GAUHATI UNIVERSITY

A training Report submitted in partial fulfillment of the requirements

For the award of the degree of

MASTER OF BUSINESS ADMINISTRATION (II) ,GAUHATI

UNIVERSITY ON

“MARKETING”

Under organization guidance of : under institutional

guidance of :

Mr. J.S. Shahi MS. Lucky singh

Apollo business school

Prepared and submitted by :Robin goyal

G.U registration No. 09010918 of 2009-2010

STUDENT’S DECLARATIONSTUDENT’S DECLARATION

I hereby declare that the Project Report conducted at

JETAGE GARAGE EQUIPMENTS , NEW DELHI

Under the guidance of

MS. LUCKY SINGH

Submitted in partial fulfillment of the requirements for the

Degree of

MASTER OF BUSINESS ADMINISTRATION

TO

GAUHATI UNIVERSITY

Is my original work and the same has been submitted for the award

of any other degree /diploma/fellowship or other similar titles or prizes.

Place: NOIDAPlace: NOIDA Robin GoyalRobin Goyal

Reg No.- 09010918

ACKNOWLEDGEMENTS

A project report can not be completed without motivation, assistance, guidance,

cooperation and inspiration from various triangles. I would like to thank to all the person

who help and persuaded me to work on this project.

Working with the “JETAGE GARAGE EQUIPMENTS” has been a wonderful experience.

It was a dream comes true for me when I was given an opportunity to undergo our

training in JETAGE GARAGE EQUIPMENTS.I express my deepest sense of gratitude

to Mr. J.S. Shahi Who has been a continuous source of inspiration throughout the work

on this project and guided me with keen interest.

Robin Goyal Robin Goyal

CONTENTS

CHAPTER 1. INDUSTRY PROFILE

Origin and Development of the industry

Growth and Present status of the industry

Future of the industry

CHAPTER 2. PROFILE OF THE ORGANISATION

Origin of the organization

Growth and development of the organization

Present status of the organization

Future plans of the organization

Departments of the organization

Organizational structure and organizational chart

Product and service profile of the organization

Market profile of the organization

CHAPTER 3. DISCUSSIONS ON TRAINING

Student’s work profile

Description of live experience

CHAPTER 4. STUDY OF SELECTED PROBLEM

Statement of research objective

Research design and methodology

Analysis of data

Summary of findings -

CHAPTER 6. SUMMARY AND CONLUSIONS

Summary

Conclusions

APPENDIX

QUESTIONNAIRE

BIBLIOGRAPHY

Company Profile

 

High Performance Automobile Garage Equipments We wish to introduce ourselves as a company with latest technology products suitable for the Indian working environment at reasonable prices. For the first time in India, we have introduced Wheel Alignment Machine with Blue Tooth Data Communication. This machine uses CCD Cameras from SONY Japan. We have introduced Computerised Video and Digital Display Wheel Balancers which can take the Rim Distance and Rim Diameter automatically. We have light and heavy duty Tyre Changers imported from Korea. Also for the first time in India, we have introduced Nitrogen Generators which can Produce Nitrogen Gas used for inflating the Automobile Tyres. Use of Nitrogen Gas in Tyres increases the tyre life by 20% and fuel saving by 10%. We have introduced low cost Tyre Inflator. Also cost saving Infrared Lamps for baking Painted parts of automobile which saves the cost of diesel for the same purpose. We have also introduced Electro Hydraulic Two Post and Four Post Lift for Wheel Alignment. We have our Training Centre with all the machines in display. We can provide training to your service staff for better handling of the machines. We invite you to visit our office and training centre. We can show the working of our machines for better understanding. We look forward for an opportunity to associate with your esteem company and prove our claims

Automobile

The Automotive industry is the key driver of any growing economy. A sound transportation system plays a pivotal role in a

country’s rapid economic and industrial development. The well-developed Indian automotive industry ably fulfils this catalytic

role by producing a wide variety of vehicles. The automobile industry comprises automobile and auto component sectors. It

includes passenger cars; light, medium and heavy commercial vehicles; multi-utility vehicles such as jeeps, scooters,

motorcycles, three-wheelers and tractors; and auto components like engine parts, drive and transmission parts, suspension

and braking parts, and electrical, body and chassis parts.

India’s automotive industry is now worth $34 billion and expected to grow $145billion in another ten years. The Indian

automotive industry is growing at a very high rate with sales of more than one million passenger vehicles per annum. The

overall growth rate is 10-15 per cent annually. India is the world’s second largest manufacturer of two-wheelers, fifth largest

manufacturers of commercial vehicles as well as largest manufacturer of tractors. It is the fourth largest passenger car

market in Asia and home to the largest motorcycle manufacturer.

