Genesis
Building a Centralized Forecasting Solution for the Oil & Gas Industry
Genesis
Building a Centralized Forecasting Solution for the Oil & Gas Industry
Industry
Oil & Gas
Client
Hess
Company
Strategy CX
Role
Product Designer
(UX-focused)
Scope
End-to-end Product Design
(Discovery → Delivery)
Problem Statement
Operational teams relied on fragmented tools and spreadsheets to manage production forecasting. This resulted in duplicated work, inconsistent versions of forecasts, and low confidence in decision-making.
Forecasting wasn’t only slow — it directly affected operational reliability and planning accuracy across teams.
Problem Statement
Operational teams relied on fragmented tools and spreadsheets to manage production forecasting. This resulted in duplicated work, inconsistent versions of forecasts, and low confidence in decision-making.
Forecasting wasn’t only slow — it directly affected operational reliability and planning accuracy across teams.
Goals
Centralize forecasting workflows into a single platform
Reduce manual data reconciliation
Improve trust and alignment across operational teams
Enable faster and more informed decision-making
Discovery & Research
We ran a 4-week discovery phase with cross-functional stakeholders:
62 interviews
56 participants
45+ hours of qualitative research
This allowed us to understand not only functional needs, but behavioral patterns around how forecasts were created, reviewed, and approved.
Key insights:
Excel was being used as database, tracker, and communication tool
Lack of version control created misalignment
Analysts needed safe spaces for scenario testing
Leaders needed quick access to risk signals
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 the entire MBR and QLA forecasting cycles to visualize how data, decisions, and responsibilities flowed across teams.
This blueprint revealed systemic issues:
Redundant steps
Information silos
Manual handoffs
High cognitive load during critical decision moments
This artifact became our north star for redefining the forecasting system — not just digitizing an old process, but redesigning the workflow.
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:
Analysts needed deep control, scenario simulation, and traceability
Decision-makers needed fast insights, comparisons, and risk visibility
These behavioral differences informed:
Navigation structure
Information hierarchy
Level of interface complexity
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
We mapped key journeys such as:
Reviewing a new forecast
Simulating alternative scenarios
Aligning teams before decision deadlines
This helped us identify friction points like manual handoffs, unclear ownership, and missing validation steps — which directly informed feature prioritization.
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 strategy
design strategy
design strategy
We defined core product principles:
Clarity over visual complexity
Comparison as a primary action
Traceability of assumptions
Progressive disclosure for advanced analysis
These principles guided all interface and interaction decisions.
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.
design solution
design solution
design solution
Modular & Role-based UI
The platform adapts to different user roles, showing:
Forecast creation tools
Status indicators
Performance dashboards
Version comparison views
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 introduced a sandbox environment where users could simulate production scenarios by adjusting variables such as cycle time, output volume, and failure rates — with real-time updates.
This reduced dependency on offline Excel simulations and supported more confident decision-making.
We introduced a sandbox environment where users could simulate production scenarios by adjusting variables such as cycle time, output volume, and failure rates — with real-time updates.
This reduced dependency on offline Excel simulations and supported more confident decision-making.
Visualizing Complex Data
Visualizing Complex Data
Special attention was given to data visualization, enabling users to:
Compare forecast versions
Identify anomalies
Filter time-series data
Export annotated insights
Special attention was given to data visualization, enabling users to:
Compare forecast versions
Identify anomalies
Filter time-series data
Export annotated insights
impact
impact
After the platform was introduced:
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.
~30% reduction in manual data gathering time
Faster alignment across teams around a single source of truth
Increased confidence in forecasting decisions
Reduced operational friction during forecasting cycles
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.
Beyond metrics, the platform shifted forecasting from a fragmented, manual process to a scalable, product-driven system.
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.
reflection
This project reinforced that impactful UX in complex systems is less about visual polish and more about:
Reducing cognitive load
Supporting decision-making
Designing for long-term product evolution