# Infosys POV 
Title: Next-gen Process Mining powers Oil & Gas transformation 
Author: Infosys Consulting 
Format: PDF 1.7 

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 An Infosys Consulting Perspective By Sachin Padhye, Naveen Kamakoti, Shruti Jayaraman and Sohini De Consulting@Infosys.com | InfosysConsultingInsights.com Next-gen Process  Mining powers Oil &  Gas transformation 

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 Oil & Gas transformation | © 2023 Infosys Consulting 2 Process Mining technology: A key enabler to transform Oil & Gas Technology continues to be a reliant and indispensable enabler to transform operations of Oil and  Gas companies. As a growing trend, the broader intent of incorporating technology is the use of  operational data to support analytics and fact-based decision-making. However, increasing  complexity of core and supplementary processes, coupled with limited agility of legacy and  monolithic IT systems adds a constant challenge to continuous process improvement.<|endoftext|>The agility of business processes and operations depends on the ability to capture real-time data  and perform large scale analyses to generate actionable insights on-demand and help steer and  nudge key metrics and key performance indicators (KPIs).  One such technology framework with ever-growing adoption is Process Mining, especially due  to the evolution from process-discovery-based limited applications to centralized platforms for  integrated process automation.<|endoftext|>

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 3 Process Mining uses detailed data from business processes Process Mining is the practice of using data from various sources to analyze, baseline  and improve business processes.  The concept of Process Mining is built on the pillars of analysis techniques using artificial  intelligence (AI) and machine learning (ML). It is an approach to analyze, optimize, and  improve complex operational processes.  Powered by event data logs and data science tools, Process Mining helps identify  process variations and bottlenecks and gathers quantitative insights in process flows. It  also helps address performance and compliance-related issues in processes.  The following high-level steps are involved in a typical process mining lifecycle journey: Step Description Tools used 1. Data collection Collect data from various sources, such  as event logs, databases, operational  data stores.<|endoftext|>Data extraction tools, such as ETL  tools, log parsers, or database  connectors.<|endoftext|>2. Data pre- processing Clean, filter, and normalize data to  ensure consistency and accuracy.<|endoftext|>Data cleaning and preparation  tools, such as Python, or R scripts.<|endoftext|>3. Process  discovery Create a process model based on the  collected data.<|endoftext|>Process Mining tools, such as  Disco, ProM, or Celonis.<|endoftext|>4. Conformance  checking Compare the process model with the  collected data to identify deviations,  errors, or inefficiencies in the process.<|endoftext|>Conformance checking tools, such  as Disco, ProM, or Celonis.<|endoftext|>5. Process  enhancement Optimize the process model to improve  efficiency, reduce costs, and enhance  quality.<|endoftext|>Process simulation and optimization  tools, such as Arena, Simul8, or  ProModel.<|endoftext|>6. Process  monitoring Continuously track and analyze process  data to identify potential issues,  bottlenecks, or opportunities for  improvement.<|endoftext|>Process monitoring tools, such as  Celonis, Splunk, ELK, or Graylog.<|endoftext|>7. Process  visualization Create graphical representations of the  process model and process data to help  stakeholders understand the process and  identify areas for improvement.<|endoftext|>Data visualization tools, such as  Celonis, Tableau, Power BI, or  QlikView.<|endoftext|>Oil & Gas transformation | © 2023 Infosys Consulting 

