sales budgeting and forecasting

8/10/2019 Sales Budgeting and Forecasting

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A sales budget gives a plan showing the expected sales for a specied period

in the future.

Selling expense budgets details the schedule of expenses that may be

incurred by the sales department to achieve planned sales.

Administrative budget species the budgetary allocations for general

administrative expenses that would be incurred by the sales department.

et!ods Fo" Budget#$g

!he di%erent methods for budgeting include the&

A%ordability method

#ercentage&of&sales method

'ompetitive parity method

(b)ective&and&tas* method

+eturn&oriented method.

Sales Fo"e%ast#$g

Sales forecasting is a di,cult area of management. -ost managers believe

they are good at forecasting. owever$ forecasts made usually turn out to be

wrong/ -ar*eters argue about whether sales forecasting is a science or an

art. !he short answer is that it is a bit of both. -ar*et 0orecast refers to the

estimates of future sales of a companys products in the mar*et. Sales

forecasting is very popular in industrially advanced countries where demandconditions are always uncertain than the supply conditions.

Reaso$s fo" u$de"ta$g Sales Fo"e%ast
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"usinesses are forced to loo* well ahead in order to plan their investments$

launch new products$ and decide when to close or withdraw products and so

on. !he sales forecasting process is a critical one for most businesses.

1ey decisions that are derived from a sales forecast include2&

3mployment levels re4uired

#romotional mix

Investment in production capacity

Types Of Fo"e%ast#$g

!here are two ma)or types of forecasting$ which can be broadly described as

macro and micro2

-acro forecasting is concerned with forecasting mar*ets in total. !his is

about determining the existing level of -ar*et Demand and considering what

will happen to mar*et demand in the future.

-icro forecasting is concerned with detailed unit sales forecasts. !his is

about determining a products mar*et share in a particular industry and

considering what will happen to that mar*et share in the future.

Sele%t#o$ Of Fo"e%ast#$g

!he selection of which type of forecasting is use depends on the several

factors which can be described as2


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567 !he degree of accuracy re4uired if the decisions that are to be made on

the basis of the sales forecast have high ris*s attached to them$ then it

stands to reason that the forecast should be prepared as accurately as

possible. owever$ this involves more cost

587 !he availability of data and information& in some mar*ets there is a

wealth of available sales information 5e.g. clothing retail$ food retailing$

holidays79 in others it is hard to nd reliable$ up&to&date information.

5:7 !he time horizon that the sales forecast is intended to cover. 0or

example$ are we forecasting next wee*s sales$ or are we trying to forecast

what will happen to the overall size of the mar*et in the next ve years;

5 stage of the product life cycle$ less sales data and

information may be available than for products at the =maturity> stage when

time series can be a useful forecasting method.

Pu"poses Of S!o"t te"' Fo"e%ast#$g

Appropriate production scheduling

+educing cost of purchasing +?-

Determining appropriate price policy

Setting sales targets and establishing controls and incentives

3volving a suitable promotional program

0orecasting short&term nancial re4uirements

#lanning of a new unit or expansion of an existing unit#lanning of long&term nancial re4uirements

#lanning of man&power re4uirements

A common method of preparing a sales forecast has three stages


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#repare a macroeconomic forecast what will happen to overall

economic activity in the relevant economies in which a product is to

be sold.

#repare an industry sales forecast what will happen to overall

sales in an industry based on the issues that in@uence the

macroeconomic forecast.

#repare a company sales forecast based on what management

expect to happen to the companys mar*et share.

Fo"e%ast#$g P"o%ess

Determined independent and dependent variables

Develop 0orecast #rocedure

0orecast (b)ective

Select forecast Analysis method

3valuate +esult versus forecast

!otal forecast #rocedure

ather B analyze data

#resent assumption about data

-a*e B nalize forecast


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0orecasting can be classied into 4ualitative forecasting and 4uantitative

forecasting. !he methods used in 4ualitative forecasting are2

Cser expectations$ sales force composite$ )ury of executive opinion$ delphitechni4ue and mar*et test.

