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This course covers demand forecasting techniques, components of demand, and the importance of accurate forecasts for effective supply chain management. Topics include qualitative analysis, time series analysis, and causal relationship forecasting.
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Introduction Lecture 6. Forecasting
Course content • Demand Management • Components of demand • Qualitative technique in forecasting • Time series analysis • Causal relationship forecasting
Forecasting • Forecast plans: It is not possible to make decisions in production scheduling, purchasing, and inventory levels until forecasts are developed that give reasonably accurate views of demand over the forecasting horizon. • Forecasting is a prediction of future events used for planning process • Management needed accurate forecast to ensure supply chain management
Forecasting • Accurate forecast allows schedulers to use capacity efficiently, reduce customer response times and cut inventories. • Managers need forecast to anticipate changes in prices or costs or to prepare for new laws or regulations, competitors, resource shortages or technologies.
Demand Management • Demand Management - Demand management is to coordinate and control all sources of demand so the productive system can be used efficiently and the product delivered on time. Demand are of two types • Dependent Demand If the demand can be tabulated from the end item configuration. For developing a Car, number of tires, wheel barrow etc can be known. Usually sub-components of the final product. 2. Independent Demand: It cannot be derived directly from that of other products. E.g sales prediction
Handling Demand • Handling Demand • Holistic approach or active approach to influence demand (Developing aggressive marketing strategy to influence demand) 2. Passive role or simply respond to demand (Firm accept the demand and runs with it passively accepting it) Our primary interest is in forecasting for independent demand
Components of Demand • Demand for product or service can be broken down to six components • Average demand for the period • Trend • Seasonal elements • Cyclical elements • Random variation • Autocorrelation
Components of Demand • Trend are the usual pattern of demand. Trends are of 4 types. Trend is a systematic increase or decrease over time. Linear Trend: It is a straight continuous relationship Asymptotic trends starts with the highest demand growth at the beginning but then tappers off. Objective of capturing the market and gradually saturating it. An exponential curve is common in products with explosive growth. The explosive trend suggests that sales will continue to increase. S-Curve is typical of product growth and maturity cycle. Important point in S-Curve is a point where the trend change from slow to fast growth sales quarters
Components of Demand 1. Seasonal Elements: Seasonal fluctuation in demand 2. Cyclical Elements: Cyclical influence on demand comes from macroeconomic factors as political, war, sociological pressure etc. Time span is unknown and cause of cycle may not be considered. 3. Random variations are caused by chance events. The cause for the this type of demand is unknown. 4. Autocorrelation: It denotes the persistence of occurrence. The value expected at any point is highly correlated to its own past.
Historical product demand consisting of growth trend and seasonal demand Number of units demanded Trend Average demand seasonal
Types of Forecasting • Forecasting is classified into four basic types • Qualitative Technique • Time series analysis • Causal Relationship • Simulation Quantitative Technique
Qualitative Techniques of Forecasting Qualitative analysis are subjective, judgmental and based on estimate and opinions • Grass Roots As per this, the person near to the customer knows the market more better. His information is taken as a base for further forecasting. 2. Market Research Market survey, personal calling, data collection etc are done to collect information from market and then test the hypothesis to better understand the management decision problem (MDP). 3. Panel Consensus Group discussion to exchange the ideas. The problem is the lower management staff may not fully participate in idea sharing. 4. Historical analogy: Grouping of the customer based on product category purchased. If an person has bought a DVD, then it is likely that he is interested in purchasing DVD movies. Also people who had previously purchase some items before are interested in new product of same category.
Qualitative Techniques of Forecasting 5. Delphi method Delphi method is a form of panel consensus but the identity of the individual participating is not open. The view of everyone has same weight. View of individual is known through open end and closed end questionnaire. The result from all the participant is summarized. Distribute the final result to participants.
Time series Analysis • Predict the future based on past data. The analogy of past weeks data can be used to predict the future data. • Short term forecast: under 3 mts • Medium term forecast: 3 mts – 2 years • Long term forecast: greater than 2 years. Short term compensate for tackling, problem in hand, Medium term compensate for seasonal effects Long term compensate for identifying change in trends and consumer habits Forecasting models available for firm are • Time Horizon to forecast • Data Availability • Accuracy required • Size of forecasting budget • Availability of qualified personals
Time Series Analysis Calculation of the forecasting based on time series analysis can be done on following basis • Simple Moving Average • Weighted Moving Average • Exponential Smoothing • Simple Moving Average • The demand for the product or service is relatively constant, neither growing nor declining, with no seasonal slump, then in such scenario, a moving average is preferred. • The simple moving average method is used to estimate the average of a demand time series and thereby remove the effects of random fluctuation.
