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Contributed on September 6, 2018

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Talent Management Digital Transformation in Oil & Gas Companies

Blog: End to End BPM

Technology adoption is pushing the workforce to re-skill

Oil & Gas companies like utilities, mining and other natural resources industries, were challenged with barrier of ageing workforce. This actually have an impact. Project overruns, lower asset availability and low production targets. But during this last 5 years, what become some experiments around IoT projects, big analytics about operational performance and most recently, real time collaboration on seismic data studies and reservoir simulation, increased relying on technology – shifting from operational technology to information technology.

Oil & Gas companies need more and more personal with computer science background to keep operations going . Therefore, there is a rise in demand of data scientists, statisticians, individuals that know how to use Artificial Intelligence technologies. This requires a different breed of human talent.

Companies need to deliver to a multi-generational / cultural workforce that demands more flexible work options. And that means it is necessary to be agile in adapting human resource practices to meet the needs of the business and the workforce.  Practically speaking, HR’s role is expanding beyond its traditional focus.

From the organizational side, a new approach to education, and especially up-skilling and re-skilling, is necessary to support digitally enabled workforce and the evolution of new business operations that require with latest technology developments. For example, today it is possible to automate pipeline inspection using drones and combining edge computing solutions [1]. As such, this presents a huge growth area of new jobs opportunities that candidates could apply for or existing workers could take new training go gain role readiness.

From the labor market side, there is the ambition of have much more flexibility in the terms work is performed, mimic the work pattern habits, like in personal life – utilization of social media style tools for internal communications and bring your own device. And a part of that, new generational workforce like the Millennials believe oil & gas companies is lacking innovation, agility and creativity, as well as, opportunities to engage in meaningful work. Strategically “employee value proposition” and pull-factors will attract the most talented. Improving brand awareness captivating candidates to apply for a job opening. Attracting the next wave of the workforce can also include using new approaches like “Recruitainment” – gamification in talent management, that includes among others, recreating a real work environment and understand how well the candidate fits into role execution [2]; [3].

Creating the workforce of the future involves to consider the combination of the following dimensions:

• Modern workplace – which the workforce can collaborate across multidimension kind of data, using the device and the platform of choice. Contrary to other industry sectors, oil & gas companies are data intensive and data tasks oriented that use neural deep learning network algorithms, fuzzy logic, and others to improve data analysis.
• HR departments, must rethink how core processes are designed. Artificial Intelligence, when combined with other available technologies can free up considerably of a recruiter’s job and can then use time more efficiently attract the best candidates. Video interview is on the loom, as well as, application and job description matching, eliminating unconscious bias. Plan and predict workforce capacity, upskilling needs and employee disengagement can also be supported by AI technologies.
• Workforce safety will see a significant leap forward due to digitalization. With digital oilfields and the automation of dangerous tasks, fewer workers will be placed at risk. In addition, Artificial Intelligence will reduce the possibility of human error, contributing further to workforce safety. An example of using AI in field inspections is Equinor (formerly Statoil) – Statoil digital field worker a company that is pushing boundaries in terms of digital transformation.

Contrary to the power shift that occurred towards the customer related with consumer industries, in labor market – unless there is talent shortage – the bargaining power is still much on the recruiter side. However, HR workforce, is aware that it is needed to be investing more in recruiting and retaining the best talent, due to the fact that oil & gas companies are becoming IT companies with a necessity of becoming, fast, a data driven company. For example, in scenarios related with  optimizing drilling, by using historical drilling data, it is feasible to quantitatively identify best and worst practices that impact the target. Advanced analytical methodologies would ultimately develop a model that would provide early warning of deviations from best practices, lessons learned or other events that will adversely impact drilling time or costs. Hence, on one hand there is technological dimension related with what IT solutions are best fit to build the models and make the analysis.

New set of technologies for the evolution of Talent Management

On evolving HR function from hire to retire, increasing hiring quality, contributing to knowledge diffusion and retain talent, HR professionals should consider to invest in these new set of technologies taking in to consideration their priorities.

