Kohl's Supply
Chain Data Visualization Tool
Internship.
Summer 2022.
Cross-Functional Team.
Product Design.
One Minute Summary ⏰
Designed a 10-week data visualization build for Kohl’s logistics stakeholders aiming to identify supply chain risks, decrease operational costs, and increase revenue.
The Team 👏🏼
Sarah Palagy .................(Design)
Colin Piette ..................(Management)
Eric Xiang..................... (Engineering)
Sahil Bolar ................... (Data Science)
My Role 👩🏻💻
Product Designer:
Championed project framework, user research, prototyping, user testing, and presentations.
Deliverables 🎁
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Functional Minimum Viable Product (MVP)
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Wireframes (12)
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User Research Report
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Project Documentation
The Business Need 📊
My team and I were initially tasked with looking into supply chain issues at Kohl's ports. Due to COVID-19, overseas ordering increased drastically while the labor available to staff their international ports plummeted. Because of how broad this problem was, we're talking international level here, I chose to focus on the issue of volatile container volume at Kohl's domestic ports.
Volatile Container Volume:
‼️ $14M at risk per month
Large fluctuations in domestic product volume arriving at ports which causes delayed products, increased operating costs, and decreased profitability.
Too much product
Ports Overwhelmed
Delayed Product
Decreased Profitability
Final Design Overview 💭
Establishing the need for my product and the final solution I designed to address it.
Our Solution 🙌🏼
I ultimately determined that the biggest issue for Kohl's was visibility (i.e. knowing when there is a problem and how big). To address this, I designed, tested, and helped develop a cloud-based data visualization tool to graph the real-time volume of products coming into Kohl's domestic ports.
A Data Visualization Tool 🥳
Figure 1: Our tool's central dashboard displaying total product volume and volume by port.
By building this tool in Qlick and connecting it to Kohl's data in Google Cloud platform, I was able to deliver a tool that visualized product volume at domestic ports and indicate potential volume risks.
My Research Process 👩🏻💻
Highlighting the interview, insights, and data. that supported my designs.
Broad Stakeholder Interviews 📝
I lead nine stakeholder interviews across both logistics and merchant departments to understand how supply chain issues were impacting operations at Kohl's on a departmental level. From these insights, I identified the most actionable pain points that my team could address
Figure 2: Thematic diagraming of interview takeaways organized in Miro.
*More in-depth takeaways noted in section below
Interview Synthesis 🤔
❓Where can I create the most impact given the time constraints
Disconnected Departments 🥲
Each team seemed unfamiliar with how their actions were connected to the wider supply chain ecosystem. This lead to small issues becoming larger and more complex without anyone knowing.
Lack of Visibility 🙈
Large gaps in teams' understanding of supply chain issues lead to no one being able to fully articulate what the issue was.
We Need to Scope Down ⤵️
In order to create the best MVP, we needed to find a niche where we could create the most impact
Defining Our User Group: Logistics 👨🏻💼
After discussing these interview takeaways, I worked with product management to create our development strategy. In this, I found that the Logistics team had the most actionable pain points. From this point on, they became our primary user group.
Specific Role 📋
Generate forecasts used to staff deCon facilities
Specific Pains 💢
Low visibility of risks
Manual data manipulation
High toil
Creating Our
Problem Statement 💬
By framing supply chain issues as they affected our Logistics users, we created a problem statement to define our project scope.
User Interviews 🗣
‼️ User Quote: "If you could find a way to generate these forecasts for me, that would save me 4 hours a day"
The Current State 😳
Figure 3: Example of a supply chain forecast that Logistics constructs in Excel (private data redacted).
These forecasts are how Kohl's anticipates risks and staffs its ports. Because the forecast requires data from many different databases, significant effort was expended by Logistics to manually construct, update, and distribute these forecasts
My Ideation Process ✍🏼
Outlining the product development strategy, sketches, and prototypes that supported my final designs.
Crazy 8's💡
I led my team through crazy 8's, a design sprint methodology that involved sketching, feature prioritization, and developing an MVP sketch to guide our wireframing process.
Sketching ✍🏼
Determined the method of data visualization and most important features.
Feature Prioritization 🥇🥈🥉
Identified the top 3 features to build based on feasibility and impact.
MVP Sketch ✍🏼
Consolidate chosen features into a clear sketch that could be concept tested with users.
Test Driven Iteration 🔁
In order to refine our MVP, I conducted two rounds of usability testing with our Logistic users. After each round of testing, I iterated upon our designs allowing me to bring them to higher fidelity and test the newly improved version
Testing Round 1️⃣
Adding Individual Port Views 🚢
"I like that it's simple and straight to the point, but I want to be able to look at individual ports too." -User quote
Separated the Control Panel 🎮
"Make sure the graph controls are clear and easy to understand." -User Quote
Testing Round 2️⃣
Added Callouts 🗣
"I want to see the risk quantified at each port, not just color." - User quote
Modified View Options 🔍
"Being able to see both container and carton views might help us get a better idea of how to staff our ports." -User Quote
Added an All-Port Graph 📊
"I like having the all-port view so we can easily see which ports need the most attention." - User quote
Final MVP 👩🏻💻
After testing our wireframes, I collaborated with our engineer to develop our solution as a web app. This process involved building our interface from scratch and connecting it to data hosted on Google Cloud Platform (GCP).
Next Steps ➡️
In addition to deploying our MVP, we delivered our backlog to the wider team of full-time engineers and designers. This backlog included long-term goals and the 3 most actionable features to begin developing.
Refine Port Capacity Thresholds
Improve Graph Labeling
Connect UI to More Data
Reflection 💕
Reflecting on what I learned from this experience and highlighting aspects of my personal journey as a designer.
Looking Back, I'd Do Some Things Differently. 🤔
If I could go back, the biggest thing I would've changed would be being more critical about user feedback. If the users said they wanted a certain feature, I tended to go with it even in moments where I had concerns from a design standpoint. If I had taken more time to have a conversation with my users, I think I could've produced cleaner designs with a more clear user-flow.
Confidence Is A Skill 🏆
Imposter syndrome was one of the many hurdles that I faced during this internship. Being surrounded by so many technical experts in data science and engineering often made me self-conscious of my own skills and right to lead. At the end of the day, it was through vulnerability and conversations with my teammates that I was able to overcome these doubts and learn how to value my own unique set of skills
Not Everyone Values Design Right Away 🫣
This experience taught me that design is still very new and foreign to many companies/individuals. There were many times when I had to teach the design process to my team members and argue why taking time to define the right project scope was important for us. However, because of this, I'm very confident in my ability to advocate for design to non-design audiences.
Big Changes Can Happen Overnight 😳
Originally, I was researching and designing our product under the impression that it'd be implemented within an existing software however, at about week 8 of 12, my project manager informed me that we'd be building it as a stand-alone tool on a different platform. Although this shocked me at first, it taught me how to roll with the punches and to not let re-scoping phase me.
Working Cross-Disciplines Is So Rewarding 🥳
This was my first time working on a team as the only designer and in such close collaboration with data science, engineering, and product management. It was scary at first, but I was able to learn so many different skills and ways of thinking from my team. I learned that its something I'd like to continue doing in the future!
Check Out My Other Projects💡
Figma Marketing Campaign
Expedia Sustainability UI
Purdue OWL Redesign