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 4 Evolution of Process Mining From being a niche technology used in research-oriented projects to a completely  integrated cross-functional collaboration platform, Process Mining has evolved and  matured for broad adoption. Here is a summary of this evolution: Oil & Gas transformation | © 2023 Infosys Consulting Process  discovery Process  conformance User-centered  process Integrated process  automation Time period First Generation Second Generation Third Generation Fourth Generation 1988 – 2004 2004 – 2011 2011 – 2016 2016 – Present Summary Focus on discovery  of process models  from event logs Introduction of  conformance checking  and process  enhancements Shift towards more user- centered and interactive  approaches Expansion of process mining  beyond event logs to include  other types of data and  processes Use of process  discovery algorithms  and process tree  visualizations Integration of multiple  perspectives and data  sources Focus on business  process management  and improvement Integration of process mining  with other technologies such  as AI, IoT, and blockchain Limited support for  large and complex  processes Focus on quality  control, compliance,  and audit trails Integration of social,  organizational, and  environmental factors Increased focus on  automation, robotics, and  digital transformation Challenges in  handling noise,  concurrency, and  infrequent behavior Use of data mining and  machine learning  techniques Increased emphasis on  big data, cloud  computing, and  distributed systems Development of new  techniques such as predictive  process monitoring and  prescriptive analytics People Primarily academic  researchers and  process experts Involvement of  business stakeholders  and end-users in  process mining projects Involvement of a wider  range of stakeholders  including end-users, IT  staff, and top  management Involvement of a wide range  of stakeholders including  business users, IT staff, data  scientists, and process  experts Minimal involvement  of business  stakeholders and  end-users Increasing emphasis on  collaboration and  communication Greater emphasis on  user needs and user  experience Greater emphasis on cross- functional collaboration and  co-creation Process Emphasis on  process modeling  and analysis Shift towards process  improvement and  optimization Increased integration of  process mining with  business strategy and  management practices Integration of process mining  with digital transformation and  innovation initiatives Limited focus on  process  improvement and  optimization Greater attention to  business objectives  and value creation Greater emphasis on  continuous improvement  and innovation Systems Basic computing  tools and algorithms Development of more  sophisticated  algorithms and  methods Greater use of cloud  computing, big data, and  advanced analytics Integration of AI, IoT,  Blockchain, and Advanced  Analytics Primarily desktop- based software Increased use of  enterprise-level  software systems Integration with other  digital technologies such  as social media and  mobile devices Use of event logs  and basic data  mining techniques Integration of multiple  data sources and  formats Greater use of process  automation and robotic  process automation  (RPA) 

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 5 Process Mining impacts multiple areas in Oil & Gas The Oil and Gas industry is complex and dynamic with significant data generated across  all areas.  For Oil and Gas companies, Process Mining can be particularly important because of the  complex and highly regulated nature of their operations. Here are some specific ways in  which Process Mining can benefit Oil and Gas companies: Oil & Gas transformation | © 2023 Infosys Consulting Value levers Impact of Process Mining Operational  efficiency Process Mining can help identify inefficiencies in processes,  such as bottlenecks or unnecessary steps, and suggest ways  to streamline them. This can lead to cost savings and better  use of resources.<|endoftext|>Regulatory  compliance Oil and Gas companies are subject to numerous regulations  and standards, such as those related to environmental  protection and worker safety. Process Mining can help  ensure that these regulations are being followed and identify  areas where improvements are needed.<|endoftext|>Operational safety Safety is a top priority for Oil and Gas companies, and  Process Mining can help identify potential hazards and risks.<|endoftext|>By analyzing data from sensors, equipment, and other  sources, companies can identify patterns that may indicate  an increased risk of accidents or equipment failure.<|endoftext|>Optimized  maintenance Process Mining can help companies optimize maintenance  schedules by analyzing data from equipment and other  sources to identify when maintenance is needed. This can  help prevent unplanned downtime and reduce maintenance  costs.<|endoftext|>Customer  satisfaction Oil and Gas companies may interact with customers in  various ways, such as through fuel delivery or service  stations. Process Mining can help companies understand  how customers interact with their services and identify ways  to improve the customer experience.<|endoftext|>

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 6 Pre-requisites for Process Mining Certain conditions need to be met before leveraging Process Mining effectively. These  can broadly be grouped under People, Process and Technology.<|endoftext|>Oil & Gas transformation | © 2023 Infosys Consulting Who can help manage the  organizational changes that may  result from Process Mining  initiatives.  BUSINESS ANALYSTS With domain knowledge of the  processes to be analyzed.  With expertise in data  management and system  integration.  With expertise in data analysis  and statistical modelling.  Or subject matter experts who can  provide feedback on the accuracy  and relevance of Process Mining  results.  DATA SCIENTISTS IT PROFESSIONALS