Oil refinery project monitoring enterprise application

You didn’t come this far to stop

This is an example that I recreated based on a real enterprise project that I was working on for over 2 years when working with my previous firm. This is a detailed UX research study, with research methods, plans and actual analysis and results applied for a refinery plant based in Bahrain.

The purpose: This was a UX research is being carried out to develop a enterprise level product that helps monitor the extraction process, testing, transportation, storage, logistics and quality management, tools used and how the tool maintenance schedules are planned as well. What were the targets to be achieved, testing of quantity of oil in the desired location. Different parts

Detailed UX Research Study for Refinery Plant Enterprise Product Development

Objective:

To design and develop an enterprise-level product for a refinery plant in Bahrain to optimize the monitoring of:

1. Extraction Process

2. Testing

3. Transportation

4. Storage

5. Logistics

6. Quality Management

7. Tool Maintenance Schedules

8. Oil Quantity Verification at Desired Locations

This UX research study outlines the methods, plan, execution, analysis, and results for developing the proposed tool.

Research Plan

Step 1: Define Research Goals

• Understand User Needs: Identify pain points and requirements for each stage of the refinery process.

• Validate Current Processes: Gather insights into existing workflows and tools.

• Pinpoint Gaps: Uncover inefficiencies in current systems.

• Ensure Usability: Establish usability benchmarks for the enterprise product.

Step 2: Stakeholder Involvement

• Participants: Refinery workers, quality inspectors, logistics coordinators, and management.

• Stakeholder Interviews: Define high-level business goals and desired outcomes for the product.

Step 3: Research Methods

1. Contextual Inquiry

• Observe refinery operations directly in Bahrain.

• Conduct shadowing sessions with operators and managers.

• Document workflows for the entire process.

2. Surveys and Questionnaires

• Distribute surveys to gather qualitative data on pain points and user preferences.

3. User Interviews

• Conduct in-depth interviews with different user groups (operators, quality testers, logistics managers).

4. Usability Testing (on Competitors' Tools)

• Identify existing tools being used and evaluate their usability.

5. Personas Creation

• Develop personas to represent key user groups.

6. Journey Mapping

• Map the journey of oil from extraction to storage and identify critical touchpoints.

7. Card Sorting

• Organize features into categories to ensure logical information architecture.

Research Execution

Phase 1: Contextual Inquiry

• Spent two weeks on-site at a Bahrain-based refinery.

• Observed and documented real-time processes (e.g., extraction monitoring, oil testing labs, logistics coordination).

• Photographed workflows and tools in action.

Image Placeholder: A refinery worker monitoring the control panel with graphs and gauges displayed on multiple screens.

Phase 2: Surveys

• Distributed online and physical surveys to 50 participants. Key questions:

• What are your biggest challenges with the current tools?

• Rate the ease of scheduling maintenance tasks.

• How often do delays occur in logistics coordination?

Phase 3: Interviews

• Interviewed 20 stakeholders, including:

• 5 Extraction Operators

• 3 Testing

You said:

tell me more in detail of the research results being conducted, how the product will be designed and planned

Detailed UX Research Study Results

1. Research Context & Key Insights

After conducting qualitative and quantitative research methods with stakeholders from the refinery plant in Bahrain, key insights were gathered for each component of the refinery process

A. Extraction Process:

• Insights: Operators lack real-time data on the efficiency of the extraction process. Decisions are based on static reports rather than live metrics.

• Challenges:

• No centralized dashboard for monitoring equipment performance.

• Difficulties in identifying irregularities in the extraction process until they escalate.

• Opportunities: Implement real-time tracking sensors and predictive alerts to improve efficiency.

B. Testing:

• Insights: Testing schedules are not integrated into a central system, leading to inconsistencies in timelines and quality verification.

• Challenges:

• Manual entry errors.

• Lack of alerts for upcoming tests.

• Opportunities: Automate test scheduling and integrate lab equipment to reduce manual dependencies.

