Part III Prescriptive Analytics And Big Data Model Of The De

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Part III discusses the application of prescriptive analytics and big data models to decision problems, emphasizing methods for handling uncertainty through sampling experiments, Monte Carlo simulations, and discrete event simulations. It explains the process of estimating uncertain parameters based on past data, generating random samples, and analyzing their statistical distributions. The section highlights key software tools such as @RISK and Crystal Ball for Monte Carlo simulations and discusses their applications across industries, including risk management, capacity planning, and operational optimization.

Additionally, the content introduces discrete event simulation, focusing on modeling interactions within systems like supply chains, queues, and production processes. An example case study of Cosan, a major Brazilian conglomerate involved in sugar and ethanol production, illustrates the use of simulation software (Simio) to optimize logistics and mitigate risks. The case demonstrates how simulation enables visualization of operational bottlenecks, assessment of supply chain disruptions, and the formulation of strategic improvements. It emphasizes that such simulation approaches are valuable when traditional optimization models are difficult to implement, especially for complex, stochastic, and dynamic systems.

The section concludes with review questions on the characteristics, advantages, disadvantages, and steps involved in simulation modeling, preparing students for practical application of simulation techniques in various contexts and industries.

Paper For Above instruction

Prescriptive analytics combined with big data modeling plays a crucial role in solving complex decision problems in modern business environments. One of the foundational methods in managing uncertainty within these models is Monte Carlo simulation, which involves running a model numerous times with random samples of uncertain parameters to understand the range of possible outcomes and their probabilities. This approach has found extensive application across industries such as finance, manufacturing, utilities, and supply chain management.

Monte Carlo simulation operates on the principle of probabilistic sampling, where estimates for uncertain variables are generated based on historical data or assumed distributions. When decisions involve significant uncertainty, such as market fluctuations or operational variability, Monte Carlo provides insights into the potential risks and behaviors of the system under different scenarios. For instance, Procter & Gamble uses such models to hedge foreign exchange risks, while in the healthcare sector, Monte Carlo simulations assist in predicting patient flow and resource needs.

Commercial software tools like @RISK (by Palisade) and Oracle Crystal Ball have simplified the process by providing user-friendly interfaces for model development, input distribution specification, and result analysis. These tools enable decision-makers to perform sensitivity analyses, identify critical variables, and quantify risks. Despite their advantages, a limitation is that Monte Carlo simulations can be computationally intensive, especially for large-scale models with numerous uncertain variables. Nonetheless, advances in computational power have made these simulations more accessible and practical for decision support.

Discrete event simulation (DES) complements Monte Carlo by allowing detailed modeling of systems where entities (e.g., customers, parts, vehicles) interact over time. DES is particularly effective in analyzing queuing systems, manufacturing processes, and supply chains by capturing the stochastic nature of arrivals, service times, and resource availability. The primary advantage of DES is its ability to visualize dynamic interactions and pinpoint bottlenecks or inefficiencies.

The case study of Cosan exemplifies the application of simulation in large-scale agricultural logistics. As a company managing extensive sugar, ethanol, and sugarcane farms, Cosan faces complex supply chain challenges that include optimizing fleet size, increasing mill capacity, and identifying operational bottlenecks. Using Simio software, Cosan modeled their supply chain over a simulated 240-day production season, incorporating detailed lab data and operational parameters.

The simulation provided insights into operational risks, enabling the logistics team to evaluate different scenarios and identify critical bottlenecks such as vehicle capacity constraints and processing delays at mills. The model captured variability in variable inputs and generated outputs like transportation times, queue lengths, and throughput rates. It became evident that strategic adjustments, supported by simulation results, could save the company over $500,000 by reducing fleet size, optimizing routing, and addressing capacity constraints.

This case underscores how simulation offers a practical approach to managing uncertainty and improving operational decision-making when traditional optimization proves too complex or infeasible. The visual and interactive nature of discrete event models helps managers understand the impacts of variability and make data-driven decisions to enhance efficiency and resilience.

In summary, prescriptive analytics, supported by Monte Carlo simulations and discrete event modeling, provide powerful tools for managing uncertainty and optimizing complex systems. Adoption of such methods helps organizations reduce risks, improve operational efficiency, and support strategic decision-making, thus giving them a competitive edge in a dynamic environment.

References

• Palisade Corporation. (2020). @RISK Risk Analysis and Monte Carlo Simulation. Retrieved from https://palisa.de.com
• Oracle. (2019). Crystal Ball Forecasting and Optimization. Oracle Corporation.
• Law, A. M., & Kelton, W. D. (2007). Simulation Modeling and Analysis. McGraw-Hill Education.
• Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation. Pearson Education.
• Fishman, G. S. (2001). Discrete-Event Simulation: Modeling, Programming, and Analysis. Springer Science & Business Media.
• Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill Education.
• Kleijnen, J. P. (1998). An Overview of the Design and Analysis of Simulation Experiments. European Journal of Operational Research, 98(1), 1–21.
• Christou, I. T., & Stavropoulos, T. G. (2018). Simulation in Supply Chain Management: State of the Art and Future Challenges. International Journal of Production Research, 56(1-2), 1–3.
• Fowler, J. W. (2021). Applications of Discrete Event Simulation in Manufacturing and Logistics. Journal of Manufacturing Systems, 58, 347–358.
• Hopp, W. J., & Spearman, M. L. (2011). Factory Physics. Waveland Press.