Sales Budgeting and Forecasting

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How to make sales budgets and forecast sales




Sales Budget

A budget is a plan expressed usually in monetary terms. It is a process of
allocating a portion of an organization’s resources for its various activities for
a specified period of time.
It helps in planning and coordination of the organization’s activities. Sales
budgets are developed for the smooth functioning of the sales function.
Developing sales budgets serve two purposes –

As a mechanism of control and
An instrument of planning.

There are several benefits an organization derives from budgeting. They are

Improved planning
Better communication and coordination
Performance evaluation
Psychological benefits
Avoiding uncontrolled expenditure

Types of Budget

In practice, sales managers prepare three types of budgets –

Sales budgets
Selling expense budget
Administrative budget

A sales budget gives a plan showing the expected sales for a specified 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 specifies the budgetary allocations for general
administrative expenses that would be incurred by the sales department.

Methods For Budgeting

The different methods for budgeting include the

Affordability method
Percentage-of-sales method
Competitive parity method
Objective-and-task method
Return-oriented method.

Sales Forecasting

Sales forecasting is a difficult area of management. Most managers believe
they are good at forecasting. However, forecasts made usually turn out to be
wrong! Marketers argue about whether sales forecasting is a science or an
art. The short answer is that it is a bit of both. Market Forecast refers to the
estimates of future sales of a company’s products in the market. Sales
forecasting is very popular in industrially advanced countries where demand
conditions are always uncertain than the supply conditions.

Reasons for undertaking Sales Forecast

Businesses are forced to look well ahead in order to plan their investments,
launch new products, and decide when to close or withdraw products and so
on. The sales forecasting process is a critical one for most businesses.

Key decisions that are derived from a sales forecast include:-

Employment levels required
Promotional mix
Investment in production capacity

Types Of Forecasting

There are two major types of forecasting, which can be broadly described as
macro and micro:

Macro forecasting is concerned with forecasting markets in total. This is
about determining the existing level of Market Demand and considering what
will happen to market demand in the future.

Micro forecasting is concerned with detailed unit sales forecasts. This is
about determining a product’s market share in a particular industry and
considering what will happen to that market share in the future.

Selection Of Forecasting

The selection of which type of forecasting is use depends on the several
factors which can be described as:

(1) The degree of accuracy required– if the decisions that are to be made on
the basis of the sales forecast have high risks attached to them, then it
stands to reason that the forecast should be prepared as accurately as
possible. However, this involves more cost
(2) The availability of data and information- in some markets there is a
wealth of available sales information (e.g. clothing retail, food retailing,
holidays); in others it is hard to find reliable, up-to-date information.
(3) The time horizon that the sales forecast is intended to cover. For
example, are we forecasting next weeks’ sales, or are we trying to forecast
what will happen to the overall size of the market in the next five years?
(4)The position of the products in its life cycle. For example, for products at
the “introductory” 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.

Purposes Of Short term Forecasting

Appropriate production scheduling
Reducing cost of purchasing R/M
Determining appropriate price policy
Setting sales targets and establishing controls and incentives
Evolving a suitable promotional program
Forecasting short-term financial requirements

Planning of a new unit or expansion of an existing unit
Planning of long-term financial requirements
Planning of man-power requirements

A common method of preparing a sales forecast has three stages

Prepare a macroeconomic forecast – what will happen to overall
economic activity in the relevant economies in which a product is to
be sold.

Prepare an industry sales forecast – what will happen to overall
sales in an industry based on the issues that influence the
macroeconomic forecast.

Prepare a company sales forecast – based on what management
expect to happen to the company’s market share.

Forecasting Process

Determined independent and dependent variables

Develop Forecast Procedure

Forecast Objective

Select forecast Analysis method

Evaluate Result versus forecast

Total forecast Procedure

Gather & analyze data

Present assumption about data

Make & finalize forecast

Forecasting can be classified into qualitative forecasting and quantitative
forecasting. The methods used in qualitative forecasting are:
User expectations, sales force composite, jury of executive opinion, delphi
technique and market test.
The methods used in quantitative forecasting are:
Time series analysis, moving averages, exponential smoothing regression
and correlation
analysis, and multiple regression models


Control was defined 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.”
Revenue control is clearly an important goal of sales control, but it is not the
only one.

Sales Control
Like any other control system, sales control requires 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 marketing functions
required for the even flow of products or services form producers to
All promotional and auxiliary efforts in marketing require as much control as
the actual selling efforts demand.

