Chapter 8 Forecasting

Chapter 8 ForecastingKey TermsDemand management,200Demand management is the function of recognizing and managing all demands for products.Order processing,200The activity required to administratively process a customer’s order and make it ready for shipment or production.Trend,202General upward or downward movement of a variable over time.Seasonality,202A repetitive pattern of demand from year to year (or other repeating time interval) with some periods considerably higher than others.Random variation,202A fluctuation in data that is caused by uncertain or random occurrences.Stable,203The demand patterns which retain the same general shape are called stable.Qualitative techniques,206Qualitative techniques are projections based on judgment, intuition, and informed opinions.Extrinsic forecasting techniques, 206Extrinsic forecasting techniques are projections based on external (extrinsic) indicators which relate to the demand for a company’s products.Economic indicators, 206An index of total business activities at the regional, national, and global levels. They describe economic conditions prevailing during a given time period.Intrinsic forecasting techniques,207Intrinsic forecasting techniques use historical data to forecast.Moving average,209An arithmetic average of certain number (n) of the most recent observations. As each new observation is added, the oldest observation is dropped. The value of n ( the number of periods to use for the average) reflects responsiveness versus stability in the same way that the choice of smoothing constant does in exponential smoothing. There are two types of moving average, simple and weighted.Exponential smoothing forecast,210A type of weighted moving average forecasting technique in which past observations are geometrically discounted according to their age. The heaviest weight is assigned to the most recent data. The smoothing istermed exponential because data points are weighted in accordance with an exponential function of their age. The technique makes use of a smoothing constant to apply to the difference between the most recent forecast and the critical sales data, thus avoiding the necessity of carrying historical sales data.Seasonal index,212A number used to adjust data to seasonal demand. This index is an estimate of how much the demand during the season will be above or below the average demand for the product.Desasonalized demand,212The average demand for all periods is a value that averages out seasonality. This is called the deseasonalized demand.Forecast error,216Forecast error is the difference between actual demand and forecast demand.Bias,216Bias exists when the cumulative actual demand varies from the cumulative forecast.Mean absolute deviation (MAD),218The average of the absolute values of the deviation of observed values from some expected value. MAD can be calculated based on observations and the arithmetic mean of those observations. An alternative is to calculate absolute deviations of actual sales data minus forecast data. These data can be averaged in the usual arithmetic way or with exponential smoothing.Normal distribution,219A particular statistical distribution where most of the observations fall fairly close to one mean, and a deviation from the mean is as likely to be plus as it is to be minus. When graphed, the normal distribution takes the form of a bell-shaped curve.Tracking signal,220The ration of the cumulative algebraic sum of the deviations between the forecasts and the actual values to the mean absolute deviation. Use to signal when the validity of the forecasting model might be in doubt. Tracking signal can be sued to monitor the quality of the forecast.Production lead time,222Production lead time is the stacked lead time for a product. It includes time for purchasing of raw materials to arrive, manufacturing, assembly, delivery, and sometimes the design of the product.Demand lead time,222Demand lead time is the customer’s lead time.. It is the time from when a customer places an order until the goods are delivered.Questions1. What is demand management? What functions does it include?1) The function of recognizing all demands for goods and services to support the marketplace. It involves prioritizing demand when supply is lacking. Proper demand management facilitates the planning and use ofresources for profitable business results. 2) In marketing, the process of planning, executing, controlling, and monitoring the design, pricing, promotion, and distribution of products and services to bring about transactions that meet organizational and individual needs.2. Why must we forecast?Forecasting is inevitable in developing plans to satisfy future demand. Most firms cannot wait until orders are actually received before they start to plan what to produce. Customers usually demand delivery in reasonable time, and manufacturers must anticipate future demand for products or services and plan to provide the capacity and resources to meet the demand.3. What factors influence the demand for a firm’s products?The factors influence the demand for a firm’s products are as follows:⑴General business and economic conditions.⑵Competitive factors.⑶Market trends such as changing demand.⑷The firm’s own plans for advertising, promotion, pricing, and product changes.4. Describe the purpose of forecasting for strategic business planning, production planning, and master production scheduling.The strategic business plan is a statement of the major goals and objectives the company expects to achieve over the next two to ten years or more.Production management is concerned with the following:The quantities of each product group that must be produced in each period.The desired inventory levels.The resources of equipment, labor, and material needed in each period.The availability of the resources needed.The master production schedule (MPS) is a plan for the production of individual end items. It breaks down the production plan to show, for each period, the quantity of each end item to be made.5. The text describes three characteristics of demand. Name and describe each.The three characteristics of demand are as follows:Demand patterns –If historical data for demand are plotted against a time