Enterprise resource planning (ERP) software used across industries has made tremendous progress in the past 30 years, not only in terms of adoption but also in sophistication. ERP, which began by bringing all business systems online, eventually moved to the integration of operations across multiple functions of the business like finance, human resources and administration.
The adoption of automation in industry picked up pace rapidly around 20 years ago, and many large-scale organisations implemented the technology with focus on the integration of their operations. As the world enters the next phase of automation, there is an increased focus on artificial intelligence (AI), machine learning (ML) and robotic process automation (RPA), which will eventually reduce the number of processes operating in an organisation. During this period, ML and AI will be the significant drivers for ERP, thereby forming an intelligent enterprise. In an intelligent enterprise, repetitive processes like invoice matching will be eliminated.
Upcoming technologies augmenting human capabilities
In the next decade, an average organisation is predicted to replace 20 to 30 per cent of its business processes with RPA, ML algorithm, or an AI-enabled chatbot. Processes such as generating invoices, making payments and issuing licences will be automated. Thus, organisations have to ensure that they have the right architecture and are ready for this transition as they move forward.
Many digital technologies, including blockchain, ML, AI and internet of things (IoT), are gaining popularity and being increasingly adopted across industry. There is a need to bring these technologies together on a common platform so that they can interact with each other and share data so as to enable the automation of all processes. Thus, the systems need to be integrated in a more holistic and synchronised manner.
Key trends shaping ERP systems in the oil and gas industry
Significant volatility has been observed in the oil and gas industry recently. Oil prices, which started to rise in 2017 after a long period of stagnation, suddenly fell in mid 2017 before subsequently picking up in end 2017. The single biggest factor that is affecting oil prices is the competition from other sectors. For instance, natural gas has become a prominent player in the transportation industry. Going forward, city gas distribution (CGD) will replace liquefied petroleum gas (LPG) penetration, at least in Tier I and metro cities. Renewable energy is also an increasingly viable source of energy generation that is going to impact the position of oil as a fuel for transportation, energy generation and heating. Thus, organisations today are facing a tremendous business impact resulting from this replacement or substitution of fuels. These changes are expected to shape the future of the ERP systems being used in the oil and gas industry.
Impact of intelligent systems on the oil and gas industry
The evolving intelligent systems are expected to have a positive impact on the oil and gas industry. For instance, getting real-time business insights is becoming increasingly important for achieving a healthy bottom line. Technology should be an enabler for generating these insights. The existing oil and gas companies in the country have at least 20 years of data available with them. What is important is finding the most productive ways to leverage technology to mine this data effectively. For example, business processes were initially set up with a preconceived idea in mind. However, if data is mined on a real-time basis, it will help the organisation restructure its business processes based on the insights drawn from the collected data. This will create data-led business processes rather than business processes based on certain preset conditions.
Further, organisations should be able to create a new revenue model based on this transformation. At the board level, organisations are looking at digital technologies to help them increase their revenue which is a key performance indicator for the board.
How ERP will shape the future of the oil and gas industry
Intelligent technologies such as AI and ML will drive multiple business activities and processes in the oil and gas industry, going forward. However, in order to reap the benefits of these technologies, there is a need to re-architect, re-engineer, and reimagine the way that enterprise systems were created 20 years ago, so that they are able to understand the information that is coming from these systems and undertake the business processes that were previously performed by humans. For example, if a chatbot is being used for invoice matching, the ERP system should be able to understand the data generated by this chatbot.
The second change is the way blockchain will transform the larger ecosystem. Initially, enterprise systems were defined for the use of individual people in businesses who would supply information to the system. However, this is changing now. For example, in the current blockchain network, somebody sitting in a remote location is capable of updating a milestone, which has an impact on the rest of the project activity. By simply updating the system to show that the project is progressing well will ensure that payment is released on time, as the blockchain had already approved the transaction previously. Thus, some things need to be redefined from an enterprise systems perspective by moving from a human-led approach to a data-led approach.
The third – and highly crucial – change is the increasing focus on data management. Currently, all organisations are generating large volumes of data that could be used for generating insights and even for predictive modelling using ML. Thus, all the data that is important to the organisation should be fed into the system and should be an integral part of the architecture.
The way forward
Going forward, most of the processes will be moved to the cloud. Thus, cloud-based computing will help determine standard business processes that will be used by companies. ERP systems will eventually evolve into intelligent systems that will integrate data from multiple platforms. This intelligent architecture will, in turn, result in enhanced capabilities. The first outcome will be the growing visibility of patterns generated from the available data. This will be facilitated by ML which can analyse both structured and unstructured data and provide the level of visibility needed. The second outcome will be the increased focus of the organisation, that is, determining the areas where the organisation should direct its resources for optimum results. The third outcome will be the increased agility gained by an organisation. For instance, if crude prices were to increase, the intelligent enterprise would be able to determine how the internal business environment should change according to the external business environment. These intelligent systems are expected to change the way organisations in the oil and gas industry will operate and will drive the intelligent organisations of tomorrow.