Knowledge is power, and knowledge can power new approaches to service and revenue. Performance-based contracting is trending in the oil and gas industry, following in the footsteps (or flight path) of the aerospace and defense industry, that has been operating outcome-based contracts for decades. Several high-value service contracts were recently inked, focusing on maximum end-user productivity and lower operating costs.
In the aerospace and defense industry, the historical importance of aircraft or fleet availability have called for different approaches to service and maintenance, like Rolls-Royce’s Power-by-the-Hour, entering its 55th year as a way to align the interests of manufacturer and operator. Oil and gas manufacturers and OEMs are catching up to this strategy, making the move from on-shelf part availability and fill-rates to focusing on equipment and asset uptime. In order to successfully make the shift from a focus on parts to a focus on oil and gas equipment or asset uptime, the oil and gas industry must gather, consolidate, and effectively use its large amounts of available data. The industry’s reserves of information can be turned into actionable knowledge, and in turn, profits. How?
Properly bidding out performance-based contracts
For performance-based contracts, where asset uptime is key, the oil and gas industry needs to closely estimate the eventual cost of asset maintenance, as well as the cost of parts. This advanced forecasting capability is critical to inventory optimization. Improperly forecasting the need for parts can cause manufacturers to charge too little for their maintenance contracts, impacting the bottom line. Overcharging the end-user, on the other hand, can impact customer relationships and contract renewals. This level of forecasting requires the synthesis of many levels of information, from part dependencies to part locations. These data projections for parts and maintenance are also useful when manufacturers are submitting contract proposals to customers.
Getting granular information on asset performance
Given the dynamic nature of the oil and gas industry, with projects, geographies, and of course, oil and gas prices changing all the time, data capture and interpretation needs to allow for measuring these changes in order to have the most accurate forecasting of parts and asset performance. There is currently a great potential to capture and combine operational conditions from assets to gain an even better understanding of performance and failure.
For example, manufacturers could come up with different failure rates for drilling equipment located in different geographies, based on data points such as pressure, temperature, rock type, etc. By sensoring equipment and gathering this granular data, oil and gas manufacturers can make informed decisions about how equipment will perform in similar geographies.
Beyond location granularity, there is also individual part performance assessment, when the same part is used in multiple application. Currently, there are no measurements being captured by how an individual part performs in each setting it’s used in. This usage data, if captured, could allow manufacturers to make stronger predictions, and more accurate forecasts. In the past, the process of collecting this data was prohibitively expensive and the benefits didn’t justify the costs. The IoT is changing this – allowing to obtain and interpret configuration data.
Comparing apples to apples
Performance-based contracts typically call for levels of fill-rate and asset availability, but may also dictate the number of backorders, length of backorders, delays, and more. To use available data for profitable growth requires an ability to translate stock levels into metrics that will show the financial impact of each stocking decision. This includes using data to calculate the risk-benefit of penalties and rewards related to meeting or not meeting service-level agreements.
Holistic view improves accuracy
We’ve mentioned this again and again when talking about oil & gas, and other industries that are dispersed geographically – location-based forecasting is not an effective way of planning and forecasting parts. Parts must be optimized – the right service part levels, in the right location, at the right time, and this necessarily involves some tradeoffs. These tradeoffs can only be achieved with a look at the entire install base data set, using a comprehensive service parts planning system.
Ultimately, the “holy grail” of service data analysis, is a full product lifecycle loop, delivering information from service right back into product design, research and development, and manufacturing. Oil and gas industry data is only as good as the platform used to interpret it.
To learn more about service parts optimization, catch up with our on-demand webinar below: