demand forecasting of nokia

Demand Forecasting

“Demand forecasting is an estimate of sales in dollars or physical units for a specified future

period under a proposed marketing plan”. This definition was given by the American

Marketing Association.

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INDEX

1) Introduction.......................................................................................03

2) Methods of demand forecasting............................................................04

3) Company Profile.................................................................................08

4) Method used for Nokia by me..............................................................10

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Demand forecasting is the tool to predict the likely demand of a product in the future.

The concept of matching supply with demand is straightforward. It is to get the balance

between the inventory control and the customers’ need. Buying too much wastes time, money

and space, also underestimating demand leads to back orders, cancellations etc. So demand

forecasting provides a sophisticated yet easy method of to the challenges of many companies.

Demand forecasting overcomes the problems by giving a simple source for statistical

demand forecasting throughout the enterprise. This frees the planners and the buyers to

concentrate their time on the fine points of merchandise planning and inventory optimization.

The better a company can assess the demand the better it can use its resources.

Factors are mainly three: company, competitive and macro economic factors.

Company factors mainly include market share trends, changes in strategies and

implementation. Competitive factors include competitor advertising, competitor product

offerings, market share etc. Macroeconomic changes include mainly income, economic

growth and shocks.

There are many methods to assed or forecast demand, but none of the methods are to be a

100% success as the various factors involved can be independent or dependent of the other

factors. But it still allows the company to have a view to how much does it need to supply the

products in the market. Most of the companies use a single or a variety of methods. They may

use the Simple sales analysis, market size and market share research. One of the most used

nowadays method is that of Market size research and mind share research.

Forecasting is the method of estimation in totally unknown situations. Forecasting in the

recent past has evolved into the practice of demand planning in day to day planning for

manufacturing companies also for supply chain management.

Forecasting benefits mainly include:

1) Saves time, money and effort

2) Reduced inventory

3) Increased sales and capacity.

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Methods of Demand Forecasting

There are a lot of methods to forecast the demand but the apt and correct has to be used to get

the so called intended result from the forecast that is to be done. The choice of the type of the

method to be used can be chosen on the various given factors:

1) Imminent objectives of forecast whether it’s a new product or to get impact from an

advertisement.

2) Cost involved, it should not be more than the benefits received or to be received

3) Time perspective, whether it’s for a short run or for a long run

4) Complexity of the technique

5) Nature and the quality of available data.

The various methods involved for forecasting are:

Methods based on Judgement

Unaided judgement

It is used when the following conditions do not hold: experts aren’t biased, or when large

changes are unlikely to take place, when relationships are well understood by experts. It was

found that experts were no better than chance when they used unaided judgement.

Prediction markets

Some commercial organizations provide internet markets to allow participants to bet by

trading contacts. Markets can be used to predict as the percentage of US households with

HDTV by the end of a given time period. Confidential betting markets are also set up to bet

on new entrants into the market. This method is used in the US and is mainly based on

betting.

Delphi method

It was developed at RAND Corporation in 1950 to help capture the knowledge of diverse

experts while avoiding the disadvantages of traditional group meetings. To forecast, the

administrator should recruit about 10-15 experts and poll them for their expert views. Then

the administrator gives anonymous statistics on the forecasts, and the experts’ reason for their

forecast. This is repeated till there is a change in forecast between 2-3 rounds. The Delphi

method or the result is the median or mode of the experts’ final forecasts. It was found out

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that the forecasts from the Delphi groups were more accurate than from unaided judgement

and the traditional groups.

Structured analogies

The outcomes of similar situations from the past may help to forecast the outcome of a new

target situation. This method is not carried out in a structured manner. For example, they

might search for an analogy that suits them and then stop, so it’s not structured. To use this

method, an administrator prepares a description of the target situation and selects experts who

have knowledge about the analogues; the experts identify and describe analogues situations,

preferably direct situations. They rate their similarity to the target situation and match the

outcomes of their analogies with potential outcomes in the target situation. Then the

administrator derives the forecast.

Judgemental decomposition

It is to divide the forecasting problem into parts that are easier to forecast than the whole. One

then forecasts the parts individually using methods to each part. Then they’re combined to get

the final forecast.

Judgemental bootstrapping

This method converts subjective judgements into structured procedures. Experts are asked for

their methods and asked to make decisions for diverse cases which can be real or

hypothetical. The resulting data is then made to a model by estimating a regression equation

relating the judgemental forecasts to the information used by the forecasters. This method

allows experts to see how they’re weighing various factors. This knowledge can help to

improve judgemental forecasting.

Simulated interaction

It’s a form of role playing for predicting decisions by people who are interacting with othes.