Identifying digital enablers for improved talent management

• Social Sourcing. Storing a static talent pool is not sufficing. Managing a dynamic talent pool that continuously is aware of the activity the candidate is broadcasting in terms of career achievements, new roles or professional activity is becoming ultimately important to spot the changes and trends of the prospective labor market. Social Sourcing is about make it visible a pipeline of sourcing with internal / external candidates integrated with social workforce analytics and from employee service providers (e.g.: LinkedIn). It also includes identify automatically candidate profiles that match your job description requirements.
• Intelligent Application Screening. Despite of the evolution related with managing job applications in digital mode become a standard, processing an immense volume of applications is not practical and is time consuming. Robotic Process Automation can help with text extraction, candidate profiling and role matching and workflow automation.
• Automated Candidate Interviewing. This is one of the trends that is moving very fast, in some cases, is becoming fully automated that with the combination of cognitive services (e.g.: image pattern recognition and natural language processing), it is possible to analyze emotions and enhance candidate profiling, makes a pre-screening of the interviews before they are short-listed to the recruiter.
• Neuroscience Assessment in combination with advanced analytics. Supports decisions about candidate’s degree of fit, matching candidates to positions and at the same time removing recruiter / hiring manager unconscious bias.
• Mixed Reality. Is able to replicate in the digital world a natural performance environment. It can be used in “Recruitainment”, as well as in training (e.g. safety, operations performance and optimization, geomodeling).
• Social Business. Provides a platform for finding relevant information, collaborate, ideate, innovate and reach out to colleagues and SME’s. Knowledge diffusion is improved combined with integrated collaboration and productivity tools. – Social Business is a platform that allows to :
  • Know about the Human: Who the worker is, how the worker identify himself and what you pretend to be, the person’s social graph.
  • Human Interactions: What the worker does. Whim whom the worker engages with. How he react to participation in company forums . What kind of work activities are pursued.
  • Search and Analytics: Search for knowledge, gather feedback, get trends, spot patterns, sentiments, learn.
• e-Learning.  Enable organizations to train and upskill the workforce at scale, with content personalization, and curation, based on particular role execution requisites and performance review feedback.
• Social performance analysis. Enables workforce analysis in order to improve performance measurement. It also includes, alignment between the nature of work performed and the type of social network configuration the employee is fitted.

The set of technologies uniquely referred above are supported enhanced by a foundational layer composed of:

• Data Analysis Services. Supports all the moments which decision making is executed, by combining a multitude of data sources. Enables the exploration of data that go beyond those available in spreadsheets. Visualization is possible by direct connection to data sources.
• Machine Learning. Like in operational improvement scenarios, Machine Learning can be used from predict employee disengagement and the propensity of leaving the organization, to recommend personalized training, and benefits packages;
• Cognitive Services. Multiple set of Artificial Intelligence technologies supporting workforce planning, automated conversational applications, emotional analysis, recommendation engines and intelligence search:
  • Role description matching and skills / candidate experience.
  • Recommendation engines, used to propose personalized training program, career path, perform workforce impact analysis in terms of future role needs and new skills to be acquired and ultimately;
  • Business Process automation, by the use of chatbots, for candidate or HR staff self-servicing.

Evolving the HR mission and processes by the adoption of Artificial Intelligence, Big data, analytics, and gamification in the hiring process is picking traction, nevertheless, HR professionals also consider the employee experience if talent must be retained and keep low the propensity of leaving the organization.

References:

[1] – Gartner defines edge computing solutions as: “facilitate data processing at or near the source of data generation and serve as a decentralized extension of cloud, data center or campus networks. Typical sources of data generation in the context of industrial Internet of Things (IoT) include sensors and control devices such as programmable logic controllers (PLCs) and distributed control systems (DCSs)” – Market Guide for Edge Computing Solutions for Industrial IoT – Gartner 2018. Microsoft’s Azure – IoT Edge is such kind of solution.

[2] – This is not a new trend only in Oil & Gas companies. Marriott hotels, who created a virtual reality Sims-like game in which players have to juggle all the responsibilities of a hotel kitchen manager, was one of the first to try a gamification approach.

[3] – Building the Next Generation of Petrotechnical Professionals through Gamification – Paul Ugoji (Total Automation Concepts Ltd) | Akii Ibhadode (Federal University of Petroleum Resources Effurun, Delta State) | Anslem Amadi (Federal University of Petroleum Resources Effurun, Delta State).