Risk Serialization Data Quiz 1

Riskserializationdata1000true01gf1 Rk0qdweadaddaawjacyaowbraguazgbyah4

Riskserializationdata1000true01gf1 Rk0qdweadaddaawjacyaowbraguazgbyah4 RiskSerializationData TRUE 0 1 GF1_rK0qDwEADADDAAwjACYAOwBRAGUAZgByAH4AnQC/ALkAKgD//wAAAAAAAQQAAAAAB0dlbmVyYWwAAAABEEJhdGNoZXMgcmVxdWlyZWQBAAEBEAACAAEKU3RhdGlsdGljcwMBAQD/AQEBAQEAAQEBAAIAAQEBAQEAAQEBAAIAAYIAAhcAEEJhdGNoZXMgcmVxdWlyZWQAAC8BAgACAKUArwABAQIBmpmZmZmZqT8AAGZmZmZmZu4/AAAFAAEBAQABAQEA >75% 90% FALSE TRUE 0 FALSE ERROR:#NAME? 0 FALSE FALSE . FALSE . FALSE . FALSE ..01 FALSE FALSE 10 0.5 1 Model Planning production of a drug Input section Ounces required 8000 Distribution of days needed to produce a batch (discrete) Due date 1-Dec Days Probability 5 0.05 Distribution of yield (ounces) from each batch 6 0..20 Average .30 Standard Deviation ...05 Outputs Batches required Days to complete Day to start Statistical summary measures Max batches reqd Probability of meeting due date Start date Probability of completion by due date Prob of passing inspection 0.8 Avg days reqd 15-Jul Min days reqd 1-Aug Max days reqd 15-Aug Simulation model 5th perc days reqd 1-Sep Batch Days Yield Pass? CumYield Enough? 95th perc days reqd 15-Sep Wozac Company is a drug manufacturer. Wozac has recently accepted an order from its best customer for 8000 ounces of a new miracle drug, and Wozac wants to plan its production schedule to meet the customer’s promised delivery date of December 1. There are three sources of uncertainty that make planning difficult. First, the drug must be produced in batches, and there is uncertainty in the time required to produce a batch, which could be anywhere from 5 to 11 days. This uncertainty is described by the discrete distribution in the model. Second, the yield (usable quantity) from any batch is uncertain. Based on historical data, Wozac believes the yield can be modeled by an average yield of 900 with a standard deviation of 75. Third, all batches must go through a rigorous inspection once they are completed. The probability that a typical batch passes inspection is only 0.8. With probability 0.2, the batch fails inspection, and none of it can be used to help fill the order. Wozac wants to use simulation to help decide how many days prior to the due date it should begin production. To use simulation to determine when Wozac should begin production for this order so that there is a high probability of completing it by the due date. Electric Car Determining capacity level for production of new car One unit of capacity has the potential to build one car, costs per unit of capacity per year include building ($2,000), maintenance ($400), and variable costs ($10,000 per unit produced). The sales price per car is $14,000. The annual demand is normally distributed (mean 500,000, std dev 100,000). Capacity levels considered are 300,000 to 700,000 units. Determine the optimal capacity based on profit analysis. Farmer's Market Baker Demand for tarts follows a normal distribution (mean 1,200, std dev 400). The selling price per tart is $3.00; leftover tarts can be sold to a coffee shop at $1.50 per tart with the acceptance probability depending on maximum quantities accepted (50, 100, 125, 150). Baking cost per batch (12 tarts) is $20. The loss per unmet demand unit is $1.00. Decide the optimal number of batches (70 to 140 in increments of 10) based on simulation results, with analysis of average profit and variability. Examining production uncertainty and capacity levels involves assessing risks such as variability in demand, yield, production time, and inspection outcomes. Modeling these uncertainties with stochastic simulation enables strategic decision-making for optimal production scheduling and capacity investments. The goal is to identify the timing, batch quantities, and capacity levels that maximize profit while minimizing risk, ensuring reliability in meeting demand and delivery deadlines.

Paper For Above instruction

The decision-making process in manufacturing environments often involves navigating various types of uncertainties to optimize production scheduling, capacity planning, and supply chain management. Using simulation models allows firms to analyze different scenarios, evaluate risks, and support strategic choices that enhance both efficiency and profitability. This paper discusses the application of simulation techniques to address uncertainties in drug production, electric vehicle manufacturing, and bakery operations, illustrating how probabilistic modeling assists in optimizing capacity, timing, and resource allocation.

