Pantree
Case Study in
Reducing Food Waste.
Solo Designer
6 Months
Figma, Autodesk Inventor
Reinventing the Way You Shop. With Pantree’s predictive smart trolley system, every trip is efficient, sustainable, and designed for you.
The Product
Pantree is a smart onboard trolley system built for the conscious shopper. It connects with a retailer’s app, displays live shopping lists on the trolley, checks items off in real time, and uses predictive intelligence to suggest what’s actually needed reducing food waste before food ever enters the home.
How It Works
Shopper creates or syncs a list via the retailer’s app
List appears when the trolley is docked
Items are checked off in real time as they’re scanned
The system learns from habits,diets and purchases
Powered By Intelligence
A.R.I.M.A
Forecasting
User
Clustering
Feedback
Loop
Feedback
Loop
The Problem
Food waste is a massive environmental and financial issue, and households are one of the largest contributors. Despite good intentions, shoppers frequently overbuy, forget what they already have at home, and make impulse decisions in-store. How might we reduce household food waste by supporting better decisions during the shopping experience without adding friction for shoppers or retailers?
1.3
Billion
Tonnes
1/3
Households
Global Waste
Contribution
Research & Discovery
Desk Research: Understanding Food Waste
I began by exploring the scale of food waste and its key contributors. What stood out was that household waste is largely driven by planning issues, overbuying, and poor storage habits particularly for perishable goods.
During this phase I discovered a design revolution in the 1950’s, that the refrigerator and the supermarket had a symbiotic development but there has always been a disconnect.
Along side this one of the stronger findings was a social capital dynamic I discovered, where families with fuller fridges are perceived to be wealthier but in had this leads to food waste.
This reinforced the opportunity to intervene before purchase, not after.
Interviews & Behavioural Insight
I conducted six semi-structured interviews with participants aged 24–60 across varying household sizes.
Key insights included:
- Busy lifestyles reduce planning quality
- Overbuying often feels emotionally justified
- Guilt around food waste exists, but tools feel effort-heavy
These insights highlighted the need for low-effort, supportive interventions.
Shopping List Experiment
To test whether planning reduces waste, I ran a two-week study with 20 participants:
- Group 1: Used shopping lists (Waste per person - 0.63kg/week)
- Group 2: No shopping lists (Waste per person - 1.54kg/week)
Participants using lists consistently produced less food waste, validating planning as a key intervention point.
Photovoice Study
Participants documented moments of food waste in their daily lives.
The most striking insight was how normalised and unremarkable waste felt shaping Pantree’s focus on subtle nudges rather than confrontational messaging.
Competitive Analysis
I reviewed existing tools across grocery shopping, planning, and food waste reduction.
Key Observations
- Convenience tools don’t address waste
- Waste platforms intervene too late
- Planning tools aren’t connected to real purchase decisions
OpportunityReal-time, in-store support that helps people make better decisions as they shop.
Core Insights
Key Findings
What we learned: Food waste is driven by habits and perceived value, not lack of intent.
- Habits outweigh intentGood intentions broke down against everyday routines like overbuying and forgetting items.
- Support beats intrusionUsers disengaged when reminders felt noisy or judgmental.
- Value must be tangibleFinancial savings motivated behaviour change more than sustainability alone.
Reframing the productThese insights repositioned Pantree as an adaptive system, not just a smart list.
Challenging the Concept
Challenging the Concept
Using assumption mapping, I tested the beliefs underpinning Pantree.
Key Learnings
- Efficiency alone doesn’t reduce waste
- Retail buy-in is essential
- User effort must remain low
- Integration with existing retailer apps is critical
These learnings refined Pantree into a predictive shopping companion.
Ideation &
Concept Selection
Ideation
I explored ideas across:
- Pre-purchase planning
- Food storage awareness
- Waste management
- Behavioural nudging
Through thematic analysis, three core themes emerged:
- Organisation
- Food storage
- Behavioural nudging
Why Pantree?
Pantree was selected because it addressed food waste at its source, before purchase, while also offering value to retailers.
It stood out by:
- Encouraging intentional shopping
- Supporting smoother in-store flow
- Aligning with sustainable, low-waste lifestyles
Prototyping the Experience
Physical Prototyping
Low-Fidelity Build
I began with cardboard prototypes to test scale, usability, and in-store presence.
Key Decisions
- Robust, angular form for durability
- Fixed trolley attachment point
- Large tactile button for ease of use
- Partial screen occlusion for a futuristic feel
High-Fidelity Build
I developed a high-fidelity prototype using:
- CAD
- 3D printing
- Sanding and painting
- Integrated electronics
- Raspberry Pi for simulated scanning
The focus was on credibility in a real supermarket environment.
Digital Prototyping
Using Figma, I designed Pantree’s digital interface to be clear, adaptive, and transparent.
Key features included:
- Flexible list management
- Predictive suggestions
- Yes/No feedback loops
- User control and overrides
- POS system alignment
Final Outcome
Pantree
Case Study in
Reducing Food Waste.
