Demand Forecasting Final

8/4/2019 Demand Forecasting Final

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y Forecasting is the art and science of predictingfuture events.

y Demand forecasting is the scientific and analytical estimation of

demand for a product (service) for particular period of time.

y It is the process of determining how much of what products isneeded when and where.

y An operations research technique of planning and decisionmaking.


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y No forecasting method can be demand

superior to other in every respect.

yThe order to generate a pattern mustremain nearly the same in the future or

the demand entries must depend to some

extent on the past value of a set of

variables items for which thesehypotheses hold are said to have a

regular demand.


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y Forecasting is an attempt to determine in

advance the most likely outcome of an

uncertain variable planning andcontrolling logistics systems need

prediction for the level of future economic

activities because of the time lag in

matching supply to demand.


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Lumpy Demand

y When demand is lumpy or irregular there is somuch randomness in the demand pattern that

no reliable prediction can be made.

y When dealing with such items, two alternativesshould be explored. If demand is low, accuracyis not usually a key issue & an overestimatecan be used. As an alternative, the processes ofthe supply chain could be made more flexible inorder to obtain a quick response.


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y Long-term forecasts span a time horizon from one to fiveyears. Predictions for longer periods are very unreliable,

since political & technological issues come into play.

y Long-term forecasts are used for deciding whether a new

item should be put on the market, or whether an old one

should be withdrawn, as well as in designing a logisticsnetwork.


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y Medium-term forecasts extend over a period from a

few months to one year. They are used for tactical

logistical decision, such as setting annualproduction & distribution plans, inventory

management & slot allocation in warehouses.


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y Short-Term forecasts cover a time interval

from a few days to several weeks. They are

employed to schedule & re-schedule

resources in order to meet medium-termproduction & distribution targets. As service

requests are received, there is less need for

forecasts. Consequently, forecasts for a

shorter time interval are quite uncommon.


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y A top-down or a bottom-up approach can be

utilized.

y In the top-down method, the entire demand is

globally forecasted & then divided amonggeographic areas.

y In the bottom-up technique, the demand pattern

of an item is estimated in each geographical area,

& then aggregated if necessary.y However, the bottom-up approach would result in

large forecasting errors; & hence, this approach

is generally not utilized in practice.


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Short-term forecasts are as a rule more accuratethan those for medium & long time periods. This issimply because the longer the time interval, thegreater the probability of unexpected events.

Aggregate demand forecasts are generally moreprecise than those of single items.

Forecasts obtained by using simple techniques areeasier to understand & explain. This is a

fundamental aspect when large sums of money areinvolved in the decision-making process.

In a business context, complex forecastingprocedures seldom yield better results than simple

ones.


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y Determine the objective/purpose of the forecast

y Select the items that are to be forecasted

y Determine the time horizon

y Select forecasting model

y Gather the data

y Validate the forecasting model

y Make the forecast

y Implement the results


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[1] JURY OF EXECUTIVE OPINION

y Opinion of a small group of high-level managers

y Combination with statistical models

y Results in a group estimate of demand

[2]SALES FORCE ASSESSMENT

y Forecasts made by a companys sales workforce for a

particular area/region.y Accurate estimates are expected since workforce is

closer to customers.

y Forecasts are then reviewed.

y Combined at a district or national level to reach anoverall forecast.


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[3]NAIVE APPROACHy Assumes demand in the next period is the same as

demand in the most recent period.

y For some product lines this method is more cost effectiveand efficient.

[4]MARKET RESEARCH

y Based on interviews with potential customers/users

regarding future purchase plans.y Time consuming and deep knowledge of sampling

theory required. Hence, used occasionally.

y Helps in improving product design and planning for

new products.


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[5]DELPHI METHOD

y Mainly used to estimate the influence of political ormacro economical changes on an item demand.

y Three different types of participants- decision makers,staff and respondents.

* Decision Makers- 5 to 10 experts who make thefinal decisions.

* Staff- Assist the decision makers by preparing,distributing , collecting, summarizing the survey results.

* Respondents- Group of people whose judgmentsare valued and sought.


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y A Series of questionnaires submitted to a panel of

experts.

y Every time a questionnaire is answered newinformation is obtained

yThen new questionnaire is prepared with this

information in mind

y This method is stopped as soon as all experts

share the same opinion.


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[1] CAUSAL METHODS

y Based on the hypotheses that future demand depends on the past orcurrent values of some variables

y Major advantage is their ability to anticipate variations in demand

y Very effective for medium and long term forecasts

y Negative side of this method includes:

* In several cases it is difficult to identify any causal variable having astrong correlation with future demand.

