Picket App
-Store Operations
B2B
Quick Commerce
Supply Chain
Store Operations
//PROJECT OVERVIEW
Why are we doing this?
ApnaMart operates both physical grocery stores and a quick-commerce mobile app promising quick delivery under 15 minutes. But ApnaMart was facing a major challenge with online order fulfillment inside its grocery stores. Our pickers had to navigate busy, live stores filled with customers while finding scattered items without any clear map or route. The existing manual process was slow and inefficient, often taking 12 to 13 minutes or more to pick a single order itself. This led to delays, picker fatigue, and increased chances of errors—all hurting customer satisfaction and store operations.
//Pickers: Store staff inside offline grocery store responsible for picking and packing the items ordered via online app and handing it over to delivery partners
//PROBLEM STATEMENT
What are we designing?
Our pickers needed a smart, easy-to-use tool to help them find items efficiently without running all over the place, bumping into shoppers, or wasting time searching shelves for products that might not even be there anymore.
//RESULTS
On - Ground Impact
The launch of this new picker flow played a significant role in reducing the overall delivery time, And since then we have seen significant drop in average picking time per order. The team worked faster, made fewer mistakes, and customers were happier too.
11.18 mins
3.74 mins
Average Picking Time
1.5 mins
46 secs
Average Packing time per SKU
//DESIGN PROCESS
The Approach
Discover
Define
Ideate
Design
Feedback
Primary Research
Competitor Analysis
Challenges
Project Overview
Problem Statement
Understanding the Process
Project Goals
Solutioning
Information Architecture
Style Guide
Final Screens
Impact Data
Metrics Graphs
//DESIGN PROCESS
The Approach
//PRIMARY RESEARCH
Knowing your competitors
Before beginning my project, it was necessary for me to understand our direct competitors (Blinkit & Instamart) in Jharkhand and West Bengal region where Apna Mart is widely active, I wanted to gain understanding of their processes, tools they use and and the measures they take to achieve quick delivery.
Before beginning my project, it was necessary for me to understand our direct competitors (Blinkit & Instamart) in Jharkhand and West Bengal region where Apna Mart is widely active, I wanted to gain understanding of their processes, tools they use and and the measures they take to achieve quick delivery.
//PRIMARY RESEARCH
Knowing your
Competitors
As part of this research phase, I reached out to multiple people to gain access to dark stores. After quite a bit of struggle, I was able to connect with some folks with the help of a few colleagues and friends
As part of this research phase, I reached out to multiple people to gain access to dark stores. After quite a bit of struggle, I was able to connect with some folks with the help of a few colleagues and friends
Instamart Dark Store
Instamart Dark Store
Blinkit Dark Store
Blinkit Dark Store
Apna Mart Store
Apna Mart Store
//COMPETITOR ANALYSIS
Side By Side Comparison with Direct Competitors
After visiting all 3 dark stores and store, I created a table to compare side by side based on different parameters to find out shortcomings and what can be improved
After visiting all 3 dark stores and store, I created a table to compare side by side based on different parameters to find out shortcomings and what can be improved
//COMPETITOR ANALYSIS
Side By Side Comparison
with Direct Competitors
//CHALLENGES
What are the major challenges for us?
//CHALLENGES
What are the major
challenges for us?
//UNDERSTANDING THE PROCESS
Order Journey
Let's just understand the order journey once.
Let's just understand the journey once
//UNDERSTANDING PROCESS
Our Journey
//UNDERSTANDING THE PROCESS
How a online order is picked and packed?
As you can clearly see below, there is no set path for picker while picking item, because what a order comes in, the picker gets a list of items—but nothing is told to them where to find each one. The result? Staff zigzagg across the store, retracing their steps, losing time and sometimes missing a item completely because the item had shifted—or vanished—thanks to other shoppers wandering the store
Picker wandering around the store to pickup the order
Picker wandering around the store
to pickup the order
As you can clearly see below, there is no set path for picker while picking item, because what a order comes in, the picker gets a list of items—but nothing is told to them where to find each one. The result? Staff zigzagg across the store, retracing their steps, losing time and sometimes missing a item completely because the item had shifted—or vanished—thanks to other shoppers wandering the store
//UNDERSTANDING PROCESS
How a online order is
picked and packed?
//DEFINE
Project Goals
Outlined the main objectives for this project to define groundwork for our design
Outlined the main objectives for this project to define groundwork for our design
//DEFINE
Project Goals
//RESOLUTION
Evaluating Problem statement
So the very first gap in our system that was observed was that, Neither the store is properly mapped and nor the SKUs in stores have any designated location that is known to our system.
So we realised before we go any further with our solutioning, we need to fix this problem first and that's when we put this project on pause and took a detour to build "Planogram".
//A Planogram is a store merchandising tool used to map and plan how products should be displayed on shelves, fixtures, or other in-store layouts. See full project in detail here: Link
Once the planogram was launched and all store data with product-to-shelf mapping was in place, the system had complete visibility of which item was placed on which shelf in which aisle.
Once the planogram was launched and all store data with product-to-shelf mapping was in place, the system had complete visibility of which item was placed on which shelf in which aisle.
Building on this, we envisioned an app that leverages this data to organize items in each order based on their physical store placement. The app generates the fastest picking sequence, minimizing backtracking and eliminating time wasted wandering through the store. And the new path will look something like compared to old picking sequence.
