Forecasting

• All supply chain decisions based on estimates of future demands

• Historical demand information can be used to forecast future demands

• For push/pull philosophy of supply chain– Push processes are performed in anticipation of demand– Pull processes performed in response to the customer

demand• Dell orders components for computers in anticipation of

customer demand, while• Assembly is performed in response to a customer

demand

Forecasting

• When individual stages in the supply chain make their independent forecast of demand, there is always a mismatch between the supply and demand

• Collaborative forecast for the entire chain partners tends to be much more accurate

• Decisions for functions like Production, Marketing, Finance, Personnel are best taken based on collaborative forecast

• Mature products with stable demand are usually easiest to forecast– Staple products like food grains, sugar at superbazars

Forecasting

• Forecasting and accompanying managerial decisions are extremely difficult when either the supply of raw materials or the demand for the finished product is highly variable– Fashion garments, high tech products etc.

• Good forecasting is important for products with short life cycle, like fashion goods

• Products with a long life cycle have less significant effect from forecasting errors

Forecasting

• Forecasts are always wrong and should include both the expected value and a measure of the forecast error

• Long term forecasts are usually less accurate than short term forecasts

• The greater the degree of aggregation , the more accurate is the forecast– Easier to forecast the GNP in a year of a country within 2%

accuracy than the annual revenue of a company

• The greater up the supply chain a company is, the greater the distortion of information they receive– Bullwhip effect

Forecasting- Characteristics

Bullwhip Effect

Tier 2Suppliers

Tier 1Suppliers

Producer Distributor Customers

Ordering

Amount ofinventory=

• Companies need to first – Identify the factors that influence the future demand, and

then– Ascertain the relationship between these factors and future

demand

• Some of the factors that need to be looked into– Past demand– Lead time of products– Planned advertising or marketing efforts– State of economy– Planned price discounts– Action competitors have taken

Forecasting- Components

Demand Forecasting - Basic Forecasting

• Six step approach for effective forecasting– Understand the objective of forecasting– Integrate demand planning and forecasting throughout the

supply chain– Understand and identify customer segments– Identify the major factors that influence the demand forecast– Determine the appropriate forecasting technique– Establish performance and error measures for the forecast

Demand Forecasting - Basic Forecasting• Six step approach for effective forecasting• Understand the objective of forecasting Objective is to

support decisions that are based on the forecast so first step is to identify these decisions. Eg – how much to make a particular product., how much inventory to keep etc. All affected parties in the SC must be aware of the link between decisions & forecasts. For eg – If BigBazar plans a promotion campaign in which all detergents will be sold on 20% discount in May, then this information should be shared with all participants in the SC which include manufacturer, transporter and others involved in filling demand. All parties should come up with a common forecast for promotion and a shared plan of action based on the forecast. Failure to make these decisions jointly may result in either too much or too little product in various stages of the SC.

Demand Forecasting - Basic Forecasting• Six step approach for effective forecasting• Integrate Demand Planning & Forecast throughout the SC

A company should link its forecast to all planning activities throughout the SC. These include capacity planning, production planning, promotion planning and purchasing among others. This link should exist at both the information system and the HR management level. As a variety of functions are affected by the outcomes of the planning process, it is important that all of them are integrated into the forecasting process. In a common but wrong scenario, a retailer develops forecast based on promotional activities, whereas a manufacturer, unaware of these promotions, develops a different forecast for their production planning. As a result, the manufacturer may not have enough product for retailer, ultimately leading to poor customer service.

Demand Forecasting - Basic Forecasting• Six step approach for effective forecasting• Understand and Identify Customer segments here a firm must identify

the customer segments the SC serves. Customers may be grouped by similarities in service requirements, demand volumes, order frequency, demand volatility, seasonality and so forth. In general, companies may use different forecasting methods for different segments.

• Identify Major Factors that Influence the Demand forecastAs a next step, a firm must identify major factors that influence demand forecast. A proper analysis of these factors is central to developing an appropriate forecasting technique. The main factors influencing forecasts are demand supply and product related phenomena.

On demand side, a company must ascertain whether demand is growing, declining or has seasonal patterns. Estimate must be made on demand – not on sales data. Eg – A supermarket may have promoted a certain brand of shampoos while demand for other comparable shampoos was low.

On supply side, a company must consider the available supply sources to decide on the accuracy of the forecast desired. This is more important when only a single supplier with long lead time is available so an accurate forecast will be more valuable.

Demand Forecasting - Basic Forecasting• Six step approach for effective forecasting• Determine the appropriate Forecasting technique. In

selecting an appropriate forecasting technique, a company should first understand the dimensions that will be relevant to the forecast. These dimensions include geographical area, product groups and customer groups. The company should understand the differences in demand along each dimension.

• Establish Performance and Error measurement for forecast

Companies should establish clear performance measures to evaluate the accuracy and timeliness of the forecast. These measures should correlate with the objectives of the business decisions based on the forecasts.

