9 MINS READ
INDUSTRY
Energy, Resources & Utilities
Rebalance the energy equation
SOLUTION
Cloud
Enabling Enterprise Agility & Resilience with a Robust Cloud Strategy
Highlights
- Oil and gas facilities have expensive assets that are often located in desolate places and need specialized workforce for maintenance.
- With the advent of sensing technology, IoT, cloud, big data, machine learning, and artificial intelligence, OEMs are now better equipped to monitor their self-built assets.
- The whitepaper proposes key strategies and business models for energy companies to envisage and benefit from, such as the capex to opex and the machine-as-a-service models.
- The opex model reduces the cost burden for OEMs, can be deployed on-demand, thereby reducing the lead time of deploying new and improved products and helps OEMs stay relevant in volatile markets.
- IoT-enabled smart production centers and refineries will enable adoption of the machine-as-a-service model, as it lowers capital expenditure and helps set up plants quickly.
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The Opex model - A viable proposition
The world has over 6.2 million kilometers of pipeline, transporting crude hydrocarbon from oil and gas wells located in more than 65,000 oil and gas fields and products from more than 697 refineries in 118 countries. These oil and gas facilities have expensive assets like pumps, compressors, fractionators, reformers, gas separators, cracking, hydro treaters, boilers, and state-of-the-art combined cycle power generators.
Figure 1: USA’s vast natural gas infrastructure
These assets are often located in desolate places and need specialized workforce for inspection, maintenance, and repair. Companies invest in the initial installation, spare parts, and manpower required to maintain smooth operations. Continual monitoring decreases asset lifecycle costs and improves performance, and a mere 2% increase in performance can increase profit by millions of dollars. However, original equipment manufacturers (OEMs) are better equipped to monitor their self-built assets; with the advent of sensing technology, IoT, and data transfer, the technical and commercial viability of offering industrial assets as a service is fast becoming a reality.
There are various budgetary, financial, depreciation, regulatory, taxation, and contractual reasons for shifting from the capital expenditures (Capex) to the operating expenses (Opex) model. This whitepaper primarily focuses on the predicaments from the IT perspective, strategies that the energy companies need to envisage, and the benefits they can reap by switching over the business model. It also deals with the involvement of third-party neutral IT organizations that facilitate the shift from Capex to Opex. Only a win-win situation for the asset manufacturers and the oil and gas companies can make the Opex model a viable proposition.
Industrial machine-as-a-service ― The new paradigm
Rolls-Royce pioneered the introduction of the equipment-as-a-service model for selling aircraft turbine services at a fixed cost per actual usage hours only. Rolls-Royce earned four times more revenue than the traditional sales model by selling engine-as-a-service. This ground-breaking approach freed the airlines from complicated turbine maintenance and allowed them to concentrate on improving airline services and increasing sales for higher profits.
According to the Business Innovation Observatory of the European Commission, 70% of machine manufacturers position services as a key differentiator. Machine manufacturers who adopted service-based business models have witnessed 5% to 10% business growth annually, with 50% of their revenues coming from services. Using industrial machines or even the entire unit or plant ‘as a service’ is gaining popularity due to distinctive advantages.
The table below (see Table 1) illustrates commercial transactions related to the purchase of machines, based on our interactions with OEMs and oil and gas companies.
Figure 2: Commercial implications of machine transferred as ‘Capex’
How this shift benefits industrial customers
The Opex model reduces the cost burden—for OEMs—that occurs due to the rising investment capital required for Capex purchase. This model can be deployed on-demand, thereby reducing the lead time of deploying new and improved products and increasing profits.
It provides businesses agility and flexibility, helping OEMs to stay relevant in volatile markets and effectively tackle fierce competition. This flexibility allows businesses to pay only for what is needed and not be burdened with outdated infrastructure. By investing in assets that can be used immediately, companies can avoid being stuck with unnecessary capacity and improve profitability.
Most importantly, the service provider offers the latest technology that minimizes failures and reduces downtime risk. This helps in complying with rising regulatory pressures. The rich sensor data captured by OEMs can be used to create data-driven strategies and optimize the use of machines and improve productivity and billing performance.
The table below (see Table 2) is based on our experience of working with oil and gas companies and OEMs worldwide. It illustrates the monetary implication of using machine-as-a-service.
