industry and academia. His past research includes analysis of human social networks as physical systems, simulation development and verification, a variety of general topics, and research into the state of the industry as an analyst and writer. Anthony has a Ph.D. in Physics from the University of Notre Dame.

Michael S. Strathman – Has more than 40 years in the global oil and gas industry. Mike has worked in many aspects of the upstream and downstream businesses. He has experienced the business with an operating company, an engineering software provider and as a strategic consultant and writer. Mike has a degree in Systems Analysis from Miami University and an MBA from Northwestern University.

METHODS DISCOVERING NEW TECHNOLOGIES

Figure 7: Normalized ranking of methods used to discover new technologies (Question 151 on the survey)

0

0.2

0.4

0.6

0.8

Look to key executives in our firm with contacts

Connect with research universities that are strong in our business

Network with technology companies we have used for other projects - trusting they will help.

Other

Attend conferences/trade association events on the topic and contact those firms we think are best suited to our need

Research online for cutting-edge technologies with capabilities we might need, then read and network in the industry for the best firms to work with

1 This supplement is based on a survey of industry leaders. The survey’s details, the questions asked and normalization calculations of some of the responses are available in the Appendices.

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