Chain Data Visualization Tool
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 👩🏻💻
Championed project framework, user research, prototyping, user testing, and presentations.
Functional Minimum Viable Product (MVP)
User Research Report
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
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.
Our 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.
Stakeholder Interviews 📝
To understand the scope of the issue,
I lead nine stakeholder interviews across both logistics and merchant departments to understand how VCV was specifically impacting operations at Kohl's.
Figure 2: Interview synthesis and affinity diagraming organized in Miro.
Interview Synthesis 🤔
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
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 🗣
I conducted two interviews with Logistics executives to gain a deeper understanding of their biggest pain points. These interviews revealed high user toil and a lack of visibility into risks caused by old operations practices.
Above is an example of a supply chain forecast that Logistics constructs in Excel. Although some of the raw data can be imported from separate databases, multiple hours a day are dedicated to updating the sheet with more recent information and manually identifying risks.
If you could find a way to generate these forecasts for me,
that would save me 4 hours a day
My Prototyping Process ✍🏼
Outlining my ideation and product development strategy that supported our final designs
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.
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