• Qualitative Method– Qualitative forecasting methods are primarily subjective and

rely on human judgment• Most appropriate when there is little historical data

available or when experts have market intelligence that is critical in making forecast

• Used to forecast future demand for long term in a new industry

• Time Series– Use historical demand to forecast

• Method appropriate when the demand pattern does not vary significantly from one year to the next

Forecasting- Methods

• Causal – Method assumes that the demand forecast is highly

correlated with certain factors in the environment• State of economy, interest rates etc.

– Used to determine the impact of price promotions on demand

• Simulation– Methods imitate the consumer choices that give rise to

demand to arrive at a forecast– Simulation is used to combine time series and causal

methods to find answers to• Impact of price promotion, competitors’ stores coming up

in the vicinity etc.– Forecast demand for higher fare seats when there

are no seats available at economy class fare– Modeling makes use of computers

Forecasting- Methods

Time Series Forecasting Methods

• A time series is a time-ordered sequence of observations taken at regular intervals over a period of time– Data may be measurement of demand, earnings, profits,

outputs etc.

• Analysis of time series data requires identification of the underlying behaviour of the series– Done by plotting the data with time and examining for some

pattern• Trend, Seasonal variations, Cycles, and Random or

Irregular variations ( errors)

• Trend– Refers to gradual, long term, upward or downward

movement in the data over time• Changes in income, population etc.

• Seasonality– Refers to short term fairly regular variations related to

factors such as weather, holidays, vacations etc.– Variations can be daily, weekly or monthly

• Cycles– Wave like variations of more than one year’s duration or

which occur every year• Business cycle related to economic, political or

agricultural conditions

• Random variations– Residual variations which are blips in the data caused by

chance and unusual situations


Linear Tre

nd

Constant Trend

Seasonal TrendP

rodu

ctio

n D

eman

d (U

nits

)

Demand Patterns

Time

– Pattern continuous when it is constant and does not consistently increase or decrease

• Sales of a product in the mature stage of its life cycle may show this

– Linear pattern emerges when demand increases or decreases from one period to the next

• Sales of product in the growth stage of the product life cycle shows increasing while in the decline stage show decreasing trend

– Cyclical pattern pertains to influence of seasonal factors• Demand of woolen wears will be high in winter and low

during summer

Quantitative Methods

• Forecasts in time series methods based on averages smoothened through averaging

• Three techniques used for Averaging– Naive Forecasts

• Simplest method• Assumption of demand for the next period based on the

actual demand in the most recent period– Moving Average method

• Simple moving average• Weighted moving average


• Simple Moving Average (SMA)– Forecasts for the next month is the arithmetic average of the

actual sales for a specific number of recent past time periods

– SMA =Sum of demands for all periods/Chosen number of periods

– SMA = in

=1/n =(D1+D2+D3……Dn)/n,• where , n=the chosen number of periods,• i= 1 is the oldest period in the n-period average• i= n is the most recent period• D1= the demand in the ’i’ th period


• Weighted Moving Average (WMA)– A weighted average of past sales is the forecast for the next

time period– A WMA allows for varying, not equal weightage of old

demands

– WMA= in

=1 Ci Di ,

• where Di is the demand during time period ‘i’, Ci is the

weight given to that demand and ‘n’ is the chosen number of periods

• Also 0 Ci 1 , and in

=1 Ci =1


• Exponential Smoothing Models– Forecasted sales for the last period modified by information

about the forecast error of the last periods– Modification of the last year’s forecasts are the forecast for

the next time periods• Weight assigned to a previous period’s demand

decreases exponentially as that data gets older• Recent demand data receive a higher weight than does

the older demand data – Normally only three items of data are required

• This period’s forecast, the actual demand for this period and α which is referred to as smoothening constant and having a value between 0 and 1


– Formula used is • Next period’s forecast = This period's forecast + α ( this

period’s actual demand – this period’s forecast)• Or

• Ft =Ft-1 + α ( At-1 – Ft-1)

• Where Ft = Forecast for this period (t)

• Ft-1 = Forecast for the previous period (t-1)

• At-1 = Actual demand for the previous period ( t-1)

• α = Smoothening constant• Smoothening constant selection is a matter of judgment• Commonly used values range between 0.05 – 0.5


• Regression Analysis– A forecasting technique that establishes a relationship

between variables- one dependent and others independent– Only one independent variable in simple regression

• Population, advertising expenses affecting sales– More than one independent variable in multiple regression

• Population, income and sales force affecting sales– It involves fitting a straight line equation ( in simple linear

regression analysis) to explain sales fluctuations in terms of related and presumable causal variables

– Three major steps in regression analysis• Identifying variables which are causally related to the

firms sales• Determine / estimate the values of these variables

related to sales• Derive the sales forecast from these estimates

Common Time Series Models

• A linear regression assumes the relationship between dependent and independent variables a straight line ( known a simple linear regression analysis)

• A curvilinear relationship is a non-linear regression producing a curve

Common Time Series Models

Forecasting- Adaptive Method

• Adaptive method uses more sophisticated approach compared to static methods

• Popular models used in this method• Holt’s Model

– This is a Trend corrected Exponential smoothened model– Appropriate when demand is assumed to have a level and a

trend but no seasonality– Systematic component of demand = Level + Trend

– In period t, given estimate of level Lt and trend Tt, the forecast for future periods is expressed as