Solo Designer
6 Months
Figma, Autodesk Inventor
Reinventing the Way You Shop. With Pantree’s predictive smart trolley system, every trip is efficient, sustainable, and designed for you.
The Product
Pantree is a smart onboard trolley system built for the conscious shopper. It connects with a retailer’s app, displays live shopping lists on the trolley, checks items off in real time, and uses predictive intelligence to suggest what’s actually needed reducing food waste before food ever enters the home.
How It Works
Shopper creates or syncs a list via the retailer’s app
List appears when the trolley is docked
Items are checked off in real time as they’re scanned
The system learns from habits,diets and purchases
Powered By Intelligence
A.R.I.M.A
Forecasting
User
Clustering
Feedback
Loop
Feedback
Loop
The Problem
Food waste is a massive environmental and financial issue, and households are one of the largest contributors. Despite good intentions, shoppers frequently overbuy, forget what they already have at home, and make impulse decisions in-store. How might we reduce household food waste by supporting better decisions during the shopping experience without adding friction for shoppers or retailers?
1.3
Billion
Tonnes
1/3
Households
Global Waste
Contribution
Research & Discovery
Desk Research: Understanding Food Waste
I began by exploring the scale of food waste and its key contributors. What stood out was that household waste is largely driven by planning issues, overbuying, and poor storage habits particularly for perishable goods.
During this phase I discovered a design revolution in the 1950’s, that the refrigerator and the supermarket had a symbiotic development but there has always been a disconnect.
Along side this one of the stronger findings was a social capital dynamic I discovered, where families with fuller fridges are perceived to be wealthier but in had this leads to food waste.
This reinforced the opportunity to intervene before purchase, not after.
Interviews & Behavioural Insight
I conducted six semi-structured interviews with participants aged 24–60 across varying household sizes.
Key insights included:
- Busy lifestyles reduce planning quality
- Overbuying often feels emotionally justified
- Guilt around food waste exists, but tools feel effort-heavy
These insights highlighted the need for low-effort, supportive interventions.
Shopping List Experiment
To test whether planning reduces waste, I ran a two-week study with 20 participants:
- Group 1: Used shopping lists (Waste per person - 0.63kg/week)
- Group 2: No shopping lists (Waste per person - 1.54kg/week)
Participants using lists consistently produced less food waste, validating planning as a key intervention point.
Photovoice Study
Participants documented moments of food waste in their daily lives.
The most striking insight was how normalised and unremarkable waste felt shaping Pantree’s focus on subtle nudges rather than confrontational messaging.
Competitive Analysis
I reviewed existing tools across grocery shopping, planning, and food waste reduction.
Key Observations
- Convenience tools don’t address waste
- Waste platforms intervene too late
- Planning tools aren’t connected to real purchase decisions
OpportunityReal-time, in-store support that helps people make better decisions as they shop.
Core Insights
Key Findings
What we learned: Food waste is driven by habits and perceived value, not lack of intent.
- Habits outweigh intentGood intentions broke down against everyday routines like overbuying and forgetting items.
- Support beats intrusionUsers disengaged when reminders felt noisy or judgmental.
- Value must be tangibleFinancial savings motivated behaviour change more than sustainability alone.
Reframing the productThese insights repositioned Pantree as an adaptive system, not just a smart list.
Challenging the Concept
Challenging the Concept
Using assumption mapping, I tested the beliefs underpinning Pantree.
Key Learnings
- Efficiency alone doesn’t reduce waste
- Retail buy-in is essential
- User effort must remain low
- Integration with existing retailer apps is critical
These learnings refined Pantree into a predictive shopping companion.
Ideation &
Concept Selection
Ideation
I explored ideas across:
- Pre-purchase planning
- Food storage awareness
- Waste management
- Behavioural nudging
Through thematic analysis, three core themes emerged:
- Organisation
- Food storage
- Behavioural nudging
Why Pantree?
Pantree was selected because it addressed food waste at its source, before purchase, while also offering value to retailers.
It stood out by:
- Encouraging intentional shopping
- Supporting smoother in-store flow
- Aligning with sustainable, low-waste lifestyles
Prototyping the Experience
Physical Prototyping
Low-Fidelity Build
I began with cardboard prototypes to test scale, usability, and in-store presence.
Key Decisions
- Robust, angular form for durability
- Fixed trolley attachment point
- Large tactile button for ease of use
- Partial screen occlusion for a futuristic feel
High-Fidelity Build
I developed a high-fidelity prototype using:
- CAD
- 3D printing
- Sanding and painting
- Integrated electronics
- Raspberry Pi for simulated scanning
The focus was on credibility in a real supermarket environment.
Digital Prototyping
Using Figma, I designed Pantree’s digital interface to be clear, adaptive, and transparent.
Key features included:
- Flexible list management
- Predictive suggestions
- Yes/No feedback loops
- User control and overrides
- POS system alignment
Final Outcome
Pantree
Case Study in
Reducing Food Waste.