!he methods used in 4uantitative forecasting are2

!ime series analysis$ moving averages$ exponential smoothing regression

and correlation

analysis$ and multiple regression models

Co$t"ol

'ontrol was dened as =a process used by managers to direct$ regulate$ and

restrain the actions of people so that the established goals of an enterprise

may be achieved.>

+evenue control is clearly an important goal of sales control$ but it is not the

only one.

Sales 'ontrol

i*e any other control system$ sales control re4uires the establishment of

standards$ the evaluation of actual performance and the correction of

deviation in performance. Sales control implies not only managerial action

with regard to actual sales$ but it also embraces all other mar*eting functionsre4uired for the even @ow of products or services form producers to

consumers.

All promotional and auxiliary e%orts in mar*eting re4uire as much control as

the actual selling e%orts demand.


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Eevertheless$ control of promotional and auxiliary e%orts in mar*eting is

more di,cult and cannot be exercised with that exactness which is possible

in case of actual selling e%orts. "ecause of their intangible performances$

ancillary activities in mar*eting are placed under some broad measures of

control$ and they are measured and appraised by managerial )udgment$ s*ill

or experience.

!he basic tool for controlling these e%orts is to be found in the sales expense

budget. 0or controlling performances of salesmen$ the sales budget or in the

absence of a sales budget$ the sales programme provides the standard for

control.


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A%%u"ate fo"e%ast#$g opt#'#(es %usto'e" se")#%e* '#$#'#(es

#$)e$to"y o)e"sto%&s a$d lays t!e g"ou$d+o"& fo" e,e%t#)e

'a"&et#$g at Nestl-.

A billion units roll o% EestlF production lines every single day. !his number

illustrates the sheer 4uantity of goods produced by the worlds biggest food

company. !o deliver on its promise of =ood 0ood$ ood ife$> EestlF has

brought to mar*et a whopping 6G$GGG products aimed at improving

consumers lives with better and healthier foods and beverages.

!o ensure the right amounts of those products ma*e it to the shelves and

into customers hands$ EestlF relies on forecasting. After all$ even the bestmar*eting promotions can bac*re if the shelves are empty when the

customers show up for their favorite foods.

It comes as no surprise that EestlFs interest in closely managing the supply

chain and *eeping inventories within tight limits is proportionate with the

size of its operations. Its sheer size ma*es planning on a global scale highly

complex. #roduct categories$ sales regions and an abundance ofparticipating departments combine to weave a tangled web.

Its also the nature of the food and beverage industry that ma*es operational

planning a challenge. Seasonal in@uences$ being dependent on the weather

to provide a good harvest$ swings in demand$ other retail trends and the

perishable nature of many products ma*e it di,cult to plan production and

organize logistics.

T#ed do+$ /y %o$0#%t#$g 1PIs

=Supply chain management is a well&established$ recognized stream and

process at EestlF$> explains -arcel "aumgartner$ who leads global demand


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planning performance and statistical forecasting at EestlFs corporate

head4uarters. =(ur professionals ta*e care of transportation networ*s$ run

e,cient warehouses and are the rst point of contact with customers. (ne

area of focus is planning or$ more precisely$ demand and supply planning.

According to "aumgartner$ this process tac*les two important metrics2

customer service levels and inventory levels. (ne can improve customer

service levels dened as the percentage of complete and on&time deliveries

by expanding inventories. "ut that ties up capital$ and its often di,cult to

nd storage space. !he freshness of the product su%ers as well.

In this industry$ products are processed in very large batches to *eep unit

prices low$ ensure 4uality and ta*e advantage of raw ingredient availability.

!his ma*e&to&stoc* production strategy contrasts with the ma*e&to&order

principle fre4uently seen in other sectors such as the automobile industry.

=!o have the right 4uantity of the right products at the right place and time$

we rely heavily on being able to predict the orders our customers will place

as precisely as possible$> says "aumgartner.

(ther business metrics$ such as budgets and sales targets$ are also

important factors. !he overarching goal$ according to "aumgartner$ is to be

able to =ta*e proactive measures instead of simply reacting.> !o accomplish

this$ EestlF focuses on strong alignment processes$ stronger collaboration

with customers and the use of the proper forecasting methodology.

