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Demand Forecasting

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  • Chapter 8-ForecastingMarch 5, 2001

  • ForecastingPlanningForecasting is a prelude to planningMost firms cannot wait until orders are received to plan what is going to be producedMost firms must have saleable goods on hand or materials and subassembliesCustomers usually demand delivery in a reasonable time

  • ForecastingDemandMajor factors that influence demand include:Business and economic conditionsCompetitive factorsMarket trendsA firms plans related to product planning and design

  • ForecastingDemand ManagementIs the function of recognizing and managing all demands for products

    A firm must include long, medium and short term planning

    DM includes four major activities;ForecastingOrder processingMaking delivery promisesInterfacing between MP&C and the market

  • ForecastingOrder ProcessingA product is usually either delivered from finished goods inventory or assembled to order.

    When an item is sold from finished goods inventory a sales order authorizes the item to be shipped.

    If an item is assembled to order a sales order is issued that specifies the product.

  • ForecastingDemand ForecastingForecasts are made for three levels of plans:The strategic business planThe production planThe master production schedule

  • ForecastingCharacteristics of Demand*Demand shows the need for an itemSales is what is actually sold

    This chapter uses the term demand rather than sales

  • ForecastingCharacteristics of DemandDemand PatternsA pattern is the general shape of a time series

    TrendOverall direction of the demand over the time series

    SeasonalityHow demand fluctuates over the course of a yearMay include some other time frame...week, month

  • ForecastingCharacteristics of DemandDemand PatternsRandom variationFluctuations in the demand that occur on a random basis due to various factors

    CycleInfluences on demand due to long term (a span of several years and even decades) fluctuations of the overall economy

  • ForecastingCharacteristics of DemandStable and dynamic patternsStable patterns are those that retain the same general shape over timeDynamic patterns are those that do not follow the same general shape over timeThe more stable the demand, the easier it is to forecast demand

    Dependent and independent demandOnly independent demand items are forecast

  • ForecastingPrinciples of ForecastingForecasts are usually wrong!

    Every forecast usually includes an estimate of error

    Forecasts are more accurate for groups or families

    Forecasts are more accurate for near timeLead time issues

  • ForecastingForecast DataForecasts are only as good as the data upon which they are basedGIGO (Garbage In, Garbage Out)

    Forecasts are usually based on historical data using statistical techniques

  • ForecastingData Collection and PreparationRecord data in the same terms as needed for the forecastWhat is the purpose of the forecast?What is to be forecast?Forecast period should match the schedule periodItems forecast should match those as controlled by manufacturing

    Record the circumstances relating to the dataParticular events, factors and conditions

  • Forecasting

    Data Collection and PreparationRecord the demand separately for different customer groupsTake into account differing distribution channelsEach set of demands should be forecast separately

  • ForecastingForecasting TechniquesQualitative techniquesProjections based on judgement, intuition, and informed options

    Subjective projections

    Used to project long term trends and demand

    Generally NOT used for production and inventory forecasting

  • ForecastingForecasting TechniquesExtrinsic TechniquesProjections based on external indicators which relate to demand for a product

    The theory is that the demand for a product group is directly related or correlates for activity in another field

    Mainly used for forecasting total demand for a firms products or families of products.Not used for individual end items

  • ForecastingForecasting TechniquesIntrinsic techniquesUse historical data to forecastData usually available from company recordsBased on the assumption that the future will be similar to the pastUsed as the input for the MPS

  • ForecastingIntrinsic TechniquesRule based methodsDemand in a future period will be the same as the last period

    Demand this period will be the same as demand during the same period last year

    May be applicable if demand is seasonal and there is little fluctuation in the trend

  • ForecastingIntrinsic TechniquesAverage DemandAverages are used rather than attempting to assess what the random fluctuations might be

    Should include an estimate of error applied to the forecast

  • ForecastingIntrinsic TechniquesMoving AveragesA forecast created by taking an average over a previous periodThe forecast will be based on the average of the actual demand over the specified periodThe fewer periods included in the forecast the more weight will be given to recent informationThe forecast will react quicker to trendsThe forecast will always lag behind the trendBest used for forecasting products with stable demand with little trend or seasonality

  • Example (pg. 204)Q: Demand over the past three months has been 120, 135, and 114 units. Using a three month moving average, calculate the forecast for the fourth month.A: Forecast for month 4 = (120 + 135 + 114)/3 = 369/3 = 123 unitsQ: Demand for the fourth month turned out to be 129. Calculate the forecast for the fifth month.A: Forecast for month 5 = (135 + 114 + 129)/3 = 378/3 = 126 units

  • ForecastingIntrinsic TechniquesExponential SmoothingA technique that utilizes the most recent demand data and the previous forecast to arrive at a forecast for the next period.

    Uses a weight called a smoothing constant () to control how much emphasis is to be placed on recent data

    A routine for updating item forecastsWorks best with stable items and short range forecasts

  • ForecastingIntrinsic TechniquesExponential SmoothingMethod will detect trendsForecast will lag actual demand

    The larger the smoothing constant the more closely the forecast will follow actual demandThe forecast may become erratic, however, if there exists large amounts of random fluctuationsSimulation may assist in the selection of

    New forecast =()(latest demand) + (l-)(previous forecast)

  • Example 8.5, pg. 221-Q: If the old forecast is 100 and the latest actual demand is 85, what is the exponentially smoothed forecast for the next period? Alpha is 0.2.A: New Forecast = Alpha(Latest Demand) + (1-Alpha)(Previous Forecast) New Forecast = (0.2)(85) + (1 - 0.2)(100)= 97

  • ForecastingSeasonalityMany products have a seasonal or periodic demand patternPeriod may be day, week, month

    A measure of the degree of seasonal variation for a product is the seasonal index.

  • ForecastingSeasonal ForecastsIf a company forecasts average demand for all period, the seasonal indices can be used to calculate the seasonal forecasts.

    Seasonal demand = (Seasonal index)(deseasonalized demand)

  • ForecastingDeseasonalized DemandForecasts are made for average demand and do not consider random variation.

    Historical data are of actual seasonal demand and must be deseasonalized before they can be used to develop a forecast of average demand.

    Comparisons of data between different periods deseasonalized data must be used.

    Deseasonalized Demand = actual seasonal demand/seasonal index

  • ForecastingRules for Forecasting with SeasonalityOnly use deseasonalized data to forecast

    Forecast deseasonalized demand, not seasonal demand

    Calculate the seasonal forecast by applying the seasonal index to the base forecast.

  • ForecastingTracking the ForecastThe process of comparing actual demand with the forecast

  • ForecastingForecast ErrorThe difference between actual demand and forecast demand.

    BiasExists when the cumulative actual demand varies from the cumulative forecast

    Random variationActual demand will vary about the average demand

  • ForecastingForecast ErrorMean Absolute Deviation (MAD)A measurement of forecast errorCalculate the total error ignoring the plus and minus signs and then take the average

    MAD = sum of absolute deviation/# of observations

  • ForecastingUsing MADNormal distribution+/-1MAD of the average about 60% of the time+/-2MAD of the average about 90% of the time+/-3MAD of the average about 98% of the time

    Tracking SignalIf error is due to bias the forecast should be connectedUnder normal conditions the actual period demand should be within +/-3 MAD of the average 98% of the timeTracking signal = sum of forecast errors/MAD

  • ForecastingUsing MADContingency planning

    Safety stock

  • For Next Week. . .Do Problems:8.18.28.48.98.108.148.16

    PREPARE for Test #2. . . 15% of the final grade


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