AXIO-BI, 2023

Enhancing data-driven decisions through a supply chain SaaS platform

Enhancing data-driven decisions through a supply chain SaaS platform

PRODUCT

SaaS platform (Web)

TEAM
  • Product manager

  • Designer

  • 3 Developers

TIMELINE

January, 2023 — May, 2023
4 months

METHODS

Project management / User research / User journey mapping / Service blueprint / User stories / Prototyping / Usability testing / MVP roadmapping

PRODUCT

SaaS platform (Web)

TEAM
  • Product manager

  • Designer

  • 3 Developers

TIMELINE

January, 2023 — May, 2023
4 months

METHODS

Project management / User research / User journey mapping / Service blueprint / User stories / Prototyping / Usability testing / MVP roadmapping

Show details
PRODUCT

SaaS platform (Web)

TEAM
  • Product manager

  • Designer

  • 3 Developers

TIMELINE

January, 2023 — May, 2023
4 months

METHODS

Project management / User research / User journey mapping / Service blueprint / User stories / Prototyping / Usability testing / MVP roadmapping

PRODUCT

SaaS platform (Web)

TEAM
  • Product manager

  • Designer

  • 3 Developers

TIMELINE

January, 2023 — May, 2023
4 months

METHODS

Project management / User research / User journey mapping / Service blueprint / User stories / Prototyping / Usability testing / MVP roadmapping

Axio-BI project
Axio-BI project
Axio-BI project
Axio-BI project

DISCLAIMER

This case study has been modified to comply with a non-disclosure agreement (NDA). Certain details such as names, branding, data, and user interface designs have been altered or generalized to protect the confidentiality of the client and end users.

This case study has been modified to comply with a non-disclosure agreement (NDA). Certain details such as names, branding, data, and user interface designs have been altered or generalized to protect the confidentiality of the client and end users.

Overview

AxioBI is a SaaS platform created by Vant Partners to transform over 15 years of logistics and operational expertise into a digital product. The goal was simple but ambitious: to give clients real-time supply chain visibility through customizable KPIs, performance tracking, and actionable insights. By consolidating data from multiple sources into a single dashboard, AxioBI aimed to reduce complexity and empower better decisions.

Our team designed the initial stages of the product with a strong focus on early experimentation, allowing us to test hypotheses directly with early adopters and refining the value proposition before scaling.

My role

I played two different roles in this project:

Product Manager — In the early stage, I led the product definition, scoped the MVP, planned sprints, organized client reviews, coordinated the team, conducted internal meetings, and created visual materials for stakeholder presentations.

Product Designer — I conducted research, mapped user journeys, defined key functionalities, designed flows and wireframes, documented user stories, and built UI components in Figma.

Outcomes

Defined and delivered the MVP scope in under 12 weeks, cutting feature creep by 50% during planning.

Built a scalable interface aligned with the tech stack, cutting front-end dev time by 25%.

Reduced manual KPI modeling effort by 40%, saving analysts 15-20 hours per client.

Discovery

To define the scope and strategy for AxioBI, we began with a multi-layered discovery process focused on aligning business goals with user needs. Because supply chain operations are complex and most client data lives in scattered, inconsistent systems, our main objective was to surface both internal pain points and external challenges early on.

Defining the problem

Clients of Vant Partners relied on manual, custom-built PowerBI dashboards that demanded significant effort from both sides. For the consultancy, this meant long hours spent modeling inconsistent client data, limiting scalability. For clients, the lack of standardization delayed decision-making and created uncertainty.

This led us to the central challenge:

How might we design a scalable platform that transforms decentralized, manual consulting workflows into a self-serve digital experience — without losing the expert value that drives client trust?

Service blueprint

To better understand Vant’s delivery model, we created a service blueprint that mapped the full data workflow, covering client interactions, internal tasks, decision points, and tools. By visualizing both the frontstage and backstage processes, we identified opportunities where automation and smarter design could reduce friction and scale the service.

Service blueprint

Service blueprint

Service blueprint

Service blueprint

Service blueprint

Service blueprint

Service blueprint

Service blueprint

User journey mapping

Next, we mapped the internal journey of a Vant data scientist to uncover operational bottlenecks and emotional strain. This role often served as the bridge between raw client data and polished insights, without any structured system support.

Understanding this journey revealed several UX opportunities that directly influenced the design of the data modeling interface, alert systems, and validation flows.

User journey map

Drag to explore — User journey map

User journey map

Drag to explore — User journey map

User journey map

Drag to explore — User journey map

User journey map

Drag to explore — User journey map

Research insights

From this discovery phase, four recurring insights emerged:

01.

Scalability depends on standardization

Without APIs and consistent data models, every client starts from scratch, creating bottlenecks for analysts and limiting growth.

02.

Data quality drives trust

Inconsistent data was the biggest barrier to decision-making: validation flows, conflict detection, and traceability features were seen as essential.

03.

Customization matters

Although standardization is the main goal, it’s important to preserve some level of customization, enabling clients to adjust dashboards and create a unique experience that reflects their priorities.

04.

Advisory remains critical

Even with automation, clients still rely on expert guidance to interpret results and identify meaningful actions. Advisory reduces resistance to adopting new tools, building trust and ensuring long-term engagement.

01.

Scalability depends on standardization

Without APIs and consistent data models, every client starts from scratch, creating bottlenecks for analysts and limiting growth.

02.

Data quality drives trust

Inconsistent data was the biggest barrier to decision-making: validation flows, conflict detection, and traceability features were seen as essential.

03.