Figure 3: Commercial implications of a machine deployed as a ‘Service’
Why OEMs should consider offering machine-as-a-service
Although initially OEMs that switch to service models may register some drop in revenues from new machine sales, however, long-term service agreements can help them increase revenues eventually. As the source of life-long revenue generation, the machine enables and stabilizes steady cash flow for OEMs in the long run.
OEMs can improve machine design and handle reactive, predictive, or prescriptive maintenance entirely with machine data collected from multiple client sources and using enhanced data algorithms. The rich sensor data that is captured can be used for data-driven quality control, product optimization, and customer-requested customizations.
As they continually engage with the customer, OEMs can offer additional machinery, packages, and services to customers as and when required. Offering industrial equipment-as-a-service also helps acquire new customers aiming for cash flow optimization.
Machine-as-a-service – A win-win solution
Offering machine-as-a-service is a win-win solution for both OEMs and their clients, as illustrated below (see Table 3):
Figure 4: Offering machine-as-a-service
The challenges of switching to Opex
As illustrated above, using machine-as-a-service is beneficial for both the industry and OEMs. However, there are several bottlenecks around variegated machines, infrastructure, location, connectivity, regulatory demands, and data security that both the parties must address. These issues, if not addressed, may cause a hindrance while shifting from Capex to an optimal Opex model:
- Oil and gas installations work with multiple OEMs for asset procurement. Often, even similar types of assets are procured from different OEMs, depending upon the capacity, time of procurement, and other factors. Although these assets are operated and maintained by the oil and gas companies, they have to depend on the OEMs for operating guidance, maintenance, and repairs.
- For constant monitoring of assets, OEMs need information that includes data on operating parameters and from sensors attached to these machines. Every aspect of an equipment’s operation cycle needs to be captured, stored, and categorized. Capturing, processing, and transferring such large volumes of data from different machines to various OEMs often becomes challenging.
- Due to physical and cyber security threats, companies cannot allow multiple OEMs to set up connectivity devices from their machines located within plants and sites to transmit sensor data.
- Since different equipment generates data in different formats, managing this data and making it available for equipment manufacturers in a standardized format becomes complicated.
Enabling the equipment-as-a-service model
Advances in industrial internet of things (IIoT), 5G, cloud, big data, machine learning (ML), and artificial intelligence (AI) have given rise to digital service solutions facilitating machine-as-a-service models. These solutions enable machines to share asset performance data, provide transparency on its usage, and charge customers based on actual usage. IoT infrastructure with real-time and historical data visibility, AI-powered analytics, and advanced predictive maintenance capabilities can serve as a backbone for usage-based billing and flexible contract management.
The equipment covers various assets, including devices like controller gateways. These devices also provide mechanisms to capture data from the equipment to provide inputs to operations systems like supervisory control and data acquisition (SCADA). Equipment manufacturers use this data for equipment functioning analysis. OEMs can leverage cloud and IoT platforms to collect and process data. Technologies such as AI and blockchain will make the manufacturing process more efficient and ensure that the products remain competitive.
It is practical to introduce the concept of a third-party neutral service provider who would be responsible for collecting, collating, storing, and distributing the data to various OEMs from a company’s sites and plants. This will reduce data collection, connectivity, and storage costs and eliminate the need for the company to provide access to several OEMs inside their premises, thereby ensuring security. This service would include collection from sources, historians, or embedded systems and transportation of the data, preferably in real-time, to cloud, for storage, and analytics.
The neutral third-party data aggregator needs to devise a service-based architecture and create a platform to receive, collect, and process data from the field equipment. Aggregating data would require components like gateway and connectivity to collect and transfer data from the field to the cloud. This mechanism will be different from the SCADA systems since the focus of the SCADA systems is more on operations rather than on data aggregation or analytics. The processed data, reports, and analytics can be distributed to vendors, internal employees, regulatory authorities, customers, and external stakeholders through a secure platform.
Data privacy will be one of the paramount features of this platform. The decision on whether the platform will be on cloud or on premise will be determined as per the needs of the customers, OEMs, and any regulatory requirements. The vendor can also bring in AI and advanced analytical tools for optimizing performance.
IoT-enabled smart manufacturing
Adopting the machine-as-a-service model will help machine manufacturers and oil and gas producers or processors increase revenue and profits. ‘Smart’ production centers and refineries will improve adoption of the machine-as-a-service model, as they require less capital expenditure and help set up plants quickly. In addition to facilitating a steady income stream, the machine-as-a-service model will help machine manufacturers improve the lifecycle of machines with better maintenance.
