Why a business needs to forecast.

Please answer these 4 questions:
1: Give three examples showing why a business needs to forecast.

2: Give 3 examples from your life in which you may forecast the future.

3: Describe the steps involved in forecasting.

4: Identify the key differences between qualitative and quantitative forecasting methods. Which is better in your opinion and why?

Operations Management: An Integrated Approach R. Dan Reid; Nada R. Sanders 6th edition

**Cite this book APA style when answering the questions above. I have broken it down by page number down below. **

Steps in the Forecasting Process (page 268)
Regardless of what forecasting method is used, there are some basic steps that should be followed when making a forecast:

1. Decide what to forecast.
   Remember that forecasts are made in order to plan for the future. To do so, we have to decide what forecasts are actually needed. This is not as simple as it sounds. For example, do we need to forecast sales or demand? These are two different things, and sales do not necessarily equal the total amount of demand for the product. Both pieces of information are usually valuable.
   An important part of this decision is the level of detail required for the forecast (e.g., by product or product group), the units of the forecast (e.g., product units, boxes, or dollars), and the time horizon (e.g., monthly or quarterly).
2. Evaluate and analyze appropriate data.
   This step involves identifying what data are needed and what data are available. This will have a big impact on the selection of a forecasting model. For example, if you are predicting sales for a new product, you may not have historical sales information, which would limit your use of forecasting models that require quantitative data.
   We will also see in this chapter that different types of patterns can be observed in the data. It is important to identify these patterns in order to select the correct forecasting model. For example, if a company was experiencing a high increase in product sales for the past year, it would be important to identify this growth in order to forecast correctly.
3. Select and test the forecasting model. Once the data have been evaluated, the next step is to select an appropriate forecasting model. As we will see, there are many models to choose from. Usually we consider factors like cost and ease of use in selecting a model. Another very important factor is accuracy. A common procedure is to narrow the choices to two or three different models and then test them on historical data to see which one is most accurate.
4. Generate the forecast. Once we have selected a model, we use it to generate the forecast. But we are not finished, as you will see in the next step.
5. Monitor forecast accuracy. Forecasting is an ongoing process. After we have made a forecast, we should record what actually happened. We can then use that information to monitor our forecast accuracy. This process should be carried out continuously because environments and conditions often change. What was a good forecasting model in the past might not provide good results for the future. We have to constantly be prepared to revise our forecasting model as our data change.
   The rapid growth of information technology (IT) has created a forecasting challenge for manufacturers of industry components such as microchips and semiconductors. Companies like Intel have had difficulty in forecasting demand for information technology used in internal applications. Forecasts are critical in order to plan production and have enough product to meet demand. However, overforecasting means having too much of an expensive product that will quickly become obsolete. The exponential growth in requirements and a short product life cycle have added much uncertainty to the forecasting process. Intel has had to consider many factors when generating its forecasts, such as key technology trends that are driving the information revolution and future directions in the use of IT.

(page 270)
Qualitative forecasting methods, often called judgmental methods, are methods in which the forecast is made subjectively by the forecaster. They are educated guesses by forecasters or experts based on intuition, knowledge, and experience. When you decide, based on your intuition, that a particular team is going to win a baseball game, you are making a qualitative forecast. Because qualitative methods are made by people, they are often biased. These biases can be related to personal motivation (“They are going to set my budget based on my forecast, so I'd better predict high.”), mood (“I feel lucky today!”), or conviction (“That pitcher can strike anybody out!”).
Quantitative forecasting methods, on the other hand, are based on mathematical modeling. Because they are mathematical, these methods are consistent. The same model will generate the exact same forecast from the same set of data every time. These methods are also objective. They do not suffer from the biases found in qualitative forecasting. Finally, these methods can consider a lot of information at one time. Because people have limited information-processing abilities and can easily experience information overload, they cannot compete with mathematically generated forecasts in this area.
Both qualitative and quantitative forecasting methods have strengths and weaknesses. Although quantitative methods are objective and consistent, they require data in quantifiable form in order to generate a forecast. Often, we do not have such data, for example, if we are making a strategic forecast or if we are forecasting sales of a new product. Also, quantitative methods are only as good as the data on which they are based. Qualitative methods, on the other hand, have the advantage of being able to incorporate last-minute “inside information” in the forecast, such as an advertising campaign by a competitor, a snowstorm delaying a shipment, or a heat wave increasing sales of ice cream. Each method has its place, and a good forecaster learns to rely on both.

