Better Placement of Perishables
Beyond just a distribution tool, we connected commercial processes with logistics
Overview
Besides adopting the restock team’s business jargon, the process evolved from using macros to machine learning models, thanks to a Product Discovery with the users in this area. By deeply understanding the full cycle of the problem, the solution went beyond a simple restock proposal. This approach enabled the connection of processes that optimize both distribution and sales evaluation.
My Role
Responsabilities
Tools
Timeline
Sep 2021- Dic 2023
Team members
This project holds great value for my development. From the very first day, it gave me the chance to grow and challenged my limits, ultimately leading me to become a Senior Product Designer. I faced major challenges as I entered the retail sector in Mexico’s largest company.
My first task was to understand the entire fresh produce restock process in the supermarket, getting familiar with many company-specific terms and concepts. Applying design methodology, mapping out the restock process was possible without necessarily reaching a technical level.
The Product Manager played a crucial role. We worked as a team and conducted Discovery with users to focus on their core needs. During this phase, we realized the importance of building a DATA MODEL capable of optimizing product placement. Through this, I learned how to develop variables and training factors so the tool could fulfill its purpose.
Beyond tackling functional aspects, I successfully integrated three key processes: the commercial strategy, which determines the quantity of merchandise to be sold; the proposal for distributing merchandise to stores; and the order placement process. This integration allowed the team to collaborate more effectively, surpassing the simple goal of proper product placement. The resulting design provided flexibility to the restock team in selecting merchandise.
In this project, you will see a concept similar to a shopping cart, since the tool helped the manager to have control of the distribution proposals to the stores.
When our business champion requested the distribution product, the goal was to help the restock team achieve better product placement for customers. This meant ensuring the right products were available in stores while considering shipping factors, weather conditions, and pay cycle dates.
The greatest skill of a Product Designer is knowing how to listen to users and empathize with their daily work.
Project Challenge
How might we ensure an efficient distribution process for fresh produce restock teams in the purchasing area, using a product that connects processes from commercial planning to order dispatch to distribution centers (CEDIS), aiming to increase sales and reduce waste?
Metodología de Producto
Using a Product-driven approach to address business needs, we began by identifying the core problem in the area and conducted shadowing sessions with users to uncover process gaps.
User interviews were a versatile and indispensable tool for design
Unstructured Interviews
To kick off the project, I asked Walmart’s restock manager to explain their workflows in an open-ended manner. Without a fixed script, I allowed them to freely describe their process. Together with the PM, we structured a process diagram to identify key steps.
Contextual Interviews
To deeply understand the fresh produce restock process, we conducted on-site visits, observing firsthand how users managed shipments, took inventory, and differentiated between promotional and restock.
Semi-Structured Interviews
Finally, I conducted task-based interviews at each stage of the process to analyze workflows and interactions. Through these discussions, I gathered insights into pain points, challenges, and opportunities for designing a tool that truly made a difference.
Commercial Analyst
Responsibilities:
Develop commercial strategy
Upload proposals for Restock Teams
Track strategy execution
Pain Points:
Negotiating with suppliers
Setting prices for 1,000 items per week
Lack of visibility into strategy execution
Gains:
Ensuring successful strategy execution
Streamlining workload
Collaborating effectively with purchasing and restock teams
Perishables Replenisher
Responsibilities:
Select merchandise for distribution analysis
Evaluate store stock
Ensure proper distribution to stores
Pain Points:
Heavy workload in analysis
Uncertainty about seasonal demand
Managing product waste
Gains:
Reducing time spent on merchandise distribution
Greater certainty about sales trends
Flexibility for last-minute commercial changes
Gerente de Resurtido
Responsibilities:
Consolidate distribution plans for the team and CEDIS
Review commercial plans and adjust them for the team
Manage distribution responsibilities
Pain Points:
Ensuring the right volume of stock in stores
Reducing unsold product waste
Negotiating with commercial teams for minimal changes
Gains:
Increased certainty in distributions = higher sales
Centralized information management
Faster processing of merchandise to CEDIS
Validation of feasibility and desirability with users
Using low-fidelity prototyping, I validated hypotheses about user needs against their expectations
Testing, testing, and more testing to refine the product
Each interaction clarified the tool’s value for users, from receiving information to generating machine-learning-based distribution proposals for stores
Strategic decision-making for fresh produce merchandise
After conducting interviews with low-fidelity prototypes, usability tests were run to simplify the item selection process. The final interaction design was inspired by a shopping cart model, offering users greater flexibility.
Parameter workspace for data modeling based on business needs
Users had high flexibility in selecting distribution items. Once parameters were set, the data model generated distribution proposals per store on a single screen.
Additional processes considered in merchandise distribution
The user interface was designed to be flexible and modular, accommodating different user levels and scaling to other distribution processes. Leaders could operate at the same level as analysts and submit the entire team's proposal in a single batch.
Implementation results
The value proposition aligned with the product strategy, aiming to reduce waste and increase sales through more accurate and timely distributions, ultimately delivering fresher products to customers.
89.5%
CSAT Score. Users rated the tool with outstanding satisfaction
85%
Efficiency. The tool improved product placement efficiency, achieving a 70% higher sales rate compared to Macros.
+$21 M
Waste Reduction. The data model’s validation significantly helped reduce spoilage, benefiting the entire team.
Beyond a rewarding experience
Connecting startup processes
As a designer, I structured the seeding load flow to ensure articles were available for Restock teams.
Operational Flexibility
The item selection process was simplified using a shopping cart flow, making it more adaptable for users.
Applied Data Science
The MVP launched with a machine learning model that surpassed the Macros-based distribution process by 50% in accuracy.
End-to-End Platform Design
The complete flow was built for the refillers and the flow was built for the “refill manager” user with the possibility of accepting or rejecting the data model distribution proposals.