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# Forecasting Demand

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Forecastis an estimate of future demand. A forecast can be constructed using quantitative methods, qualitative methods, or a combination of methods, and it can be based on extrinsic (external) or intrinsic (internal) factors. Various forecasting techniques attempt to predict one or more of the four components of demand: cyclical, random, seasonal, and trend”.

Forecast will contain some element of error, therefore it should be expressed as a value, with a +/- % of error associated with it. To improve the forecast process the error % should be tracked & monitored.

“Forecast erroris the difference between actual demand and forecast demand, stated as an absolute value or as a percentage”. “Forecast management is the process of making, checking, correcting, and using forecasts. It also includes determination of the forecast horizon”.

Forecasts depend upon what is to be done. They must be made for the strategic business plan (SBP), the production plan (PP) and the master production schedule (MPS). The purpose, planning horizons and level of detail vary for each.

The **strategic business plan** is concerned with overall markets and the direction of the economy over the next two to ten years or more. Its purpose is to provide time to plan for those things that take long to change. For production, the strategic business plan should provide sufficient time for resource planning: plant expansion, capital equipment purchase, and anything requiring a long lead time to purchase. The level of detail is not high, and usually forecasts are in sales units, sales dollars, or capacity. Forecasts and planning will probably be reviewed quarterly or yearly.

**Production planning** is concerned with manufacturing activity for the next one to three years. For manufacturing, it means forecasting those items needed for production planning, such as budgets, labor planning, long lead time, procurement items, and overall inventory levels. Forecasts are made for groups or families of products rather than specific end items. Forecasts and plans will probably be reviewed monthly.

**Master production** scheduling is concerned with production activity from the present to a few months ahead. Forecasts are made for individual items, as found on a master production schedule, individual item inventory levels, raw materials and component parts, labor planning, and so forth. Forecasts and plans will probably be reviewed weekly.

** Demand patterns**A pattern is the general shape of a time series. Pattern shows that actual demand varies from period to period. If historical data for dem

and are plotted against a time scale, they will show any shapes or consistent patterns that exist. A pattern is the general shape of a time series.Although some individual data points will not fall exactly on the pattern, they tend to cluster around it.

The above fig shows a hypothetical historical demand pattern. The pattern shows that actual demand varies from period to period. There are four reasons for this: trend, seasonality, random variation, and cycle.

- **Trend**: The trend can be level, having no change from period to period, or it can rise or fall;

In trend method a forecast is calculated by inserting a time value into the regression equation. The regression equation is determined from the time-serieas data using the “least squares method”.

F=a+bt

This graph illustrates a linear trend, but there are different shapes, such as geometric or exponential. The trend can be level, having no change from period to period, or it can rise or fall.

- **Seasonality**: Each year’ s demand fluctuating depending on the time of year;

The demand pattern in the figure shows each year’s demand fluctuating depending on the time of year. This fluctuation may be the result of the weather, holiday seasons, or particular events that take place on a seasonal basis. Seasonality is usually thought of as occurring on a yearly basis, but it can also occur on a weekly or even daily basis. A restaurant’s demand varies with the hour of the day, and supermarket sales vary with the day of the week

- **Random variation:** It occurs where many factors affect demand during specific periods and occur on a random basis;

Random variation occurs where many factors affect demand during specific periods and occur on a random basis. The variation may be small, with actual demand falling close to the pattern, or it may be large, with the points widely scattered. The pattern of variation can usually be measured, and this will be discussed in the section on tracking the forecast.

- **Cycle**: Over a span of several years, wavelike increases and decreases in the economy influence demand. Forecasting of cycles is a job for economists.

** PRINCIPLES OF FORECASTING**Forecasts have 4 major characteristics:

- Forecasts are usually wrong,

- Every forecast should include an estimate of error,

- Forecasts are more accurate for families or groups,

- Forecasts are more accurate for nearer time periods.

Forecasts 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.

Every forecast should include an estimate of error. Since forecasts are expected to be wrong, the real question is, “By how much?” 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. Estimates of this error can be made statistically by studying the variability of demand about the average demand.

Forecasts are more accurate for families or groups. The behavior of individual items in a group is random even when the group has very stable characteristics. For example, the marks for individual students in a class are more difficult to forecast accurately than the class average. High marks average out with low marks. This means that forecasts are more accurate for large groups of items than for individual items in a group.

For production planning, families or groups are based on the similarity of process and equipment used. For example, a firm forecasting the demand for knit socks as a product group might forecast men’s socks as one group and women’s as another since the markets are different. However, production of men’s and women’s ankle socks will be done on the same machines and knee socks on another. For production planning, the forecast should be for (a) men’s and women’s ankle socks and (b) men’s and women’s knee socks.

Forecasts are more accurate for nearer time periods. The near future holds less uncertainty than the far future. Most people are more confident in forecasting what they will be doing over the next week than a year from now. As someone once said, tomorrow is expected to be pretty much like today.

In the same way, demand for the near term is easier for a company to forecast than for a time in the distant future. This is extremely important for long lead-time items and especially so if their demand is dynamic. Anything that can be done to reduce lead time will improve forecast accuracy.

** Stable versus dynamic**The demand pattern that retain the same general shape are called stable and those that do not are called dynamic. Dynamic change can affect the trend, seasonality or randomness of the demand. The more stable the demand, the easier it is to forecast.

** Dependent versus independent demand**A product is independent when is not related to the demand for any other product. Dependent demand for a product occurs where the demand for the item is derived from that of a second item. Requirements for dependent demand are calculated

and for independent demand need be forecast.