scale, they will show any shape of consistent patterns that exist.Stable versus dynamic – The shapes of the demand patterns for some products or services change over time whereas other do not.Dependent versus independent demand – Demand for a product or service is independent when it is not related to the demand for any other product or service. Dependent demand for a product or service occurs where the demand for the item is derived from that of a second item.6. Describe trend, seasonality, random variation, and cycle as applied to forecasting.Trend – The trend can be level, having no change from period to period, or it can rise or fall. Seasonality – Seasonality is usually thought of as occurring on a yearly basis, but it can also occur on weekly or even daily basis.Random variation – Random variation occurs where many factors affect demand during specific periods and occur on a random basis.Cycle – Over a span of several years and even decades, wavelike increases and decreases in the economy influence demand.7. The text discusses four principles of forecasting. Name and describe each.⑴Forecasts are usually wrong. Forecasts attempt to look into the unknown future and, except by sheer luck, will be wrong to some degree. Errors are inevitable and must be expected.⑵Every forecast should include an estimate of error. Every forecast should include an estimate of error often expressed as a percentage (plus and minus) of the forecast or as a range between maximum and minimum values.⑶Forecasts are more accurate for families or groups. This means that forecasts are more accurate for large groups of items than for individual items in a group.⑷Forecasts are more accurate for nearer timer periods. The near future holds less uncertainty than the far future.8. Name and describe the three principles of data collection.⑴Record data in the same terms as needed for the forecast. There are three dimensions to this:a. If the purpose is to forecast demand on production, data based on demand, not shipments, are needed.b. The forecast period, in weeks, months, or quarters, should be the same as the schedule period.c. The items forecast should be the same as those controlled by manufacturing.⑵Record the circumstances relating to the data.⑶Record the demand separately for different customer groups.9. Describe the characteristics and differences between qualitative, extrinsic, and intrinsic forecasting techniques.Qualitative techniques are projections based on judgment, intuition, and informed opinions. By their nature, they are subjective.Extrinsic forecasting techniques are projections abased on external (extrinsic) indicators which relate to the demand for a company’s products.Intrinsic forecasting techniques use historical data to forecast.10. Describe and give the advantages and disadvantages of (a) moving averages and (b) exponential smoothing.Moving averages are best used for forecasting products with stable demand where there is little trend or seasonality. Moving averages are also useful to filter out random fluctuations. One drawback to using moving averages is the need to retain several periods of history for each item to be forecast. This will require a great deal of computer storageExponential smoothing provides a routine method for regularly updating item forecasts. It works quite well when dealing with stable items. Generally, it has been found satisfactory for short-range forecasting. It is not satisfactory where the demand is low or intermittent.11. What is a seasonal index? How is it calculated?It is a number used to adjust data to seasonal demand. The formula is as follows:Seasonal index = period average demand / average demand for all periods12. What is meant by the term deseasonalized demand?The average demand for all periods is a value that averages out seasonality. This is called the deseasonalized demand. The formula can be describe as:Season index = period average demand / deseasonalized demand13. What is meant by the term tracking the forecast? In which two ways can forecasts go wrong? Tracking the forecast is the process of comparing actual demand with the forecast. The purpose of tracking the forecast is to be able to react to forecast error by planning around it or by reducing it.Bias exists when the cumulative actual demand varies from the cumulative forecast.The variability will depend upon the demand pattern of the product.14. What is bias error in forecasting? What are some of the causes?Bias exits when the cumulative actual demand varies from the cumulative forecast. This means the forecast average demand has been wrong.Bias is a systematic error in which the actual demand is consistently above or below the forecast demand.15. What is random variation?It is a fluctuation in data that is caused by uncertain or random occurrences.16. What is the mean absolute deviation (MAD) ? Why is it useful in forecasting?MAD is the average of the absolute values of the deviations of observed values from some expected value. MAD can be calculated based on observations and the arithmetic mean of those observations. An alternative is to calculate absolute deviations of actual sales data minus forecast data. This data can be averaged in the usual arithmetic way or with exponential smoothing.Total error would be useless to measure the variation. One way to measure the variability is to calculate the total error ignoring the plus and minus signs and take the average. This is called mean absolute deviation:Mean implies an average,Absolute means without reference to plus and minus,Deviation refers to the error.MAD = sum of absolute deviations / number of observations17. What action should be taken when unacceptable error is found in tracking a forecast?When an unacceptably large error or bias is observed, it should be investigated to determine its cause.18. What is the P/D ratio? How may it be improved?“P”, or production lead time, is the stacked lead time for a product. “D”, or demand lead time, is the customer’s lead time.There are five ways to improve this index:⑴Reduce P time.⑵Force a match between P and D.a. Make the customer’s D time equal to your P time.b. Sell what you forecast.⑶Simplify the product line.⑷Standardize products and processes.⑸Forecast more accurately.。