Stat#st#%s )s2 #$st#$%ts

!here are two main options for generating forecasts. !he sub)ective method

is mainly dependent upon on the estimation and appraisal of planners based

on the experience they draw upon. !he statistical method approaches the

forecasting problem with data.


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"efore using SAS$ EestlF was primarily using SA# A#(s underlying

forecasting techni4ues$ together with models from the open&source statistical

software +$ integrated into A#(. !hose forecasts were then revised by the

EestlF demand planners. SAS enhances this$ and thus complements SA# A#(

perfectly.

Statistical forecasting tends to be more reliable if su,cient historical data is

available. ="ut one thing has become clear to us H you cant predict the

future with statistics by simply loo*ing at the past. It doesnt matter how

complex your models are.>

So its not the statistical methodology thats the problem for "aumgartner

and his team. !he critical factor in this complex environment is being able to

assess the reliability of forecasts. !wo elements have attracted the most

attention within this context2 dealing with volatility$ and SAS.

=#redictability of demand for a certain product is highly dependent on that

products demand volatility$> says "aumgartner. =3specially for products that

display wide @uctuations in demand$ the choice and combination of methods

is very important. SAS 0orecast Server simplies this tas* tremendously.

(f particular importance for demand planning are the so&called =mad bulls$>

a term EestlF uses to characterize highly volatile products with high volume.

A mad bull can be a product li*e EescafF$ which normally sells 4uite regularly

throughout the year$ but whose volumes are pushed through trade

promotions. A simple statistical calculation is no more useful in generating a

demand forecast than the experience of a demand planner for these less

predictable items. !he only way out is to explain the volatility in the past by

annotating the history. "aumgartner and his team rely on the forecast value

added 50A7 methodology as their indicator. !he 0A describes the degree to


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which a step in the forecasting process reduces or increases the forecast

error.

o"e &$o+ledge* less guess#$g

According to "aumgartner$ SASJ0orecast Server is the ideal tool for this

scenario. !he solutions scalability allows a handful of specialists to cover

large geographical regions. And selecting the appropriate statistical models

is largely automated$ which is seen as one of the strongest features of SAS

0orecast Server. =At the same time$ were now able to drill down through

customer hierarchies and do things such as integrate the impact of

promotions and special o%ers into the statistical models.>

!he results paint a clear picture. In a comparison between the conventional

forecasting method and SAS 0orecast Server procedures for the most part

using default settings the results showed that EestlF often matches and

improves its current performance for the predictable part of the portfolio and

thus frees up valuable time for demand planners to focus on mad bulls.

ast but not least$ EestlF emphasizes that even a system as sophisticated as

SAS 0orecast Server cannot replace professional demand planners.

=#articularly for mad bulls$ being connected in the business$ with high

credibility$ experience and *nowledge is *ey.> Kith more time available to

tac*le the complicated products$ planners are able to ma*e more successful

production decisions. And that means really having enough EestlF ice cream

at the beach when those hot summer days nally arrive.


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Innovator and expert in sales forecasting 'harles 'hase has helped EestlF

improve its forecast accuracy and ma*e multi&million dollar reductions in

their inventory by removing human )udgement and enabling the predicting of

future demand through Ldemand shaping.

'hase is 'hief Industry 'onsultant for business analytics software leader

SAS$ which recently wor*ed with EestlF and drin*s ma*er -iller 'oors$

among others$ helping bring their forecasting and analytics up to speed. e

is a pioneer and advocate of revolutionary Demand&Driven 0orecasting

solutions.

'ompanies are more global today than ever before9 lead times have been

extended with the e%ective practise of lean management made di,cult by

the volatility of demand. !he use of safety stoc* or inventory to protect

against variability is no longer so viable and$ as 'hase asserts$ companies

now need to understand and measure that variability in demand and be able

to predict it more accurately with an enterprise&wide solution$ which can loo*

at millions of forecasts up and down a Lproduct hierarchy.

!he technology that 'hase pioneered senses demand signals rather than

trend and seasonality$ automatically telling a business what demand signals

are actually in@uencing consumers purchasing of products up and down a

hierarchy.

