Jeremy Ure Associate Partner July 2015 Developing a more intelligent approach to strategic asset management One bold (?) assertion, and two questions
Let s start with something we all know and can agree on. Maintenance is terribly important. Manolo Blahnik..but asset management is about more than maintenance 2
Our assets increasingly want to talk to us, how do we get better at understanding what they say? What s changed. Why should we listen? How do we make sure we can use what we hear? 3
Our world has changed.our assets are telling us more Source: IDC 4
if we listen, our assets are capable of telling us things that used to take days (and considerable expertise) to find out Example Outcomes What happened/ is happening now? What actions are needed now? Why is this happening? What will happen? What s the best that can happen? Reporting Alerts Analysis Predictive Modeling Optimization Reduction of downtime Reduction of unscheduled maintenance and asset failure Reduction of spare parts inventory Optimised view of an asset Increased asset reliability Increase in plant and workforce safety 5
but there is an even bigger change taking place Worldwide spend interconnectivity, 2015 Source: IDC 6
This interconnectivity, allowing the rapid exchange and use of data, extends the role of asset management Products & Markets Rapid feedback from customers Predict human perception Incorporate digital services w/ physical products Sales and Distribution? Entire Ecosystem Collaborative and transparent Optimize sales calls Optimize routes and productivity of teams Warehousing and Inventory Improve the productivity and accuracy of warehouse employees 7 Optimize and simplify inventory management via senor based technologies Marketing & Customer Loyalty Create hyperlocal demand forecasts Develop and monitor dynamic promotions Manufacturing Equipment optimization Real-time visibility Use 3D printing It turns out that the real key isn t the fact that we ve got visibility into the assets, though that was our initial goal It s that we now have information available on performance that we didn t have before we can do a lot with that information Peter Vinter, power grid specialist, DONG Energy
However, value will be created differently Value creation and capture shifts through interconnectivity Traditional Product Mindset Value Creation Customer Needs Solve for existing needs and lifestyle in a reactive manner Service Mindset Address real-time and emergent needs in a predictive manner Offering Stand alone product that becomes obsolete over time Product refreshes through over-the-air updates and has synergy value Role of Data Single point data is used for future product requirements Information convergence creates the experience for current products and enables services Value Capture Path to Profit Sell the next product or device Enable recurring revenue Control Points Capability Development Potentially include commodity advantages, IP ownership, & brand Leverage core competencies, existing resources & processes Add personalization and context; network effects between products Understand how other ecosystems partners make money in a connected world, products are no longer one-and-done new features and functionality can be pushed to the customer on a regular basis. The ability to track products in use makes it possible to respond to customer behavior leading to new analytics and new services for more effective forecasting, process optimization, and customer service experiences 8 Source: HBR, How the Internet of Things Changes Business Models, 2014
Go-To Market Strategy Mining Equipment Manufacturer combine their asset information with customer production forecasts Trigger for Transformation The global downturn in commodity prices hits sales and led to their customers focusing much harder on sweating assets rather than capital spend Smart Services offerings are tiered and tailored to meet specific site needs Transformation Having built a traditional aftermarket / Smart services to provide real time analytics and Predictive Asset Optimisation services for several years, this company realised they had just reached parity with their competitors. They took learning from airlines and integrated asset data into production planning, getting directly integrated into their clients operations Are they managing assets or production? 9
Deere & Company (John Deere) use their mobile sensors to collect and share information for farmers.. Trigger for Transformation Increased competition and globalization led John Deere to believe it was not cost competitive or easy to do business with. John Deere also experiences pressure from macroeconomic factors such as population growth, climate change, and low commodity prices leading to reduced demand for agricultural equipment. Transformation They developed innovations such as a Field Connect System that monitors moisture levels, air and soil temperature, and more, sending the data over a wireless connection to farmers. In 2011, they launched FarmSight. In 2012 they released more products that can connect John Deere s equipment with each other as well as to owners, operators, dealers and agricultural consultants. What assets are John Deere Managing? 10 Sources: https://datafloq.com/read/john-deere-revolutionizing-farming-big-data/511 Deere & Company: The Global Operating Model and http://www.zdnet.com/article/ten-examples-of-iot-and-big-data-working-well-together/ the Worldwide Harvesting Cab Initiative https://www.deere.com/en_us/corporate/our_company/news_and_media/speeches/2011may10_allen.page Deere & Company 1H15 Earnings http://www.chicagotribune.com/business/breaking/ct-deere-outlook-0227-biz-20150226-story.html http://qctimes.com/business/deere-ceo-lays-out-aggressive-strategy/article_c9f93aca-3f7f-11e0-adec-001cc4c03286.html