C. Transportation & Storage:

• Insights: Transportation and storage logistics are managed in silos, leading to delays and resource inefficiencies.

• Challenges:

• Inefficient route planning for oil transportation.

• No visibility into real-time storage capacity.

• Opportunities: Incorporate route optimization algorithms and real-time storage monitoring.

D. Quality Management:

• Insights: Quality monitoring requires extensive manual tracking, leading to delays in decision-making.

• Challenges:

• No standardized interface to compare historical quality metrics.

• Difficulty correlating process anomalies with quality outcomes.

• Opportunities: Implement AI-driven quality anomaly detection.

E. Tool Maintenance Schedules:

• Insights: Maintenance schedules are reactive rather than predictive, resulting in unexpected downtimes.

• Challenges:

• Lack of predictive analytics for tool wear-and-tear.

• Opportunities: Integrate IoT sensors and predictive maintenance analytics.

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Product Design and Planning

1. Product Goals

• Develop an Integrated Monitoring and Management Tool for the refinery plant to improve productivity, transparency, and decision-making across all workflows.

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2. User Persona Development

Based on user interviews, the following personas were developed:

1. Operations Manager

• Needs: Holistic view of refinery processes, predictive alerts, and resource allocation.

• Pain Points: Overwhelmed by disjointed data sources.

2. Quality Engineer

• Needs: Accurate real-time and historical data on oil quality.

• Pain Points: Manual data tracking and reporting.

3. Logistics Coordinator

• Needs: Efficient transportation routes and storage space tracking.

• Pain Points: Frequent scheduling conflicts.

4. Maintenance Specialist

• Needs: Tools for proactive maintenance planning.

• Pain Points: Reactive maintenance leading to downtimes.

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3. Workflow Mapping

Detailed process maps were created for each persona’s workflow. These maps identified bottlenecks and opportunities for automation and integration.

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4. Key Features for the Tool

• Dashboard:

• Unified real-time monitoring of extraction, testing, transportation, storage, and maintenance schedules.

• AI Integration:

• Predictive analytics for tool maintenance and quality control.

• IoT Sensor Integration:

• Real-time equipment performance monitoring.

• Automated Alerts:

• Notification system for upcoming tests, maintenance, and anomalies.

• Data Visualization:

• Customizable charts and reports for managers and engineers.

Proposed UI/UX Design

1. Wireframes

Main Dashboard Wireframe

• Real-time graphs displaying extraction rates, testing schedules, storage capacity, and transportation status.

• Maintenance notifications in a side panel.

Quality Management Wireframe

• Historical and real-time quality trends visualized as line graphs.

• Alerts for out-of-spec parameters with actionable insights.

Maintenance Tracker Wireframe

• Calendar view of tool maintenance schedules.

• Predictive maintenance suggestions with priority flags.

2. Design Prototypes

1. Interactive Main Dashboard:

• Filters for viewing specific processes or time frames.

• Drill-down capabilities for detailed metrics.

2. Responsive Mobile Application:

• For field engineers to monitor workflows on the go.

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Testing & Validation

1. Usability Testing

• Participants: 15 refinery staff from different roles.

• Scenario Testing:

• Responding to a sudden drop in extraction efficiency.

• Planning oil transportation to a specific storage site.

• Scheduling and tracking tool maintenance.

Feedback:

• Users appreciated the clarity of the dashboard but requested better customization options.

• Mobile app users wanted offline functionality for remote areas.

Analysis of Research Results

Quantitative Data Analysis:

• 30% reduction in process inefficiencies by integrating predictive analytics.

• Testing errors dropped by 20% after automating schedules.

Qualitative Feedback:

• Operations managers found the unified dashboard invaluable.

• Logistics coordinators saved 15 hours per week using the route optimizer.

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Final Product Features

1. Enterprise Dashboard:

• Unified monitoring system with customization options.

2. Predictive Analytics:

• For tool maintenance and quality control.

3. IoT Integration:

• Real-time sensor-based tracking.