Nevertheless, control of promotional and auxiliary efforts in marketing is
more difficult and cannot be exercised with that exactness which is possible
in case of actual selling efforts. Because of their intangible performances,
ancillary activities in marketing are placed under some broad measures of
control, and they are measured and appraised by managerial judgment, skill
or experience.
The basic tool for controlling these efforts is to be found in the sales expense
budget. For controlling performances of salesmen, the sales budget or in the
absence of a sales budget, the sales programme provides the standard for

Accurate forecasting optimizes customer service, minimizes
inventory overstocks and lays the groundwork for effective
marketing at Nestlé:
A billion units roll off Nestlé production lines every single day. This number
illustrates the sheer quantity of goods produced by the world’s biggest food
company. To deliver on its promise of “Good Food, Good Life,” Nestlé has
brought to market a whopping 10,000 products aimed at improving
consumers’ lives with better and healthier foods and beverages.

To ensure the right amounts of those products make it to the shelves and
into customers’ hands, Nestlé relies on forecasting. After all, even the best
marketing promotions can backfire if the shelves are empty when the
customers show up for their favorite foods.

It comes as no surprise that Nestlé’s interest in closely managing the supply
chain and keeping inventories within tight limits is proportionate with the
size of its operations. Its sheer size makes planning on a global scale highly
complex. Product categories, sales regions and an abundance of
participating departments combine to weave a tangled web.

It’s also the nature of the food and beverage industry that makes operational
planning a challenge. Seasonal influences, being dependent on the weather
to provide a good harvest, swings in demand, other retail trends and the
perishable nature of many products make it difficult to plan production and
organize logistics.

Tied down by conflicting KPIs
“Supply chain management is a well-established, recognized stream and
process at Nestlé,” explains Marcel Baumgartner, who leads global demand

planning performance and statistical forecasting at Nestlé’s corporate
headquarters. “Our professionals take care of transportation networks, run
efficient warehouses and are the first point of contact with customers. One
area of focus is planning – or, more precisely, demand and supply planning.

According to Baumgartner, this process tackles two important metrics:
customer service levels and inventory levels. One can improve customer
service levels – defined as the percentage of complete and on-time deliveries
– by expanding inventories. But that ties up capital, and it’s often difficult to
find storage space. The freshness of the product suffers as well.

In this industry, products are processed in very large batches to keep unit
prices low, ensure quality and take advantage of raw ingredient availability.
This make-to-stock production strategy contrasts with the make-to-order
principle frequently seen in other sectors such as the automobile industry.
“To have the right quantity 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 Baumgartner.

Other business metrics, such as budgets and sales targets, are also
important factors. The overarching goal, according to Baumgartner, is to be
able to “take proactive measures instead of simply reacting.” To accomplish
this, Nestlé focuses on strong alignment processes, stronger collaboration
with customers and the use of the proper forecasting methodology.

Statistics vs. instincts
There are two main options for generating forecasts. The subjective method
is mainly dependent upon on the estimation and appraisal of planners based
on the experience they draw upon. The statistical method approaches the
forecasting problem with data.

Before using SAS, Nestlé was primarily using SAP APO’s underlying
forecasting techniques, together with models from the open-source statistical
software R, integrated into APO. Those forecasts were then revised by the
Nestlé demand planners. SAS enhances this, and thus complements SAP APO

Statistical forecasting tends to be more reliable if sufficient historical data is
available. “But one thing has become clear to us — you can’t predict the
future with statistics by simply looking at the past. It doesn’t matter how
complex your models are.”

So it’s not the statistical methodology that’s the problem for Baumgartner
and his team. The critical factor in this complex environment is being able to
assess the reliability of forecasts. Two elements have attracted the most
attention within this context: dealing with volatility, and SAS.

“Predictability of demand for a certain product is highly dependent on that
product’s demand volatility,” says Baumgartner. “Especially for products that
display wide fluctuations in demand, the choice and combination of methods
is very important. SAS Forecast Server simplifies this task tremendously.

Of particular importance for demand planning are the so-called “mad bulls,”
a term Nestlé uses to characterize highly volatile products with high volume.
A mad bull can be a product like Nescafé, which normally sells quite 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. The only way out is to explain the volatility in the past by
annotating the history. Baumgartner and his team rely on the forecast value
added (FVA) methodology as their indicator. The FVA describes the degree to

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

More knowledge, less guessing
According to Baumgartner, SAS® Forecast Server is the ideal tool for this
scenario. The solution’s 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
Forecast Server. “At the same time, we’re now able to drill down through
customer hierarchies and do things such as integrate the impact of
promotions and special offers into the statistical models.”

