11025658
Beschreibung
Karteikarten von Carina Vergara, aktualisiert more than 1 year ago
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Erstellt von Carina Vergara
vor mehr als 6 Jahre
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| Frage | Antworten |
| What is the Role of Forecasting? | Forecasting is a vital function and impacts every significant management decision. -Finance & accounting (budgeting and cost control) -Marketing(new product planning& personnel compensation), -Production(select suppliers, capacity req. decisions about purchasing, staffing and inventory. |
| What other different roles require forecasting approaches? | -Decisions about overall directions require strategic forecasts -Tactical forecasts are used to guide day-to-day decisions |
| Types of Forecasting | 1.Qualitative 2. Time series analysis (date relating to past demand, can predict the future demand) 3. Causal relationships 4. Simulation |
| What are the components of Demand? | 1. Average demand for a period of time. 2. Trend 3. Seasonal element 4. Cyclical Elements 5. Random variation 6. Autocorrelation |
| What are Trends? | Identification of trend lines is a common starting point when developing a forecast |
| What are the types of trends? | 1. Linear 2. S-Curve 3. Asymptotic 4. Exponential |
| What are the type of Time Series Analysis? | 1. Short Term 2. Medium Term 3. Long term |
| Short term Forecasting? | Less than 3 months -Used mainly for tactical decisions |
| Medium Term Forecasting? | 3 months to 2 years - Used to develop a strategy which will be implemented over the next 6 to 18 months (e.g. meeting demand) |
| Long term Forecasting? | Greater than 2 years - Useful for detecting general trends and identifying major turning points. |
| Choose the appropriate forecasting model depends on | it depends upon 1. Time horizon to be forecast 2. Data availability 3. Accuracy required 4. Size of forecasting budget 5. Availability of qualified personnel |
| What are the Forecasting Method selection guide? | |
| 1. Simple Moving Average | -Useful when demand is not growing or declining rapidly and no seasonality is present -Removes some of the random fluctuation from the data -Simply calculate the average demand over the most recent period (e.g.Forecasting June with a 5-month moving average = (Jan+Feb+Mar+Apr+May)/5) -Each time a new forecast is made, the oldest period is discarded in the average and the newest period included. -Selecting the period length is important -Longer periods provide more smoothing -Shorter periods react to trends more quickly |
| 2. Weighted Moving Average | Allows unequal weighted of prior time periods. -The sum of the weights must be equal to one (1) -Often more recent periods are given higher weights than periods farther in the past |
| How to select the weights om a weighted moving average? | 1. Experience and/ or trial-and-error. 2. The recent past is often the best indicator of the future, so weighted are generally higher for more recent data. 3. If the data are seasonal, weights should reflect this appropriately (e.g. To forecast swimsuit sales of August, the sales in July should be weighted more than the ones last Dec). 4. More inconvenient and costly than the exponential smoothing method |
| 3. Exponential Smoothing | 1.using weights that decrease exponentially (1-α) for each past period. 2. Weighted average includes all past data in the forecasting calculation. 3. Recently are weighted more heavily 4. most used techniques 5. An integral part of computerized forecasting. |
| What are the accepting reasons for Exponential Smoothing? | 1.Exponential models are surprisingly accurate. 2. Formulating an exponential model is relatively easy. 3. The user can understand how the model works. 4. Little computation is required to use the model. 5. Computer storage requirements are small. 6. Tests for accuracy are easy to compute. |
| When is Exponential Smoothing a SHORTCOMING? | 1. Lagging changes in demand 2. The forecast lags during an increase or decrease, but overshoots when a change in direction occurs. 3. The higher the value of α, the more closely the forecast follows the actual. 4. Solutions: “Adaptive Forecasting” -Considering a trend factor (δ) -Adjusting α value |
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