4. AI-Powered Alerts:

• Notifications for anomalies, deadlines, and inefficiencies.

5. Mobile App:

• For field engineers to monitor and control operations remotely.

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Mockup Visuals

1. Main Dashboard

• Displays extraction rates, transport schedules, and quality metrics in real time.

2. Maintenance Tracker

• A predictive graph showing upcoming tool maintenance.

3. Quality Anomaly Detector

• AI-suggested corrections for identified issues.

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Impact & Next Steps

Impact:

• Anticipated 25% improvement in operational efficiency.

• Reduced downtime and cost by 15% due to predictive maintenance.

Next Steps:

• Conduct pilot testing for 3 months.

• Gather additional feedback to fine-tune the design.

• Begin phased rollout across refinery units.

Would you like a visual mockup or step-by-step wireframe breakdown?

ChatGPT can make mistakes. Check important info.

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Dashboard Design Idea: Information Architecture

The dashboard for the refinery plant enterprise product will serve as a centralized interface for monitoring and managing operations across extraction, testing, transportation, storage, logistics, quality management, and tool maintenance schedules. Below is the proposed information architecture for the dashboard.

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1. Main Navigation Structure

The dashboard will have a primary navigation bar on the top and a secondary navigation menu on the left for quick access to modules.

Primary Navigation Tabs:

1. Home: Overview of key metrics and customizable widgets.

2. Processes: Detailed insights into extraction, testing, and quality management.

3. Logistics: Transportation, storage, and scheduling.

4. Maintenance: Predictive and scheduled tool maintenance.

5. Reports: Historical and real-time data visualizations.

6. Settings: Customization, user permissions, and system integrations.

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2. Information Layout by Module

A. Home (Executive Overview)

• Purpose: Provide a high-level overview of operations with key metrics.

• Content:

o KPIs (Key Performance Indicators):

 Extraction efficiency (%)

 Testing schedules (upcoming tests)

 Transportation efficiency (on-time deliveries)

 Storage capacity (% utilized)

 Maintenance status (tools requiring immediate attention)

o Quick Alerts: Notifications for anomalies or delays.

o Customizable Widgets:

 Graphs for process-specific data.

 Maintenance calendar preview.

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B. Processes

• Purpose: Drill down into real-time monitoring of the refinery processes.

• Content:

o Extraction Process:

 Live performance graphs for extraction equipment.

 Alerts for deviations in extraction efficiency.

o Testing:

 Automated testing schedule tracker.

 Results from recent tests with flagged issues.

o Quality Management:

 Live oil quality metrics (viscosity, contamination levels).

 AI-generated recommendations for quality improvement.

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C. Logistics

• Purpose: Manage transportation and storage of oil effectively.

• Content:

o Transportation:

 Interactive route planner with real-time GPS updates.

 Estimated time of arrival for each shipment.

o Storage:

 Real-time visualization of storage capacity across facilities.

 Alerts for approaching storage limits.

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D. Maintenance

• Purpose: Plan and track tool maintenance schedules proactively.

• Content:

o Predictive Maintenance:

 Graph showing predicted tool wear and tear.

 Suggestions for preventive maintenance with priority flags.

o Scheduled Maintenance:

 Calendar view with overdue and upcoming maintenance tasks.

 Maintenance history for individual tools.

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E. Reports

• Purpose: Enable data-driven decision-making with historical insights.

• Content:

o Custom Reports:

 Generate reports on extraction trends, testing outcomes, or logistics.

o Data Visualizations:

 Line charts, bar graphs, and heatmaps for process trends.

o Export Options:

 Download reports in formats like PDF, Excel, or CSV.

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F. Settings

• Purpose: Allow customization and user management.

• Content:

o User Permissions:

 Role-based access control for operators, managers, and engineers.

o System Integrations:

 Configure IoT devices and lab equipment connections.

o Dashboard Customization:

 Add, remove, or rearrange widgets on the Home page.

