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Greg Goodwin

Quality Insider

Revenue Stream Revolution

Takeaways from the MIT/Accenture Big Data in Manufacturing conference

Published: Monday, December 2, 2013 - 12:58

LNS Research was pleased to be among the 80 to 100 attendees at the MIT Forum for Supply-Chain Innovation’s Big Data in Manufacturing conference held Nov. 12, 2013, in Cambridge, Massachusetts. The event featured MIT faculty speakers and experts from the technology services and outsourcing company, Accenture, as well as experts from mining and electronics companies.

Although each speaker analyzed a different facet of the coming big data revolution, they all stressed how manufacturing and business operations are poised to fundamentally change forever. A key takeaway was big data’s role in helping companies develop new business models that leverage previously unknown data correlations. Additionally, speakers emphasized how big data are helping companies become more proactive in operational decision making in areas such as predicting asset failures, tuning product pricing, and addressing reputation management. A final key takeaway was how big data will have a transformative effect on the way companies are managed, moving away from executive experience and intuition and more toward a data-driven model.

Below, I’ll expand on some of the highlights from the conference and provide LNS Research’s views on the role of big data in manufacturing.

Enabling new business models with big data capabilities

The first speaker was David Simchi-Levi from MIT’s Engineering Systems Division. Evoking a vision for future data capabilities, Simchi-Levi spoke of current, real-world big data capabilities that are altering long-established business models and opening up new revenue streams.

He described emerging big data capabilities as empowering people to analyze extensive data “at the speed of thought to drive decisions and actions,” and allowing people to identify, diagnose, replicate, and prescribe appropriate business actions. He gave examples of companies that are currently using these enhanced analytical abilities to restructure divisions, adjust business models, and find new streams of revenue. Rolls-Royce is one of those companies.

Rolls-Royce aircraft division restructures its business model

Instead of selling its engines to customers, Rolls-Royce now rents them, retaining the responsibility for repair, maintenance, and replacement. This shift was triggered by new big data capabilities that have allowed an unprecedented level of predictive maintenance. Rolls-Royce can now identify correlations between different part failures and operational environments. This is allowing the company to predict engine failures several days before they occur, with high accuracy and low false alarms.

This change in business model moves the aircraft-engine cost structure from a fixed asset to variable operations cost, and Rolls-Royce’s engine-rental business now comprises 70 percent of its total aircraft division revenue. In addition it has resulted in improved safety, improved customer service, and lower service costs.

Rolls-Royce is a pioneer in this business model, and LNS Research expects to see many others in asset-intensive industries adopting similar strategies in the near future.

Leveraging big data to create closed-loop environments and continuous improvement models

Moving from safer aircraft to more efficient public utilities, Narendra Mulani, senior managing director of Accenture Analytics, spoke of how predictive analytics enabled by big data are transforming energy expenditures. By installing sensors in piping infrastructure, Accenture is working on a “smart water” infrastructure network that is able to predict leaks with high accuracy before they occur. Having this type of real-time visibility and predictive maintenance program in place allows water pressure levels to be raised and lowered according to usage and potential infrastructure weakness points, creating a more energy efficient system overall.

There are parallels between this type of closed-loop feedback enabled by new data capabilities and other industries where business and operations management are now being approached in a fundamentally different way. For instance, many companies are already using formal customer-complaint information to improve engineering and manufacturing processes, analyzing trends in those complaints, and tracing them back to specific components or materials. This is a trend that will only develop further as the growth and function of social media expands, creating a vast landscape of information for companies to use to improve intelligence and quality operations.

Pioneering a management revolution

Delivering the afternoon keynote address, Erik Brynjolfsson, director of MIT’s Center for Digital Business, spoke of the way that big data capabilities are changing how decisions are made in companies. Whereas the traditional model involved senior executives brainstorming ideas and then ultimately falling on the highest paid person’s opinion, successful companies are increasingly adopting a business strategy of allowing data-driven insights to steer the ship.

Brynjolfsson cited Amazon as a data-driven company that is not afraid to conduct large amounts of experiments to improve user experience and drive additional revenue; the decisions to do so aren’t based on senior leadership’s personal experience, but rather data-minded individuals from all areas of the company who foresee ways in which data collection can provide information that can improve the company in some way.

The largest hurdle to achieving a data-driven organization is the massive cultural shifts that must take place, as well as a fundamental shift in the way strategy is formulated, measured, and continuously improved on. The importance of aligning culture, objectives, goals, metrics, and support systems to meet today’s operational and business needs is something we’ve talked extensively about at LNS Research. We see big data analytics as one of the most important technology frontiers that will have a significant effect in helping companies accelerate their operational excellence journey.

The pursuit of performance management excellence

Along with cloud-computing and mobile technologies, harnessing big data analytics holds the promise of enabling major improvements in operational and business performance. For more information, LNS Research’s report, “The Pursuit of Performance Excellence,” focuses on the role of plant performance metrics programs, improvements, and IT applications. It contains best practices on how to build a performance culture.


About The Author

Greg Goodwin’s picture

Greg Goodwin

Greg Goodwin is a research associate with LNS Research based in Cambridge, Massachusetts. LNS Research provides executives a platform for accessing unbiased research and benchmark data to improve business performance. Goodwin writes research papers, case studies, and contributes regularly to the LNS Research blog, where he covers topics including manufacturing operations management, industrial automation, sustainability, enterprise quality management software, and asset performance management.