Simple Moving Average • Applying a moving average model simply involves calculating the average demand for the nmost recent periods and using it as the forecast for the next time period. Where D= actual demand in period t n = total number of periods in the average =Forecast for period t+1 With the moving average method, the forecast of next period’s demand equals the average calculated at the end of this period
Simple Moving Average a. Compare a three-week moving average forecast for the arrival of medical clinic patients in week 4. The number of arrivals for the past three weeks were b. If the actual number of patient arrivals in week 4 is 415, what is the forecast for week 5
Simple Moving Average Solution • The moving average forecast at the end of week 3 is b. The forecast for week 5 requires the actual arrivals from weeks 2-4, the three most Recent weeks of data The forecast at the end of week 3 would have been 397 patients for week 4. The forecast For week 5, made at at the end of week 4, would have been 402 patients. In addition, at the end of week 4, the forecast for week 6 and beyond is also 402 patient.
Simple Moving Average • Determining the value of n The stability of the demand series generally determines how many periods to include (i.e the value of n). Stable demand series are those for which the average (to be estimated by the forecasting method) only infrequently experiences changes. Large values of n should be used for demand series that are stable and small values of n for those that are susceptible to change in underlying average. If the underlying average in the series is changing, however, the forecasts will tend to lag behind the changes for a longer time interval because of the additional time required to remove the old data from the forecast.
Simple Moving Average Example: Exhibit 13.5 Pg. 546 Eleventh Edition Chase/ jacob
Weighted Moving Average • In simple moving average, each demand has the same weight in the average. But in the weighted moving average method, each historical demand in the average can have its own weight. • The sum of the weights equal to 1. • The Formula for a weighted moving average is Where W1=weight to be given to the actual occurrence for the period t-1 W2=weight to be given to the actual occurrence for the period t-2 Wn= weight to be given to the actual occurrence for the period t-n n=total number of periods in the forecast
Weighted Moving Average • A department store may find that in a four month period, the best forecast is derived by using 40% of the actual sales for the most recent month, 30% for two months ago, 20% for three month ago and 10% of four months ago. If actual sales experience was Find the forecast for month 6 if the sales for 5 mts turned out to be 110 (Ans: 102.5)
Weighted Moving Average • Using the weighted moving average method to estimate average Demand a. The analyst for the medical clinic has assigned weights of 0.70 to the most recent demand, 0.2 to the demand one week ago, and 0.10 to the demand two weeks ago. Use the data for the first three weeks from the table below to calculate the weighted average for week 4. (Ans: 403) b. If the actual demand for 4th week is 415 Patients, what would be the forecast for week 5. (Ans. 410)
Exponential Smoothing Exponential Smoothing is method is actually a weighted moving average method that calculates the average of the time series by giving recent demands more weights than earlier demands. It is most frequently used for forecasting due to its simplicity and the amount of data needed to support it. Weighted moving average requires n periods of past demand and n weights, whereas exponential smoothing requires only three items to calculate demand • The last period’s forecast • The demand for this period • Smoothing parameter alpha (α) (Value of α is between 0 and 1)
Exponential Smoothing • The equation for forecast is Smoothing constant α is the level of smoothing and the speed of reaction between forecasts and actual occurrences. Value for smoothing constant can be taken from organization requirement as per their volume of demand. Or mathematically it can be taken as 2/(n+1) The equation for exponential smoothing highlights, the old forecast + error portion between previous forecast and what actually occurred.
Exponential Smoothing • E.g In the given table below, consider the arrival of patients, at the end of 3 weeks, using α=0.10, calculate the exponential smoothing for week 4. Assume initial forecast as 390
Exponential Smoothing • In the above example, if the demand for 4th week becomes 415, the new forecast for week 5 would be as follow Conclusion: Using the exponential smoothing model, the analyst’s forecasts would have been 392 patients for week 4 and then 394 patients for week 5 and beyond. As Soon as the actual demand for week 5 is known, then the forecast for week6 will be Updated.
Trend Effect in Exponential Smoothing • Exponential smoothing has an advantages of simplicity, minimal data requirement, inexpensive and attractive to firm. • But its simplicity is a disadvantage if the underlying average is changing, as in case of demand series with a trend. • Higher values of Smoothing constant (α) may help to reduce forecast error to some extent, when there is a change in the average of the time series; however, the lag will still be there if the average is changing systematically.
