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Question: Brainstorm with your group and qualitatively describe at least two different approaches to forecasting future demand. Briefly explain the advantages, limitations, and any assumptions behind each approach. What data would you need for each forecasting method, and who would you have to engage with to collect this data?

11 Oct 2022,9:07 PM

 

Part 1: Double Marginalization (15 points)
Double marginalization refers to the phenomenon where even if every firm in a supply chain chooses actions to maximize its own expected profit, the total profit earned in the supply chain may be less than the entire supply chain’s maximum profit. In other words, rational and self-optimizing behavior by each firm of the supply chain does not lead to optimal supply chain performance.

In even simpler terms, double marginalization occurs when firms try to increase their “piece” of the supply chain’s “profit pie” and, as a result, the total size of the “pie” decreases. We discussed how contracts such as buy-back contracts and revenue-sharing contracts could mitigate this effect by “shifting” costs and risks around the supply chain (and, as a result, increase the size of the “pie”).

For this question, please identify a phenomenon similar to double marginalization from your own personal life, working experience, or that you observed in practice. Namely, identify and describe a case where two people or companies act in a rational self-interested manner and, as a result, obtain an outcome that is worse than if they coordinated and shared risks or costs. Then, discuss how a different kind of contract or coordination mechanism could mitigate double marginalization in this case. How is this contract or coordination mechanism shifting risks and costs? How is it making the "size of the profit pie" grow? (A qualitative discussion suffices – no need to give numbers).

Part 2: Qualitative Forecasting (30 points)
After graduating from Georgia Tech, you have accepted the position of Demand Planning Analyst in the Retail Fulfillment Operations team at Apple. One of your most exciting tasks is to estimate the demand for new products being launched in the near future. This task is challenging because, due to long lead times and manufacturing planning, Apple's management must make these forecasts well in advance, sometimes even a year before the launch date.

Yesterday, you were assigned to the team responsible for Apple’s next-generation phone, the iPhone Z. Your task is to forecast how many iPhone Z units will sell globally during the first year after launch. The launch date is nine months from now, but you must make the final forecast soon. Once the forecast has been made, the company places one large order to its suppliers. After the order is placed, you cannot update the order quantity.

Your task is certainly not an easy one, and some “out-of-the-box” thinking is required. This is an open-ended exercise and is an opportunity to be creative and to think about different forecasting methods.

Note: The goal of this exercise is to promote discussion within your group and quantitative thinking at a high-level. No need to source data or be very specific. A few paragraphs (or slides) for each question will suffice.

Question 1 (10 points)

Brainstorm with your group and qualitatively describe at least two different approaches to forecasting the future demand (if possible, base your answers on the methods described in class). Briefly explain the advantages, limitations, and any assumptions behind each approach. What data would you need for each forecasting method, and who would you have to engage with to collect this data?

Question 2 (10 points)

Suppose that you want to use the Newsvendor model to estimate the optimal order quantity for the iPhone Z. This, however, requires a forecast that describes the distribution of the future demand. Explain if and how you would use your approaches in Question 1 to construct a demand distribution for the iPhone Z.

Question 3 (10 points)

After delivering your report on demand forecasting for the iPhone Z, the executive team is impressed. They invite you to be a part of Apple’s most ambitious secret project: The Apple iCar. This revolutionary electric vehicle features level 4 autonomous driving capabilities, and its modular and elegant design is set to disrupt the auto industry. You are tasked with estimating the demand for this vehicle during the first year after launch. Propose two methods for estimating the demand for this new product. What are some of the advantages and limitations of each method?

 

Expert answer

 

1. Qualitative forecasting methods: a. Expert opinion: This approach relies on the judgment of experienced individuals within an organization to make predictions about future demand. Advantages of this method include the ability to consider a wide range of factors, as well as the experience and knowledge of the analysts involved. Limitations can include personal biases and inaccurate predictions. Data needed for this approach includes past sales data, market trends, and customer feedback. b. Delphi technique: This is a variation of expert opinion that uses a panel of experts to make predictions. The advantage of this approach is that it takes into account different opinions, which can lead to more accurate predictions. The disadvantage is that it can be expensive to assemble a panel of experts, and they may not be available when needed. Data needed for this approach includes past sales data, market trends, and customer feedback.

 

 

 

 

 

2. Quantitative forecasting methods: a. Time series analysis: This approach looks at historical sales data to identify patterns and trends in order to predict future demand. Advantages include its accuracy and its ability to take into account outside influences such as economic conditions or weather patterns. Limitations include its reliance on historical data, which may not be accurate in predicting future demand, and its inability to account for changes in customer behavior or preferences. Data needed for this approach includes past sales data and market trends. b. Linear regression: This approach uses historical data to identify relationships between different variables in order to predict future demand. Advantages include its accuracy and the ability to take into account outside influences such as economic conditions or weather patterns. Limitations include its reliance on historical data, which may not be accurate in predicting future demand, and its inability to account for changes in customer behavior or preferences. Data needed for this approach includes past sales data and market trends.

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