At a glance
During my time at IBM, I participated in Patterns, IBM’s six-week Design Education Program for early-career designers. The program offered an immersive experience, beginning with two weeks of learning sessions on topics such as IBM’s design system, Carbon, and insights from senior designers on discipline growth, soft skills, and business strategy. This foundational phase introduced us to Enterprise Design Thinking (EDT), IBM’s scalable framework for design collaboration. Through daily EDT activities and workshops, we gained the tools needed for our Incubator Project, which spanned the remaining four weeks of the program.
Six weeks of immersive design learning and hands-on application (enrolled in February 2024 cohort)
We were tasked with addressing a real-world problem in an IBM product, MRO Inventory Optimization (MRO IO). This platform leverages AI to help industries optimize inventory, minimize downtime, and improve operational efficiency. Over four weeks, our team of five designers collaborated to explore the product, identify its challenges, and develop a user-centric solution, culminating in a final presentation to the Patterns cohort.
Week 1: Discovery and Research
Initial exploration
As MRO IO was new to us, we began with a silent brainstorming session to align on key questions:
What is MRO IO?
Who are the users?
What is the problem statement?
What assumptions and gaps exist?
Through discussions with the Product Manager (PM) and Development team, we gained a comprehensive understanding of the product’s screens, workflows, and purpose. Guided by our two coaches, we refined our approach to the project.
User interview
Cataloging Challenges: Ensuring accurate and non-duplicated product details was a recurring issue. Cataloguers had to manually search for duplicates in SAP ERP by combing through detailed records, a process both time-consuming and prone to human error.
Distinct Roles: Inventory analysts primarily manage stock availability, while inventory controllers focus on monitoring and maintaining inventory levels. Both roles demand seamless collaboration to ensure efficient operations.
Standardization Willingness: Several users expressed openness to aligning with IBM's data taxonomy and standards, doing so would streamline operations and improve efficiency.
Data Cleansing Complexity: The process of cleaning existing product data varied widely across organizations. Larger companies often employed third-party services, whereas smaller ones relied on manual efforts.
Hub-and-Spoke Model: Many businesses employed a central hub warehouse model, with regional spokes managing localized inventory. This structure introduced challenges in maintaining consistent data quality across all locations.
Labor-Intensive Data Entry: Data entry for creating product catalogs is tedious process, requiring inputs like short and long descriptions, part numbers, unit measurements, supplier details, etc.
Irregular Data Maintenance: Regular checks for cleansing data were rare. Most organizations only performed data cleaning when requested, leading to inconsistent data quality over time.
By analyzing this rich data, we began constructing a user-centric foundation for our project.
As- is mapping
We mapped the user’s current experience to identify pain points and opportunities for improvement, focusing on user actions, thoughts, and feelings.
Heuristic evaluation
By evaluating the product screens, we identified usability issues and assessed the alignment of user actions with intended outcomes.
Week 2: Understanding
We identified two primary personas:
Cataloguer: Standardizes material data in SAP ERP to prevent duplicates and update details like price and lead time.
Inventory Controller: Monitors and maintains stock levels, collaborating with departments to ensure smooth operations.
Process mapping
We mapped workflows for two key processes:
Creating New Master Data: A planning team initiates a request, which moves through cataloging and procurement teams before integration into MRO IO.
Cleansing Master Data: Cataloguers and inventory controllers collaborate to identify and merge duplicates, updating stakeholders on changes.
Competitor study
We analyzed competitors such as Web Manuals, SAP EWM, and Safety Culture. Few features that grabbed my attention were
AI Features: Automatic replenishment, demand prediction, defect detection, and voice-enabled operations.
Non-AI Features: Labor management tools, RFID integration, mobile device support, and compliance automation.
Week 3: Consolidation
Need statement
A need statement is a concise way to frame the user's problem, focusing on their core needs rather than features. It shifts the design approach to be user-centric, ensuring the solutions align with user pain points and desired outcomes.
We used the below prompt to create need statement for our product.
Persona: Cataloguer
Action: Needs a way to identify duplicates and cleanse data efficiently
Outcome: To minimize effort and improve data quality
Through stakeholder voting, we prioritized the most impactful needs for users.
Hill statement
Hill statements articulate the intent behind the solution, focusing on user impact and business value. These statements serve as guiding principles, ensuring the design efforts target key opportunities that benefit users and stakeholders alike. Below are the articulated statements:
Cataloguers can identify errors while cleansing existing product data with minimal effort and time.
Cataloguers can create product master data via minimal and accurate data input.
Cataloguers can generate compliant product data descriptions following taxonomy standards efficiently.
Ideation
Using the “Big Idea Vignettes” method, from the Design toolkit, we explored AI and non-AI solutions for each hill statement. Ideas were scoped based on user impact and business value, leading to a refined set of actionable concepts.
Concept sketches and finalisation
We sketched rapid concepts, created low-fidelity designs, and iteratively refined them through stakeholder feedback and formative testing. The process began with rough sketches, followed by team critiques to identify strengths and gaps. Next, we developed low-fidelity wireframes, which were tested with stakeholders to gather feedback. After this, mid-fidelity prototypes were created and further refined based on team and stakeholder reviews. This iterative approach ensured a user-focused design outcome.
Week 4: Delivery
Hi-fi designs and prototype
Leveraging IBM’s Carbon Design System, we developed high-fidelity designs and an interactive prototype. Key features included:
Dashboard Overview: Cataloguers can view assigned, open, and completed tasks, along with analytics on errors, duplicates, and cleansed data over time.
AI-Driven Validation: While adding items to the master data, AI ensures standard compliance and suggests corrections. Missing information is auto-fetched from databases or internet sources.
Data Cleansing: Users can filter and cleanse muddy data (duplicates, errors, and missing entries) with AI’s help. The system highlights resolved issues, displays confidence scores, and provides feedback for informed decisions.
Stroytelling
To present our four-week journey effectively, we embraced a comic-style narrative, blending visuals and storytelling to create a memorable experience. The comic format illustrated our journey from problem discovery to delivering the solution, making it engaging even for those unfamiliar with the product or problem domain.
To enhance the delivery, we incorporated voice modulations and role-playing, giving life to the personas.
It was a standout moment in the Patterns program, leaving a lasting impression on our peers and stakeholders.
For more information, reach out to me …
Key takeaways
Collaboration: Worked with diverse stakeholders, including PMs, design leads, and external users, forming valuable global connections.
Storytelling Skills: Effectively conveyed complex ideas through creative and engaging presentations.
Time Management: Successfully delivered a comprehensive solution within a tight four-week timeline, from understanding the product to refining the design.
Design Insights: Gained expertise in the Carbon Design System, IBM’s design toolkit, and the Enterprise Design Thinking (EDT) framework.
This project not only honed my design skills but also provided a platform to contribute meaningfully to a real-world IBM product.
