demand management nitie

8/8/2019 Demand Management NITIE

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Demand Management

What is Demand Management ? Defined as focused efforts to estimate and

manage customers demand, with the intention ofusing this information to shape operating

decisions.

Set of activities and decisions, tools andtechniques, that firms adopt to assess, andpredict the purchase of companys products and

services.

Seeks to forecast and even regulate, the quantity,mix, price and timing of such purchases.


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Demand Management Objectives

Gathering and analyzing knowledge aboutconsumers, their problems, and their unmetneeds.

Identifying supply chain partners to performthe functions needed in the demand chain.

Moving the functions that need to be doneto the channel member that can performthem most effectively and efficiently.


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Sharing with other supply chain members,

knowledge about consumers andcustomers, available technology, logisticschallenges and opportunities.

Developing products and services thatsolve customers problems.

Developing and executing the bestlogistics, transportation, and distribution

methods to deliver products and servicesto consumers.


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Demand Forecasting

A major component of demandmanagement is forecasting the amount ofproduct that will be purchased by

consumers or end users.In the integrated supply chain all otherdemand will be derived from the primarydemand.

A key objective is to anticipate and respondto primary demand as it occurs in themarket place.


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Forecasting process comprises of two

elements(a)Nature of demand, and

(b)Forecast components

Nature of Demand

Dependent Demand Independent Demand


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Dependent versus IndependentDemand

Vertical dependent is characterized by sequenceof purchasing and manufacturing, such asnumber of tyres used for assembly of

automobiles. Horizontal dependent occurs in a situation where

an attachment, promotion item or operatorsmanual is included with each item shipped.

(a)The demanded item may not be required tocomplete the manufacturing process but may beneeded to complete the marketing process.


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(b) Once manufacturing plan for base item isdetermined , requirements of components/

attachments can be calculated directly andno separate forecasting is done. Independent demands are ones that are

not related to the demand for another item.

For instance, demand for refrigerator is notrelated to the demand for milk. Independent demand items are forecasted

individually.


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Forecast Components1. Base demand

2. Seasonal factors

3. Trends

4. Cyclic factors

5. Promotions

6. Irregular quantities.

Mathematically forecast is expressed as

jFt+1= (Bt x St x Tt x Ct x Pt) + I, where

- Ft+1= forecast quantity for period t+1


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- Bt= base level sales demand (average saleslevel) for period t+1

- St= seasonal factor for period t- T= trend component (quantity increase or

decrease per time period)- Ct= cyclic factor for period t- Pt= promotional factor for period t- I= irregular or random quantity.j All forecasts may not include all components.A. Base demand is based on average demand over

an extended period of time.(a)There is no seasonality, trend, cyclic or

promotional component.


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B. Seasonal component is characterized by upwardand downward movement in demand pattern,

usually on annual basis e.g. emand for woollenblankets is at peak during winter months andlowest during summer.

(a) Seasonality at wholesale level precedesconsumer demand by approximately one

quarter.(b) An individual seasonality factor of 1.2 indicates

that sales are projected at 20% higher than anaverage period.

C. Trend Component exhibits long rangemovement in sales over an extended period oftime.

(a) Trend may change number of times over theentire product life cycle.


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(b) For instance, a reduction in birth rateimplies reduction in demand of disposal

diapers.

(c) Trend component influences base demandas Bt+1 = Bt x T, where

- Bt+1 = base demand in period t+1- Bt = base demand in period t, and

- T= periodic trend index.

D. Cyclic component are known as businesscycles.

(a)Economies swing from recession to

expansion every three to five years.


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E. Promotions are initiated by the firms marketingactivities such as advertising, and various otherschemes.

(a) Sales increase during promotion as the consumerstake advantage of promotional schemes thus ledingto liquidation of inventories.

(b) Promotion can either be the deals offered to theconsumers or deals offered to the trade

(wholesalers/ retailers).(c) Promotions if offered on regular basis at the same

time every year will resemble a seasonalcomponent.

F. Irregular components include random orunpredictable quantities that do not fit into any othercategory hence are impossible to predict.

(a) By tracking and predicting other components themagnitude of random component can be minimized.


