Using Data Visualisation to Find F&B Opportunities during a Pandemic Custom Case Solution & Analysis

1. Evidence Brief

Financial Metrics

• The Singapore Food and Beverage sector experienced a revenue decline of approximately 25 percent to 30 percent during the initial pandemic waves in 2020.
• Food and Beverage Services Index (2017 = 100) plummeted to 44.8 in May 2020 during the Circuit Breaker period.
• Online food delivery contribution to total Food and Beverage sales rose from under 10 percent pre-pandemic to over 20 percent during lockdown phases.
• Rental costs in Central Business District areas remained high despite a 60 percent to 80 percent drop in foot traffic.

Operational Facts

• Total Food and Beverage establishments in Singapore exceeded 28000 units prior to the pandemic.
• The Urban Redevelopment Authority provides geospatial data on land use, while the National Environment Agency manages data for 114 hawker centers.
• Geospatial analysis utilized hexagonal binning to aggregate data points into 250-meter cells for granular demand-supply mapping.
• Work From Home mandates shifted food demand from the Central Business District to residential heartlands such as Tampines, Jurong West, and Woodlands.

Stakeholder Positions

• Dr. Kam Tin Seong: Associate Professor at Singapore Management University; advocates for the transition from intuition-based site selection to geospatial data analytics.
• F&B Business Owners: Facing insolvency; seeking immediate guidance on whether to pivot to delivery-only models or relocate.
• Data Analysts: Tasked with cleaning messy, multi-source data to create actionable visualizations for non-technical decision makers.

Information Gaps

• The case lacks specific net profit margin data for cloud kitchens compared to traditional brick-and-mortar outlets.
• Exact commission structures charged by delivery aggregators like GrabFood or Foodpanda are not detailed.
• Long-term consumer behavior data regarding the permanence of Work From Home trends post-pandemic is absent.

2. Strategic Analysis

Core Strategic Question

• How can Food and Beverage operators utilize geospatial intelligence to reallocate capital from high-cost, low-yield urban centers to underserved, high-demand residential nodes?

Structural Analysis

Application of the Value Chain and Jobs-to-be-Done frameworks reveals a fundamental shift in the industry structure:

• Value Chain Displacement: The primary value driver has shifted from physical atmosphere and location to logistics and digital discoverability. Inbound logistics and operations are now secondary to outbound delivery efficiency.
• Jobs-to-be-Done: The consumer job has changed from a social experience to a convenience-based caloric need. This reduces the necessity for high-street frontage and increases the value of proximity to residential clusters.
• Supply-Demand Mismatch: Geospatial visualization shows that while the Central Business District is oversupplied, residential areas in the North and West of Singapore have significant food deserts relative to the population density.

Strategic Options

Preliminary Recommendation

Pursue the Decentralized Cloud Kitchen model. The data indicates that demand in residential heartlands is structural, not temporary. By eliminating front-of-house costs and utilizing geospatial data to pick sites with the highest demand-to-supply ratio, firms can achieve profitability even with high delivery commissions. Speed of entry into these residential nodes is the primary competitive advantage.

3. Implementation Planning

Critical Path

• Phase 1: Data Integration (Weeks 1-3): Aggregate Urban Redevelopment Authority land-use data with National Environment Agency hawker center locations and population density metrics.
• Phase 2: Hotspot Identification (Weeks 4-5): Execute hexagonal binning analysis to identify cells with high residential density but low Food and Beverage outlet counts.
• Phase 3: Site Acquisition (Weeks 6-10): Secure short-term leases in industrial or secondary commercial spaces within identified hotspots.
• Phase 4: Digital Launch (Weeks 11-12): Onboard onto delivery platforms and initiate hyper-local digital marketing within a 3-kilometer radius of the new sites.

Key Constraints

• Regulatory Zoning: Singapore Urban Redevelopment Authority has strict rules on where commercial food preparation can occur; industrial spaces must be cleared for food use.
• Data Latency: Publicly available data often lags behind real-time market shifts; the plan requires manual validation of competitor presence in target zones.
• Labor Availability: The shift to residential areas may increase the difficulty of finding staff who previously commuted to central hubs.

Risk-Adjusted Implementation Strategy

The strategy assumes a 70 percent success rate for new sites. To mitigate failure, the firm will utilize modular kitchen equipment that can be relocated within 48 hours if a specific hexagonal cell underperforms. Contracts with delivery providers will be negotiated on a volume-incentive basis to protect margins during the initial 90-day ramp-up period.

4. Executive Review and BLUF

BLUF

Firms must immediately exit underperforming Central Business District locations and redeploy capital into a decentralized cloud kitchen network located in residential heartlands. Geospatial analysis of Singapore confirms that the shift in demand is not a temporary disruption but a structural realignment of the Food and Beverage market. By utilizing hexagonal binning to identify underserved residential cells, operators can reduce fixed costs by 40 percent while capturing the 20 percent growth in delivery demand. Execution must prioritize speed over brand perfection to secure first-mover advantages in high-density zones like Tampines and Woodlands. APPROVED FOR LEADERSHIP REVIEW.

Dangerous Assumption

The analysis assumes that Work From Home behaviors will remain sufficiently high to sustain residential demand post-pandemic. If corporate Singapore mandates a full return to office, the decentralized kitchens will face a stranded asset problem in low-traffic residential areas.

Unaddressed Risks

• Platform Dependency (High Probability, High Consequence): Delivery aggregators control the customer relationship and can increase commission rates at will, potentially erasing the cost savings gained from cheaper residential rents.
• Hyper-Competition (Medium Probability, Medium Consequence): As data visualization tools become democratized, multiple competitors will identify the same underserved hexagonal cells, leading to rapid oversupply and price wars in residential zones.

Unconsidered Alternative

The team did not evaluate a Mobile Food Truck strategy. In a volatile regulatory environment, mobile units would allow the firm to test different hexagonal cells without the 12-month commitment of a physical lease. This would provide the ultimate operational flexibility to follow demand as it fluctuates between residential and commercial districts.

MECE Analysis of Strategic Options

• Category 1: Location-Based Strategies
  • Stay in Central Business District (Status Quo)
  • Move to Residential Heartlands (Pivot)
• Category 2: Model-Based Strategies
  • Traditional Dine-in (High Opex)
  • Cloud Kitchen (Low Opex)
• Category 3: Revenue-Based Strategies
  • B2C Food Sales (Core)
  • B2B Data Consulting (Diversification)