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3. Wireframe Overview

A. Main Dashboard Layout

• Top Navigation Bar:

o Modules: Home | Processes | Logistics | Maintenance | Reports | Settings.

o Quick Access: Notifications, User Profile, and Help.

• Left Navigation Panel:

o Subcategories for each selected module (e.g., "Testing" under "Processes").

• Central Content Area:

o Dynamic area displaying KPIs, live graphs, and detailed information for the selected module.

• Right Sidebar:

o Alerts and upcoming events.

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4. Interaction Design

• Dynamic Filters: Users can filter data by time (hourly, daily, monthly), location, or equipment.

• Drill-Down Capability: Clickable graphs for deeper insights into specific data points.

• Alerts and Notifications: Color-coded indicators for priority-based issues.

• Mobile-Friendly Design: Adaptive interface for mobile devices to access essential features.

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5. Example Dashboard Widgets

1. Real-Time Extraction Rate Widget:

o Displays current and average extraction rates.

o Shows deviation alerts with causes.

2. Storage Utilization Bar:

o Interactive visual showing used and available capacity.

3. Route Planner Map:

o Live visualization of transport routes with shipment status.

4. Maintenance Calendar:

o Upcoming tasks color-coded by urgency.

5. Quality Trends Line Chart:

o Real-time and historical data on oil quality parameters.

________________________________________

Would you like visual mockups of this architecture or recommendations on technologies for building it?

4o

You said:

yes please

ChatGPT said:

ChatGPT

Here is the visual mockup of the proposed dashboard for the refinery monitoring system. It incorporates all key elements, such as real-time data visualizations, interactive maps, maintenance tracking, and KPI displays, organized within an intuitive layout. Let me know if you'd like to refine or adjust any aspect of the design!

You said:

thats nice, but looks too rustic the fonts and all. can it be a little formal

ChatGPT said:

C hatGPT

Here is the refined dashboard design with a formal and polished look. The typography is now more elegant and consistent, giving it a professional appearance while retaining the functional layout. Let me know if this meets your expectations or if additional tweaks are needed!

You said:

also suggets images for the research methodolies we discussed earlier

ChatGPT said:

ChatGPT

User Flow for the Refinery Monitoring Product

The user flow outlines how different users interact with the system to achieve their goals, ensuring a seamless and intuitive experience across all functionalities.

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Primary Users and Their Objectives

1. Operations Manager:

o Monitor extraction and overall process efficiency.

o View key performance indicators (KPIs).

o Receive anomaly alerts and take corrective actions.

2. Quality Engineer:

o Track oil quality in real-time and review testing results.

o Investigate quality anomalies.

o Schedule and monitor quality tests.

3. Logistics Coordinator:

o Plan and track transportation routes.

o Monitor storage levels and allocate resources efficiently.

o Resolve transportation bottlenecks.

4. Maintenance Specialist:

o Track tool performance and schedule maintenance.

o Resolve urgent maintenance issues.

o Review maintenance history for individual tools.

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User Flow: Step-by-Step Journey

1. Login and Access

• Input: Credentials (Username and Password) or Single Sign-On (SSO) options.

• Action: Role-based authentication determines access levels (e.g., Operations Manager, Quality Engineer).

• Output: Redirected to the Home Dashboard displaying relevant KPIs.

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2. Navigation Through the Dashboard

A. Operations Manager’s Flow

1. Goal: Monitor process efficiency and resolve anomalies.

o Access the Dashboard:

 View extraction performance graphs and anomaly alerts on the home screen.

o Action: Click on an alert (e.g., "Extraction Rate Deviation").

 Redirected to the Processes Module for detailed insights.

o Drill Down: Review sensor data from IoT devices and identify the equipment causing the issue.

o Resolve Issue: Assign a task to the maintenance team or adjust settings remotely.

B. Quality Engineer’s Flow

1. Goal: Review testing results and investigate anomalies.

o Access the Dashboard:

 View oil quality KPIs and flagged quality issues.

o Action: Click on an anomaly (e.g., "High Contamination Levels Detected").