Utilities are also integrating and automating. Trigger for Transformation Ever tighter regulatory requirements impose tightening output requirements Transformation They are connecting the a wide range of date sources, combined with analytics to predict disturbance to their network. Sensors show where blockages are forming, and combining these feeds improves the targeting of traditional preventative works. Looking ahead, they can use the asset data to proactively change customer behaviour for example minimising run off from fields and targeting behaviour that causes blockages Intelligent asset management is changing behaviours 11
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Who are the leaders? Industry 4.0 has an significant thrust from the German government German Government Vision Digitally enabled industrial economy & mass customization Pervasive use of Cyber-Physical Systems Convergence and alignment of IT and OT Creation of Smart Factories Horizontal integration through value networks End-to-end digital integration of engineering across the entire value chain Vertical integration and networked manufacturing systems Work-life balance Mixed human/robotic workforce & cognative capabilities Expansion to industries outside of industrial 13 Industry 4.0 is a long-term vision that holds huge potential; some elements, such as the convergence of IT and OT are near term, while many other elements will not be felt for 5 10 years (e.g. mass customization) Sources: Acatech: Recommendations for implementing the strategic initiative Industrie 4.0, April 2013 Gartner: Industrie 4.0- The Ten Things the CIO Needs to Know The Goldman Sachs Group: The Internet of Things: Making sense of the next mega-trend Germany Trade & Invest: INDUSTRIE 4.0 SMART MANUFACTURING FOR THE FUTURE
Making this real is still a challenge. We are not aiming for this! 14
What have we learned? General observations 1. This is at the heart of how companies will / do compete 2. It requires application of four distinct elements: An understanding of the business to identify sources of value 15 Business processes to actually use all of the capability you have built! Analytical methods and tools to filter, combine and make sense of what we hear Technology to capture, store and make available relevant data 3. Companies struggle if they.. Try to fix all of the data before they start Focus on technology rather than business opportunities. Lock themselves in to a single proprietary product Ignore the business process and change management aspect 15 We can change integrate the data and build analytic models faster than we can change the way we work Chief Engineer UK Utility
Learning #1: don t try to fix all the data issues before you start A disproportionate portion of the time spent in analytics project is about data preparation: acquiring/preparing/formatting/normalizing the data Focus only on the data you need for a specific use case otherwise you will never start If there are major data issues, start a separate data governance improvement activity 16
Learning #2: the business need to define a roadmap of opportunities Primary obstacles to widespread analytics adoption Lack of understanding how to use analytics to improve the business Lack of management bandwidth due to competing priorities Lack of skills internally in the line of business Ability to get the data Culture does not encourage sharing information Ownership of the data is unclear or governance is ineffective Lack of executive sponsorship 28% 24% 23% 23% 22% 34% 38% Concerns with the data Perceived costs outweigh the projected benefits No case for change 15% 21% 21% Organizational Data Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute of Business Value study. 2010 Massachusetts Institute of Technology. Financial 17
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Learning #3: the technology requirements are unlikely to be met very well by a single product performance management Analytics Sophistication strategic planning Use structured and unstructured data Numeric Text Image Audio Video Captured Detected Inferred Made consumable and accessible to everyone, optimized for their specific purpose, at the point of impact, to deliver better decisions and actions through: What happened? Reporting How many, how often, where? Questioning What exactly is the problem? Visualisation What actions are needed? Decision support Descriptive Analytics What could happen? Simulation What if these trends continue? Forecasting What will happen next if? Predictive Modelling Predictive Analytics How can we achieve the best outcome? Optimization How can achieve the best outcome and address variability? Stochastic Optimization Prescriptive Analytics 19
Learning #4: this is mostly about business and process change, not everyone will see (or feel) the benefit Engineering here we re not sure about this Interconnectivity thing Could you just dump it all onto Excel for us? 20
A recap to close.. It turns out that the real key isn t the fact that we ve got visibility into the assets, though that was our initial goal It s that we now have information available on performance that we didn t have before we can do a lot with that information Peter Vinter, power grid specialist, DONG Energy 21