Industry overview
What is shaping food & beverage
Food and beverage brands compete on taste, price, health perceptions and shelf presence. Small changes in pack, recipe or price can shift share quickly.
- Health and natural claims influence choice
- Quick commerce and modern trade change shopping habits
- Taste remains the deciding factor at repeat
Client background
A dairy processor, working in Gujarat. Client identity is withheld to protect confidentiality.
- Organisation
- A dairy processor
- Sector
- Food & beverage
- Team engaged
- IT & Development
- Services
- ERP development, Mobile app development, Data analytics & dashboards
Client challenge
The dairy processor collected milk from hundreds of village centres but could not trace quality problems in finished products back to specific sources. Retail and export buyers were demanding stronger food safety controls, and rejected milk was costly.
Research objectives
- Deliver on the brief: traceability from farm to shelf for a dairy processor.
- Design around the real needs of collection centres.
- Define requirements and success criteria the solution would be tested against.
- Deliver a reliable, secure solution the team can run and extend.
Target audience
The work focused on 400 collection centres.
- collection centres400
Audiences we typically reach in food & beverage
Shoppers and consumersHouseholdsRetailers and kirana ownersQuick-commerce usersFood-service buyers
Geographic coverage
Coverage: Gujarat. Market: India.
- India
Research methodology
An iterative delivery approach: discovery, design, build and test in short cycles, with the client reviewing each step.
- 1Brief
- 2Process mapping
- 3Collection app
- 4Batch traceability
- 5Insight
We built a collection app recording quantity, fat content and quality tests at each centre, working offline in villages and syncing when connected. Collection data was linked to tanker routes and production batches in the ERP, and dashboards showed quality trends by centre and route.
ERP developmentMobile app developmentData analytics & dashboards
Sampling strategy
- collection centres
- 400
- Users and stakeholders from every affected team took part in discovery workshops and acceptance testing.
Data collection
- Requirements were gathered through workshops, process walk-throughs and a review of existing systems and data.
Data quality & validation
- Peer code review on every change
- Automated and manual testing
- User-acceptance testing with the client
- Release checklist and rollback plan
Data analysis
- Usage and performance monitoring after launch
- Defect and support-ticket trends
- Process time before and after
Key findings
- Quality issues could be traced to specific centres within hours, and targeted training for centre operators reduced rejected milk.
- Retail food safety audits became much simpler.
Traceability improved quality because problems became visible at the source.
Recommendations
- Build on the core insight: traceability improved quality because problems became visible at the source.
- Focus effort where the evidence was strongest: quality issues could be traced to specific centres within hours, and targeted training for centre operators reduced rejected milk.
- Re-measure after the changes to confirm the effect.
Business impact
- The processor also used quality data to reward the best-performing centres.
What changed in the business as a result of the work. Impact is described qualitatively.
Deliverables & next steps
- Interactive dashboard
- Deployed solution with documentation and handover
- Support and maintenance plan
- Repeat the measure after changes are in place to track progress.
- A natural follow-on question: what happens at the shelf?
Client identity is withheld to protect confidentiality.