Investing in the Currency of the Future: Big Data for the Manufacturing Domain

Transcription

1 Investing in the Currency of the Future: Big Data for the Manufacturing Domain Transition Towards Data-driven Real-time Visibility and Decision Making Compels Manufacturers to Adopt Big Data Solutions July 2015

2 Contents Section Slide Number Executive Summary 3 Internet of Industrial Things A Research Perspective 5 Research Scope and Objectives 8 Examining the Functional Components of a Big Data Solution and Unearthing its Potential for Manufacturing Seizing Lucrative Big Data Opportunities to Gain a First-Mover Advantage ATM Framework Market Direction Innovative Companies Developing Value-added Products, Solutions and Services The Frost & Sullivan Story 45 2

3 Key Findings Big Data for the Manufacturing Domain: Key Findings, Global, 2014 The four building blocks of Big Data include i) Data Storage: to store and manage a large volume of multiple datatypes, ii) Data integration: to integrate and process data in a form that is fit for analysis, iii) Data Analytics: to derive actionable insights from the data, and iv) Data Visualization: to present the data in a suitable fashion to the user. The influx of large volumes of structured and unstructured data owing to the increase in connected assets has resulted in a push towards a scalable framework (such as the Hadoop cluster a $ billion opportunity by 2021) to manage, integrate, and process Big Data. As manufacturing end users look for proactive ways to improve asset uptime and streamline their maintenance activities, the demand for predictive and prescriptive analytical platforms is expected to spike ( -year compound annual growth rate [CAGR] of % from 2014 to 2021). From a vertical market standpoint, greater opportunities are expected across discrete industries (that account for % of the global stored data) such as life sciences, automotive and aerospace, food and beverage, hi-tech, and general manufacturing. In addition to traditional applications and markets for Big Data and analytics, there is tremendous potential for emerging applications such as the energy management platform (valued at $ billion in 2014 and expected to reach $ in 2021) and new markets such as waste disposal (a $ billion opportunity in 2014 in the United States alone). 4

4 Internet of Industrial Things The Four Functional Facets Industry Convergence: IT-OT Services 2.0 Supply Chain Evolution The Industry 4.0 Business Ecosystem The cross-pollination of ideas, technologies, and processes between the worlds of information technology and operations technology will form the crux of the fourth industrial revolution. These explore newer avenues for service innovations, such as cloud-based service platforms, and evaluate the potential for new profit centers. They also offer opportunity analysis for ICT technology in services. The dawn of the future factory is set to disrupt existing supply chain networks. Digitalization and increased connectivity is set to disrupt and realign existing value-chain networks in the future. The advent of advanced information and communications technology (ICT) will promote new interrelationships and interdependencies, giving way to unexpected business collaborations and partnerships in the future. Image Source: Thinkstock 6

5 Frost & Sullivan s Offering Research Portfolio 1. Internet of Industrial Things: The Vision and the Realities 2. Investing in the Currency of the Future: Big Data for the Manufacturing Domain 3. Services 2.0: The New Business Frontier for Profitability 4. The Safety-Security Argument: Expanding Needs in a Connected Enterprise 5. The Industry 4.0 Business Ecosystem: Decoding the New Normal 6. Connectivity in Context: M2M in the World of Cyber-physical Production 7. Manufacturing Across Borders: Exploring Global Paradigms and Policies 8. Supply Chain Evolution: Tectonic Shifts in the Value Chain 9. Concept to Production: Future of Additive Manufacturing 10. Evolution of Robotics: Growth Opportunities in the Age of Industrie 4.0 7

6 Research Scope and Objective Big Data for the Manufacturing Domain: Research Scope and Objective, Global, 2014 Objective Base Year Study Period Geographical Scope No. of Companies Profiled The aim of this study is to provide a detailed assessment of opportunities for Big Data and Analytics in the manufacturing domain (operations/processes associated with the development of a product within the facility) from an application, technology, and market standpoint North America (NA): the United States, Canada, and Mexico Europe: Western and Eastern Europe (includes Russia) Middle East and Africa: Key countries include the United Arab Emirates, South Africa, Nigeria, Libya, Kenya, and Angola Latin America: Key countries include Brazil, Chile, Argentina, Colombia, and Venezuela Asia-Pacific (APAC): Key countries include India, China, Japan, South Korea, Southeast Asia, Australia, and New Zealand 5 includes Mtell, ThingWorx, Sisense Inc., MongoDB Inc., Hortonworks Inc. 9

7 Key Questions This Study Will Answer Big Data for the Manufacturing Domain: Key Questions This Study Will Answer, Global, 2014 How does the Big Data and Analytics market compare across different sectors? What impact does it have on the manufacturing sector? What are the four functional blocks of a holistic Big Data and Analytics solution offering? What additional infrastructure is required for manufacturing end users to leverage vital Big Data? What are the key parameters they need to take into account to select the right deployment model? What are the benefits of adoption? How can analytics be applied across business segments in a manufacturing facility? In which area are manufacturing end users expected to witness the greatest value? How do individual personnel prefer to visualize their data? What are some of the new roles and responsibilities required? What is the market size for maintenance analytics? Why are companies transitioning from reactive to proactive analytics? How does Big Data aid in facilitating lean manufacturing? What are some of the emerging applications for Big Data that are still underpenetrated? Why are NoSQL and Hadoop critical for any future Big Data project? What is the size of the opportunity? Why do distributed assets require a machine-to-machine application development platform? What is the size of the opportunity? What are some of the key trends across geographical regions? Which among process or discrete manufacturing is likely to witness greater acceptance for Big Data and Analytics? What are some of the key vertical markets expected to experience higher growth? What are some of the emerging markets? 10