The results paint a clear picture. In a comparison between the conventional
forecasting method and SAS Forecast Server procedures – for the most part
using default settings – the results showed that Nestlé 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.

Last but not least, Nestlé emphasizes that even a system as sophisticated as
SAS Forecast Server cannot replace professional demand planners.
“Particularly for mad bulls, being connected in the business, with high
credibility, experience and knowledge is key.” With more time available to
tackle the complicated products, planners are able to make more successful
production decisions. And that means really having enough Nestlé ice cream
at the beach when those hot summer days finally arrive.

Innovator and expert in sales forecasting Charles Chase has helped Nestlé
improve its forecast accuracy and make multi-million dollar reductions in
their inventory by removing human judgement and enabling the predicting of
future demand through ‘demand shaping’.

Chase is Chief Industry Consultant for business analytics software leader
SAS, which recently worked with Nestlé and drinks maker Miller Coors,
among others, helping bring their forecasting and analytics up to speed. He
is a pioneer and advocate of revolutionary Demand-Driven Forecasting

Companies are more global today than ever before; lead times have been
extended with the effective practise of lean management made difficult by
the volatility of demand. The use of safety stock or inventory to protect
against variability is no longer so viable and, as Chase 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 look
at millions of forecasts up and down a ‘product hierarchy’.

The technology that Chase pioneered senses demand signals rather than
trend and seasonality, automatically telling a business what demand signals
are actually influencing consumers’ purchasing of products up and down a

It will automatically measure the effect of advertising and price, allowing for
‘demand shaping’ up and down the hierarchy and the running of ‘what if?’

Chase said: “Companies can ask: ‘what if I raise price in July by three
percent? What if I add another sales promotion in August and I increased

advertising for the rest of the year by 10 percent, how will that impact future

“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.

“The whole idea is you want to combine data analytics and domain
knowledge 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 well-ahead

Demand-Driven Forecasting, it is claimed, has the capability to do forecasts
for the short-term and long-term, even going two-five-10 years out.

Chase and SAS originally sold the demand-driven forecasting solution to
Nestlé Direct Store Delivery, a San Francisco Bay area-based division formed
by Nestlé’s acquisition of Dreyer’s Ice Cream and a frozen pizza business of
Kraft Foods.

The Nestlé 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 influence consumers to buy their
products. Once they were able to measure that mathematically, they wanted
to be able to use that information to run ‘what-if’ scenarios to shape future

Chase added: “Using our technology today, when Nestlé’s sales and
marketing people get together and want to run a sales promotion, say a buyone-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 significant in driving incremental demand, or unit
demand. Once it does that it then goes out to the financial system and
determines whether or not it actually made any money.

“A lot of these sales promotions are designed for trial, not to make revenue,
but companies are not getting as much trial as they think. Rather, 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 profit.

“So if it does also drive revenue and profit they say: ‘we want to use that
promotion, we want to run that promotion again, in weeks 36, 37 and 38’,
and that’s how they shape future demand.”

Skewed judgement

Four and a half years ago, said Chase, 80 percent of all Nestlé’s forecasts
were touched by human judgement every cycle with only 20 percent being
driven by mathematics, data and what he calls ‘domain knowledge’.

“‘Judgement’”, said Chase, “means I can just arbitrarily change the number
to meet my needs, ‘domain knowledge’ means I am able to run this
promotion, discover what the impact of that promotion is and then if that
impact is significant then I want to use it to influence future demand, or
shape future demand.”

This is another feature of this Demand-Driven Forecasting approach, human
interference is minimised and the technology “does the heavy lifting”.

Chase said: “Around 85 percent of companies still use Microsoft Excel to do
traditional forecasting, which is to create a statistical base line forecast
based on trend and seasonality only, and then hand it off to domain experts
in the organisation to add their judgement. What we found is that when
companies add their judgement they add personal bias and they actually
make 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 wasn’t able to forecast
automatically. I also believe in forecast value added, also known 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 judgement to see if they are adding value, if they are
not adding value we want to either eliminate that touch point or minimise it.

“Today, 80 percent of the Nestlé’s forecasts are driven right out of the
solution with no human judgement at all, and only 20 percent require any
kind of human judgement, the first 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 stock. They were
eventually able to take out anywhere between 14- 20 percent of their
inventory safety stock, reduce it and still meet consumer demand with this
improved forecasting capability. If you have US $100 million in inventory
that’s a US $20 million reduction.”





























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