Ch-3 Demand Forecasting

PRODUCTION AND OPERATIONS

MANAGEMENT

Chapter 3DEMAND FORECASTING

Himalaya Publishing HouseProduction and Operations ManagementBy K. Aswathappa & K. Shridhara Bhat

Chapter 3Demand Forecasting

Forecasting Defined : Forecasting is the first step in planning. It is defined as estimating the future demand for products and services and the resources necessary to produce these outputs.



Uses of Forecasts Forecasts help managers plan the productive system and also help them plan the use of the system. Planning the productive system involves long-range plans regarding the type of products and services to offer, what facilities and equipments to have, where to locate and the like. Planning the use of the system refers to short-range and intermediate range planning involving tasks such as planning inventory and workforce levels, planning purchasing and production, scheduling and budgeting.



Demand forecasting is needed for:• New facility Planning• Production Planning• Work force scheduling• Financial planning



Forecasting Time Horizons

• Short-range forecast: Has a time span of upto one year, but usually less than 3 months.

• Medium-range forecast: Has a time span from 3 months to 3 years.

• Long-range forecast: Has a time span of 3 years or more.



Types of Forecasts

• Technological forecasts: Concerned with rates of technological progress

• Economic forecasts: Statements of expected future business conditions.

• Demand forecasts: Projections of demand for a company's products or services throughout some future period.



Objectives of Demand Forecasting

Short range objectives of demand forecasting:i. Formulation of production strategy and policyii. Formulation of pricing policyiii. Planning and control of salesiv. Financial planning• Medium or Long-Range Objectives:i. Long-range planning for production capacityii. Labour requirements (Employment levels)iii. Restructuring the capital structure



Steps in the Forecasting Process

The seven basic stepsi. Determine the purpose (objectives) of the forecastii. Select the items for which forecasts are needediii. Determine the time horizon for the forecastiv. Select the forecasting model (method or technique)v. Gather and analyse the data needed for the forecastvi. Prepare the forecast vii. Monitor the forecast



Forecasting Approaches : The two general approaches to forecasting are : (i) Qualitative and (ii) Quantitative. Qualitative methods consist mainly of subjective inputs, often of non-numerical description. Quantitative methods involve either projection of historical data or the development of association models which attempt to use causal variables to arrive at the forecasts.



Overview of Qualitative Methods

1. Jury of executive opinion method involves taking opinion of a small group of high-level managers and results in a group estimate of demand.

2. Salesforce composite method is based on estimate of expected sales by sales persons.

3. Market research method or consumer survey method determines consumer interest in a product or service by means of a consumer survey.

4. Delphi method is a judgemental method which uses a group process that allows experts to make forecasts.



Market Research• Advantages –

• Consumer’s opinion regarding their future purchasing plans are better than executive opinion

• Information that might not be available elsewhere can be obtained


Chapter 3Demand Forecasting• Disadvantages –

• It may not be possible to contact every customer or potential customer and opinions obtained – forecast error

• Surveys require considerable amount of knowledge & skill to handle correctly

• Surveys can be expensive & time consuming

• The response rate for mailed questionnaire may be poor

• The survey results may not reflect the opinions of the market


Chapter 3Demand ForecastingDelphi Method

• Advantages –

• This method can be used to develop long – range forecast of product demand and sales projections for new products

• A panel of experts may be used as participants.



• Disadvantages –

• The process can take long time.

• Responses may be less meaningful because respondents are not accountable due to anonymity

• High accuracy may not be possible

• Poorly designed questionnaire will result in ambiguous or false conclusions



Overview of Quantitative Methods

• Time series models use a series of past data to make a forecast for the future.

• Time series is a time-ordered sequence of observations taken at regular intervals over a period of time.



Time Series Models

• Naïve approach

• Moving Averages Method

• Exponential Smoothing Method



Causal Models

• Trend Projection

• Linear Regression Analysis



Time Series Forecasting Methods• Decomposition of a Time Series –

• Trend – it refers to gradual, long term, upward or downward movement in the data over time. Changes in income, population, age distribution or cultural views may account for such movements

• Seasonality – It refers to short-term, fairly regular variations related to factors such as weather, holidays, vacation etc. seasonal variations can be daily, weekly or monthly



• Cycles – are wavelike variations of more than one year’s duration or which occur every several years. They are usually tied with business cycle related to a variety of economic, political or agricultural conditions

• Random variations – are residual variations which are blips in the data caused by chance and unusual situations which can not be predicted (e.g. war, earthquake, flood etc.)


Chapter 3Demand Forecasting• Naïve Approach: The simplest way to forecast is

to assume that forecast of demand in the next period is equal to the actual demand in the most recent period (i.e. current period).

• Moving Averages Method – A moving average forecast uses a number of most recent historical actual data values to generate a forecast.

• Moving average = ∑demand in previous n periods / n

• It removes the effect of random fluctuation. It is most useful when demand has no pronounced trend or seasonal fluctuations.



• In the weighted moving average method each historical demand in the moving average can have its own weight and the sum of the weight equals one.

• For example, in a 3 period weighted moving average model, the most recent period might be assigned a weight of 0.50, the second most recent period might be assigned a weight of 0.30 and the third most recent period with a weight of 0.20

• Then forecast, Ft+1=(0.50Dt +0.30Dt-1 +0.20Dt-2)/ Sum of the weights (i.e. 0.5+0.3+0.2)



Exponential Smoothing Method• It is a sophisticated weighted moving average

method. It requires only three items of data: this period’s forecast, the actual demand for this period and which is referred to as smoothing constant and having a value between 0 and 1. the formula used is

• Ft = Ft-1 + (At-1 – Ft-1)

• Selecting a smoothing constant is basically a matter of judgment or trial and error.



Selection of a Fore-casting Method• Factors to be considered –

• Cost & Accuracy

• Data Available

• Time Span

• Nature of Products & Services

• Impulse Response & Noise Dampening


Chapter 3Demand ForecastingExample

• The table below shows the monthly demand over 6 months period for a product.

• (a) Determine the forecast of demand for the 7th month using 3 month simple moving average method.

• (b) if the weightage given for the demand for 6th, 5th and 4th months are 0.5, 0.3 and 0.2 respectively, determine the forecast of demand for the 7th month using weighted moving average method.

Month 1 2 3 4 5 6

Demand (units) 120 130 110 140 110 130



• ABC company predicted the sales for a product as 150 units for February 2003. Actual demand for February 2003 was 158 units. Using a smoothing constant () of 0.3, forecast the demand for March 2003.



1. Identify and clearly state the objectives of forecasting.

2. Select appropriate method of forecasting.

3. Identify the variables.

4. Gather relevant data.

5. Determine the most probable relationship.

6. For forecasting the company’s share in the demand, two different assumptions may be made:

(a) Ratio of company sales to the total industry sales will continue as in the past.

(b) On the basis of an analysis of likely competition and industry trends, the company may assume a market share different from that of the past. (alternative / rolling forecasts)



7. Forecasts may be made either in terms of units or sales in rupees.

8. May be made in terms of product groups and then broken for individual products.

9. May be made on annual basis and then divided month-wise, etc.

Ch-3 Demand Forecasting

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