 Redirected to the Testing Module with detailed reports.

o Schedule Testing:

 Use the calendar feature to assign a new test schedule.

o Output: Test results integrated automatically from lab equipment.

C. Logistics Coordinator’s Flow

1. Goal: Optimize transportation and monitor storage.

o Access the Dashboard:

 View transportation schedules and current storage levels.

o Action: Click on the Logistics Module.

 Visualize an interactive map showing transportation routes and storage capacity.

o Optimize Routes:

 Use the route planner tool to allocate trucks to underutilized routes.

o Resolve Issues: Adjust schedules or prioritize shipments based on storage alerts.

D. Maintenance Specialist’s Flow

1. Goal: Track and schedule tool maintenance.

o Access the Dashboard:

 View upcoming maintenance tasks on the home screen.

o Action: Click on the Maintenance Module.

 View predictive analytics on tool wear-and-tear.

o Schedule Maintenance:

 Assign a task from the calendar or adjust timelines for upcoming tasks.

o Output: Receive automatic notifications when maintenance is due.

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3. Alerts and Notifications

• Input: Triggered by anomalies in process efficiency, quality metrics, storage levels, or maintenance schedules.

• Action:

o Alerts are displayed on the dashboard sidebar and sent via email/SMS.

o Users can click alerts for more details or to initiate corrective actions.

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4. Data Analysis and Reporting

1. Goal: Generate insights for decision-making.

o Access the Reports Module:

 View predefined reports or create custom ones.

o Action: Filter data by process, time frame, or location.

o Output: Export reports in PDF or Excel for stakeholder presentations.

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5. Mobile Access (Field Engineers)

1. Goal: Real-time monitoring and task execution in the field.

o Access Mobile App:

 Login and view a condensed version of the dashboard.

o Action:

 Receive alerts, track tasks, and enter updates (e.g., maintenance completion).

o Output: Data syncs with the central dashboard in real-time.

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User Flow Diagram

Below is a textual representation of the user flow:

plaintext

Copy code

Login -> Home Dashboard -> Select Module (Processes/Testing/Logistics/Maintenance)

-> View Details -> Take Action (Analyze Data, Assign Tasks, Adjust Schedules)

-> Alerts & Notifications (Context-Specific) -> Generate Reports -> Logout

Visual Representation

Would you like a visual diagram (e.g., a flowchart) of this user flow for better clarity?

4o

You said:

yes, show flowchart, Also after thr user flow show me the storyboard sequence of the same set of activities

ChatGPT said:

ChatGPT

Here is the flowchart representing the user flow for the refinery monitoring system. It illustrates how users navigate the product from login to various modules, handle alerts, generate reports, and log out.

Next, I will create a storyboard sequence to visualize these activities.

It seems like I can’t generate any more images right now. Please try again later. If you'd like, I can describe the storyboard sequence in detail, or we can revisit the visual creation later. Let me know how you'd like to proceed!

4o

You said:

yes storyboard sequence in detail is okay

ChatGPT said:

ChatGPT

Here is the detailed storyboard sequence for the user flow in the refinery monitoring system:

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Frame 1: Login

Description:

• Scene: A user is sitting at their workstation, entering their credentials (username and password) on the login page of the system. The page is clean and professional, with a company logo at the top and input fields in the center.

• User Action: The user clicks the "Login" button to access the system.

Purpose:

Authenticate and personalize the user experience based on their role (e.g., Operations Manager, Quality Engineer).

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Frame 2: Home Dashboard Overview

Description:

• Scene: The user sees the Home Dashboard displaying KPIs like "Extraction Efficiency," "Storage Utilization," and "Upcoming Maintenance Tasks." There are navigation tabs at the top and a sidebar for modules (Processes, Testing, Logistics, Maintenance, Reports).

• User Action: The user scans the dashboard for alerts and selects the "Processes" module from the navigation menu.

Purpose:

Provide a high-level overview of system operations and quick access to key modules.

