This company plans to enter a new market next year and asks you to estimate the...

 

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This company plans to enter a new market next year and asks you to estimate the customer demand in year 2023. You get the past 5-year demand information as below which has been quickly increasing. As an initial simple try, you adopt exponential smoothing. (Q3-1) (4) What is your best choice of the "alpha" value out of (0.2, 0.5, 0.8) and what is the reason? (Q3-2) (4) Write down the table below and assume "alpha" = 0.8 now. Find the forecasts for 2019-2023 showing your detailed computations. Year 2018 2019 2020 2021 2022 2023 Actual Demand 1,500 2,000 2,400 3,000 3,500 Forecast 1,450 MAD Tracking Signal (Q3-3) (4) Find the MAD (mean absolute deviation) in 2022 for 2019~2022 forecasts. (Q3-4) (4) Find the Tracking Signal in 2022 for 2019-2022 forecasts. (Q3-5) (4) What can you say about your forecasting method based on the Tracking Signal? How can you improve your forecasts using exponential smoothing considering Tracking Signal? (Q4) (6) You realize that the past customer demand data values are actually sales volumes instead of true demand. Thus, as a consultant, you decide to explain the importance of demand unconstraining process in the future forecast tasks in the client company. For more accurate future "demand" forecasts, list up 2 reasons that we should not use past "sales" data without careful processing. (Q5) (6) They want to develop their approximate inventory management plan from year 2023 in the new market after they start a long-term business there. They consider a fixed-quantity ordering model by which they place an order of the same quantity each time. They guess that monthly demand is uniformly distributed in [200,400] and independent between months. When the transportation lead time to the new market is 1 month, what is the optimal reorder point to achieve 90% service level for the uncertain demand? (Q6) (6) They also ask you to analyze their production quality for an oil product. In each 1 kg of this product, a specific chemical element's weight in gram should be in the range of [0.99,1.01] according to their quality specification. You take many samples over time and find that the sample weight follows a normal distribution with the mean μ = 1.002 gram and the standard deviation = 0.004 gram. (Q6-1) (3) What is the process capability index for this production process? (Q6-2) (3) What is the probability that they produce defective oil? (Hint: For a normal random variable X, Pr(|X->o) = 31.73 % , Pr( | X-μ| > 20) = 4.55%, Pr ( | X-μ| >30) = 0.27%.) This company plans to enter a new market next year and asks you to estimate the customer demand in year 2023. You get the past 5-year demand information as below which has been quickly increasing. As an initial simple try, you adopt exponential smoothing. (Q3-1) (4) What is your best choice of the "alpha" value out of (0.2, 0.5, 0.8) and what is the reason? (Q3-2) (4) Write down the table below and assume "alpha" = 0.8 now. Find the forecasts for 2019-2023 showing your detailed computations. Year 2018 2019 2020 2021 2022 2023 Actual Demand 1,500 2,000 2,400 3,000 3,500 Forecast 1,450 MAD Tracking Signal (Q3-3) (4) Find the MAD (mean absolute deviation) in 2022 for 2019~2022 forecasts. (Q3-4) (4) Find the Tracking Signal in 2022 for 2019-2022 forecasts. (Q3-5) (4) What can you say about your forecasting method based on the Tracking Signal? How can you improve your forecasts using exponential smoothing considering Tracking Signal? (Q4) (6) You realize that the past customer demand data values are actually sales volumes instead of true demand. Thus, as a consultant, you decide to explain the importance of demand unconstraining process in the future forecast tasks in the client company. For more accurate future "demand" forecasts, list up 2 reasons that we should not use past "sales" data without careful processing. (Q5) (6) They want to develop their approximate inventory management plan from year 2023 in the new market after they start a long-term business there. They consider a fixed-quantity ordering model by which they place an order of the same quantity each time. They guess that monthly demand is uniformly distributed in [200,400] and independent between months. When the transportation lead time to the new market is 1 month, what is the optimal reorder point to achieve 90% service level for the uncertain demand? (Q6) (6) They also ask you to analyze their production quality for an oil product. In each 1 kg of this product, a specific chemical element's weight in gram should be in the range of [0.99,1.01] according to their quality specification. You take many samples over time and find that the sample weight follows a normal distribution with the mean μ = 1.002 gram and the standard deviation = 0.004 gram. (Q6-1) (3) What is the process capability index for this production process? (Q6-2) (3) What is the probability that they produce defective oil? (Hint: For a normal random variable X, Pr(|X->o) = 31.73 % , Pr( | X-μ| > 20) = 4.55%, Pr ( | X-μ| >30) = 0.27%.)

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Q31 Best choice of alpha value and reason The choice of the alpha value depends on the desired level of responsiveness to recent changes in demand A higher alpha value places more weight on recent obs... View the full answer