FORCASTING MODELS By Group-2

FORECASTING A forecast is an estimate of a future event achieved by systematically combining and casting forward in a predetermined way data about the past.

Nominal group technique Forecasting Models Forecasting Techniques Qualitative Models Time Series Methods Causal Delphi Method Historical Data Nominal group technique Naive Moving Average Weighted Moving Average Exponential Smoothing Trend Analysis Seasonality Analysis Simple Regression Multiple Multiplicative Decomposition

MODAL DIFFERENCES 1. Qualitative – incorporates judgmental & subjective factors into forecast. 2. Time-Series – attempts to predict the future by using historical data. 3. Causal – incorporates factors that may influence the quantity being forecasted into the model

QUALITATIVE FORECASTING MODALS Delphi method Iterative group process allows experts to make forecasts Participants: decision makers: 5 -10 experts who make the forecast staff personnel: assist by preparing, distributing, collecting, and summarizing a series of questionnaires and survey results respondents: group with valued judgments who provide input to decision makers

Nominal Group Technique (NGT) Nominal group technique (NGT) is a structured method for group brainstorming that encourages contributions from everyone.

When to Use Nominal Group Technique When some group members are much more vocal than others. When some group members think better in silence. When there is concern about some members not participating. When the group does not easily generate quantities of ideas. When all or some group members are new to the team. When the issue is controversial or there is heated conflict.

Nominal Group Technique Procedure Materials needed: paper and pen or pencil for each individual, flipchart, marking pens, tape. 1. State the subject of the brainstorming. Clarify the statement as needed until everyone understands it. 2. Each team member silently thinks of and writes down as many ideas as possible in a set period of time (5 to 10 minutes). 3. Each member in turn states aloud one idea. Facilitator records it on the flipchart.

Nominal Group Technique Procedure 4. Discuss each idea in turn. Wording may be changed only when the idea’s originator agrees. Ideas may be stricken from the list only by unanimous agreement. Discussion may clarify meaning, explain logic or analysis, raise and answer questions, or state agreement or disagreement. 5. Prioritize the ideas using multivoting or list reduction.

Quantitative Forecasting Models Time Series Method Naïve Whatever happened recently will happen again this time (same time period) The model is simple and flexible Provides a baseline to measure other models Attempts to capture seasonal factors at the expense of ignoring trend

Naïve Forecast

Naïve Forecast Graph

Quantitative Forecasting Models Time Series Method Moving Averages Assumes item forecasted will stay steady over time. Technique will smooth out short-term irregularities in the time series.

Moving Averages

Moving Averages Forecast

Moving Averages Graph

Quantitative Forecasting Models Time Series Method Weighted Moving Averages Assumes data from some periods are more important than data from other periods (e.g. earlier periods). Use weights to place more emphasis on some periods and less on others.

Simple Moving Average It is used to forecast demand of next period using data from several of most recent periods.

We may take three or more periods. Continued with same number of periods. All periods are equally weighted. Demand of oldest period is discarded and newest is added.

Simple Moving Average = Sum of demands of periods Chosen number of periods

If we have to forecast the demand of car in a city ‘X’ for the month of April, with Simple Moving Average using last three month’s data: Month No. of cars January 96 February 94 March 98 April ?

Simple Moving Average =96 + 94 + 98 = 96 3 So the forecast for the month of April is 96 cars.

Weighted Moving Average

Weighted Moving Average

Quantitative Forecasting Models Time Series Method Exponential Smoothing Moving average technique that requires little record keeping of past data. Uses a smoothing constant α with a value between 0 and 1. (Usual range 0.1 to 0.3) Both moving averages and weighted moving averages are effective in smoothing out sudden fluctuations in the demand pattern in order to provide stable estimates. Increasing the size of k (number of periods averaged) smoothes out fluctuations even better. This requires keeping extensive historical records.

What is Exponential Smoothing? Exponential Smoothing is a technique that can be applied to time series data, either to produce smoothed data for presentation, or to make forecasts. The time series data themselves are a sequence of observations. The observed phenomenon may be an essentially random process, or it may be an orderly, but noisy, process. Whereas in Single Moving Averages the past observations are weighted equally, Exponential Smoothing assigns exponentially decreasing weights as the observation get older. Exponential smoothing is commonly applied to financial market and economic data, but it can be used with any discrete set of repeated measurements. The raw data sequence is often represented by {xt}, and the output of the exponential smoothing algorithm is commonly written as {st} which may be regarded as our best estimate of what the next value of x will be. When the sequence of observations begins at time t = 0, the simplest form of exponential smoothing is given by the formulas.