Solo Designer
6 Months
Figma, Autodesk Inventor
Reinventing the Way You Shop. With Pantree’s predictive smart trolley system, every trip is efficient, sustainable, and designed for you.
The Product
Pantree is a smart onboard trolley system built for the conscious shopper. It connects with a retailer’s app, displays live shopping lists on the trolley, checks items off in real time, and uses predictive intelligence to suggest what’s actually needed reducing food waste before food ever enters the home.
How It Works
Shopper creates or syncs a list via the retailer’s app
List appears when the trolley is docked
Items are checked off in real time as they’re scanned
The system learns from habits,diets and purchases
Powered By Intelligence
A.R.I.M.A
Forecasting
User
Clustering
Feedback
Loop
Feedback
Loop
The Problem
Food waste is a massive environmental and financial issue, and households are one of the largest contributors. Despite good intentions, shoppers frequently overbuy, forget what they already have at home, and make impulse decisions in-store. How might we reduce household food waste by supporting better decisions during the shopping experience without adding friction for shoppers or retailers?
1.3
Billion
Tonnes
1/3
Households
Global Waste
Contribution
Research & Discovery
Desk Research: Understanding Food Waste
I began by exploring the scale of food waste and its key contributors. What stood out was that household waste is largely driven by planning issues, overbuying, and poor storage habits particularly for perishable goods.
During this phase I discovered a design revolution in the 1950’s, that the refrigerator and the supermarket had a symbiotic development but there has always been a disconnect.
Along side this one of the stronger findings was a social capital dynamic I discovered, where families with fuller fridges are perceived to be wealthier but in had this leads to food waste.
This reinforced the opportunity to intervene before purchase, not after.
Interviews & Behavioural Insight
I conducted six semi-structured interviews with participants aged 24–60 across varying household sizes.
Key insights included:
- Busy lifestyles reduce planning quality
- Overbuying often feels emotionally justified
- Guilt around food waste exists, but tools feel effort-heavy
These insights highlighted the need for low-effort, supportive interventions.
Shopping List Experiment
To test whether planning reduces waste, I ran a two-week study with 20 participants:
- Group 1: Used shopping lists (Waste per person - 0.63kg/week)
- Group 2: No shopping lists (Waste per person - 1.54kg/week)
Participants using lists consistently produced less food waste, validating planning as a key intervention point.
Photovoice Study
Participants documented moments of food waste in their daily lives.
The most striking insight was how normalised and unremarkable waste felt shaping Pantree’s focus on subtle nudges rather than confrontational messaging.
Competitive Analysis
I reviewed existing tools across grocery shopping, planning, and food waste reduction.
Key Observations
- Convenience tools don’t address waste
- Waste platforms intervene too late
- Planning tools aren’t connected to real purchase decisions
OpportunityReal-time, in-store support that helps people make better decisions as they shop.
Core Insights
Key Findings
What we learned: Food waste is driven by habits and perceived value, not lack of intent.
- Habits outweigh intentGood intentions broke down against everyday routines like overbuying and forgetting items.
- Support beats intrusionUsers disengaged when reminders felt noisy or judgmental.
- Value must be tangibleFinancial savings motivated behaviour change more than sustainability alone.
Reframing the productThese insights repositioned Pantree as an adaptive system, not just a smart list.
Challenging the Concept
Challenging the Concept
Using assumption mapping, I tested the beliefs underpinning Pantree.
Key Learnings
- Efficiency alone doesn’t reduce waste
- Retail buy-in is essential
- User effort must remain low
- Integration with existing retailer apps is critical
These learnings refined Pantree into a predictive shopping companion.
Ideation &
Concept Selection
Ideation
I explored ideas across:
- Pre-purchase planning
- Food storage awareness
- Waste management
- Behavioural nudging
Through thematic analysis, three core themes emerged:
- Organisation
- Food storage
- Behavioural nudging
Why Pantree?
Pantree was selected because it addressed food waste at its source, before purchase, while also offering value to retailers.
It stood out by:
- Encouraging intentional shopping
- Supporting smoother in-store flow
- Aligning with sustainable, low-waste lifestyles
Prototyping the Experience
Physical Prototyping
Low-Fidelity Build
I began with cardboard prototypes to test scale, usability, and in-store presence.
Key Decisions
- Robust, angular form for durability
- Fixed trolley attachment point
- Large tactile button for ease of use
- Partial screen occlusion for a futuristic feel
High-Fidelity Build
I developed a high-fidelity prototype using:
- CAD
- 3D printing
- Sanding and painting
- Integrated electronics
- Raspberry Pi for simulated scanning
The focus was on credibility in a real supermarket environment.
Digital Prototyping
Using Figma, I designed Pantree’s digital interface to be clear, adaptive, and transparent.
Key features included:
- Flexible list management
- Predictive suggestions
- Yes/No feedback loops
- User control and overrides
- POS system alignment
Final Outcome