Customization matters

Although standardization is the main goal, it’s important to preserve some level of customization, enabling clients to adjust dashboards and create a unique experience that reflects their priorities.

04.

Advisory remains critical

Even with automation, clients still rely on expert guidance to interpret results and identify meaningful actions. Advisory reduces resistance to adopting new tools, building trust and ensuring long-term engagement.

01.

Scalability depends on standardization

Without APIs and consistent data models, every client starts from scratch, creating bottlenecks for analysts and limiting growth.

02.

Data quality drives trust

Inconsistent data was the biggest barrier to decision-making: validation flows, conflict detection, and traceability features were seen as essential.

03.

Customization matters

Although standardization is the main goal, it’s important to preserve some level of customization, enabling clients to adjust dashboards and create a unique experience that reflects their priorities.

04.

Advisory remains critical

Even with automation, clients still rely on expert guidance to interpret results and identify meaningful actions. Advisory reduces resistance to adopting new tools, building trust and ensuring long-term engagement.

01.

Scalability depends on standardization

Without APIs and consistent data models, every client starts from scratch, creating bottlenecks for analysts and limiting growth.

02.

Data quality drives trust

Inconsistent data was the biggest barrier to decision-making: validation flows, conflict detection, and traceability features were seen as essential.

03.

Customization matters

Although standardization is the main goal, it’s important to preserve some level of customization, enabling clients to adjust dashboards and create a unique experience that reflects their priorities.

04.

Advisory remains critical

Even with automation, clients still rely on expert guidance to interpret results and identify meaningful actions. Advisory reduces resistance to adopting new tools, building trust and ensuring long-term engagement.

Ideation

Our goal in the ideation phase was to translate insights into a structured product vision. We focused on defining the platform’s scope through user stories, organizing features into an intuitive information architecture, and testing early layouts through wireframes.

User stories

To ground the product in real scenarios, we created a set of collaborative user stories. Each one captured the perspective of our primary users (clients, analysts, and advisors) describing what they wanted to achieve and why. These stories helped us prioritize features, avoid scope creep, and align the entire team on the MVP vision.

User stories

Extract of the user stories

User stories

Extract of the user stories

User stories

Extract of the user stories

User stories

Extract of the user stories

Wireframing

We created wireframes in Figma to visualize primary user flows and key features derived from the user stories, with a strong emphasis on structure, hierarchy, and interactivity. These wireframes were later reviewed in validation sessions with stakeholders and early adopters, and A/B testing was conducted to identify the most intuitive and effective layout variations.

Drag to explore — Some mid fidelity wireframe screens

Drag to explore — Some mid fidelity wireframe screens

Drag to explore — Some mid fidelity wireframe screens

Drag to explore — Some mid fidelity wireframe screens

Design

With the MVP’s functional scope defined and validated through user stories and wireframes, we moved into the design phase. Our goal was to transform early concepts into a functional, scalable interface that could handle complex data interactions while remaining approachable and intuitive for non-technical user.

Dashboard

The dashboard was designed with a collapsible side navigation and a 12-column grid system to organize cards and panels. To support fast decision-making, cards display the most relevant KPIs from each supply chain stream, ongoing initiatives, and alerts on conflicting data.

Displaying metrics and user interactions in the dashhoard

Displaying metrics and user interactions in the dashboard

Analytics module

At the core of the product is the analytics module, where users can explore relational data through interactive charts and tables segmented by stream tabs. Features such as keyword search, filtering, export options, and a guided four-step process to create new sections give users flexibility and control over their analysis.

Navigating through the analytics module

Dynamic data presentation ensures that results adjust in real time based on selected elements. The module also connects directly to the initiatives system, allowing users to surface relevant opportunities for improvement tied to the data displayed in each section.

Micro-interactions within an analytic section

Initiatives module

The initiatives module suggests goals based on internal benchmarks and best practices. These recommendations are automatically linked to specific KPIs, ensuring progress is measurable and aligned with industry standards and business outcomes.

Initiatives module and details modal

Initiatives module and details modal

Initiatives module and details modal

Initiatives module and details modal

Responsive layout

The interface was designed with a responsive grid system, adapting seamlessly from desktop to tablet and mobile. Key actions and data visualizations were prioritized to maintain clarity and usability across all breakpoints.

Responsive behavior

Drag to explore — Desktop, tablet and mobile layout

Responsive behavior

Drag to explore — Desktop, tablet and mobile layout

Responsive behavior

Drag to explore — Desktop, tablet and mobile layout

Responsive behavior

Drag to explore — Desktop, tablet and mobile layout

Conclusion

The AxioBI project was both challenging and rewarding. Working in the supply chain domain required intensive onboarding and a steep learning curve. One key decision was to split the work into two tracks: first, cleaning and standardizing client data through an API, and second, designing dashboards that transformed this data into actionable insights. This approach gave us a solid technical foundation while ensuring the user interface delivered immediate value to clients.

What could be improved?

Looking back, two opportunities for improvement stand out:

  • Clearer scope definition – Many definitive KPIs were still under construction, making it difficult to lock the scope. We had to push consistently to secure the necessary inputs from Vant in order to plan development accurately.

  • Smoother phase transition – The handoff between MVP scoping and development could have been more seamless, especially in translating design outputs into implementation tasks.

My journey as Product Manager

Balancing dual roles as both Product Manager and Designer gave me a unique perspective. On one hand, I gained direct access to stakeholder feedback and achieved deep involvement across the project. On the other, leading both fronts simultaneously often felt overwhelming, as the workload demanded more resources to meet my vision. This experience helped me define the boundaries of my role and reinforced the importance of building the right team structure for project success.

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