IntroBlocks
IntroBlocks
IntroBlocks
2025
2025
Volvo Group is a global company with complex processes and products, operating in numerous locations and employing over 100,000 people. To manage this complexity, we’ve implemented what we call the “Intro Block” for our production lines.
An Intro Block is a scheduled interval on the truck production line during which specific build-and-optimization processes are executed. Each Intro Block:
•Starts in a predefined calendar week
•Runs for a fixed number of weeks
•Ends when the next Intro Block begins
This structure ensures that every phase of truck assembly and improvement is rolled out in a predictable, repeatable cadence.
Volvo Group is a global company with complex processes and products, operating in numerous locations and employing over 100,000 people. To manage this complexity, we’ve implemented what we call the “Intro Block” for our production lines.
An Intro Block is a scheduled interval on the truck production line during which specific build-and-optimization processes are executed. Each Intro Block:
•Starts in a predefined calendar week
•Runs for a fixed number of weeks
•Ends when the next Intro Block begins
This structure ensures that every phase of truck assembly and improvement is rolled out in a predictable, repeatable cadence.
To manage the Intro Blocks, we use multiple Power BI dashboards and tools; however, they aren’t centralized, consistent, or user-friendly, as shown in the images below.
To manage the Intro Blocks, we use multiple Power BI dashboards and tools; however, they aren’t centralized, consistent, or user-friendly, as shown in the images below.
For this reason, I was tasked with developing a brand-new system to address those issues and improve how we manage our data. Before I began, however, some stakeholders, who were already biased, offered directions on how to proceed. I respectfully requested time to devise a clear plan. Today, with a sea of UX methodologies, tools, and metrics available, I wanted to ensure I chose the most efficient ones given the time and resources at my disposal.
For this reason, I was tasked with developing a brand-new system to address those issues and improve how we manage our data. Before I began, however, some stakeholders, who were already biased, offered directions on how to proceed. I respectfully requested time to devise a clear plan. Today, with a sea of UX methodologies, tools, and metrics available, I wanted to ensure I chose the most efficient ones given the time and resources at my disposal.
UX Mentor
Keeping that in mind, I conceived my first AI tool to help designers generate an action plan based on their project’s context, using a set of predefined, easy-to-adjust parameters. In the video below, you can see the UX Mentor in action.
UX Mentor
Keeping that in mind, I conceived my first AI tool to help designers generate an action plan based on their project’s context, using a set of predefined, easy-to-adjust parameters. In the video below, you can see the UX Mentor in action.
Lean UX + Design Sprint
Once the tool I developed, drawing on reliable sources, provided me with valuable insights, I decided to combine Lean UX and Design Sprint methodologies. With just one month to deliver a high-fidelity prototype for a pitch, these approaches proved to be the perfect fit for my context.
Lean UX + Design Sprint
Once the tool I developed, drawing on reliable sources, provided me with valuable insights, I decided to combine Lean UX and Design Sprint methodologies. With just one month to deliver a high-fidelity prototype for a pitch, these approaches proved to be the perfect fit for my context.
The plan
Following that clear plan for the new dashboard tool, I created a Word document outlining each phase and its deliverables, communication being key to successful delivery, so it was documented and available to everyone from the very beginning.
The plan
Following that clear plan for the new dashboard tool, I created a Word document outlining each phase and its deliverables, communication being key to successful delivery, so it was documented and available to everyone from the very beginning.
Interviews
In the first week, we conducted five 45-minute user interviews with participants from the U.S., Belgium, France, and Sweden, representing a range of roles within Volvo.
My goal was to pinpoint what worked well and what didn’t, so I could determine how to make improvements. I dug deep to draw meaningful connections and gain a holistic understanding of the users’ context.
Interviews
In the first week, we conducted five 45-minute user interviews with participants from the U.S., Belgium, France, and Sweden, representing a range of roles within Volvo.
My goal was to pinpoint what worked well and what didn’t, so I could determine how to make improvements. I dug deep to draw meaningful connections and gain a holistic understanding of the users’ context.
Quotes - Problems
"It used to be information worked on in different files... hard to create one way of dashboarding because data is not structured enough in one place.”
Arne Vandeputte
"No stored historical data... replace reports constantly. Analytics and predictions are missing.”
