# Simulation Exercise: Littlefield Technologies + venture pitch **Duration:** August 6 (9:00 AM) - August 8 (8:30 AM) **Simulation URL:** opy.responsive.net/lf/farias/ **Report Due:** August 14 **Assignment Type:** Team (Graded) ---- i was proud that alexandra, gary, kwame, dereck's team nailed both simulation and venture pitch - their kpi for "revenue per job" was interesting - proactive planning beats reactive decision making Marco's Q: preanalysis vs trying new things - contract 2 would be better (lead time and prioritization) - there were experimentation phase (machine purchase (dropping to 40k to 900)) - someone didn't sleep (no monitor 4hr block) ### team 2 day of demand (500k), 1.2k kits (1.2m); more inventory rather than loosing demand (total revenue 384m) gpu capacity for token ----- ## Learning Objectives - Apply operations management concepts in dynamic environment - Experience capacity, inventory, and lead-time trade-offs - Practice quantitative decision-making under uncertainty - Understand systems thinking and operational interactions ## Simulation Overview ### Littlefield Technologies Business - **Product:** Digital Satellite System (DSS) receivers - **Market:** Growing demand with uncertain patterns - **Operations:** 3-station production line - **Challenges:** Capacity planning, inventory management, lead-time control - **Competition:** Multiple teams managing similar factories ### Factory Configuration **Production Stations:** 1. **Station 1:** Stuffing (circuit board assembly) 2. **Station 2:** Testing (quality control and calibration) 3. **Station 3:** Tuning (final assembly and packaging) **Key Resources:** - **Machines:** Capacity at each station - **Inventory:** Raw materials and work-in-process - **Cash:** Available for machine purchases - **Contracts:** Customer orders with lead-time requirements ## Simulation Mechanics ### Timeline and Access - **Preparation Phase:** August 1 onwards (factory analysis and planning) - **Live Simulation:** August 6 (9:00 AM) - August 8 (8:30 AM) - **Decision Windows:** Regular opportunities to make changes - **Real-Time Data:** Continuous updates on performance ### Decision Variables 1. **Machine Purchases:** Add capacity at bottleneck stations 2. **Inventory Policy:** Set (R,Q) parameters for materials 3. **Contract Selection:** Choose between different lead-time contracts 4. **Machine Sales:** Reduce capacity if demand drops ### Performance Metrics - **Revenue:** From completed orders - **Costs:** Machine purchases, inventory holding, expediting - **Lead Times:** Delivery performance - **Utilization:** Efficiency of resources - **Cash Flow:** Available funds for investments ## Required Readings - **Simulation Overview:** LITTLEFIELD TECHNOLOGIES: OVERVIEW - **Management Guide:** MANAGING CAPACITY, INVENTORY AND LEAD-TIME AT LITTLEFIELD TECHNOLOGIES ## Strategic Considerations ### Demand Analysis - **Pattern Recognition:** Identify growth trends and seasonality - **Forecasting:** Predict future demand levels - **Capacity Planning:** Match resources to demand - **Lead-Time Implications:** Balance service level vs. cost ### Bottleneck Management - **Identification:** Find the constraining resource - **Capacity Expansion:** When and where to add machines - **Load Balancing:** Distribute work evenly - **Flexibility:** Adapt to changing bottlenecks ### Inventory Optimization - **Raw Materials:** Buffer against supply uncertainty - **Work-in-Process:** Balance between stations - **Safety Stock:** Service level protection - **Holding Costs:** Minimize inventory investment ### Contract Strategy - **Lead-Time Pricing:** Shorter lead-times command higher prices - **Capacity Utilization:** Match commitments to capabilities - **Risk Management:** Balance revenue vs. penalty risk - **Market Positioning:** Competitive differentiation ## Graded Simulation Report Questions Students must submit a 4-page report (excluding appendices) addressing: ### Question 1: Demand Forecasting **Prompt:** How did you forecast demand? For what decisions in the game did you find it most useful to have a demand forecast available? Ex-post, were you happy with your demand forecasting technique and would you use the same one if the game was to start over again? **Analysis Framework:** - **Forecasting Methods:** Moving averages, exponential smoothing, trend analysis - **Decision Applications:** Capacity planning, inventory setting, contract selection - **Performance Evaluation:** Accuracy assessment and lessons learned - **Improvement Opportunities:** What would you do differently? ### Question 2: Capacity Decision Process **Prompt:** What models and/or considerations did you use to decide how many machines of each type to buy initially? Later on during the simulation, how did you decide how many more machines to buy or sell? Ex-post, were you happy with your capacity decision process and would you use the same one if the game was to start over again? **Analysis Framework:** - **Initial Decisions:** Pre-simulation capacity planning - **Dynamic Adjustments:** Response to demand changes and bottlenecks - **Decision Models:** Quantitative vs. intuitive approaches - **Performance Assessment:** Effectiveness of capacity management ### Question 3: Inventory Replenishment Policy **Prompt:** How did you decide on the parameters of your (R,Q) inventory replenishment policy? How did you update these parameters over time? Ex-post, were you happy with your inventory model and would you use the same one if the game was to start over again? **Analysis Framework:** - **Parameter Setting:** Initial R (reorder point) and Q (order quantity) decisions - **Dynamic Updates:** How parameters changed during simulation - **Model Selection:** EOQ, newsvendor, or other approaches - **Performance Review:** Inventory costs vs. service levels achieved ### Question 4: Contract Type Selection **Prompt:** How did you decide initially and later in the game what type of contract to go after? Ex-post, were you happy with your method/model for quoting lead-times and would you use the same one if the game was to start over again? **Analysis Framework:** - **Initial Strategy:** Early contract type selection logic - **Dynamic Strategy:** Adaptation to changing conditions - **Lead-Time Modeling:** How you estimated delivery capabilities - **Revenue Optimization:** Balance between price and delivery risk ### Question 5: Factory Performance Analysis **Prompt:** Describe your factory's performance during the simulation, both in absolute and relative terms, and provide an interpretation. Is there anything not already mentioned in any of your previous answers which you would do in order to improve your performance if you were to play the game a second time? **Analysis Framework:** - **Absolute Performance:** Revenue, costs, utilization, service levels - **Relative Performance:** Ranking vs. other teams - **Performance Drivers:** Key factors explaining results - **Additional Improvements:** New insights and strategies ### Question 6: Key Lessons and Insights **Prompt:** What are the most important lessons you learned or insights you gained from playing this simulation game? **Reflection Areas:** - **Operational Concepts:** How theory applies in practice - **Decision-Making:** Balancing multiple objectives - **Team Dynamics:** Coordination and communication - **Strategic Thinking:** Long-term vs. short-term trade-offs ## Simulation Success Factors ### Preparation and Planning - **Early Access:** Use August 1-5 to analyze and plan - **Quantitative Models:** Develop decision frameworks before going live - **Team Coordination:** Clear roles and communication protocols - **Scenario Planning:** Prepare for different demand patterns ### Execution Excellence - **Regular Monitoring:** Frequent check-ins during live period - **Quick Decision-Making:** Rapid response to changing conditions - **Data Analysis:** Use simulation data to guide decisions - **Adaptive Strategy:** Adjust approach based on results ### Common Pitfalls to Avoid - **Over-Investment:** Buying too much capacity too early - **Under-Investment:** Being slow to respond to demand growth - **Inventory Mismanagement:** Wrong safety stock levels - **Lead-Time Overcommitment:** Promising unrealistic delivery times ## Team Organization ### Recommended Roles - **Demand Analyst:** Forecasting and market analysis - **Capacity Manager:** Machine purchase/sale decisions - **Inventory Controller:** Material replenishment policies - **Contract Manager:** Lead-time and pricing decisions - **Performance Monitor:** Track metrics and coordination ### Communication Protocol - **Regular Check-ins:** Scheduled team meetings - **Decision Authority:** Clear escalation procedures - **Documentation:** Record decisions and rationale - **Learning:** Capture insights throughout simulation ## Simulation Debrief Process ### Performance Analysis - **Financial Results:** Revenue, costs, and profitability - **Operational Metrics:** Utilization, lead-times, service levels - **Competitive Position:** Ranking and relative performance - **Decision Quality:** Effectiveness of strategies ### Learning Integration - **Concept Application:** How course concepts were used - **Practical Insights:** Real-world applicability - **Team Dynamics:** Collaboration effectiveness - **Strategic Thinking:** Decision-making process improvement ## Key Takeaways from Simulation - **Systems Thinking:** Operations are interconnected and dynamic - **Trade-off Management:** Balancing multiple competing objectives - **Decision Under Uncertainty:** Making choices with incomplete information - **Quantitative Analysis:** Value of models and data-driven decisions - **Continuous Improvement:** Learning and adapting over time ## Preparation for Report - **Data Collection:** Gather performance metrics throughout simulation - **Decision Documentation:** Record rationale for major decisions - **Performance Tracking:** Monitor both absolute and relative results - **Reflection Notes:** Capture insights and lessons learned real-time ## Teaching Notes - Emphasize advance planning and quantitative modeling - Encourage teams to develop decision frameworks before going live - Highlight the value of continuous monitoring and adaptation - Connect simulation experiences to course concepts and real-world applications