– Ft+1 = Lt + Tt and Ft+n = Lt+ nTt


– After observing for Period t, the estimate for level and trend is corrected as

– Lt+1 = α Dt+1 + (1- α )(Lt + Tt)

– Tt+1 = β (Lt+1 – Lt) + (1- β )Tt ,

• Where α is a smoothening constant for the level, and β is a smoothening constant for trend and varies from 0 to 1 like α

• Winter’s Model – Trend and Seasonality Corrected Exponential Smoothened

model– Method appropriate when the demand is assumed to have a

level, trend and a seasonal factor

– Systematic component of the demand = ( Level + trend) x seasonal factor

– Assume periodicity of demand to be p, initial estimates of level L0, trend T0 and seasonal factors ( S1…….Sn)

– In period t, the forecast for future periods is given by• Ft+1 = (Lt + Tt)* St+1, and Ft+l = (Lt + lTt)*St+l

– On observing the demand for period t+1, the estimates for level, trend and seasonal factors are revised as

– Lt+1 = α (Dt+1 /St+1) + (1- α )(Lt-Tt)– Tt+1 = β (Lt+1 – Lt) + (1- β )*Tt

– St+p+1 = γ (Dt+1 / Lt+1) + (1- γ )*St+1, • Where γ is a smoothening constant for seasonal factor

varying from 0 -1


Measure of Forecasting Errors

• Managers perform a thorough error analysis on a forecast to– Determine whether the current forecasting method is

accurately predicting the systematic components of demand• A method consistently giving positive error can indicate

over prediction by the method and manager can make necessary corrections

– Estimate forecast error as any contingency plan must account for such an error

• Contracting with an outsource agency , even though more expensive, to supply shortfalls in the order on urgent basis


• Forecasting Error is simply the difference between the forecast and actual demand for a given period• et = Ft – At , where et = forecast error for the period t, At

= actual demand for period t, and Ft = the forecast for the period t

• Mean Error (ME) = 1/n ∑nt=1 et

• Cumulative Sum or Error (CFE) = ∑nt=1 et

– CFE is useful in measuring the bias in a forecast

• Mean Absolute Deviation (MAD) = 1/n ∑nt=1 | et |

• MAD is merely the average error for each forecast.• Popular because it is easy to understand

• Mean Squared Error (MSE) = 1/n ∑nt=1 et

2

• Used as an estimate of the variance of the random error et which is σ2

• Mean Absolute Percentage Error (MAPE)=1/n ∑ⁿ t=1 ( |et| / At) X100

– MAPE is useful for putting forecast performance in the proper perspective

• Forecast error of 100 when the actual demand is 200 units results in larger percentage error than the error occurring when the demand was 1000 units


Qualitative or Judgemental Methods

• Not based on quantitative numbers exclusively– Based on judgment about the causal factors that underline

the sales of particular products or services, and– On opinions about the relative likelihood of these causal

factors being present in the future– Useful when historical data are not available

• Executive Committee Consensus– A committee of executives from different departments

constituted and entrusted with the responsibility of developing a forecast

– Uses inputs from all parts of organisation and analysts analyse data as required

– Such forecasts tend to be compromised ones, not reflecting the extremes that might be present

– Most commonly used method of forecast


• The Delphi Method– Method seeks to remove the undesirable consequences of

group thinking existing in committees– Committee consists of experts from within and outside the

organisation• Expert in one aspect of the problem and no one

conversant with all aspects of the issue– Each expert makes independent predictions in the form of

brief statements – Coordinator edits and clarifies these statements– Coordinator provides a series of questions in writing to the

experts that includes feedback supplied by other experts– Above repeated several times till consensus reached


• Survey of Salesforce/ Field Expectation Method– Individual members of the salesforce required to submit

sales forecasts of their respective regions– These combined to form total estimate of sales– Estimates transformed into sales forecasts to ensure

realistic estimates• A popular method for companies having good

communication system and salesforce directly selling to customers


• Survey of Customers/User’s Expectation Method– Estimates of future sales obtained directly from customers

through survey– Sales forecast determined by combining individual

customers’ responses• Method useful where customers are limited in number


• Historical Analogy– Estimates of future sales of product tied to knowledge of a

similar product’s sales– Knowledge of one product’s sales during various stages of

its product life cycle applied to estimates of sale for a similar product

• Method useful for a new product


• Market Surveys– Questionnaires, telephone talks or field interviews form the

basis for predicting market demand for products– Normally preferred for new products or existing products in

new markets


Demand Forecasting

• Forecasting is a key driver of virtually every design and planning decision made in both an enterprise and a supply chain

• Collaborative forecasting taking all partners in the supply chain give benefits an order of magnitude higher than the cost

• Value of data depends upon where one is in the supply chain

• Demand is not the same as sales– True demand can be obtained by making adjustments for

the unmet demands due to stock outs, competitor’s actions, pricing, promotions etc.

Forecasting

Documents

Transcript of Forecasting