Chapter 4: Multiple Choice Questions 1. Forecasts a. become more accurate with longer time horizons b. are rarely perfect c. are more accurate for individual items than for groups of items d. all of the above e. none of the above One purpose of short-range forecasts is to determine a. production planning b. inventory budgets c. research and development plans d. facility location e. job assignments Forecasts are usually classified by time horizon into three categories a. short-range‚ medium-range
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Based on a thorough analysis of sales data we have summarized our findings‚ forecasts‚ and recommendations for you. The analysis focuses on sales performance of the past three years. Findings: Sales totals consistently follow a trend‚ featuring peak sales in January and low sales in September. (see workbook titled Data & Time Series Plot #1) There appears to be a steady decline in food and beverage sales at the start of the calendar year‚ followed by recovery in sales entering the fourth
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qualitative approach. C) It is based on the assumption that the analysis of past demand helps predict future demand. D) Because it accounts for trends‚ cycles‚ and seasonal patterns‚ it is always more powerful than associative forecasting. E) All of the above are true. Answer: C 4. Time-series data may exhibit which of the following behaviors? A) trend B) random variations C) seasonality D) cycles E) They may exhibit all of the above. Answer: E 5. The fundamental difference between cycles
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50 Week 2= 54 Week 3= 57 Week 4= 60 Week 5= 64 Week 6= 67 Week 7= 90 Week 8= 76 Week 9 =79 Week 10= 82 Week 11 =85 Week 12 =87 Week 13= 92 Week 14= 96 Week 15 Now‚ we will be using the general form for a trend model; which is ; Y = a + bt or F = a + bt Whereas‚ Y= demand‚ t=period [Ty=50‚108‚171‚240‚320‚402‚630‚608‚711‚820‚935‚1044‚1196‚1344 t²=1‚ 4‚9‚16‚25‚36‚49‚64‚81‚100‚121‚144‚169‚196 (Details calculation shown on the spreadsheet) ] = =3
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TABLE OF CONTENTS I. Forecasting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 A. Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 B. Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C. Importance of Forecasting. . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1. Product Life Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
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The current demand forecasting method is based on qualitative techniques more than quantitative ones. If the forecast is not accurate‚ the company would carry both inventory and stock out costs. It might lose customers due to shortage of supply or carry additional holding costs due to excess production. If the actual demand doesn’t match the forecast ones‚ and the forecast was too high‚ this will result in high inventories‚ obsolescence‚ asset disposals‚ and increased carrying costs. When a forecast
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you think of an example when deseasonalized data would be necessary for analysis? 3) A simple regression model was run on the following time series‚ representing the number of company employees per quarter‚ to establish the linear trend. Use this information to answer questions (a)‚ (b) and (c) below. Year | Quarter | t | Yt | St | dt | 2001 | 3 | 1 | 1050 | 0.9 | 1166.67 | 2001 | 4 | 2 | 1200 | 1.1 | 1090.91 | 2002 | 1 | 3 | 980 | 0.85 | 1152.94 | 2002 | 2 | 4 | 1140 |
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Nicole-line breaks mean new slide important questions Forecasts are needed to predict demand all different teams within the company need the forecast different users have different time requirements and detail reqts you might have to collect more data if you don’t have enough cost depends on the scope of the project need to engage the users‚ so have to provide a feedback system The top chart appears to be a ore difficult to forecast but they just narrowed the y axiz 2nd chart down
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Part 1: Step 1: Good Morning‚ I am (write your name here) from Tempus Global Data. I am calling to speak to Mr. Bob Wilson‚ major account executive of the weatherchannel.com? Step 2: Mr. Wilson I can realize that your time is very precious so I will take only 2 minutes to explain the reason why I am calling you now. Is this good for you to talk now or I can call after some time? Thanks a lot. Step 3: Mr. Wilson basically I am working as sales account executive at Tempus Global Data. Our Company
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Time Series Regression 3.1 A small regional trucking company has experienced steady growth. Use time series regression to forecast capital needs for the next 2 years. The company’s recent capital needs have been: ══════════════════════════════════════════════ Capital Needs Capital Needs (Thousands Of (Thousands Of Year Dollars) Year Dollars) -------------------------------------------
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