Trend Effect in Exponential Smoothing • Assume that actual demand is steadily increasing at 10units per period. Forecast using exponential smoothing with α=0.3 As we see, forecast using exponential smoothing with α=0.3 will lag severely behind the actual demand even if the first forecast is perfect. To improve the forecast, we need to calculate an estimate of the trend, we start by calculating the current estimate of the trend which is the difference between the average of the series computed in the current period and the average computed last period. Another smoothing constant delta () is added to reduce impact of error
Trend Effects in Exponential Smoothing Model FITt = Ft + Tt Ft = FITt-1 + a(At-1 - FITt-1) Tt = Tt-1 + (Ft - FITt-1 ) Ft = the exponentially smoothened forecast for period t Tt = the exponentially smoothened trend for period t FITt = the forecast including trend for period t FITt-1 = the forecast including trend made in prior period or period t-1 At-1 = actual demand for prior period or period t-1 a , = smoothing constants
Assume a initial starting Ft of 100 units, a trend of 10 units, an alpha of 0.20 and a delta of 0.30. If actual demand turned out to be 115 rather then the forecast 100, calculate the forecast for the next period. Hence, the forecast for next period turned out to be 121.3 with a trend of initial 100 units.
Mean Absolute Deviation (MAD) • MAD is the average error in forecasts, using absolute values. • MAD is computed using the differences between the actual demand and the forecast demand without regard to sign. • It equals the sum of the absolute deviation divided by the number of data points or stated in equation as follow; • Where • t=period number • A=actual demand for the period • F= forecast demand for the period • N=total number of period
Mean Absolute Deviation (MAD) • Compute a 3 month moving average forecast of demand for April through January (of the next year) • Compute a 5 months average for June through January • Compare the two forecasts computed in parts a and b using MAD. Which one should the dealer use of January of the next year. e.g
Mean Absolute Deviation (MAD) • MAD is often use to forecast errors. • When errors that occurs in the forecast are normally distributed, the mean absolute deviation relates to the standard deviation as • Standard deviation = Conversely, 1 MAD= 0.8 Standard Deviation • The ideal MAD is zero which would mean there is no forecasting error • The larger the MAD, the less the accurate the resulting model
Mean Absolute Deviation (MAD) • The value of MAD to forecast in case of exponentially smoothing is as follow;
Measurement of Error • Tracking Signal It is a measurement that indicates whether the forecast average is keeping pace with any genuine upward or downward changes in demand. Tracking signal is the number of mean absolute deviations that the forecast value is above or below the actual occurrence. Tracking signal (TS)= RSFE/ MAD RSFE= running sum of forecast error, considering the nature of the error
Measurement of Error • Computing MAD and Tracking signal In a perfect forecasting model, the sum of actual forecast errors would be zero; the error that results in overestimates should be offset by errors that are underestimate. The tracking signal would then be also zero, indicating an unbiased model, neither leading nor lagging the actual demand.
Linear Regression Analysis • Regression is a functional relationship between two or more correlated variables. • It is used to predict one variable to other. Or more precisely, relation of dependent and independent variables. • Linear regression line is of the form Y=mx +C Where Y is the value of dependent variable that we are solving for, C is the intercept and m is slope, x is the independent variable. In linear regression forecasting, the past data and future projection are assumed to fall about a straight line.
Linear Regression Analysis • Linear regression is used in for both time series forecasting and for casual relationship forecasting. • When the dependent variable changes as a result of time, it is time series analysis. • If one variable changes because of the change in another variable, this is called casual relationship. E.g Death of lung cancer increasing with the increase in number of people smoking. • Casual Method provides the most sophisticated forecasting tools and are very good for predicting turning points on demand and preparing long range forecast.
Linear Regression Analysis • Least square method fits the line to the data that minimizes the sum of the squares of the vertical distance between each data point and its corresponding point on the line. • Equation of st. line is Y=a+bx Standard Error of Estimate
Linear Regression Analysis Example Following are the sales and advertising data for past five months. The marketing manager says that the next month, the company will spend $1750 on advertising of product. Use linear regression to develop an equation and forecast for this product.
Correlation Coefficient for regression • Correlation coefficients shows the strength between the dependent and independent variable. • The value of correlation coefficient lies between -1 to +1. • If r=-1, it shows, negatively correlated • If r=0, there is not linear relationship • If r=1, highly correleted
Casual Relationship Forecasting • Casual relationship forecasting is the one in which the causing element is known enough in advance, it can be used as a basis for forecasting. • E.g increase in rain will increase sales of umbrella • Increase in car accidents, increase in number of insurance Identify the occurrence that are really the cause. Often leading indicators are not the casual relationship, but in some indirect way, they may suggest that some other things might happen. Other non casual relationships just seem to exist as a coincidence.
Important Questions discussion PU 2003 Fall 5.a) From the choice of a simple moving average, weighted moving average, exponential smoothing, and linear regression analysis, which forecasting technique would you consider the most accurate? Why? (7) 4.a)what is the difference between dependent demand and independent demand. Why do firms keep inventory? (5) 6.c) Explain the features of a good forecasting technique. (5) 2.B What do you mean by demand management? Differentiate between dependent demand and independent demand. (5)