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Forecast Approaches

A. Top-Down Approach

Plant Distribution Centre

Field

DistributionCentre # 1

Forecast4000 units

FieldDistribution

Centre # 2

Forecast3000 units

Field

DistributionCentre #3

Forecast2000 units

Field

DistributionCentre #4

Forecast1000 units


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Assume the firm has an aggregate monthly

forecast for the entire country as 10,000units and it use four distribution centres toservice the demand with a historical split of40, 30, 20, and 10 per cent respectively.

Forecasts for individual distribution centreswill be projected to be 4,000, 3000, 2,000and 1,000 respectively.

In top-down approach a national level SKUforecast is developed and then theforecasted volume is spread acrosslocations on the basis of historical salespattern.


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B. Bottom-up Approach

Decentralized approach since each

distribution centre forecast is developedindependently.

Results into more accurate forecast as it

tracks and considers demand fluctuationswithin specific markets.

Requires more detailed record keeping andis more difficult to incorporate demand

factors such as impact of promotion.jTrade-off the detail tracking of bottom-up

approach with data manipulation ease oftop-down approach.


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Components of ForecastingProcess

Forecastdatabase

OrdersHistory

Tactics

Forecast Process

Forecast Administration

Forecast

Technique

Forecast

SupportSystem

ForecastUsers

FinanceMarketing

SalesProduction

Logistics


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A. Forecast data base keeps information about

Orders

Order history Tactics used to obtain orders such as

promotions, schemes, special promotionalprogrammes.

State of economy and competitive actions.B. Forecast process integrates forecast techniques,

support system and administration.

Two prominently used forecasting techniques

are time series and correlation modelling. Forecast support system is the capability to

gather and analyze data, evaluate impact ofpromotion, develop forecast and communicate

to the relevant personnel.


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Issues addressed by ForecastAdministration

Who is responsible for developing the forecast?

How is forecast accuracy and performancemeasured?

How does forecast performance affect jobperformance, evaluation and rewards?

Do the forecast analysts understand the impact

of forecasting on logistics operations? Do they understand the differences in various

forecasting techniques?


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Pyramid Forecasting Technique

Individual Items

X 1 X2 Z1, Z2, Z3, Z9

Roll Up

Product Groups

X ZForce down

Level 1

Level 2

Level 3

TotalBusinessRs


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Initial Roll-up Forecast

X1 X2 Initial forecast (units)

Unit Price (Rs)8200

Rs 20.614845

Rs 10

Z1, Z2, Z3, Z9

217460 561000X Z15000 25000 Group Forecast13045 28050 Roll-up Forecast

Rs 16.67 Rs 20 Average Price

950000778460

Business Forecast(Rs)Roll-Up Forecast (Rs)

Roll-Up


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Forcing down the managementForcing down the managementforecast of total salesforecast of total sales

2121

X19480

X 25602

Z1, Z2, Z3Z9

Force down

Rs 900,000Management forecast (Rs)

Forced forecast (X)= (900,000/778460)x13045= 15082 units

X15082

Forced forecast (Z) = (900,000/778460)X28050=32429 Units

Z

32429

Forced forecast(Units)

Forced forecast

(Units)

Forced forecast (X1)=(15082/13045)x 8200=

9480 unitsForced forecast (X2)=(15082/13045) x4845=5602 units


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Forecasting MethodsForecasting techniques can be divided into

three categories(a) Qualitative methods

(b) Time-series methods, and

(c) Causal methods Qualitative Methods

- Subjective and judgmental and based on

opinions and estimates. Time series and causal methods

- Employ numerical data collected over aperiod of time to predict future trends.


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Demand Forecasting

Qualitative analysis Quantitative analysis

Customersurvey

Sales forcecomposite

Executiveopinion

Delphimethod

Past analogy

Time seriesanalysis

Causalanalysis

Forecast by linearregression

Simplemovingaverage

Simpleexponentialsmoothing

Trend analysis

Holts doubleExponentialsmoothing

Winters tripleExponentialsmoothing


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- The Delphi Method generally consists of thefollowing steps.

1. Selection of groups of experts, depending onthe type of expertise required.

2. Ideas and forecasts are obtained from allparticipants, usually through a questionnaire.

3. The results are summarized and redistributed

among participants, along with appropriate newquestions.4. Any member whose response deviates from

the opinions of majority is requested toreconsider and provide justification for thedeviation.


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5. The responses are again summarized, and newquestions are developed on the basis of theresponses.