Building on this, we envisioned an app that leverages this data to organize items in each order based on their physical store placement. The app generates the fastest picking sequence, minimizing backtracking and eliminating time wasted wandering through the store. And the new path will look something like compared to old picking sequence.
Old picking route
Old picking route
New picking route ✦
New picking route ✦
//A Planogram is a store merchandising tool used to map and plan how products should be displayed on shelves, fixtures, or other in-store layouts. See full project in detail here: Link
We realised before we go any further with our solutioning, we need to fix this problem first and that's when we put this project on pause and took a detour to build "Planogram".
So the very first gap in our system that was observed was that, Neither the store is properly mapped and nor the SKUs in stores have any designated location that is known to our system.
//RESOLUTION
Evaluating Problem
statement
//IDEATE
Information Architecture
I kept the user flow very straightforward. A self explanatory process with no fuss and no getting lost in menus.
//IDEATE
Information
Architecture
I kept the user flow very straightforward. A self explanatory process with no fuss and no getting lost in menus.
//FINAL
Key Features
To facilitate a smoother workflow for pickers, we incorporated all our research findings and, based on the insights, drafted a new flow that is simple, straightforward, and easy to adopt without requiring prior training. Following are the key features of this new flow:
//FINAL
Key Features
To facilitate a smoother workflow for pickers, we incorporated all our research findings and, based on the insights, drafted a new flow that is simple, straightforward, and easy to adopt without requiring prior training. Following are the key features of this new flow:
//VISUALS
Style Guide
One more thing before starting, Defined A Style guide for the app so all the screens stays consistent and easy for developers for working with set set of colours
//VISUALS
Style Guide
One more thing before starting, Defined A Style guide for the app so all the screens stays consistent and easy for developers for working with set set of colours
//DESIGN
Final Screens & Flows
// 4 Different states for Mark -in/ Mark-out
// 4 Different states for Mark -in/ Mark-out
//Mark -in/ Mark-out Flow
//Mark -in/ Mark-out Flow
//Picker Screen During Picking
//Picker Screen During Picking
//Picking a Order
//Picking a Order
//Picker Performance
//Picker Performance
//DESIGN
Final Screens & Flows
//DESIGN
Other Flows and Edge Cases
//DESIGN
Other Flows &
Edge Cases
//IMPACT METRICS
So How effective was the new flow?
For Launch phase, we started with 1 store, Hathiya Bazar, Ranchi which is also one of our biggest stores in the city. We conducted a in-person product demo for Store managers and Store Operations head of the Jharkhand state. took feedbacks and also formed SOPs for Picker training purpose.
// The response was great, people liked the new proposed flow, it was easier for our pickers to understand and use the new picker flow. And everyone praised the new multi language support especially store staff
After validating the results, we scaled the new optimized picking flow across all stores, along with the planogram rollout—since the new picking system relied on standardized product placement to function effectively. And the data was observed as follow:
Past 1 year change in picking time
11.18 mins
3.74 mins
Average Picking Time
1.5 mins
40.88 secs
Average Packing time per SKU
Past 6 month trend for picking time per sku
//FIN
Thank You For Scrolling!
That's all folks, Please feel free to ask any questions you have
I would love to answer them. Bring it on!
Picket App -
Store Operations
B2B
Supply Chain
Quick Commerce
Store Operations
//PROJECT OVERVIEW
Why?
ApnaMart operates both physical grocery stores and a quick-commerce mobile app promising quick delivery under 15 minutes. But ApnaMart was facing a major challenge with online order fulfillment inside its grocery stores. Our pickers had to navigate busy, live stores filled with customers while finding scattered items without any clear map or route. The existing manual process was slow and inefficient, often taking 12 to 13 minutes or more to pick a single order itself. This led to delays, picker fatigue, and increased chances of errors—all hurting customer satisfaction and store operations.
//Pickers: Store staff inside offline grocery store responsible for picking and packing the items ordered via online app and handing it over to delivery partners
Our pickers needed a smart, easy-to-use tool to help them find items efficiently without running all over the place, bumping into shoppers, or wasting time searching shelves for products that might not even be there anymore.
//PROBLEM STATEMENT
What are we
designing?
The launch of this new picker flow played a significant role in reducing the overall delivery time, And since then we have seen significant drop in average picking time per order. The team worked faster, made fewer mistakes, and customers were happier too.
//RESULTS
On-Ground Impact
11.18 mins
3.74 mins
Average Picking Time
1.5 mins
46 secs
Average Packing Time per SKU
//FIN
Thankyou For Scrolling!
That's all folks, Please feel free to ask any questions you have I would love to answer them. Bring it on!
Past 1 year change in picking time
Past 6 month trend for picking time per sku
//IMPACT METRICS
So how effective was
the system?
For Launch phase, we started with 1 store, Hathiya Bazar, Ranchi which is also one of our biggest stores in the city. We conducted a in-person product demo for Store managers and Store Operations head of the Jharkhand state. took feedbacks and also formed SOPs for Picker training purpose.
// The response was great, people liked the new proposed flow, it was easier for our pickers to understand and use the new picker flow. And everyone praised the new multi language support especially store staff
After validating the results, we scaled the new optimized picking flow across all stores, along with the planogram rollout—since the new picking system relied on standardized product placement to function effectively. And the data was observed as follow:
11.18 mins
3.74 mins
Average Picking Time
1.5 mins
46 secs
Average Packing Time per SKU