Global Manufacturing Study Shows Early Signs of Adoption of Groundbreaking Machine-to-Data Technologies with China Leading the Way
Many industrial companies see potential in Internet of Things but lack strategy to improve asset efficiencies using data
Bangalore, May 14 2015: Infosys (NYSE: INFY), a leader in consulting, technology, outsourcing and next-generation services, together with the Institute for Industrial Management (FIR) at the University of Aachen, Germany (RWTH) today published the results of a
global study on asset efficiency. The study’s objective was to measure the maturity of asset efficiency strategies in industrial manufacturing worldwide. The study polled more than 400 industrial manufacturing and process industry executives in China, France, Germany, the United Kingdom and the United States across several sectors including aerospace, automotive, electronics and machinery.
Key findings of the study:
- 85 percent of manufacturing companies globally are aware of the potential of technologies in increasing asset efficiency. However, only 15 percent of enterprises surveyed have already implemented dedicated strategies to this end by analyzing machine data
- 57 percent of companies measure the operational efficiency of production machinery and production systems with indicators, but only 13 percent do this in real-time
- 13 percent of companies use real-time data for maintenance. This varies by country with Germany and France reporting lower levels, nine and six percent respectively, and the United States at 21 percent
- While 81 percent of respondents are aware of the potential of machine condition surveillance for enhancing maintenance, only 17 percent have put such principles into practice
- 89 percent are aware of the high potential of information efficiency, yet only 11 percent have systematically implemented this
The research revealed that the largest improvements planned over the next five years are in the areas of information interoperability, data standardization and advanced analytics. Manufacturing being energy intensive, the majority (88 percent) of the companies surveyed have identified energy management as a critical factor for achieving asset efficiency. However, only 15 percent have systematic and integrated implementation of energy efficiency throughout the lifecycle of assets in place.
China has highest percentage of early adopters
Across the five regions surveyed, the level of maturity with regard to machine data technologies varies significantly. While no country can claim to be the global early adopter, the percentage of companies in China (57 percent) that were identified as early adopters is significantly higher than anywhere else. The United States is at 32 percent, United Kingdom at 26 percent, Germany at 21 percent and France at 14 percent.
The rate of implementation of asset efficiency strategies in each country over the next five years is expected to be broadly the same. Nearly half of the respondents surveyed (48 percent) want to use machine data technologies by 2020 to systematically implement solutions to enhance asset efficiency. One fifth (20 percent) believe that by 2020 they will not achieve anything beyond recognizing the potential of the Industrial Internet of Things (or Industry 4.0 as Germany refers to this) concept.
Sudip Singh, Vice President and Global Head of Engineering, Infosys
“With equipment and system processes becoming intelligent, virtually every process and activity in the manufacturing enterprises involves data. If machine data can be transformed into meaningful insights, it will be able to provide maintenance engineers with powerful tools to accurately predict failures and make better informed decisions. Enterprises implementing technology-enabled data analytics approaches can optimally manage their assets and associated performance. This, in turn, improves availability, maximizes performance, consumes less energy, produces less waste and enhances overall quality of products.”
Prof. Volker Stich, CEO of the Institute for Industrial Management, Aachen University
“The study we conducted together with Infosys reveals highly relevant differences between industry nations with regard to their maturity levels and abilities in ‘advanced manufacturing’, also known as ‘Industry 4.0’ in Germany. Without a doubt, digitalization is the future of manufacturing industries. Even though leading manufacturing countries like Germany are still pioneers in machinery construction and engineering, they have to be aware of the so called ‘fast lane’ digital and smart services. This is where the future progress and profit lie. We hope that our study drives awareness about this in the respective industries.”
Details of the survey and stories on how companies are addressing their Industry 4.0 roadmap can be found at
The research used the Industry 4.0 framework, conceptualized by the German government and developed by industry leaders, to investigate the effectiveness of existing asset management processes. This reference framework is therefore applicable to any industrial organization in the world. The study polled 433 industrial manufacturing executives in five regions – China, France, Germany, the United Kingdom and the United States. The results provide the first glimpse into the understanding of industry preparedness for Industry 4.0 and specifically into the critical aspect of asset efficiency. Infosys and RWTH Aachen focused on the four most important asset efficiency levers namely, maintenance management, operational management, information management, and energy management. Respondents were asked to outline their current maturity levels on these levers and their target for 2020 on a four-point scale from, ‘Not Implemented (lowest maturity)’, ‘Potential Recognized’, ‘Partially Implemented’ and ‘Systematically Implemented and Benefits Realized (highest maturity)’. For the purpose of analysis, enterprises were categorized as ’Early Adopters’ or ’Followers’ based on their response to the levers of asset efficiency. This paper reports the status today and the aspiration for 2020 by asset efficiency levers, industry and production type, and country.