It will automatically measure the e%ect of advertising and price$ allowing for

Ldemand shaping up and down the hierarchy and the running of Lwhat if;

scenarios.

'hase said2 ='ompanies can as*2 Lwhat if I raise price in Muly by three

percent; Khat if I add another sales promotion in August and I increased


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advertising for the rest of the year by 6G percent$ how will that impact future

demand;

=So now you are being proactive versus reactive$ you are not reacting to the

forecast but proactively driving the forecast and can do demand sensing and

demand shaping for thousands of products automatically.

=!he whole idea is you want to combine data analytics and domain

*nowledge on an exception basis and you want to practice lean forecasting

and forecast value&added$ these other technologies can only sense demand

signals for trend in seasonality.>

Get +ell3a!ead

Demand&Driven 0orecasting$ it is claimed$ has the capability to do forecasts

for the short&term and long&term$ even going two&ve&6G years out.

'hase and SAS originally sold the demand&driven forecasting solution to

EestlF Direct Store Delivery$ a San 0rancisco "ay area&based division formed

by EestlFs ac4uisition of Dreyers Ice 'ream and a frozen pizza business of

1raft 0oods.

!he EestlF team wanted the ability to sense demand signals associated with

sales promotions$ price$ advertising$ in&store merchandising and economic

factors to better understand what things in@uence consumers to buy their

products. (nce they were able to measure that mathematically$ they wanted

to be able to use that information to run Lwhat&if scenarios to shape future

demand.


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'hase added2 =Csing our technology today$ when EestlFs sales and

mar*eting people get together and want to run a sales promotion$ say a buy&

one&get&one&free that they ran in the past$ our system then calculates the

unit lift that was associated with that particular promotion in the past and

tells them whether it was signicant in driving incremental demand$ or unit

demand. (nce it does that it then goes out to the nancial system and

determines whether or not it actually made any money.

=A lot of these sales promotions are designed for trial$ not to ma*e revenue$

but companies are not getting as much trial as they thin*. +ather$ they are

actually subsidising brand&loyal customers who buy from promotion to

promotion$ so we want those promotions to not only drive incremental

demand$ but also revenue and prot.

=So if it does also drive revenue and prot they say2 Lwe want to use that

promotion$ we want to run that promotion again$ in wee*s :N$ :O and :P$

and thats how they shape future demand.>

S&e+ed 4udge'e$t

0our and a half years ago$ said 'hase$ PG percent of all EestlFs forecasts

were touched by human )udgement every cycle with only 8G percent being

driven by mathematics$ data and what he calls Ldomain *nowledge.

=LMudgement>$ said 'hase$ =means I can )ust arbitrarily change the number

to meet my needs$ Ldomain *nowledge means I am able to run this

promotion$ discover what the impact of that promotion is and then if that

impact is signicant then I want to use it to in@uence future demand$ or

shape future demand.>


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!his is another feature of this Demand&Driven 0orecasting approach$ human

interference is minimised and the technology =does the heavy lifting>.

'hase said2 =Around PQ percent of companies still use -icrosoft 3xcel to do

traditional forecasting$ which is to create a statistical base line forecast

based on trend and seasonality only$ and then hand it o% to domain experts

in the organisation to add their )udgement. Khat we found is that when

companies add their )udgement they add personal bias and they actually

ma*e the forecast less accurate.

=I believe in forecast by exception$ let the technology do the heavy lifting

and only touch those products that the system wasnt able to forecast

automatically. I also believe in forecast value added$ also *nown as lean

forecasting and what we mean by that is we want to measure every touch

point in the process$ before and after someone touches the statistical drive

forecast with human )udgement to see if they are adding value$ if they are

not adding value we want to either eliminate that touch point or minimise it.

=!oday$ PG percent of the EestlFs forecasts are driven right out of the

solution with no human )udgement at all$ and only 8G percent re4uire any

*ind of human )udgement$ the rst year they implemented it was three years

ago$ they found that every one percent improvement in forecast accuracy

translated into a two percent reduction in inventory safety stoc*. !hey were

eventually able to ta*e out anywhere between 6


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sales budgeting and forecasting

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