Frame 3: Investigating an Alert in Processes

Description:

• Scene: The user is now in the Processes module, viewing detailed charts and live data on extraction efficiency. An alert banner at the top highlights an anomaly in the extraction rate.

• User Action: The user clicks on the alert to drill down into detailed sensor data from IoT devices.

Purpose:

Enable quick identification and investigation of operational anomalies.

Frame 4: Optimizing Transportation in Logistics

Description:

• Scene: The user navigates to the Logistics module, viewing an interactive map with live transportation routes. A storage facility is flagged for being near capacity, requiring immediate action.

• User Action: The user adjusts transportation routes using a drag-and-drop interface to redirect shipments to underutilized facilities.

Purpose:

Optimize transportation and manage storage capacity effectively.

Frame 5: Scheduling Tool Maintenance

Description:

• Scene: The user switches to the Maintenance module, viewing a predictive analytics dashboard showing tools that require immediate attention. A calendar below displays scheduled maintenance tasks.

• User Action: The user assigns a high-priority maintenance task and reschedules a less urgent task for later.

Purpose:

Ensure proactive maintenance and efficient tool scheduling.

Frame 6: Generating Reports

Description:

• Scene: In the Reports module, the user selects parameters (time frame, location, and type of process) to generate a custom report. A graph and table preview the data.

• User Action: The user exports the report as a PDF for a stakeholder meeting.

Purpose:

Enable data-driven decision-making and easy sharing of insights.

Frame 7: Logout

Description:

• Scene: The user clicks the "Logout" button in the top-right corner of the screen. A confirmation message appears, and the user successfully logs out.

• User Action: The user confirms the logout and exits the system.

Purpose:

Securely end the user session to protect sensitive information.

Storyboard Summary

The storyboard visually maps the user journey:

1. Login.

2. Viewing the Home Dashboard.

3. Investigating alerts in the Processes module.

4. Optimizing transportation in Logistics.

5. Scheduling maintenance tasks.

6. Generating custom reports.

7. Logging out.

Each step is designed to minimize cognitive load, provide clear guidance, and align with the user's role and goals. Let me know if you'd like additional refinements or visual aids for this sequence!

4o

You said:

yes storyboard can be more detailed and

ChatGPT said:

ChatGPT

Here is a more detailed storyboard for the refinery monitoring system user flow. Each frame now includes specific actions, emotions, and context to enhance understanding of the experience.

Frame 1: Login

Scene Description:

• Setting: The user is in an office with multiple monitors, one displaying the login screen. The interface is minimalist and secure, with corporate branding and fields for "Username" and "Password."

• User Action:

o Types in their credentials.

o Clicks on the "Login" button.

• User Emotion: Focused, expecting a smooth and personalized experience.

• System Response: Redirects to the home dashboard.

Frame 2: Home Dashboard Overview

Scene Description:

• Setting: The home dashboard fills the screen, showing KPIs (e.g., "Extraction Efficiency: 92%" and "Storage Utilization: 80%"). Color-coded widgets highlight anomalies.

• User Action:

o Scans KPIs for alerts.

o Notices a red flag in the "Extraction Efficiency" widget and hovers over it to view details.

• User Emotion: Curious and slightly concerned about flagged issues.

• System Response: Displays tooltip information on the alert and offers a "View Details" link.

Frame 3: Investigating an Alert in Processes

Scene Description:

• Setting: The Processes module opens, with a detailed graph of extraction performance over time. A pop-up displays sensor data from IoT devices pinpointing an underperforming piece of equipment.

• User Action:

o Clicks on the flagged equipment to open detailed performance logs.

o Assigns a task to the engineering team to address the issue.

• User Emotion: Empowered to take immediate corrective action.

• System Response: Sends an automated task notification to the assigned team.

Frame 4: Optimizing Transportation in Logistics

Scene Description:

• Setting: The Logistics module displays an interactive map with transportation routes, storage locations, and live vehicle statuses. A storage unit is highlighted in red for nearing capacity.

