What are Industrial Data? Setting the stage for an emerging industry study

by Supraja Sudharsan, Jennifer Clark, Thomas Lodato

This blog post is a product of the industrial data and regional economic development study currently being undertaken by Dr. Jennifer Clark and the team at the Center For Urban Innovation (CUI) including Supraja Sudharsan and Dr. Thomas Lodato. The study is being conducted in collaboration with the team at the Energy Policy and Innovation Center (EPICenter), including Dr. Tim Lieuwen, Dr. Richard Simmons, and Kerri Metz. The objective of the study is to delineate the emerging industry around industrial data (ID), and unpack its regional clustering characteristics.

Background

Digitalization of products and processes has ensured that all equipment, processes, and their interactions in both the physical and digital space generate data. The rising ability of firms to handle so-called “big data,” and to convert them into useful products, services, and product-embedded services, is projected to galvanize legacy industry sectors in advanced economies. This is increasingly referred to as a fourth industrial revolution.

The Industrial Data and Regional Economic Development Study undertaken by the team at CUI, in collaboration with EPICenter, affirms the criticality of data in industries. By proposing and applying a definition for “industrial data” (ID), the study reifies the emerging industry around ID. This blog post is based on the first phase of research, which was completed in September 2017.

What are industrial data?

Industrial data are data obtained by measuring and assessing the performance of an industrial equipment during production, and while in operation. Consider aviation manufacturing. Data are generated during the manufacture of jet engines, from the manufacture and supply of avionics, from tools and equipment during aircraft assembly, and while these equipment are in operation during flight. These data come from different parts of the world, based on the global foot print of aviation supply chain, and flight operations. The data come in a variety of formats including text, images, written records of historical maintenance information, and more. They are obtained by measuring performance parameters (such as temperature, pressure, vibration) of industrial equipment, at varying frequencies [1].

In general, ID includes:

1) Data from advanced manufacturing industries, such as aviation and automotive industries, including equipment manufacturers that operate in these industries,
2) Data from energy generation,
3) Data from energy transmission and (non-consumer) distribution data, and
4) Data from equipment in operation, such as wind turbines following installation.

The generalized definition of ID and its sectoral delineation helps us in three ways. First, it compiles existing models of ID across a wide range of initiatives enunciated by public, private and hybrid organizations globally. Second, it enables us to disaggregate the actors, processes, and their relationships to trace an emerging ID production chain. Third, it reveals that the processes involved in ID production are leading to the evolution of new firms and services around ID, and an emerging industry around ID itself.

Competing models and value propositions of ID

By defining ID as data obtained by measuring as well as assessing the performance of an industrial equipment, we consider the sources and uses of ID across different models of ID, and their value for a wide range of stakeholders including firms, industries, regions, and society (see Tables 1 and 2).

Table 1 shows how the German concept of Industrie 4.0 undertakes a process-oriented approach to industrial data. It considers ID as the consequence of a hierarchical process that is differentiated from the previous phases of industrial revolution via the presence of cyber-physical systems. Similarly, General Electric’s industrial internet definition refers to the “integration of physical machinery with networked sensors and software.” Other initiatives based on concepts such as advanced industry and smart manufacturing define their models by the impact of ID on the composition of high-skilled workers and by the influence of ID on the time to decision-making in business. Moreover, as seen in Table 2, the initiatives also vary in how they value ID for firms, industries, regions, and nation-states. One instance of this is GE’s industrial internet initiative, which estimates that the business value of ID will be $225 billion by 2020.

By undertaking a generalized definition of ID, we ensure that the study encompasses both the process and outcome-based approaches, as evidenced above. This in turn allows us to study the entire ID production, and delineate associated actors, and processes, as well as their outcomes.

Table 1. Leading models that study industrial data production (authors’ analysis)

Model name (Initiative)

Model definition

Source

Authors’ analysis of model approach

Industrie 4.0 A fourth industrial revolution arising from the development of cyber-physical systems in manufacturing

[2]

Technology and process-focused: Refers to a hierarchical process in the evolution of manufacturing
Industrial Internet Refers to the integration of hardware, software, and communication technologies in industries

[3]

Technology-focused: Refers to connections between technologies
Advanced industry “An industry’s R&D spending per worker must fall in the 80th percentile of industries or higher, exceeding $450 per worker. The share of workers in an industry whose occupations require a high degree of STEM knowledge must also be above the national average, or 21 percent of all workers.”

