Dosa Planet (Pan-India QSR Chain)

About Dosa Planet

Pioneered the south Indian category as a quick-service offering across India. From a single Surat outlet, the brand scaled to 500+ outlets across 25 cities by 2025, including company-owned and franchised units. Average ticket size ₹180, 400-600 transactions per outlet daily. High volume, low margin, operational complexity multiplied by scale.

The challenges they faced before RanceLab

• Food cost variance across outlets—budgeted 28%, actual ranged 26-38% depending on location and franchise partner
• Recipe drift—"Chicken Momo" tasted different at different outlets, with frequent customer complaints on social media
• Franchise compliance nightmare—214 outlets across 18 cities, each interpreting brand standards differently
• Aggregator order chaos—Swiggy/Zomato orders are manually entered into the POS, with an 8-12% error rate, and delays are common
• Peak hour prep shortages—lunch (12:30-2 PM) and dinner (7-9 PM) stockouts on bestsellers, lost sales daily
• Staff meal consumption untracked—estimated ₹40K/outlet/month, no visibility into actual numbers
• Cash variance—₹1500-2500 daily per outlet, reconciliation time-consuming, and accountability unclear
• New outlet onboarding slow—45-60 days to operationalise, training inconsistent, mistakes repeated
• Inventory theft—high-turnover staff, weak controls, monthly variance ₹80-120K across the chain
• Labour scheduling is inefficient—fixed shift patterns regardless of footfall, overtime costs are high

The solution provided by RanceLab

• Recipe Locking with Gram-Level Precision — Centralised recipe database for all 60+ SKUs. Batch variance tracking by outlet. Any deviation >3% flagged for immediate review.
• Franchise Governance Dashboard — Real-time compliance scorecard: recipe adherence, pricing discipline, inventory variance, customer ratings. Outlet-wise benchmarking with peer comparison.
• Aggregator API Integration — Direct integration with Swiggy, Zomato, others. Orders auto-flow to the kitchen display, no manual entry. Reconciliation automated—commission disputes flagged daily.
• Daypart-Based Prep Scheduling — Historical demand patterns + live pre-orders → system recommends batch sizes for each 2-hour window. Reduces over-prep waste and stockouts.
• Staff Meal Tracking Module — Every staff meal is entered at the POS with the employee ID. Manager approves. Daily/monthly reports by outlet. Budget alerts trigger at 80% threshold.
• Cash Reconciliation Workflow — Shift-level cash counting with dual verification. Variance >₹200 requires a manager investigation with the reason code before shift close.
• Rapid Onboarding Framework — Standardised training modules, digital checklists, and role-based access. New outlet operational in 15-20 days with brand consistency from day one.
• Inventory Audit Trails — Every movement logged: delivery receipt, storage, usage, waste. Monthly cycle counts are automated. Variance >2% triggers investigation.
• AI-Powered Labour Scheduling — Footfall prediction based on historical data, weather, and events. Staff are scheduled in 30-minute blocks. Overtime alerts before it happens.

The outcome the customer got

• Food cost variance: 26-38% range → consistent 28.5% across 500+ outlets
• Recipe consistency audit score: 68% → 94% (mystery shopper program)
• Franchise compliance: 61% → 89% average score across network
• Aggregator order error rate: 8-12% → <1%
• Peak hour stockout incidents: 18/day across chain → 3/day
• Staff meal costs: ₹40K/outlet estimated → ₹28K/outlet tracked and controlled
• Cash variance: ₹1500-2500/day/outlet → ₹300/day/outlet
• New outlet onboarding: 45-60 days → 15-20 days
• Inventory theft/variance: ₹80-120K/month chain-wide → ₹25K/month
• Labour cost percentage: 26% → 22% with optimised scheduling
• Gross margin: 52% → 61%
• Customer satisfaction (aggregator ratings): 3.9 → 4.4 average
• Scaled from 214 outlets to 500+ outlets in 18 months without a proportional increase in the backend team

Testimonial from the Co-Founder

"At 65 outlets, we were drowning in WhatsApp messages—'Stock out,' 'Cash shortage,' 'Recipe issue.' Now at 375 outlets with RanceLab, a dashboard at 9 AM shows exactly where the problems are. We finally have a scalable operating system.