• User Action:

o Drags and drops a shipment marker to redirect deliveries to an underutilized storage facility.

o Adjusts delivery schedules in the route planner.

• User Emotion: Relieved to resolve a potential storage bottleneck.

• System Response: Updates the transportation schedule and confirms the changes via a notification.

Frame 5: Scheduling Tool Maintenance

Scene Description:

• Setting: The Maintenance module shows a predictive analytics dashboard. A graph highlights tools at risk of failure, and a calendar lists scheduled tasks.

• User Action:

o Filters tools by urgency and assigns a high-priority maintenance task.

o Reschedules a low-priority task for next week.

• User Emotion: Proactive and confident about preventing downtime.

• System Response: Logs the changes and notifies the maintenance team.

Frame 6: Generating Reports

Scene Description:

• Setting: In the Reports module, the user selects parameters for a custom report: date range, location, and process type. A preview displays graphs and tables summarizing trends.

• User Action:

o Fine-tunes the report filters and clicks "Export."

o Downloads the report as a PDF.

• User Emotion: Satisfied with easily generating actionable insights.

• System Response: Confirms successful export and offers sharing options.

Frame 7: Logout

Scene Description:

• Setting: The user clicks on their profile icon in the top-right corner. A dropdown menu appears with a "Logout" option.

• User Action:

o Clicks "Logout."

o Reviews a confirmation message and confirms.

• User Emotion: Reassured about the system's security.

• System Response: Ends the session and returns to the login screen.

Additional Context

1. Scenes with Dynamic Elements:

o Alerts pop up in real-time, with animations that draw the user's attention.

o Interactive features like drag-and-drop for transportation schedules.

2. Scene Transitions:

o Smooth transitions between modules using fade animations.

o Highlighted paths guide the user from one task to the next.

3. System Feedback:

o Every action (e.g., assigning tasks or exporting reports) triggers immediate system feedback, like notifications or confirmation messages.

Storyboard Summary

This storyboard incorporates the user’s:

• Environment: Office or on-site locations for field engineers.

• Tools: Desktop interface for managers; mobile-friendly design for field tasks.

• Emotions: Confidence, empowerment, and satisfaction at completing their goals efficiently.

Let me know if you'd like mockups, diagrams, or further refinements!

For the refinery monitoring system, quantitative research will help collect numerical data to evaluate the system’s effectiveness, user satisfaction, and performance in real-world conditions. The goal is to derive actionable insights that can improve usability, functionality, and overall product impact. Below are some key quantitative research methods that can be applied to the system:

1. Usability Testing with Task Completion Rates

Objective: To measure how effectively users can complete tasks in the system, focusing on efficiency and usability.

Method:

• Conduct usability tests where users are asked to complete specific tasks (e.g., investigating an alert, scheduling maintenance, generating a report).

• Measure the time on task and whether users successfully complete tasks without errors.

• Task Completion Rate: The percentage of tasks completed correctly by users in a given amount of time.

Data Points:

• Average time to complete a task.

• Percentage of users completing tasks successfully.

• Number of errors or incorrect steps taken by users.

Outcome:

Quantifying task efficiency and identifying pain points in the user flow.

2. System Performance Metrics

Objective: To measure how well the system performs under real-world conditions, ensuring it meets user demands.

Method:

• Track performance metrics such as system response time, uptime, and error rates when users interact with the system.

• Use automated tools to record how long it takes to load different parts of the system, such as dashboards, reports, and interactive maps.

Data Points:

• Average load time for the dashboard, reports, and modules.

• Number of errors or system crashes over a defined period.

• System uptime percentage (how often the system is available without downtime).

Outcome:

Identifying any bottlenecks or performance issues that could affect user experience.

3. Surveys and Likert Scale Questions

Objective: To gauge user satisfaction and perceptions of the system’s effectiveness, usability, and overall experience.

Method:

• Use surveys with Likert scale questions (1-5 or 1-7 scale) to measure user satisfaction, perceived ease of use, and system functionality.

