Yum Chum (Multi-Brand Cloud Kitchen, Bangalore)

About Yum Chum

Pioneered the rotating menu concept in cloud kitchens, offering global cuisine with daily menu changes. High complexity with daily menu rotation, multi-aggregator dependence, and shared ingredient usage across brands.

The challenges they faced before RanceLab

• Aggregator reconciliation nightmare—orders from 4 platforms (Swiggy, Zomato, own app, corporate catering), manual reconciliation took 8-10 hours daily, errors frequent
• Commission overcharges—platforms charging 23-25% when contracts specified 18-20%, ₹45-60K monthly overcharges going undetected
• Multi-brand cost allocation impossible—shared ingredients (onions, spices, oil) used across 4 brands, no accurate way to allocate costs, profitability by brand unknown
• Menu rotation complexity—daily menu changes meant recipe costing recalculated manually, errors frequent, some dishes sold at loss unknowingly
• Packaging inventory chaos—18 different SKUs (boxes, bags, cutlery sets), frequent stockouts delayed orders, customer complaints about wrong packaging
• Demand forecasting failures—rotating menu made historical data less useful, prep quantities guesswork, waste or stockouts daily
• Kitchen capacity imbalance—some kitchens at 90% utilization, others at 45%, no systematic load balancing
• Staff productivity variance—top cooks processed 22 orders/hour, bottom 11 orders/hour, training gaps unclear
• COD reconciliation delays—delivery partners submitted reports weekly, reconciliation took 2-3 days, cash flow delays, dispute resolution slow
• Food waste untracked—estimated 8-10% waste but no categorization (prep vs expiry vs service), improvement actions unclear

The solution provided by RanceLab

• Multi-Aggregator Reconciliation Engine — Automated daily reconciliation across all platforms. Commission validation against contracts. Discrepancy report generated with recovery workflow. Integration with bank statements for settlement verification.
• Commission Dispute Management — Real-time alerts when commission exceeds contracted rate. Auto-generated dispute tickets with supporting data. Recovery tracking by platform. Monthly savings dashboard.
• Multi-Brand Cost Allocation Engine — Shared ingredients tracked with usage by brand. Automated cost apportionment based on actual recipe consumption. Real-time P&L by brand. Identifies profitable vs losing brands accurately.
• Dynamic Recipe Costing — Daily menu changes automatically trigger recipe costing recalculation. Ingredient price fluctuations reflected in real-time margin calculation. Alerts when dish margin falls below threshold.
• Packaging-Food Synchronization — Packaging inventory tracked separately with automatic pairing to food items. System prevents order acceptance if packaging below threshold for projected demand. Automated reorder triggers.
• AI-Powered Demand Forecasting for Rotating Menus — Algorithm analyzes dish category, cuisine type, price point, weather, day of week. Predicts demand for new menu items based on similar historical dishes. Batch size recommendations with confidence intervals.
• Kitchen Load Balancing System — Real-time capacity monitoring across all kitchens. Order routing considers current load, preparation time, delivery zone. Rebalancing suggestions when utilization variance exceeds 25%.
• Staff Performance Analytics — Orders per hour tracked by cook, by shift, by kitchen. Quality metrics (customer ratings, remakes, waste). Training gaps identified. Gamification leaderboard drives improvement.
• COD Reconciliation Automation — Daily COD reports from delivery partners imported automatically. Matched against order records. Variance alerts trigger immediate investigation. Cash flow visibility in real-time.
• Waste Tracking and Analytics — Categorized waste logging (prep waste, expiry waste, service waste, packaging waste). Daily waste percentage by brand by kitchen. Root cause analysis identifies improvement opportunities.

The outcome the customer got

• Aggregator reconciliation time: 8-10 hours/day → 30 minutes/day
• Commission overcharges recovered: ₹45-60K/month identified → ₹680K recovered in first 90 days
• Multi-brand cost allocation: manual estimates → automated actuals (revealed Italian brand 6% less profitable than assumed, Healthy Bowl brand 8% more profitable)
• Menu rotation recipe costing: manual calculation → automated real-time (prevented 12 loss-making dishes from launching in 90 days)
• Packaging stockout incidents: 14-18/week → 2-3/week
• Demand forecast accuracy: 62% → 86% (rotating menu challenge overcome)
• Kitchen capacity utilization: 45-90% variance → 72-84% balanced range
• Staff productivity: 11-22 orders/hour variance → 18-23 orders/hour consistent range
• COD reconciliation: weekly 2-3 day process → daily 20-minute process
• Food waste: estimated 8-10% → tracked 6.2% → reduced to 3.8% within 60 days
• Food cost percentage: 34% → 28% (accurate multi-brand allocation + waste reduction)
• Overall gross margin: 46% → 56%
• Revenue per kitchen: ₹3.8L/month → ₹5.1L/month (better capacity utilization)
• Customer rating average: 3.9 → 4.4

Testimonial from the Founder.

Before RanceLab, we were depending on multiple excel files for everything—costs, demand, profitability by brand. We knew we had leaks but couldn't pinpoint them. Now we know exactly which brand is making money, which dish should be discontinued, and where the waste is happening. The aggregator reconciliation alone saved us the money we were just giving away.