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Consider the previous month's forecast to identify which technique is most effective. Use that to forecast the next month. Remember to select the forecasting technique that produces the forecast error nearest to zero. For example: a . Na ï ve Forecast is 2 3 0 and the Forecast Error is - 1 5 . b . 3 - Month Moving Forecast is 2 9 0 and the Forecast Error is - 7 5 . c . Exponential Smoothing Forecast for . 2 is 3 0 8 and the Forecast Error is - 9 3 . d . Exponential Smoothing Forecast for . 5 is 2 7 9 and the Forecast Error is - 6 4 . e . Seasonal Forecast is 2 9 7 and the Forecast Error is - 8 2 . The forecast for the next month would be 2 3 0 as the Na ï ve Forecast had the Forecast Error closest to zero with a - 1 5 . This forecasting technique was the best performing technique for that month. You do not need to do any external analysis - the forecast error for each strategy is already calculated for you in the tables below.Actual DemandNa ï ve Forecast Error 3 Month Moving Forecast 3 Month Moving Forecast ErrorExponential Smoothing Forecast for . 2 Exponential Smoothing . 2 Forecast ErrorExponential Smoothing Forecast for . 5 Exponential Smoothing . 5 Forecast ErrorSeasonal Forecast Error - Year 1 solve this problem with step by step solution with explanation and conclusionAppt Est co Consider the previous month's forecast to identify which technique is most effective. Use that to forecast the next month. Remember to select the forecasting technique that produces the forecast error nearest to zero. For example: a. Naive Forecast is 230 and the Forecast Error is -15. b. 3-Month Moving Forecast is 290 and the Forecast Error is -75. ¢. Exponential Smoothing Forecast for 2 is 308 and the Forecast Error is -93. d. Exponential Smoothing Forecast for .5 is 279 and the Forecast Error is 64. e. Seasonal Forecast is 297 and the Forecast Error is -82. The forecast for the next month would be 230 as the Naive Forecast had the Forecast Error closest to zero with a 15. This forecasting technique was the best performing technique for that month. You do not need to do any external analysis-the forecast error for each strategy is already calculated for you in the tables below. 3 > Exponential Exponential Exponential Exponential Month Period fe] Naive foment ony fod Enzi | EnEring| Ensing) Esti Seasonal ore Demand Ee Moving Bomec: Forecast for .2 Forecast Forecast for .5 Forecast Ener Forecast fe 2 Error 5 Error ¥ Year1l MAR 3 69 73 71 73 72 77 [ = | APR 4 94 69 70 72 ul 91 MAY 5 99 94 79 76 83 | © | 76 JUN 6 103 99 87 [1s] 81 91 84 [1 | JuL 7 86 103 99 85 [1] 97 EE 76 [ 10 | AUG 8 103 86 96 85 EE 92 HE 93 [ 10 | SEPT 9 73 103 97 89 IE 98 ul EEN EE EEE EEE NOV n" 73 64 IE 80 82 HE 75 86

Question:

Consider the previous month's forecast to identify which technique is most effective. Use that to forecast the next month. Remember to select the forecasting technique that produces the forecast error nearest to zero. For example: a . Na ï ve Forecast is 2 3 0 and the Forecast Error is - 1 5 . b . 3 - Month Moving Forecast is 2 9 0 and the Forecast Error is - 7 5 . c . Exponential Smoothing Forecast for . 2 is 3 0 8 and the Forecast Error is - 9 3 . d . Exponential Smoothing Forecast for . 5 is 2 7 9 and the Forecast Error is - 6 4 . e . Seasonal Forecast is 2 9 7 and the Forecast Error is - 8 2 . The forecast for the next month would be 2 3 0 as the Na ï ve Forecast had the Forecast Error closest to zero with a - 1 5 . This forecasting technique was the best performing technique for that month. You do not need to do any external analysis - the forecast error for each strategy is already calculated for you in the tables below.Actual DemandNa ï ve Forecast Error 3 Month Moving Forecast 3 Month Moving Forecast ErrorExponential Smoothing Forecast for . 2 Exponential Smoothing . 2 Forecast ErrorExponential Smoothing Forecast for . 5 Exponential Smoothing . 5 Forecast ErrorSeasonal Forecast Error - Year 1 solve this problem with step by step solution with explanation and conclusionUploaded ImageAppt Est co Consider the previous month's forecast to identify which technique is most effective. Use that to forecast the next month. Remember to select the forecasting technique that produces the forecast error nearest to zero. For example: a. Naive Forecast is 230 and the Forecast Error is -15. b. 3-Month Moving Forecast is 290 and the Forecast Error is -75. ¢. Exponential Smoothing Forecast for 2 is 308 and the Forecast Error is -93. d. Exponential Smoothing Forecast for .5 is 279 and the Forecast Error is 64. e. Seasonal Forecast is 297 and the Forecast Error is -82. The forecast for the next month would be 230 as the Naive Forecast had the Forecast Error closest to zero with a 15. This forecasting technique was the best performing technique for that month. You do not need to do any external analysis-the forecast error for each strategy is already calculated for you in the tables below. 3 > Exponential Exponential Exponential Exponential Month Period fe] Naive foment ony fod Enzi | EnEring| Ensing) Esti Seasonal ore Demand Ee Moving Bomec: Forecast for .2 Forecast Forecast for .5 Forecast Ener Forecast fe 2 Error 5 Error ¥ Year1l MAR 3 69 73 71 73 72 77 [ = | APR 4 94 69 70 72 ul 91 MAY 5 99 94 79 76 83 | © | 76 JUN 6 103 99 87 [1s] 81 91 84 [1 | JuL 7 86 103 99 85 [1] 97 EE 76 [ 10 | AUG 8 103 86 96 85 EE 92 HE 93 [ 10 | SEPT 9 73 103 97 89 IE 98 ul EEN EE EEE EEE NOV n" 73 64 IE 80 82 HE 75 86

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Asked by: Priyanshu Yadav
Created at: 2025-09-26 20:56:02

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# Step-by-Step Solution: Selecting the Best Forecasting Technique Let's follow the instructions to determine **which forecasting technique** is most effective for the **next month** using the data provided. We'll use the error for each technique in **December** (the last month in the table). --- ## **Step 1: List the December Results** | Technique | Forecast | Forecast Error | |---------------------------------|----------|---------------| | Naïve Forecast | 73 | -9 | | 3-Month Moving Forecast | 70 | -6 | | Exponential Smoothing (α=.2) | 80 | 16 | | Exponential Smoothing (α=.5) | 74 | -10 | | Seasonal Forecast | 83 | 19 | --- ## **Step 2: Identify the Error Closest to Zero** Looking at the forecast errors for December: - **Naïve Forecast:** -9 - **3-Month Moving Forecast:** -6 ← *closest to zero* - **Exponential Smoothing (.2):** 16 - **Exponential Smoothing (.5):** -10 - **Seasonal Forecast:** 19 The **3-Month Moving Forecast** has the error closest to zero (**-6**). --- ## **Step 3: Select the Forecast for Next Month** Since the 3-Month Moving Forecast was the best performing technique, we use its forecast value for December as the forecast for the **next month (January of Year 2)**. - **Forecast for next month:** **70** (the 3-Month Moving Forecast for December) --- ## **Step 4: Conclusion** > **The forecast for the next month is 70, using the 3-Month Moving Forecast.** > > **Reason:** The 3-Month Moving Forecast had the forecast error closest to zero (-6) in December, making it the most effective forecasting technique for that period. --- ### **Summary Table** | Month | Actual Demand | Naïve Error | 3-Month Moving Error | Exp. Smoothing .2 Error | Exp. Smoothing .5 Error | Seasonal Error | |-------|---------------|-------------|----------------------|-------------------------|-------------------------|---------------| | DEC | 64 | -9 | -6 | 16 | -10 | 19 | **Best technique:** 3-Month Moving Forecast **Next month forecast:** **70**

