Consider the complex challenges of a mining operation. The maintenance of equipment used in mining operations is a high value process. The equipment must be functioning or mining operations stop. Given the distributed, remote sites where mining is conducted, an unplanned failure of operational equipment can take days to repair and lead to millions of dollars of lost revenue.
I recently spoke to an executive from a large mining company in Mexico City. He told me that his company paid the manufacturer of its equipment for “break/fix” maintenance service. More recently, he signed on to an enhanced service for predictive maintenance in which the drilling equipment manufacturer provides advance warning of potential equipment failure, enabling prioritization of maintenance spend to where it is most needed. In this scenario, the business proposition for the customer is to increase uptime of the equipment in mining and reduce operational risk.
The reduction of operational risk is further enhanced with the expansion of intelligent, connected machines. When such machines are used for mission-critical operations, we are witnessing the convergence of information technology (IT) and operational technology (OT). The ability to monitor and control operational devices over the Internet brings with it an imperative to apply IT disciplines such as security and data governance to the realm of industrial operations. The figure below depicts what IDC calls "the 3rd Platform" launching a new generation of innovative, data-intensive, intelligent applications for high-value decisions. As shown in the figure, one of the key accelerators for the 3rd platform is the Internet of Things. These next generation applications of industrial data science will need to apply IT disciplines such as “next generation security” and will incorporate expert system (“cognitive”) technologies in order to provide advice and guidance to front-line operators, analysts, and planners..
Business impact of analytics for asset and operations optimization
A recent IDC survey of business and operations professionals showed that the leading driver of big data and analytics projects is "product or service improvement and innovation." This factor was cited by nearly 50% of respondents. The same study reported that over 40% of respondents selected "process and operations optimization and control." In addition, IDC research on analytic applications over nearly 20 years has shown consistently that analytics on operations yields a higher return on investment (ROI) than any other major category or use case — more than customer-facing projects and more than financial analysis.
It's not difficult to understand why analytics for asset and operations optimization can yield a high ROI. The failure of a machine that is critical to an operational process can have a major impact on the revenue that a firm can generate. For example, assume an oil exploration company extracts $100 million worth of crude oil over the course of a month. A prediction of an exploration machine's likely failure in time to repair it can avert revenue losses in millions of dollars for each day the operation is shut down.
Aside from loss of revenue when operations must be shut down, there is also the cost of maintenance. One extreme example is a blowout preventer in the oil and gas industry. A blowout preventer is a complex machine positioned on the ocean floor that is used to monitor the offshore oil well as a last line of defense before a catastrophic blowout. Planned maintenance requires pulling blowout equipment up to the surface to replace worn out or potentially defective parts — an operation that can cost between $10 million and $16 million. Predictive maintenance software recommends which parts need to be replaced. If this predictive software does its job well, the oil and gas company can avoid an unscheduled resurfacing of the machine to replace a defective part. Accurate, timely predictions can save millions of dollars in maintenance costs.
In the aviation industry, airlines can now use sensor data to monitor aircraft performance in flight to enable both preventive maintenance and rapid deployment of spare parts in anticipation of a repair requirement. The same data can be analyzed in order to make real-time adjustments to the operation of jet engines during flight. U.S. airlines spend approximately $48 billion in jet fuel costs annually according to the U.S. Bureau of Transportation. Reducing fuel consumption by just 2% would yield savings of almost $1 billion, and this does not count potential savings to international carriers.
Making the value case for asset and operations analytics
The vast majority of the effort — of an analytics project is all about the data. Capturing, preparing, and integrating data so that it is ready for analysis consumes about 80% of an analytics project. So justifying an investment in analytics must consider the costs and benefits of these data-centric activities. Operations and maintenance are only two of the processes that touch a physical asset of a business. Think about the full life cycle of a physical asset — from design to manufacture to purchase to operating to maintenance to retirement. There are decisions to be made throughout the asset's life cycle, made by different individuals across a variety of roles in an organization. These are connected decisions across the life cycle of an asset. Data that has been used in support of one decision may often be repurposed, along with other relevant information, in support of future asset and operations-based decisions.
In other words, there is a one-to-many relationship between data and decisions. The value of a data set to an organization multiplies each time it is used/analyzed in support of another decision that has measurable financial impact to the business. The more uses of the data an organization can leverage for process improvement, the clearer the value case will be for investing in the people and technology needed to make the data accessible and analyzable in order to harvest the benefits.
In making the value case for asset and operations analytics:
- Focus on high value decisions with measurable results.
- Establish a base line in order to be able to assess the before and after results.
- Consider the technology as well as the people transformation costs.
- Seek out platforms and applications that have capabilities specific to your industry built-in to hold down costs and reduce implementation complexity and risk.
Check out the Industrial Internet infographic from IDC on optimizing operations with big data and analytics.