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1 – 3 of 3Risk managers have more tools than ever to help protect their companies from risk. Complex financial instruments, intricate mathematical models, and access to massive amounts of…
Abstract
Risk managers have more tools than ever to help protect their companies from risk. Complex financial instruments, intricate mathematical models, and access to massive amounts of data can help the risk manager structure a multifaceted strategy to decrease volatility and protect the company from a catastrophic event. However, these tools have their own risks that can complicate a risk manager's job.
Analyzing corn price volatility helps students understand four best practices for risk managers, regardless of the specific risks they face or the strategies they employ: quantify the company's exposure; understand the nature of the risk; understand how the hedge works in practice; and separate hedging and speculation.
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Mohanbir Sawhney, Lisa Damkroger, Greg McGuirk, Julie Milbratz and John Rountree
Illinois Superconductor Corp. a technology start-up, came up with an innovative new superconducting filter for use in cellular base stations. It needed to estimate the demand for…
Abstract
Illinois Superconductor Corp. a technology start-up, came up with an innovative new superconducting filter for use in cellular base stations. It needed to estimate the demand for its filters. The manager came up with a simple chain-ratio-based forecasting model that, while simple and intuitive, was too simplistic. The company had also commissioned a research firm to develop a model-based forecast. The model-based forecast used diffusion modeling, analogy-based forecasting, and conjoint analysis to create a forecast that incorporated customer preferences, diffusion effects, and competitive dynamics.
To use the data to generate a model-based forecast and to reconcile the model-based forecast with the manager's forecast. Requires sophisticated spreadsheet modeling and the application of advanced forecasting techniques.
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Raj V. Amonkar, Tuhin Sengupta and Debasis Patnaik
The learning outcomes of this paper are as follows: to understand the context of seaport logistics and supply chain design structure, to apply Monte Carlo simulation in the…
Abstract
Learning outcomes
The learning outcomes of this paper are as follows: to understand the context of seaport logistics and supply chain design structure, to apply Monte Carlo simulation in the interface of the supply chain and to analyze the Monte Carlo simulation algorithm and statistical techniques for identifying the key seaport logistics factors.
Case overview/synopsis
It was 9:00 p.m. on November 10, 2020, and Nishadh Amonkar, the CEO of OCTO supply chain management (SCM) was glued to the television watching the final cricket match of the Indian Premier League, 2020. Amonkar’s mobile phone rang and it was a call from Vinod Nair, a member Logistics Panel of Ranji Industries Federation. Nair informed Amonkar that it was related to the rejection of several export consignments of agricultural products from Ranji (in the western part of India). The rejection was due to the deterioration in the quality of the exported agricultural products during transit from Ranji to various locations in Europe.
Complexity academic level
This course is suitable at the MBA level for the following courses: Operations research (Focus/Session: Applications on Monte Carlo Simulation). SCM (Focus/Session: Global SCM, Logistics Planning, Distribution Network). Logistics management (Focus/Session: Transportation Planning). Business statistics (Focus/Session: Application of Hypothesis Testing).
Supplementary materials
Teaching Notes are available for educators only.
Subject code
CSS 9: Operations and logistics.
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