* It is even more difficult to find a causal variable that leads theforecasted variable in time.


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y Causal: use the relationship between demand and

some other factor to develop forecast

y Simulation

y Imitate consumer choices that give rise to

demand

yCan combine time series and causal methods


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Time Series Extrapolation Methods


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Time Series Decomposition

Method

y This method is used on the assumption that the demand

pattern of a product can be decomposed into the four effects

1)Trends

2)C

yclical Variation3)Seasonal Variation

4)Residual Variation


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Moving Averagey Used when demand has no observable trend or seasonality

y Systematic component of demand = level

y The level in period t is the average demand over the last N periods (the

N-period moving average)

y Current forecast for all future periods is the same and is based on the

current estimate of the level

Lt = (Dt + Dt-1 + + Dt-N+1) / N

Ft+1

= Lt

and Ft+n

= Lt

After observing the demand for period t+1, revise the estimates as

follows:

Lt+1 = (Dt+1 + Dt + + Dt-N+2) / N

Ft+2

= Lt+1


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Exponential Smoothing

y 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 the simple

moving averagethe past observations are weighted equally,

exponential smoothing assigns exponentially decreasing

weights over time.


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Measures of Forecast Error

y Mean absolute percentage error (MAPE)

MAPEn = (Sum(t=1 to n)[|Et/ Dt|100])/n

y Bias

y Shows whether the forecast consistently under- or overestimates

demand; should fluctuate around 0

biasn = Sum(t=1 to n)[Et]

y Tracking signal

y Should be within the range of +6

y Otherwise, possibly use a new forecasting method

TSt = bias / MADt


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Trend Projections

y Before estimating level and trend, demand data must be

deseasonalized

y Deseasonalized demand = demand that would have been

observed in the absence of seasonal fluctuations

y Periodicity (p)

y the number of periods after which the seasonal cycle repeats

itself

y for demand atTahoe Salt (Table 7.1, Figure 7.1) p = 4


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Components of an Observation

Observed demand (O) =

Systematic component (S) + Random component (R)

Level(current deseasonalized demand)

Trend(growth or decline in demand)

Seasonality(predictable seasonal fluctuation)

Systematic component: Expected value of demand Random component: The part of the forecast that deviates

from the systematic component

Forecast error: difference between forecast and actual demand


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Forecasting Methods

y Static

y Adaptive

yMoving average

ySimple exponential smoothing

yHolts model (with trend)

y

Winters model (w

ith trend andseasonality)


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y Order processing is a key element ofOrder

fulfillment.

y Order processing operations or facilities are

commonly called "distribution centers".

y "Order processing" is the term generally used

to describe the process or the work flow

associated with the picking, packing and

delivery of the packed item(s) to a carrier.


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Stores credit the material

Inspect the material

Get the material

Follow up with vendor

Place the ORDER to the right Vendor

Negotiatewith Quotation to fix the price

Study and Compare Quotations with our estimated cost

Get QUOTATION from Vendors

Send ENQUIRY to vendors

Requirement from user department


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y Electronic data interchange is the inter organizational exchange of

business document in structured, machine process able form.

y Electronic data interchange can be used to electronically transmit

documents such as purchase order, invoices, shipping bills, receiving

advices and other standard business correspondence between trading

partners.

y Also to transmit financial information and payments in electronic form.

Payments carried out over EDI are usually referred to as Electronic

FundsTransfer (EFT).

y EDI should not be viewed as simply a way of replacing paper documents and

traditional methods of transmission such as mail, phone or in person delivery

with electronic transmission.


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y It should be seen as an end but as a means to streamline

procedures and improving efficiency and productivity.

y Computers have speeded up the production of invoices,

purchase order, receiving tickets and the likes.

y When these documents are produced by high speed printers,

however, they still must be busted, inserted and distributed(usually mailed) and the copies must be filed by the

originating organization.


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The use of EDI eliminates many of these problems associated withtraditional information flow as listed below-

The delay associated with handling filing and transportation of paper

documents are eliminated

Since data is keyed in only once the chances of errors are reduced

Time required to re-enter data is saved

The data is not re-entered at each step in the process, labor costs can

be reduced.

Because time delays are reduced, there is more certainty in

information flow, the other advantage in the use of EDI is that it

generates a functional acknowledgement.


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EDI is often applied in the following situation -

y A large number of repetitive standard actions

y Very tight operating margins

y Strong competition requiring significant productivity

improvements

y Operational time constraints


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Documents

Demand Forecasting Final