AB InBev: Brewing Up Forecasts during COVID-19 Custom Case Solution & Analysis

1. Evidence Brief: AB InBev Forecasting and COVID-19 Disruption

Financial Metrics

• Global revenue exceeded 52 billion USD in the year preceding the crisis.
• On-trade channel (bars and restaurants) accounted for approximately 30 percent of global volume but represented a higher margin profile due to premium brand concentration.
• Marketing spend typically averaged 14 percent of revenue, now requiring immediate reallocation.
• Cost of Goods Sold (COGS) spiked due to aluminum price volatility and logistics premiums.

Operational Facts

• The company operates 260 breweries across 50 countries with a portfolio of 500 brands.
• Forecasting cycles historically operated on an 18-month rolling window with monthly updates.
• The supply chain faced a sudden shift from kegged beer to canned and bottled products, creating a 20 percent shortfall in aluminum can supply.
• Brewery utilization rates dropped below 60 percent in regions with strict lockdowns, while logistics costs increased by 15 percent due to empty backhauls.
• Data processing for demand signals previously relied on 3-year historical averages which became irrelevant within 14 days of the global lockdown.

Stakeholder Positions

• Global Supply Chain VP: Demands immediate SKU rationalization to stabilize production lines.
• Chief Sales Officer: Opposes SKU reduction, fearing permanent loss of shelf space to local competitors.
• Data Science Team: Proposes a move toward high-frequency, external-signal-based modeling.
• Zone Presidents: Require autonomy to bypass global forecasting protocols to react to local government mandates.

Information Gaps

• Exact inventory obsolescence rates for perishable kegged beer in transit during the initial lockdown.
• Real-time consumer pantry-loading behavior vs. long-term consumption growth.
• Granular government reopening timelines for the on-trade sector in emerging markets.

2. Strategic Analysis: From Prediction to Sensing

Core Strategic Question

• How can AB InBev replace obsolete historical forecasting models with a real-time demand-sensing architecture to manage extreme channel volatility?

Structural Analysis

The Value Chain analysis reveals that the primary bottleneck is not brewing capacity but packaging and distribution flexibility. The shift from on-trade to off-trade destroyed the traditional mix. Using the Jobs-to-be-Done lens, consumer behavior shifted from social experience (bars) to home-based stress relief and stocking. This change rendered the 18-month planning cycle a liability rather than an asset.

Strategic Options

Option 1: External Signal Integration (Demand Sensing)

• Rationale: Use mobility data, infection rates, and retail depletion data to predict weekly demand.
• Trade-offs: High IT investment and risk of over-reacting to noise in the data.
• Resources: Data scientists and API integrations with third-party providers.

Option 2: Radical SKU Rationalization

• Rationale: Cut the bottom 40 percent of low-volume SKUs to maximize throughput of core brands like Budweiser and Stella Artois.
• Trade-offs: Reduced consumer choice and potential brand equity erosion in niche segments.
• Resources: Procurement and manufacturing reconfiguration.

Preliminary Recommendation

Pursue Option 1. AB InBev possesses the scale to dominate retail if it can solve the inventory placement problem. Relying on SKU cuts alone ignores the fundamental problem: the inability to see the market in real-time. Implementing a demand-sensing model allows the company to reallocate marketing and supply before competitors can react.

3. Implementation Roadmap: 90-Day Execution Plan

Critical Path

• Week 1-2: Establish a Centralized Nerve Center to bypass regional reporting delays.
• Week 3-6: Integrate Google Mobility and epidemiologic data into the existing ERP system.
• Week 7-10: Pilot the sensing model in two high-volatility zones: Brazil and the United States.
• Week 11-12: Full global rollout and transition to weekly S&OP (Sales and Operations Planning) cycles.

Key Constraints

• Data Latency: Retailer data in emerging markets often lags by 30 days, making real-time sensing difficult.
• Packaging Flexibility: Converting lines from kegs to cans requires physical hardware changes that take 4-6 months.

Risk-Adjusted Implementation Strategy

The plan assumes a 70 percent accuracy rate for the new model. To mitigate failure, maintain a 15 percent safety stock of core SKUs in regional distribution centers. This buffer protects against model errors while the system learns from new pandemic-era patterns.

4. Executive Review and BLUF

BLUF

AB InBev must immediately abandon historical-average forecasting. The pandemic has invalidated the last three years of data. Success depends on transitioning to a demand-sensing model that uses external signals—mobility, health data, and retail scans—to drive weekly production schedules. The company should prioritize inventory availability for core brands in the off-trade channel while aggressively pruning slow-moving SKUs that clog the supply chain. Speed of reaction is now a greater competitive advantage than forecast precision.

Dangerous Assumption

The analysis assumes that the shift to off-trade (home consumption) is largely additive. If home consumption does not offset the total loss of bar and restaurant volume, the company will face a massive stranded cost problem in its draft beer infrastructure that no amount of data sensing can solve.

Unaddressed Risks

• Regulatory Risk: High probability. Sudden government bans on alcohol sales (as seen in South Africa and Mexico) render demand sensing useless.
• Input Cost Inflation: Medium probability. The focus on forecasting demand ignores the supply-side shock of rising aluminum and grain prices, which could compress margins even if volume targets are met.

Unconsidered Alternative

The team did not evaluate a Direct-to-Consumer (DTC) bypass. In markets with rigid tier-distribution systems, AB InBev could have utilized its own delivery platforms—like Ze Delivery in Brazil—not just as a sales channel, but as the primary source of real-time consumer data to feed the global forecasting engine.

Verdict

APPROVED FOR LEADERSHIP REVIEW