Useful when conflicts arise. To simulate it, an administrator prepares a description and gives

all possibilities. Roles are essayed and from that a decision is reached and this is used to

make the forecast.

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Intention and Expectation surveys

Here, people are asked how they would intend to behave to a particular situation and in an

expectation survey the people are asked about their expectations. Then the probabilities

scales such as the eleven scale method are used. By aggregating their intentions and

expectations the demand is forecasted.

Conjoint analysis

Surveying of consumers is done about their preferences for alternative product design in a

structured way; it is a possible to infer how different features will influence demand. In

general, this method gives accuracy which increases with increasing realism of the choices

presented to respondents.

Methods requiring quantitative data

Extrapolation

This method uses historical data to forecast the demand. Exponential smoothening is the most

popular and cost effective of the methods. It implements the principle that recent data should

be weighted and ‘smoothes’ out fluctuations to forecast the trend. Here the administrator first

cleans and de seasonalises the data and selects reasonable smoothing factors. He then

calculates an average and trend from the data and forecsts the demand.

Quantitative analogues

Experts identify situations that are analogues to those situations. These are then used to

extrapolate the target situation. Experts collect those data’s and construct a model target

situation and another using analogues data, combines the parameters of the model and

forecasts it.

Rule based forecasting

Here a manager’s knowledge is integrated with the domain of time series data in a structured

and in expensive way. One must find identify features of the series using statistical analysis,

inspection and domain knowledge. The rules are then used to adjust data and to estimate short

and long range models.

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Neutral nets

These are computer intensive methods that use decision processes analogous to the human

brain. They have the capabilities of learning as patterns changes and updating their

parameters estimates. Also its found these methods are more accurate in real.

Data mining

It uses sophisticated analyses to identify relationships. It ignores theory and prior knowledge

in search of patterns.

Casual models

These are based on prior knowledge and theory. Time series and cross sectional regression

are the commonly used ones. They allow one to examine the effects of marketing activity

thus providing information for contingency planning. For this method to work, suitable

variables are to be chosen that can help this method. Then based on the prior knowledge and

theory the forecasting can be carried out and the demand can be found out.

Segmentation

It involves breaking a problem into independent parts, using each data for each part to make a

forecast and then the whole data is taken to forecast. It can be done for particular products.

For example, wool carpets are separately forecasted according to the climate and the region,

so this method is very helpful and useful.

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Company Profile

The company that has been taken into account here is NOKIA. Nokia’s history spans more

than a hundred years and contains many stories, events and milestones brought about by the

many twists and turns of world history and industrialization. It started in 1865 when engineer

Fredrik Idestam established a wood pulp mill in Finland. Soon cardboard nokia became

successful and it attracted a large workforce and a small community grew as such. It then

became Finnish rubber works and then after the IInd world war it bought Finnish cable works

and was collectively called Nokia group. It manufactured cables for telegraph and telephone

networks and in 1970’s forayed into telecommunications and then on to become what it is

today.

Today Nokia has become one of the largest producers of mobile phones ranging from cheap

ones to high end communicating devices. As it suggests, Nokia is really good at ‘connecting

people’. This Finnish icon is the world’s largest manufacturer of mobile devices and has

around 40% of the global device market. Nokia is very, very successful and, in 2006,

generated revenue that for the first time was in excess of Finland’s state budget. Nokia has

always used innovation as a key driver for growth: first, by pioneering GSM and then by

reinventing the concept of product personalisation. These days Nokia’s challenge is to

maintain its position in a world increasingly converged and dominated by the likes of Google

and Microsoft. However, while these companies have strong brands and interesting plans for

the future, they don’t have control over the handset. Nokia is bundling great services with

tailored, user-friendly hardware. With a billion customers and relationships with hundreds of

operators around the world, Nokia may well manage to hold its place.

Core innovation strengths within Nokia include speed of action, interconnection between

products and services and the strategic use of design. Over the past few years, as margins

have been three times those of its nearest competitors, the company has re-emphasised the

importance of design to place it literally at the heart of the organisation’s operations. This has

moved Nokia from being a feature-driven to a design-driven manufacturer where, amongst

others, ethnography is now a core capability linking consumer behaviour around new uses of

digital media directly into the development process. Nokia’s leadership in mobile devices has

given it a solid platform upon which to build a services business that expands outside the

core. And here’s the rub, while Nokia has consolidated its traditional strengths of R&D and

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product design, the big story in 2007 was its strategic shift into the multimedia services

space.

Nokia created the N Series to deliver high-end multimedia phones. In addition, its near-

ubiquitous camera-phones have provided revenue opportunities from user-generated content.