[4]

Outcome-focused: Refers to composition of workers in industry
Advanced Manufacturing “Manufacturing that entails rapid transfer of science and technology (S&T) into manufacturing products and processes.”

[5]

Technology and process-focused
Smart Manufacturing “Smart Manufacturing is the ability to solve existing and future problems via an open infrastructure that allows solutions to be implemented at the speed of business while creating advantaged value.”

[6]

Outcome-focused: Refers to a business model

Table 2. Conceptions of value from industrial data (authors’ analysis)

Example

Source

Source of Value

Asset Performance Management – monitor operations, predict failure, optimize performance.

[3], [7]

Business Value

Prediction of power outage and communication of the same to residents.

[8]

Societal Value

Prediction of improvement in productivity and jobs.

[2]

Regional economic development

Projection of opportunities in advanced and developing countries -US and Europe, South Korea and China are expected to be early takers of industrial big data solutions.

[9]

Geographic value

Investment in industrial big data products and processes is highest in Aviation, followed by Wind, Manufacturing, Rail, Power generation, distribution, Oil and Gas, and Mining industry, in the order listed.

[3]

Sectoral value

Delineating ID production

Figure 1 gives a general overview of the production process based on data in power generation. Data are collected from power generation assets, such as gas and wind turbines, associated controls, and the surrounding environment. Through strategic partnerships between technology providers, utility, equipment providers and others, data from different sources are aggregated, and integrated across software systems for analysis. Finally, they are used to monitor, analyze, and visualize, present and future equipment performance. Consequently, firms perform four types of operations using collected data:

1) Monitor data real time or otherwise,
2) Diagnose equipment performance by comparing them to historical and baseline conditions,
3) Predict future performance based on this analysis/prescribe how to respond to predicted outcome, and
4) Optimize equipment performance based on predicted outcome.

Figure 1. Industrial Data Production, by authors

Tracing an emerging industry

Our analysis reveals that ID do not simply refer to digitalization of existing industries. The process of ID production, beginning from data collection to storage to analysis and use, are similar to other production processes. That is, similar to say, an automotive or textile production process, ID production has an input phase, the input then undergoes transformation, to be distributed and consumed by end-users. This leads us to the conclusion that the production process associated with ID is leading to the emergence of an industry around ID itself. Where a firm lies in pursuing the ID production functions, and the extent of human-computing interaction that is involved, reflects the extent of the firm’s evolution within the ID industry.

Future directions: Building on the definition of industrial data, the team is currently conducting interviews with large and small firms operating across the ID production process in various industries. Through this, we hope to learn more about the conditions under which the ID industry is evolving, and how countries and regions may develop competitive advantage within this space. The results of our study will be published in the near future, in collaboration with EPICenter.

References

[1] Boria, S., 2015. Developing Smart Tools for the Airbus Factory of the Future, INDUSTRIAL INTERNET IN ACTION CASE STUDY. Industrial Internet Consortium.

Plataine, 2017. Case Study: Harbin Hafei Airbus. Plataine, Waltham, MA.

GE Aviation, 2016. How GE’s Predix Is Taking Aviation Productivity to New Levels | GE Aviation.

[2] Davies, R., 2015. Industry 4.0. Digitalisation for productivity and growth. European Parliament Briefing 10.

[3] Evans, P.C., Annunziata, M., 2012. Industrial Internet : Pushing the Boundaries of Minds and Machines.

Accenture, GE, 2015. Industrial Internet Insights Report. Industrial Insights Report 1–35.

[4] Muro, M., Rothwell, J., Andes, S., Fikri, K., Kulkarni, S., 2015. America’s Advanced Industries: What They Are, Where They Are, and Why They Matter, Brookings Advanced Industries Project. Brookings Institution.

[5] Subcommittee of Advanced Manufacturing, 2016. ADVANCED MANUFACTURING: A Snapshot of Priority Technology Areas Across the Federal Government. The White House, Washington D.C.

[6] McKewen, E., 2015. CMTC Manufacturing Blog. What is Smart Manufacturing? (Part 1A).

[7] Courtney, B., 2014. Industrial big data analytics: The present and future. InTech 61.

[8] TechNet, SusSpec Alliance, DBL Partners, 2016. Unlocking Grid Data (White Paper).

[9] Manyika, J., Chui, M., Bisson, P., Woetzel, J., Dobbs, R., Bughin, J., Aharon, D., 2015. The Internet of Things: Mapping the value beyond the hype. McKinsey Glob. Inst. 144. doi:10.1007/978-3-319-05029-4_7.

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