6. This cycle is repeated till the results are in arange narrow enough to be used as a forecast.

- Success of this technique depends on the talentof the coordinator and absence of bias on the

part of experts.- The coordinator should be competent enough toanalyze diverse and wide ranging statementsand arrive at a structured questionnaire as wellas a coherent forecast.


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Problems in this method are:

1. Members opinions might be influenced bya socially dominant individual.

2. Members may fear the loss of credibility ifthey back away from a publicly stated

opinions. How to overcome these problems?

- Membership is generally not revealed tothe panel and panel members are keptseparate.

- The panel does not meet to discuss ordebate the issue.


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Nominal Group Technique

- Developed by Andrew Van de Ven, a Wharton

Professor.- The steps involved are:

1. Generation of ideas

- Group members write down their ideas

regarding the question/problem posed by amediator.

2. Collection of ideas

- Group members ideas are collected andrecorded on a flip chart or blackboard that isvisible to all members.

- No discussion is permitted during this stage.


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3. Discussion

- Each idea is discussed.

- To avoid any wastage of time, similar orduplicate ideas are clubbed together anddiscussed.

- The ideas are discussed in terms of theirperceived importance, clarity and logic.

- Members are allowed to make brief

impersonal comments, on a voluntary basison each idea.

4. Preliminary voting.


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- Members are asked to cast theirpreliminary vote to select the best idea.

- If there is no consensus regarding the bestidea, the ideas concerned are discussedfurther to clarify their meaning and logic.

5. Final Voting- Members are asked to cast their final vote.

- The result of the final vote is counted and

the most preferred idea, solution orforecast is identified.


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Time-Series Method

- Assumes that the past data is a good indicator of the

future.- Instances when this assumption is not true are rareand not significant enough.

- Hence, many operations managers use a time series

model to forecast the demand for their goods orservices.

Simple Moving Average

- SMA technique forecasts demand on the basis of the

average demand calculated from actual demand inthe past.

-


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SMA method is effective when a product does notexperience fluctuation in demand over a period of

time and the past demand for the product is notseasonal.

Useful for removing any random fluctuation indemand to get accurate forecasts.

Mathematically SMA is calculated asFt = (Dt-1 + Dt-2 + Dt-3 + Dt-4 + + Dt-n)/ n

- Ft = forecast for the period t

- n= number of preceding periods taken foraveraging

- Dt-1, Dt-2, Dt-3 and so on =Actual demand inthe preceding time periods.


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One of the key decisions to be taken whenusing SMA method is the length of the time

period to be considered. The greater the moving average period, the

less vulnerable the forecast to random

variations. A larger moving average period is taken

when fluctuations in demand are minimal.

A small time period is taken whenfluctuations in the demand are high or whenthere is no need to identify short-termfluctuations.


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Weighted Moving Average

- Sometimes the forecaster wants to use a

moving average but does not want all the nperiods equally weighted owing to sometrend and seasonality in demand.

-Experience and trial and error methods areused to assign weights to a particular data.

- Each element is weighted by a factor andsum of the weights should be equal to one.

- Mathematically,- WMAt+1 =Ct At,-


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- WMAt+1= W.M.A at the time period t+1,

- At = Actual demand in time period t

- Ct = Percentage weight given to time period

t; 0


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- Demand for the most recent data is given themaximum weightage.

- Weights assigned to the preceding periodsdecrease exponentially.

- The data required for making forecast are the

most recent forecasts, the actual demand forthat period and a smoothing constant ( )

- The value for E lies between 0 and 1.

First-orderexponential smoothing

Ft = EDt-1 + (1- E) Ft-1


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Ft-1 = Forecast for the period t-1

Dt-1 = Actual demand for period t

E =Smoothing constant

Selecting a smoothing coefficient (E)

The smoothing coefficient E takes any value between0 and 1.

High E results in more weightage for the most recentmonths and low E results in a relatively lowerweightage for it.

A high E is more appropriate for new products forwhich demand is more dynamic and unstable.

If demand is stable and believed to represent thefuture, a low E can be selected to smooth out the

effect of any noise.


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Trend adjusted exponential smoothing(double smoothing)

- Trend indicates a continuous increase ordecrease in the average of the series overa period of time.

- The presence of a trend in time series leadsto forecasts that are above or below theactual demand.

- In trend-adjusted exponential smoothingmethod, both the average and the trend aresmoothed.