Mattias Emilsson
"I need to jump between IBMT and KOLA... It’s time-consuming to sit and jump between different systems to find the data you’re looking for."
Hans Zackariasson
"Frustration comes from a lack of visibility of the data."
Daniel Preslar
Quotes - Problems
"It used to be information worked on in different files... hard to create one way of dashboarding because data is not structured enough in one place.”
Arne Vandeputte
"No stored historical data... replace reports constantly. Analytics and predictions are missing.”
Mattias Emilsson
"I need to jump between IBMT and KOLA... It’s time-consuming to sit and jump between different systems to find the data you’re looking for."
Hans Zackariasson
"Frustration comes from a lack of visibility of the data."
Daniel Preslar
Quotes - Improvements
"Officialize one report... make everyone use the same."
Andrey Malitsky
"We requested AI support to predict risks, but it’s not implemented yet.”
Frank Ghyselinck
"Customize views to avoid manual OneNote tables.”
Andrey Malitsky
"Managers should see workload progression... estimate if resources are sufficient."
Peter Larsen
"View object numbers, part status per plant, and release completeness."
Nico Reynaert
Quotes - Improvements
"Officialize one report... make everyone use the same."
Andrey Malitsky
"We requested AI support to predict risks, but it’s not implemented yet.”
Frank Ghyselinck
"Customize views to avoid manual OneNote tables.”
Andrey Malitsky
"Managers should see workload progression... estimate if resources are sufficient."
Peter Larsen
"View object numbers, part status per plant, and release completeness."
Nico Reynaert
Personas
Once the interviews were complete and the data compiled, I crafted four primary personas and validated them with key users to ensure they accurately represented our audience for further exploration.
Here are the personas, with one shown in detail below:
Personas
Once the interviews were complete and the data compiled, I crafted four primary personas and validated them with key users to ensure they accurately represented our audience for further exploration.
Here are the personas, with one shown in detail below:
Karl Johansson
What he does: Manages the team of Technical Preparation Engineers whose task is to supervise the product development, making sure that the future product can be assembled with the right tool, sequence, ergonomics, quality and within the right tactic time.
Experience level in the area: +25 years
How he interacts with the product: How he interacts with the product:
Checking information on parts releases, Follows the C and P build completeness, ensuring that verification and validation are done with correct parts from serial suppliers.
How often he uses the product: Daily
What devices he uses to access the tool: Desktop and laptop
Goals: To enable my organization to remove all problems with the product from a manufacturing and Logistic standpoint before Start of Production.
Concerns: Late part releases, inadequate CAD data and builds with too many prototype parts will make it difficult for my organization to verify that GTO requirements are met.
Quotes: “Finding problems late is a costly process.”
“Designing a product so that it can only be assembled correctly is the key to success.”
Karl Johansson
What he does: Manages the team of Technical Preparation Engineers whose task is to supervise the product development, making sure that the future product can be assembled with the right tool, sequence, ergonomics, quality and within the right tactic time.
Experience level in the area: +25 years
How he interacts with the product: How he interacts with the product:
Checking information on parts releases, Follows the C and P build completeness, ensuring that verification and validation are done with correct parts from serial suppliers.
How often he uses the product: Daily
What devices he uses to access the tool: Desktop and laptop
Goals: To enable my organization to remove all problems with the product from a manufacturing and Logistic standpoint before Start of Production.
Concerns: Late part releases, inadequate CAD data and builds with too many prototype parts will make it difficult for my organization to verify that GTO requirements are met.
Quotes: “Finding problems late is a costly process.”
“Designing a product so that it can only be assembled correctly is the key to success.”
"How might we" questions
With the personas in place, we crafted four key “How might we” questions to focus our efforts and kick off ideation.
1 - How might we standardize dashboards and reports across roles (plant, logistics, global) while allowing minimal customization?
2 - How might we integrate AI-driven predictive analytics to forecast risks and workload trends using historical data?
3 - How might we simplify filtering for different roles (engineers, managers) without cluttering the interface?
4 - How might we visually represent real-time progress (e.g., deadlines, approvals) so users avoid manual status checks?
"How might we" questions
With the personas in place, we crafted four key “How might we” questions to focus our efforts and kick off ideation.