In support of the social networking trend, Nokia has formed alliances with partners such as

Yahoo to allow users to share photos using its Flickr service. 2006 saw the first tablet device

with internet, not cellular connectivity, and the success of this product has led to a next-

generation of internet-only devices including webcams and higher levels of VoIP. By

eliminating the SIM card and breaking the connection to the networks, Nokia is using

alliances with the likes of Google and Skype to offer more freedom of choice direct to

consumers. At the lower end of the market, Nokia is the leading brand in China and India and

is well positioned for further growth. It continues to lead on process innovation to drive down

cost and is opening its tenth factory in India with the capability to turn out 20m phones a

year.

Nokia’s innovation roadmap weaves its software and services into a seamless package. Smart

acquisitions have increasingly played a considerable role in this. Seeing that that location and

content services provide major opportunities Nokia bought Navteq, the leading provider of

digital map information for over $8bn in cash. Nokia expects that the truly mobile internet

with multiple connectivity options that enable faster access to music, video, TV and mobile

navigation and massive multiplayer gaming services will be a major factor in driving further

growth. With overall industry handset volumes growing, Nokia is in the strongest position of

any manufacturer. Add into this mix Nokia’s acquisitions and partnerships that are building

new service portfolios and it is clear that this company continues to be the leading source of

innovation in the telecommunications sector.

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Method used to forecast sales of Nokia

Trend Projection

Trend is a general pattern of change in the long run. This method is referred to the ‘classical

method’ and is also a- very powerful tool used to predict future values of a variable on the

basis of time-series data. Time series is composed of

Secular trend- refers to the change occurring over a long time.

Seasonal trend- refers to seasonal variations of the data within a year

Cyclical trend- refers to cyclical movement in the demand for a product that may have a

tendency to recur in a few years

Random events- can be calamities, social unrest, foreign aggression etc.

Here, the equation for linear trend is

Y=a+bX

Smoothing technique

Most of the series do not show a continuous trend, some increase and decrease in values can

be seen in any time series. To take care of these seasonal or random variations are made to

‘smooth’ the series. Smoothing techniques are used when the time series data exhibit little

trend or seasonal variation.

Here, the moving average method is used to forecast on the basis of demand values during

the recent past. The formula used is:

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Least square method

For quarterly’s

Year Sales in

millions(Euro)

Y

Deviations from

2006 (X) XY

2004 Q3 6939 -2 4 -13878

2005 Q3 8403 -1 1 -8403

2006 Q3 10100 0 0 0

2007 Q3 12898 1 1 12898

2008 Q3 12237 2 4 24474

n = 5 ∑Y= 50577 ∑X=0 ∑ =10 ∑XY= 15091

Solving the equations: ∑Y = na + b∑X

∑XY = a∑X + b∑

We get a = ∑Y/n = 10115.4 b = ∑XY/∑ = 1509.1

So the eqn is Y = 10115.4 + 3956.5X

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So for 2009 Q3 we assume: Y = 10115.4 + 1509.1(3) = 14642.7 million

Year Sales in

millions(Euro)

Y

Deviations from

2006 (X) XY

2004 Q4 9063 -2 4 -18126

2005 Q4 10333 -1 1 -10333

2006 Q4 11701 0 0 0

2007 Q4 15717 1 1 15717

2008 Q4 12662 2 4 25324

n = 5 ∑Y= 59476 ∑X = 0 ∑ = 10 ∑XY= 12583


∑XY = a∑X + b∑

We get a = ∑Y/n = 11895.2 b = ∑XY/∑ = 1258.3

So the eqn is Y = 11895.2 + 1258.3X

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Yearly

Year Sales in

millions(Euro)

Y

Deviations from

2006 (X) XY

2004 29267 -2 4 -58534

2005 34191 -1 1 -34191

2006 41121 0 0 0

2007 51058 1 1 51058

2008 50710 2 4 101420

n = 5 ∑Y= 206347 ∑X = 0 ∑ = 10 ∑XY= 59753


∑XY = a∑X + b∑

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We get a = ∑Y/n = 41269.4 b = ∑XY/∑ = 5975.3

So the eqn is Y = 41269.4 + 5975.3X


Smoothing technique

In this method the average of the previous three months are taken and its average is used to

get the demand for the next month.

For 2005 Q1 is 7396, so for the next month it is 7396/3 = 2465.3


For 2005 Q3 is 8403, so for the next month it is 8403/3 = 2801





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For 2009 Q3, the estimated is 14642.7, so for the next month it is 14642.7/3 = 4880.9

For 2009 Q4, the estimated is 15670.1, so for the next month it is 15670.1/3 = 5223.3

Bibliography

1) Managerial Economics by Geetika, Piyali ghosh and Choudhary

2) www.buseco.edu

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