Principles of Forecasting (268)
There are many types of forecasting models. They differ in their degree of complexity, the amount of data they use, and the way they generate the forecast. However, some features are common to all forecasting models. They include the following:

1. Forecasts are rarely perfect. Forecasting the future involves uncertainty. Therefore, it is almost impossible to make a perfect prediction. Forecasters know that they have to live with a certain amount of error, which is the difference between what is forecast and what actually happens. The goal of forecasting is to generate good forecasts on the average over time and to keep forecast errors as low as possible.
2. Forecasts are more accurate for groups or families of items rather than for individual items. When items are grouped together, their individual high and low values can cancel each other out. The data for a group of items can be stable even when individual items in the group are very unstable. Consequently, one can obtain a higher degree of accuracy when forecasting for a group of items rather than for individual items. For example, you cannot expect the same degree of accuracy if you are forecasting sales of long-sleeved hunter green polo shirts that you can expect when forecasting sales of all polo shirts.
3. Forecasts are more accurate for shorter than longer time horizons. The shorter the time horizon of the forecast, the lower the degree of uncertainty. Data do not change very much in the short run. As the time horizon increases, however, there is a much greater likelihood that changes in established patterns and relationships will occur. Because of that, forecasters cannot expect the same degree of forecast accuracy for a long-range forecast as for a short-range forecast. For example, it is much harder to predict sales of a product two years from now than to predict sales two weeks from now.

Chapter Highlights (page 302)

1. Three basic principles of forecasting are: forecasts are rarely perfect; forecasts are more accurate for groups or families of items rather than for individual items; and forecasts are more accurate for shorter than longer time horizons.
2. The forecasting process involves five steps: decide what to forecast; evaluate and analyze appropriate data; select and test a forecasting model; generate the forecast; and monitor forecast accuracy.
3. Forecasting methods can be classified into two groups: qualitative and quantitative. Qualitative forecasting methods generate a forecast based on the subjective opinion of the forecaster. Some examples of qualitative methods include executive opinion, market research, and the Delphi method. Quantitative forecasting methods are based on mathematical modeling. They can be divided into two categories: time series models and causal models.
4. Time series models are based on the assumption that all the information needed for forecasting is contained in the time series of data.
   • There are four basic patterns of data: level or horizontal, trend, seasonality, and cycles. In addition, data usually contain random variation. Some forecasting models that can be used to forecast the level of a time series are naïve, simple mean, simple moving average, weighted moving average, and exponential smoothing. Separate models are used to forecast trend, such as trend-adjusted exponential smoothing. Forecasting seasonality requires a procedure in which we compute a seasonal index, the percentage by which each season is above or below the mean.
5. Causal models assume that the variable being forecast is related to other variables in the environment.
   • A simple causal model is linear regression, in which a straight-line relationship is modeled between the variable we are forecasting and another variable in the environment. The correlation coefficient is used to measure the strength of the linear relationship between these two variables. An extension of linear regression is multiple regression where a model is developed between the variable we are forecasting and multiple independent variables.
6. Three useful measures of forecast accuracy are mean absolute deviation (MAD)(MAD), mean square error (MSE)(MSE), and a tracking signal.
7. There are four factors to consider when selecting a forecasting model: the amount and type of data available, the degree of accuracy required, the length of forecast horizon, and patterns present in the data.
8. Collaborative Planning, Forecasting, and Replenishment (CPFR) is a collaborative process between trading partners that establishes formal guidelines for joint forecasting, replenishment, and planning.