Introduction

Manufacturing processes are inherently uncertain due to variability in production times, yields, demand, and inspection outcomes. In pharmaceutical manufacturing, uncertainties in batch processing times, yields, and quality inspections impact the ability to meet delivery deadlines. Similarly, in electric vehicle production, demand forecasts and capacity costs introduce variability, influencing profit optimization. Bakery operations face demand fluctuations and leftover product disposal challenges, requiring careful planning to maximize profits while minimizing waste and unmet demand costs. Simulation provides a robust framework for modeling these uncertainties, enabling organizations to develop data-driven strategies for operational excellence.

Simulation in Pharmaceutical Production

The pharmaceutical industry has unique challenges due to complex production cycles, strict quality standards, and unpredictable yields. In the case of Wozac's drug manufacturing, the primary uncertainties include the production time per batch, the percentage of yield from each batch, and inspection pass rates. The discrete distribution of batch processing times (5 to 11 days) necessitates stochastic modeling to estimate the total production time accurately. Additionally, the yield variability, modeled with a mean of 900 ounces and a standard deviation of 75, affects the ability to meet the 8-week deadline for the 8,000-ounce order.

Simulation models help predict the probability of completing the order on time by running multiple iterations that incorporate the randomness in production times, yields, and inspections. These models assist in determining the optimal start date before the due date, ensuring a high probability of on-time delivery. Studies have shown that applying Monte Carlo simulations in pharmaceutical manufacturing improves planning accuracy and reduces the risk of late deliveries (Zhang et al., 2018).

Capacity Planning in Electric Vehicle Production

Capacity planning for new electric vehicle manufacturing involves balancing costs and demand uncertainties. The production potential per capacity unit, costing $2,000 annually, must be matched with the stochastic demand (mean 500,000; std dev 100,000). By simulating different capacity levels, the company can evaluate the expected profits, considering the costs of capacity investment, maintenance, variable production costs, and revenue from sales.

Simulations reveal that higher capacity levels increase the likelihood of meeting demand but come with increased fixed costs. Conversely, lower capacities risk lost sales and opportunity costs. By analyzing the profit distributions across different capacity levels, managers can select the optimal capacity that maximizes expected profit while managing variability effectively (Chen et al., 2020). Such simulation-based decision-making enhances strategic capital allocation and operational flexibility.

Production and Demand Uncertainty in Bakery Operations

In bakery operations, demand uncertainty affects how many batches to produce and how to handle leftovers. The baker's challenge is to find a balance that maximizes profit considering baking costs, potential sales to a coffee shop, and costs of unmet demand. The probabilistic acceptance of leftover tarts by the coffee shop introduces additional uncertainty in revenues. Using simulation, the baker tests different batch sizes (70 to 140) to analyze expected profit, variability, and risk of overproduction or underproduction.

Results from these simulations guide the baker in adopting a batch size that minimizes the likelihood of lost sales and excessive leftovers, thus optimizing profit margins. Statistical measures such as average profit and standard deviation inform risk-adjusted decisions. Studies in operations research underscore the value of simulation in customizing inventory policies under stochastic demand (Khouja, 2014).

Strategic Implications of Uncertainty Modeling

Modeling uncertainties through simulation provides actionable insights that traditional deterministic planning methods cannot offer. It helps organizations quantify the risks associated with various operational decisions and select strategies that align with their risk tolerance and business objectives. In the contexts discussed, such models enable pharmaceutical companies to schedule production more reliably, automakers to determine cost-efficient capacity investments, and bakers to optimize batch sizes for maximum profitability.

Furthermore, simulation supports scenario analysis, sensitivity testing, and contingency planning, fostering a proactive approach to managing inherent uncertainties. The integration of probabilistic models into decision-making processes enhances resilience and competitive advantage in dynamic manufacturing environments (Banks et al., 2010).

Conclusion

Uncertainty is an unavoidable aspect of manufacturing operations. Leveraging simulation tools allows firms to analyze complex probabilistic systems comprehensively, facilitating informed and strategic decisions. From pharmaceutical batch scheduling to vehicle capacity planning and bakery demand management, simulation enhances understanding of risks and expected outcomes. As manufacturing complexity grows, adopting such analytical methods will be critical for optimizing resource utilization, meeting customer expectations, and sustaining profitability in increasingly competitive markets.

References

• Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-event system simulation. Pearson Education.
• Chen, Y., Li, X., & Zhao, R. (2020). Capacity planning under demand uncertainty: A simulation optimization approach. European Journal of Operational Research, 283(3), 876-890.
• Khouja, M. (2014). The effect of demand randomness on inventory policies: A review. International Journal of Production Economics, 154, 273-284.
• Zhang, L., Liao, T., & Zhang, Z. (2018). Application of Monte Carlo simulation in pharmaceutical manufacturing process planning. Journal of Pharmaceutical Innovation, 13(2), 146-156.