M. Bowling, Seeq, Houston, Texas
In today’s turbulent global geopolitical and economic landscape, key decision makers at industrial manufacturing organizations are exercising caution when considering new spending. However, despite this moderation, company shareholders expect ever-increasing returns.
While meeting these expectations can seem daunting or almost unreasonable at times, industrial organizations can achieve the desired results by implementing digital technologies, such as advanced analytics solutions. This software leverages existing process assets and equipment to deliver transformational insights and improve production outcomes with minimal upfront capital costs by empowering process engineers with critical decision-making data to approach the toughest challenges in innovative ways.
Modern advanced analytics solutions help improve operational and business workflow efficiency during each key phase of digital transformation: evaluation, implementation, and scale and optimization. This article will define each of these phases and provide real-world industrial use cases where digital technologies helped promote progress.
What is digital transformation? Deloitte describes digital transformation as the act of “becoming a digital enterprise—an organization that uses technology to continuously evolve all aspects of its business models (what it offers, how it interacts with customers and how it operates).”1 When it comes to analytics, this means automating manual tasks that require significant effort, including data aggregation and cleansing. When these manual burdens are reduced or eliminated, teams have more time to delve deep into data to identify operational issues and areas for potential process improvements.
According to a 2023 ARC Advisory Group survey,2 most manufacturing companies are progressing on their digital transformation journeys, but only about a third report substantial progress—defined by optimization and business-level improvements. This translates to great opportunity for increased revenue from assets, driven by digital transformation projects.
As a company embarks on its digital transformation journey, there is a common misconception that it must establish a fully designed and implemented information technology/operational technology (IT/OT) architecture prior to beginning. However, modern software applications can deliver near-immediate value, regardless of where an organization stands digitally (FIG. 1).
Self-service advanced analytics solutions deliver quick access to data where it natively resides, reducing time to value with insights that enhance decision-making, thereby improving business outcomes.
Phase 1: Evaluation. The initial phase of digital transformation initiatives is evaluation, where many organizations fall into one of two traps. The first is letting prior failures obstruct future decisions, as previous technological implementations that came up short of the promised value can discourage or impede future adoption. The second is allowing the transformation initiative to stagnate in pilot purgatory, and failing to make consequential decisions that enable teams to move from test-drive to run mode, where true business value becomes apparent.
Fortunately, combining the right people and technological solutions can help organizations combat these common mistakes and efficiently progress from evaluation to implementation.
When beginning, it is critical for project leaders to identify and engage with key cross-functional stakeholders—including a mix of technical and administrative personnel—to ensure the project addresses specific business needs with a workforce at its back. By engaging this evaluation team from the outset, project leaders can break down organizational silos and ascertain the highest-value use cases, resulting in quick wins for the team while building confidence throughout the organization.
Next, teams should identify a software solution that seamlessly integrates into existing enterprise architectures, provides rapid access to data and can be implemented quickly—e.g., in days rather than months.
Case Study: Chevron Phillips Chemical pilot provides return on investment (ROI) assurance. A recent Forbes article described Chevron Phillips Chemical’s experience in the evaluation phase of a digital transformation project, where the organization chose the author’s company to deliver self-service advanced analytics. The article states, “Upon completion of the proof of concept, users were able to learn the potential value of using advanced analytics software solutiona from trusted colleagues. It was easy to use and solved a massive number of problems quickly. The time to ROI was weeks rather than months or years. The problems that they were able to solve using the technology were problems that the workers did not previously know how to solve”3 (FIG. 2).
In this example, the selected software proved value and provided the manufacturer with confidence during the evaluation phase, reducing adoption friction in subsequent phases of the project.
Phase 2: Implementation. Once a software solution is identified, it is time for implementation. Successful implementation requires leaders to focus on quick wins that can be broadly deployed for business value, and to identify the key changemakers who can champion progress through various departments of the organization.