If Alpha is set to 1, the forecast for the next period is based entirely on the actual value from the last period. If Alpha is set to 0, the actual value from the last period is completely ignored. Since neither of these cases will provide much insight into future data, we'll constrain Alpha to be between .01 and .99. In order to minimise costly overstocking and inventory holding, your retail outlet needs useful forecasts of future sales. For this simple exponential smoothing problem, you have sales data (in $1,000's) for eight months. You need to find Alpha, the smoothing constant, that minimises the sum of the error - which in this case is the difference between the actual and forecast sales for each period. The objective for this sales forecasting technique is to determine projected sales and the Alpha smoothing constant while minimising the squared error.

Example of Exponential Smoothing: The demand for a product in each of the last five months is shown below. Month 1 2 3 4 5 Demand ('00s) 13 17 19 23 24 # Use a two month moving average to generate a forecast for demand in month 6. # Apply exponential smoothing with a smoothing constant of 0.9 to generate a forecast for demand for demand in month 6. # Which of these two forecasts do you prefer and why? Solution The two month moving average for months two to five is given by: m2 = (13 + 17)/2 = 15.0 m3 = (17 + 19)/2 = 18.0 m4 = (19 + 23)/2 = 21.0 m5 = (23 + 24)/2 = 23.5

As before the forecast for month six is just the average for month 5= M5 = 2386 To compare the two forecasts we calculate the mean squared deviation (MSD). If we do this we find that for the moving average * MSD = [(15 - 19)² + (18 - 23)²+ (21 - 24)²]/3 = 16.67 and for the exponentially smoothed average with a smoothing constant of 0.9 * MSD = [(13 - 17)² + (16.60 - 19)² +(18.76 - 23)²+ (22.58 - 24)²]/4 = 10.44 Overall then we see that exponential smoothing appears to give the best one month ahead forecasts as it has a lower MSD. Hence we prefer the forecast of 2386 that has been produced by exponential smoothing.

Exponential Smoothing Data

Exponential Smoothing

Exponential Smoothing

Trend & Seasonality Trend analysis Seasonality analysis technique that fits a trend equation (or curve) to a series of historical data points. projects the curve into the future for medium and long term forecasts. Seasonality analysis adjustment to time series data due to variations at certain periods. adjust with seasonal index – ratio of average value of the item in a season to the overall annual average value. example: demand for coal & fuel oil in winter months.

Linear Trend Analysis Midwestern Manufacturing Sales

Least Squares for Linear Regression Midwestern Manufacturing

Least Squares Method X = value of the independent variable (time) b = Where = predicted value of the dependent variable (demand) X = value of the independent variable (time) a = Y-axis intercept b = slope of the regression line b =

Linear Trend Data & Error Analysis

Least Squares Graph

Seasonality Analysis Ratio = demand / average demand Seasonal Index – ratio of the average value of the item in a season to the overall average annual value. Example: average of year 1 January ratio to year 2 January ratio. (0.851 + 1.064)/2 = 0.957 A seasonal index with value below 1 indicates demand below average that month, and an index above 1 indicates demand above average that month. Using these seasonal indices, the future demand for any future month can be adjusted. For example, if the average demand for answering machines in year three is expected to be 100 units, then the forecast for January’s demand is 100 X 0.957 = 96 units, which is below average. May’s forecast is 100 X 1.309 = 131 units, which is above average. If Year 3 average monthly demand is expected to be 100 units. Forecast demand Year 3 January: 100 X 0.957 = 96 units Forecast demand Year 3 May: 100 X 1.309 = 131 units

Deseasonalized Data Going back to the conceptual model, solve for trend: Trend = Y / Season (96 units/ 0.957 = 100.31) This eliminates seasonal variation and isolates the trend Now use the Least Squares method to compute the Trend

Forecast Now that we have the Seasonal Indices and Trend, we can reseasonalize the data and generate the forecast Y = Trend x Seasonal Index

THANK YOU