• Example questions:

o "How easy was it to navigate the dashboard?" (Scale: 1 = Very Difficult, 5 = Very Easy)

o "How effective was the system in alerting you about critical issues?" (Scale: 1 = Not Effective, 5 = Very Effective)

o "How likely are you to recommend this system to others?" (Net Promoter Score, NPS)

Data Points:

• Average scores for usability, satisfaction, and perceived effectiveness.

• NPS score (to measure user loyalty and satisfaction).

Outcome:

Understanding overall user sentiment and identifying areas for improvement in terms of usability and feature effectiveness.

4. User Behavior Analytics (Logs and Heatmaps)

Objective: To analyze how users interact with the system and identify areas where they may be encountering difficulty.

Method:

• Use click-tracking tools and heatmaps to monitor how users interact with the system. This can be particularly useful to see how users engage with different modules like the Processes or Logistics section.

• Track which parts of the dashboard are accessed most frequently and how users navigate between modules.

Data Points:

• Frequency of clicks on specific modules (Processes, Testing, Maintenance, etc.).

• Drop-off points where users abandon tasks or switch tasks.

• Areas of the dashboard that receive the most attention (heatmap).

Outcome:

Highlighting usability problems or features that are not intuitive, helping improve design or training materials.

5. A/B Testing (for UI/UX Optimizations)

Objective: To compare two or more design variations and determine which one provides a better user experience.

Method:

• Conduct A/B tests where different user groups are shown different UI layouts or feature implementations. For instance, testing a more compact version of the dashboard vs. a detailed layout.

• Measure task success rates and user satisfaction across variations.

Data Points:

• Task completion time for each group.

• Task success rate (percentage of users who completed the task correctly).

• User ratings on design elements (e.g., color scheme, layout, ease of navigation).

Outcome:

Quantifying which design or layout leads to a better user experience and which aspects of the UI/UX need refinement.

6. Time on Task and Productivity Metrics

Objective: To measure the impact of the system on users’ work efficiency, particularly for critical tasks.

Method:

• Track how long users spend completing tasks such as reviewing reports, scheduling maintenance, or monitoring extraction processes.

• Measure how much more or less efficient users are in completing their daily workflows after using the system compared to their previous methods (e.g., manual tracking).

Data Points:

• Time spent on key tasks before and after the system is implemented.

• Comparison of task completion rates before and after system implementation.

• Improvement in decision-making speed based on system insights (e.g., time to resolve critical alerts).

Outcome:

Identifying productivity gains and understanding how the system impacts overall work efficiency.

7. Error Tracking and System Accuracy

Objective: To measure the accuracy of data and insights provided by the system, ensuring it supports critical decision-making processes.

Method:

• Track any errors in data processing, such as incorrect extraction data, inaccurate quality readings, or failed integration with sensors.

• Measure the precision of predictions (e.g., maintenance scheduling, logistics optimization) based on actual results.

Data Points:

• Frequency of data errors or discrepancies.

• Accuracy of predictive maintenance or logistics scheduling.

• Percentage of false positives or false negatives in the system’s alerts.

Outcome:

Ensuring that the system is reliable and that decisions made based on its data are accurate.

Combining Results and Data Analysis

Once these quantitative research methods are conducted, the data can be analyzed statistically to identify:

• Patterns and trends in user behavior.

• Pain points where users struggle with the interface or specific modules.

• Performance issues in terms of system responsiveness and uptime.

• User satisfaction levels and overall engagement with the system.

With these insights, the product can be continuously refined to improve efficiency, usability, and user satisfaction.

Potential KPIs for Measurement:

• Task Completion Rate: Percentage of tasks users complete correctly within a time limit.

• Time to Complete Tasks: Average time spent on key tasks (e.g., logging in, completing maintenance schedules).

• Net Promoter Score (NPS): Measures user satisfaction and likelihood of recommending the system.

• System Downtime/Uptime: Percentage of time the system is available without issues.

• Maintenance Prediction Accuracy: Success rate of predicting and scheduling maintenance before tool failure.