McDonalds New Equipment Evaluation - All Day Breakfast

Introduction to the Conference

Opening Remarks

• The speaker expresses honor in opening the user conference, mentioning colleagues from HAVI and a last-minute cancellation of Mike Kramer from McDonald's.

• Emphasizes the exciting advancements in simulation technology, highlighting its evolution over 40-50 years and current tools like Apache Spark and predictive analytics.

Overview of HAVI

• Brief introduction to HAVI as a privately held global company with a focus on supply chain management and packaging services.

• Notes that HAVI has been a silent partner to McDonald's since its inception, providing distribution and packaging services.

McDonald's Innovation Journey

Historical Context

• Challenges the perception that only tech companies are innovative by presenting McDonald's as a leader in quick service restaurants.

• Discusses notable innovations by McDonald's such as Happy Meals, coffee stations, value menus, and all-day breakfast.

All-Day Breakfast Launch

• Highlights customer demand for all-day breakfast leading to its launch about a year ago; it was based on significant customer feedback.

• Mentions the growth of McDonald’s alongside HAVI's partnership throughout their journey.

Challenges in Implementing Innovations

Complexity of Promotions

• Discusses complexities faced when introducing new products like Mighty Wings, which required extensive lead time due to supply chain logistics.

• Shares an anecdote about needing 12 months lead time for chicken suppliers during the Mighty Wings promotion.

Supply Chain Issues

• Examines challenges related to launching all-day breakfast, particularly focusing on ingredient availability (e.g., eggs during an avian flu outbreak).

Strategic Changes at Scale

System-Wide Changes

• Explores what happens when large companies like McDonald's introduce fundamental changes requiring extensive planning and decision-making.

Example: Introduction of McCafe

• Provides an example of introducing the McCafe line in 2001 as a significant investment with uncertain returns due to regional demand variations.

Challenges in Supply Chain Modeling

Variability in Supply Chain Elements

• Each supply chain is unique, with variations in equipment and demand patterns, complicating modeling efforts.

• Modelers face challenges due to differing regional preferences and operational footprints among facilities.

Planning and Forecasting Solutions

• Hobby provides essential planning and forecasting solutions that form the foundation of their supply chain management offerings.

• Services include cost containment strategies within supply chains, marketing analytics, and promotional analytics that enhance simulation studies.

Importance of a Disciplined Framework

• Successful data modeling requires more than just software or skilled analysts; it necessitates a disciplined framework for problem-solving.

• The process begins with forming a hypothesis followed by data validation before running models to obtain results.

Iterative Process in Problem Solving

• After obtaining initial results from simulations, business validation is crucial to ensure models align with real-world constraints.

• This iterative approach may require refining or completely revising hypotheses based on feedback from stakeholders.

Case Study: McDonald's All-Day Breakfast Simulation

Launch Challenges

• McDonald's faced significant challenges when launching all-day breakfast due to outdated kitchen designs not accommodating both breakfast and lunch demands effectively.

• Regional preferences influenced the rollout strategy; for instance, biscuit popularity in the South contrasted with other regions' preferences for Egg McMuffins.

Success Metrics

• The initiative reportedly boosted same-store sales by 3% to 4%, indicating strong market acceptance of the all-day breakfast concept.

Expansion Considerations

• Following initial success, McDonald's aimed to expand their breakfast menu but encountered logistical complexities related to cooking space and inventory management.

Simulation as a Solution

• To address these complexities, Hobby collaborated with McDonald's to simulate kitchen operations, focusing on equipment needs and labor adjustments necessary for successful implementation.

McDonald's Operational Modeling Insights

Overview of the Model

• The operational model focuses on equipment and labor as primary decision variables, allowing for drag-and-drop adjustments to optimize layout and capacity.

• The complexity of the model includes approximately 50 parameters and six data tables, resulting in millions of permutations to analyze.

Customer Satisfaction Metrics

• Key performance indicators include average wait time, freshness of food (e.g., seconds off the grill), and waste production, which are critical objectives to monitor.

Physical Testing in Test Kitchens

• McDonald's has three physical test kitchens that were utilized for baseline scenario testing to calibrate the operational model effectively. This included ensuring inventory levels matched real-world conditions.

• The validation process involved running scenarios over four days, assessing various equipment configurations against customer service metrics. If a configuration was impractical in a test kitchen, it was deemed unfeasible for implementation.

Cost Efficiency through Simulation

• While physical testing is costly (thousands of dollars for a week), simulation allows for faster iterations and more extensive scenario analysis without significant financial burden. This efficiency highlights the value of simulation modeling in operational planning.

• Recommendations from simulations led to identifying substantial capital expenditure savings by optimizing kitchen layouts and equipment choices across multiple locations (14,000 units).

Future Opportunities with Simulation

• There are opportunities for pedestrian flow modeling within restaurants; different tile colors indicate high traffic areas based on customer movement patterns. Additionally, inventory modeling at store levels can enhance efficiency further.

• Menu analysis using simulation can assess impacts like cannibalization when introducing new items (e.g., all-day breakfast) on existing sales figures such as Big Macs. This type of analysis is well-suited for simulation techniques.

Integration with Supply Chain Logistics

• The introduction of logistics into restaurant models enables optimization in delivery routes and overall supply chain management—an exciting frontier for operational efficiency in fast-food settings.

Data Utilization Trends

• Increasing availability of data—including video footage and sensor information—allows restaurants to tailor offerings based on individual customer preferences through apps that provide targeted promotions similar to e-commerce platforms like Amazon.

Challenges Ahead

• As more tailored models emerge due to increased data availability, there will be a need for balancing numerous consumer-specific models while maintaining effective operations across various segments within the industry.