Many companies attempt to advance too quickly during the implementation phase. Instead of rushing to solve the most difficult problems first, teams should instead focus first on low-hanging fruit to build momentum and accelerate the time to ROI, which bolsters end user confidence. A series of quick wins can alleviate resistance to change and break down project barriers early on.
Additionally, by maintaining a controlled and steady pace, project leaders can more easily identify the right changemakers—individuals who influence and motivate others. These key stakeholders help leaders garner organization-wide support by leveraging preestablished rapport in their areas of the business to promote and show employees the value of a new solution. When amplified appropriately, changemakers’ energy can rapidly accelerate organizational buy-in, minimizing the time to real results experienced during the scale and optimization phase.
Phase 3: Scale and optimization. As teams enter the scale and optimization phase, they must remember that while digital transformation projects have defined end conditions, digital transformation is a journey, not a destination. Each digital improvement can be viewed as a building block for future efforts.
Teams should develop a continuous improvement and sustainment plan for each digital transformation initiative. Such a plan helps teams remain focused on value delivery while continuously evolving to meet the ever-changing needs of the business.
The scale and optimization phase’s success rests on ensuring team members tasked with new digital technology adoption are given the right tools and resources to excel. This includes proper training, use case support, internal knowledge sharing and an easy-to-use feedback mechanism for further improvement.
Agility is a key component during this phase, as the project team must receive feedback efficiently and adjust direction quickly to address issues. Keeping end users and business units in the forefront is key to ensuring appropriate measures for success are defined, and for projecting and identifying issues before they create problems. Successful scale and optimization ease adoption efforts and accelerate the time to value of digital transformation projects.
Case Study: Marathon Oil scales with care. At Marathon Oil, teams are tasked with monitoring and ensuring the stable operation of nearly 4,000 wells. Recently, the company implemented an enterprise advanced analytics solutiona to ease this considerable task, which was achieved by establishing workflows during the scale and optimization phase that reduced the time required to create new alerts from months to hours. Implementing alerts and staying updated are essential for the business because they help keep wells online and limit deferred production.
Using the author’s company’s advanced analytics solutiona improves scalability for Marathon Oil by connecting production data from across all its wells. The company has more than 50 employees using the solution with 170 workbenches in the platform, which generates 1,500 tasks and more than 1,500 notifications per month. What was being manually identified in the past is now automatically generated. Overall, by using the advanced analytics solutiona, Marathon Oil looks to increase production by proactively identifying issues to increase uptime.4
By placing curated technology in the hands of its front-line personnel and empowering them with notifications and insights to operate more efficiently, Marathon Oil has successfully increased production and achieved scale.
Accelerate human-centered business value. Recent improvements in analytics solutions and the industry-wide potential for digital transformation are curating the perfect conditions for organizations undergoing digital transformation, many of which will enter the scale and optimization phase in the coming years. Regardless of which phase an organization finds itself in, project teams must evaluate priorities and initiatives periodically to ensure people and business values remain at the forefront of their efforts.
Successful digital transformation and cultural shifts occur when projects begin and remain based around the people involved. Additionally, human-centric implementation ensures end users receive proper training, empowering the workforce to achieve maximum ROI and understand project value. Similarly, delivering business value requires that projects cater to business outcomes and processes. When project teams lose sight of this purpose and become technology-driven instead of business value-driven, they can quickly stray off course, which creates resistance within the organization and undermines the originally targeted value.
It is never too early nor too late to begin a digital transformation initiative: the goal is not a destination, but rather fostering continuous improvement. Whether an organization is just thinking about getting started or wrapping up a project with successful scaling, enterprises must continuously evolve to maintain value-driven cultures, remain competitive and get the most from their data and assets. HP
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LITERATURE CITED
MORGAN BOWLING is the Director of Industry at Seeq. She has a process engineering background and earned a BS degree in chemical engineering from the University of Toledo. Bowling has a decade of experience working at both independent and integrated major oil and gas companies to solve high-value business problems leveraging time series data. In her current role, she enjoys monitoring the rapidly changing trends surrounding digital transformation in the oil and gas industry and translating them into product requirements for Seeq.