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- To do so, two smoothing constants E and Fare used.

- To calculate both the average and the trendfor each time period, the followingequations are used.

At = E Dt + (1-E) (At-1 + Tt-1)

Tt = F (At - At-1) + (1-F) (Tt-1)

Ft+1 = At + Tt

Dt = Demand in period t At = Exponential smoothed average for

period t.


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Tt = Exponential smoothed trend for periodt.

Tt-1 = Trend estimate for period t-1. At-1 = Actual demand for t-1 period.

Ft+1 = Forecast for period t+1

E= Smoothing constant lying between 0 and1.

F= Smoothing constant lying between 0 and1.

The estimates for the last periods averageand trend are obtained from historical databy making an educated guess in case no

historical data is available.


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Causal Quantitative Models

The demand for product or service isdependent on different factors or variableslike price, quality, availability of substituteand/or complementary products/ services,

income levels of customers, number ofcompetitors, etc.

Organizations must identify the variablesthat affect the demand for a product and

service. A causal method evaluates the

relationship between different variablesand their influence on each other.


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Causal methods include linear regressionand multiple regression analysis.

Linear Regression

- Refers to the functional relationship betweentwo or more correlated variables.

- Linear regression refers to the functionalrelationship between a dependent variable,for which the forecast is needed, and a

group of other variables, known asindependent variables, which influence thedependent variable.


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- For instance, let us assume that the sale oftelevisions is dependent on the advertisingbudget and the number of retailers.

- In this case, television sales is thedependent variable and the advertising

budget and the number of retailers areindependent variables.

- In linear regression, the relationship

between the dependent variable and oneindependent variable is defined by astraight line.

Y bX


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Y= a+bX,

Where Y= Value of dependent variable

X= Value of independent variable.

a=Y intercept (constant value)

b=Slope of the line

- Widely used by operations managers because it

predicts demand with high level of accuracy.- Useful in long term forecasting of major occurrences

and aggregate planning.

- Useful for forecasting for product families, wheredemand for individual products within the family mayvary widely during a time period though the demandfor total product family remains smooth.


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Least Square Method

Used to generate a regression model byassigning data to a single line.

Past demand data is used to form a linearmodel by regressing data points to a single

line. Once a linear equation is formed, future

demand (Y) can be predicted by

substituting value of X.


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Selecting a Forecasting System-Time Span

Time

Horizon

Decision Areas Techniques

used

Short-term

Purchasing, job scheduling,project assignment, andmachine scheduling

Time seriesSMA,WMA andExponential

Smoothing.

Mediumterm

Capital and cash budgeting,sales planning, productionplanning, and inventory

budgeting

Regressionanalysis

Long-term

Product planning, facilitylocation and expansion,capital planning

Regressionanalysis, Delphimethod and

market research.


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Measures of Forecasting Accuracy

Since the demand for a product is dependent onvarious factors, all of which cannot berepresented in a forecasting model, obtainingaccurate results from forecasting methods is

highly improbable. A forecasting error is the difference between the

forecasted demand for a particular period andthe actual demand in that period.

To determine how well the forecasts from aforecasting model fit with the actual demandpattern, the average error of the model iscalculated.


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The average error of a forecast modelprovides a measure for examining how well

the forecast value of demand matches thepattern of past data. The measures of forecasting errors are:(a) Mean Absolute Deviation

Mean of the errors made by the forecastover a period of time without consideringthe direction of error.

Does not determine whether the forecast

was an overestimate or underestimate. MAD= 1/n At Ft At Ft indicates the absolute value of

deviation.


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(b) Mean Square Error

Mean of squares of deviations of forecast

values from the actual result is calculated. MSE= 1/n (At Ft )2

Large errors are penalized more than the

small ones because of squaring .(c) Mean Forecast Error

Calculated in the same way as MAD,only

difference is that in MAD, the absolutevalues are taken in consideration whereasin the MFE method, te real values are

taken.


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MFE= 1/n (At Ft)

The closer the value of MFE to zero, the

more accurate the forecast is.(d) Mean Absolute Percentage Error

MAPE indicates relative error.

MAPE= 100/n At Ftz A

t

Tracking Signal

Measure of accuracy that assesses the

accuracy with which forecasting methodsare able to predict the demand.

TS=?Actual demand Forecast DemandAz MAD or RSFE zMAD

demand management nitie

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