Central to the digital transformation of the manufacturing industry is the power of data analytics from connected devices and sensors in the production processes. Predictive data analytics is considered potentially the most important advanced manufacturing technology for driving future competitiveness, helping manufacturers reduce wastage and streamline production processes. IoT-connected machines, fleets, production lines and people are revolutionizing traditional ways of doing things across the entire manufacturing space.
In order to derive greater value from data, businesses today must move away from analyzing historic patterns to predicting the future. Until recently, data was a historic thing that you accumulated over time, to crunch, analyze and extrapolate patterns manually to find ways to operate more efficiently. Today, connected devices and sensors collecting data are becoming more prominent features of both business and everyday life. This tsunami of data is empowering new digital technologies – like machine learning (ML), natural language processing (NLP) and artificial intelligence (AI) – to turn data analysis from a reactive exercise into a proactive process that enables strategic business decision making and actions, from top decisions to the factory floor.
Manufacturing can leverage AI solutions to improve multiple areas of the industry: AI paired to analytics and machine learning enables contextual intelligence and insight, which when applied to the shop floor can revolutionize manufacturing processes and practices. The knock-on benefits of cognitive computing to issues like multi-site manufacturing, multi-tier distribution, product configuration, distributed order management and post-purchase service can be used for greater accuracy, better customer responsiveness and boosted speed to market for manufacturers.
At Orange, we believe that data should be at the core of every enterprise strategy, where AI-enabled systems can assess and reassess data analysis models, make assumptions, test and learn autonomously without human intervention. Done right, cognitive systems can increase the frequency, flexibility and immediacy of data analysis, bringing huge value to a company in terms of efficiency, productivity, competitive advantage and cost savings.
However, regardless how advanced the technology is, people still have an important part to play. To be successful, data analytics requires human skills and interpretation to make intelligent decisions at the end of the analysis. Companies need to ensure they strike the right balance between self-organizing and autonomous systems and the human capital that a company already has.
The multinational engineering and electronics company Bosch provides a great example of how data can be used to transform the production line. By gathering data and communicating it between machines and human operators they can keep track of their inventory, enabling maintenance workers to take pre-emptive action on potential breakdowns and more. In their factory in Mondeville, France, Bosch is already reaping the benefits with fewer quality defects, fewer losses and fewer breakdowns. Also, the time taken to change production lines has improved, down to just five minutes versus the average time of 30 minutes in France.
If you are interested in reading more about Bosch’s production site in Mondeville and other interesting cases, I recommend you read The Data Journey.
Simon Ranyard is Managing Director for the Nordic Region at Orange Business Services and is based in Stockholm, Sweden. With 20 years' experience in ICT in sales functions, Simon is driving a revenue growth plan by focusing on the innovative services that Orange can bring to its customers and on continuously improving the way we work with them.
In his spare time, Simon is a keen cricket fan and enjoys supporting youth development in the game.