Genesis
Building a Centralized Forecasting Solution for the Oil & Gas Industry
Genesis
Building a Centralized Forecasting Solution for the Oil & Gas Industry
Industry
Gas and Oil
Client
Hess
Scope
UI/UX Design
Company
Strategy CX
In 2024, I joined Strategy CX to help Hess Corporation—one of the largest energy companies in the world—transform its production forecasting capabilities. Their internal workflows were fragmented, manual, and heavily reliant on spreadsheets. Our challenge was to design a platform that would consolidate this complexity into a centralized system, enabling accurate, real-time forecasting across teams.
In 2024, I joined Strategy CX to help Hess Corporation—one of the largest energy companies in the world—transform its production forecasting capabilities. Their internal workflows were fragmented, manual, and heavily reliant on spreadsheets. Our challenge was to design a platform that would consolidate this complexity into a centralized system, enabling accurate, real-time forecasting across teams.
Forecasting was slow and error-prone due to siloed systems, lack of automation, and inconsistent data. Teams worked in isolation, manually updating Excel files, and constantly reconciling disconnected inputs. These inefficiencies didn’t just affect planning—they impacted operational reliability.
To ground our design strategy, we launched a comprehensive discovery phase: four weeks of field research, 62 interviews across 56 people, totaling over 45 hours of conversations. These sessions revealed widespread friction, from duplicated processes to missing version control and constant miscommunication between departments.
Forecasting was slow and error-prone due to siloed systems, lack of automation, and inconsistent data. Teams worked in isolation, manually updating Excel files, and constantly reconciling disconnected inputs. These inefficiencies didn’t just affect planning—they impacted operational reliability.
To ground our design strategy, we launched a comprehensive discovery phase: four weeks of field research, 62 interviews across 56 people, totaling over 45 hours of conversations. These sessions revealed widespread friction, from duplicated processes to missing version control and constant miscommunication between departments.
blueprint service
We mapped out the entire MBR and QLA forecasting cycles using a detailed service blueprint. This visualization helped expose key pain points: data inconsistencies, rework, lack of update tracking, and a general overload of unstructured information. Many processes were still dependent on Excel—used not just for calculation but as a database, tracker, and communication tool all at once.
This blueprint became a north star for our product direction. We didn’t just want to digitize an old process—we needed to rethink the foundation.
We mapped out the entire MBR and QLA forecasting cycles using a detailed service blueprint. This visualization helped expose key pain points: data inconsistencies, rework, lack of update tracking, and a general overload of unstructured information. Many processes were still dependent on Excel—used not just for calculation but as a database, tracker, and communication tool all at once.
This blueprint became a north star for our product direction. We didn’t just want to digitize an old process—we needed to rethink the foundation.
personas
Instead of designing for departments, we designed for behaviors. We developed personas based on how users interacted with forecasting data—whether they were aggregating inputs, modifying assumptions, or reporting outcomes. Each persona had different priorities and friction points.
For example, analysts needed deeper control and scenario testing, while decision-makers required fast access to insights and risk flags. These distinctions shaped everything from navigation to interface layout.
Instead of designing for departments, we designed for behaviors. We developed personas based on how users interacted with forecasting data—whether they were aggregating inputs, modifying assumptions, or reporting outcomes. Each persona had different priorities and friction points.
For example, analysts needed deeper control and scenario testing, while decision-makers required fast access to insights and risk flags. These distinctions shaped everything from navigation to interface layout.
user journeys
user journeys
user journeys
User journeys helped us visualize how different personas moved through the platform. We focused on simplifying common workflows like reviewing a new forecast, testing a scenario, or aligning across departments before a cycle deadline.
This mapping revealed moments of unnecessary friction: manual handoffs, unclear ownership, and lack of validation points. We then designed features that aligned with each journey—prioritizing visibility, control, and collaboration.
User journeys helped us visualize how different personas moved through the platform. We focused on simplifying common workflows like reviewing a new forecast, testing a scenario, or aligning across departments before a cycle deadline.
This mapping revealed moments of unnecessary friction: manual handoffs, unclear ownership, and lack of validation points. We then designed features that aligned with each journey—prioritizing visibility, control, and collaboration.
design
design
design
The platform’s UI was modular and role-based. Each persona had a dedicated view with widgets tailored to their needs, such as forecast creation tools, status indicators, and performance dashboards.
One of the most powerful additions was the Forecast Sandbox—a space where users could simulate production scenarios by adjusting variables like cycle time, output volume, or failure rates. These simulations updated in real time and could be saved, shared, or annotated for later review.
The platform’s UI was modular and role-based. Each persona had a dedicated view with widgets tailored to their needs, such as forecast creation tools, status indicators, and performance dashboards.
One of the most powerful additions was the Forecast Sandbox—a space where users could simulate production scenarios by adjusting variables like cycle time, output volume, or failure rates. These simulations updated in real time and could be saved, shared, or annotated for later review.
We also introduced real-time alerts, insights based on historical trends, and assumption tracking. These features helped teams stay aligned, surface anomalies early, and reduce dependency on meetings or offline syncs.
We also introduced real-time alerts, insights based on historical trends, and assumption tracking. These features helped teams stay aligned, surface anomalies early, and reduce dependency on meetings or offline syncs.
Visualizing Complex Data
Visualizing Complex Data
Because forecasting involves large volumes of time-series and operational data, we put extra care into the data visualization layer. Clean, intuitive graphs helped users quickly grasp performance trends, compare versions, and spot discrepancies. Each graph could be filtered, annotated, or exported, allowing users to integrate insights directly into their planning cycles.
Because forecasting involves large volumes of time-series and operational data, we put extra care into the data visualization layer. Clean, intuitive graphs helped users quickly grasp performance trends, compare versions, and spot discrepancies. Each graph could be filtered, annotated, or exported, allowing users to integrate insights directly into their planning cycles.
impact
impact
The platform made forecasting faster, smarter, and more collaborative. Data gathering time dropped by 30%, teams began aligning around a single environment, and forecasting reliability improved across the board. More importantly, we laid a foundation for a scalable system that could evolve with Hess’s operational needs.
Genesis wasn’t just a tool—it was a shift in how forecasting was done.
The platform made forecasting faster, smarter, and more collaborative. Data gathering time dropped by 30%, teams began aligning around a single environment, and forecasting reliability improved across the board. More importantly, we laid a foundation for a scalable system that could evolve with Hess’s operational needs.
Genesis wasn’t just a tool—it was a shift in how forecasting was done.