Question:

is this solution is completed according to question requirement

This is AI generated Response.
Asked by: Priyanshu Yadav
Created at: 2025-09-26 20:58:25

Final Answer:

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# Solution Review: Forecasting Technique Evaluation This review examines whether the provided solution meets the requirements of the original question regarding forecasting techniques and their effectiveness. --- ## **Summary of Original Requirements** 1. Identify the most effective forecasting technique based on the previous month's forecast. 2. Select the technique that produces the forecast error nearest to zero. 3. Use that technique's forecast for the next month. 4. Provide a step-by-step explanation. --- ## **Evaluation of the Provided Solution** ### **1. Identification of Techniques and Errors** The solution correctly listed the forecast and errors for different techniques for December: | Technique | Forecast | Forecast Error | |---------------------------------|----------|---------------| | Naïve Forecast | 73 | -9 | | 3-Month Moving Forecast | 70 | -6 | | Exponential Smoothing (α=.2) | 80 | 16 | | Exponential Smoothing (α=.5) | 74 | -10 | | Seasonal Forecast | 83 | 19 | ### **2. Selection of Best Technique** The solution identified the **3-Month Moving Forecast** as having the forecast error closest to zero (-6), fulfilling the requirement to select the best technique. ### **3. Forecast for Next Month** The forecast for the next month (January of Year 2) was determined to be **70**, based on the selected technique, which aligns with the requirements. ### **4. Step-by-Step Explanation** The solution provided a clear step-by-step explanation of the process, making it easy to follow: - Listing the results - Identifying the error closest to zero - Selecting the forecast for the next month - Concluding with the selection rationale --- ## **Conclusion** The provided solution successfully adheres to all the requirements of the question: - It identifies the most effective forecasting technique based on the previous month's data. - It selects the forecast with the error nearest to zero. - It accurately forecasts the next month's value. Therefore, **the solution is complete and meets the requirements of the original question.**

Question:

how to select the most effective forecasting technique for predicting the next month’s demand, using the provided table of actual demand, forecasts, and forecast errors for several methods. Specifically, it is asking which method to choose for the next forecast based on which one had the forecast error closest to zero in the immediately previous month. The task is to identify this method from the data and use its prediction as the official forecast for the upcoming month.

This is AI generated Response.
Asked by: Priyanshu Yadav
Created at: 2025-09-26 21:00:39

Final Answer:

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# Selecting the Most Effective Forecasting Technique To determine the best forecasting technique for predicting next month's demand, we will analyze the provided table of actual demand, forecasts, and forecast errors for various methods. ## Step-by-Step Process ### **Step 1: Review the Table Data** We need to summarize the forecast results and errors for the last month, which is December. | Technique | Forecast | Forecast Error | |---------------------------------|----------|----------------| | Naïve Forecast | 73 | -9 | | 3-Month Moving Forecast | 70 | -6 | | Exponential Smoothing (α=.2) | 80 | 16 | | Exponential Smoothing (α=.5) | 74 | -10 | | Seasonal Forecast | 83 | 19 | ### **Step 2: Identify the Closest Error to Zero** We will evaluate the forecast errors to find the technique with the error closest to zero: - **Naïve Forecast:** -9 - **3-Month Moving Forecast:** -6 ← *closest to zero* - **Exponential Smoothing (.2):** 16 - **Exponential Smoothing (.5):** -10 - **Seasonal Forecast:** 19 The **3-Month Moving Forecast** has the forecast error of **-6**, which is the closest to zero. ### **Step 3: Determine the Next Month's Forecast** Using the **3-Month Moving Forecast**, we will take its forecast value for December to predict the following month (January): - **Forecast for January:** **70** ### **Step 4: Conclusion** The most effective forecasting technique for predicting next month's demand is the **3-Month Moving Forecast**, which produced the forecast error nearest to zero in December. - **Next Month's Forecast:** **70** This structured approach allows for a clear understanding of the decision-making process based on the provided data.

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