1 - How might we standardize dashboards and reports across roles (plant, logistics, global) while allowing minimal customization?
2 - How might we integrate AI-driven predictive analytics to forecast risks and workload trends using historical data?
3 - How might we simplify filtering for different roles (engineers, managers) without cluttering the interface?
4 - How might we visually represent real-time progress (e.g., deadlines, approvals) so users avoid manual status checks?
Crazy 4's
After reviewing each persona and “How Might We” question, we held a Crazy 4’s session, an adaptation of the traditional Crazy 8’s. With only one hour available and participants unfamiliar with sketching, producing eight dashboard concepts on limited paper space would have been impractical. Instead, I modified the format to four two-minute sketching rounds. Each participant selected the persona they most closely resembled and generated one solution per “How Might We” question.
Crazy 4's
After reviewing each persona and “How Might We” question, we held a Crazy 4’s session, an adaptation of the traditional Crazy 8’s. With only one hour available and participants unfamiliar with sketching, producing eight dashboard concepts on limited paper space would have been impractical. Instead, I modified the format to four two-minute sketching rounds. Each participant selected the persona they most closely resembled and generated one solution per “How Might We” question.
Sketches
After sketching, each participant presented their concepts, and we began filtering and selecting the strongest ideas.
Sketches
After sketching, each participant presented their concepts, and we began filtering and selecting the strongest ideas.
Wireframig
I consolidated the strongest ideas into wireframes to discuss the system’s information architecture and logic.
Wireframig
I consolidated the strongest ideas into wireframes to discuss the system’s information architecture and logic.
User testing
Once we aligned on the system’s workflow, I built a low-fidelity prototype in Figma, stripped of color to focus solely on usability, and made it interactive in Protopie. During video calls, I gave users with different roles, simple tasks, recording their reactions, comments, and click patterns to uncover any major issues before moving on to a more advanced version.
High-fidelity prototype
Finally, everything converged into a high-fidelity prototype, again built in Figma and Protopie. I leveraged the Volvo Group Design System to ensure consistency with our other applications, giving users an immediate sense of familiarity.
Key improvements include:
Plant Overview: Users can identify and monitor all our plants using customizable filters.
Side-by-Side Comparisons: Users can compare different plants and Intro Blocks at a glance.
AI Predictions: The tool displays AI-driven forecasts for Intro Blocks, enabling proactive interventions to prevent production delays.
Detailed Insights: With advanced filtering options, users can drill down to the exact data they need.
Brand-Specific Themes: Users can switch between Volvo, Renault, or Mack branding to match their context.
Saved Views & Starters: Users can save favorite views, accessible via a top-navigation dropdown, and designate a default starter view for instant access upon login.
User testing
Once we aligned on the system’s workflow, I built a low-fidelity prototype in Figma, stripped of color to focus solely on usability, and made it interactive in Protopie. During video calls, I gave users with different roles, simple tasks, recording their reactions, comments, and click patterns to uncover any major issues before moving on to a more advanced version.
High-fidelity prototype
Finally, everything converged into a high-fidelity prototype, again built in Figma and Protopie. I leveraged the Volvo Group Design System to ensure consistency with our other applications, giving users an immediate sense of familiarity.
Key improvements include:
Plant Overview: Users can identify and monitor all our plants using customizable filters.
Side-by-Side Comparisons: Users can compare different plants and Intro Blocks at a glance.
AI Predictions: The tool displays AI-driven forecasts for Intro Blocks, enabling proactive interventions to prevent production delays.
Detailed Insights: With advanced filtering options, users can drill down to the exact data they need.
Brand-Specific Themes: Users can switch between Volvo, Renault, or Mack branding to match their context.
Saved Views & Starters: Users can save favorite views, accessible via a top-navigation dropdown, and designate a default starter view for instant access upon login.
What's next?
This initial concept garnered outstanding feedback, and we’re now pursuing funding to develop the tool in the second half of 2025. We’ll further enhance key functionalities, introduce new features, and implement comprehensive metrics to drive continuous performance improvement.
What's next?
This initial concept garnered outstanding feedback, and we’re now pursuing funding to develop the tool in the second half of 2025. We’ll further enhance key functionalities, introduce new features, and implement comprehensive metrics to drive continuous performance improvement.