Major players in this sector include Tata, Mahindra, Daewoo Motor India, Hyundai Motors India and General Motors India,

Maruti, Ashok Leyland, Bajaj, Hero Honda, Ford, Fiat and few other players.

The Indian auto components industry is worth $10 billion. Indigenous firms like Bharat Forge, Sundaram Fasteners, Minda

Industries and Gabrial India Ltd. are in the limelight. There is a boom in the auto components segment because of strong

demand and robust economy. Also, the industry has strong forward and backward linkages with almost every other

engineering segment. The component production range includes engine parts 31%, drive transmission and steering parts

19%, suspension and braking parts 12%, electrical parts 10%, equipments 12%, body and chassis 9% and others 7%.

Indian companies are very optimistic. The Auto Components Manufacturers Association (ACMA) along with McKinsey has

pegged domestic demand for components at $20-25 billion in 2015 from $1.4 billion in 2004-05. This would take the overall

industry size to $40-45 billion by 2015 in India.

The Indian automotive industry has made rapid strides since delicensing witnessing the entry of several new manufacturers

with state-of-the-art technology. The scope of the report includes assessing market potential, negotiating with

collaborators, investment decision making, corporate diversification planning etc. in a very planned manner by formulating detailed manufacturing techniques and forecasting financial aspects by estimating the cost of raw material, formulating the cash flow statement, projecting the balance sheet etc.

We also offer self-contained Pre-Investment and Pre-Feasibility Studies, Market Surveys and Studies,

Preparation of Techno-Economic Feasibility Reports, Identification and Selection of Plant and Machinery,

Manufacturing Process and or Equipment required, General Guidance, Technical and Commercial

Counseling for setting up new industrial projects on the following topics.

Many of the engineers, project consultant & industrial consultancy firms in India and worldwide use our

project reports as one of the input in doing their analysis.

GROWTH IN THE SECTOR At present the industry is enjoying a growth rate of 14-17% per annum, with domestic sales growth at 12.8%. The growth rate is predicted to double by 2015. 

As it is seen, the total sales of passenger vehicles - cars, utility vehicles and multi-utility vehicles - in the year 2005 reached the mark of 1.06 million. The current growth rate indicates that by 2012 India will overtake Germany and Japan in sales volumes. 

Financing schemes have become an important factor in the growth of automobile sales. More and more financial schemes are coming up with easy installment plans to lure the customers. 

Apart from domestic production, the industry is consistently focusing on the automobile exports. The auto component segment is contributing a lot in the export arena. The liberalized policies of the government are now making the companies go for more and more exports. 

The automobile exports are increasing year by year. According to the Society of Indian Automobile Manufactures (SIAM) automobile exports in the last five years are as follows:

Export trend over the last five years

NEW LAUNCHES The Indian automobile sector is experiencing changes in every arena. Changes in the looks of the vehicles are taking place; the vehicles are being made more user-friendly. Each and every firm is competing to give the customers more customized vehicles with respect to speed, mileage, and maintenance. At present there are many new models entering the Indian market. To name a few, Suzuki Heat 125 and Suzuki Zeus 125X are the two bikes in the motorcycle segment; Kinetic Blaze and Honda DIO in the scooter segment; Maruti's Zen Estillo in the car segment, so on and so forth.  

EMPLOYMENT IN THE SECTORInvestment is leading to the employment growth in the sector. With the emergence of new projects and introduction of technological advancements, the focus is more on the skilled and experienced human resource. The companies are looking for skilled and hard working people who can give their best to the organization. 

The engineers in the automotive or electrical or mechanical field are in demand. Some of the firms going for automation, i.e. planning for CAD (Computer Aided Designs) systems, are also recruiting people with IT specializations. 

PRODUCTS

Wheel Care Equipments

Wheel Alignment

Wheel Balancer

Tyre Changer

Automatic Tyre InflatorRim Straightener

Lifting EquipmentsTwo Post Lift

Four Post Lift

Low Rise Lift

Super Heavy Duty

 

A/C CareAC Recovery,Recycling and Recharging Machine

     PUC Equipments

4 Gas Analyser, Model-Air Ultratec

 

Cleaning   Equipments High Pressure Car Washer

Wet and Dry Vacuum Cleaner

Fuel Injector Cleaner

Body Shop EquipmentsSpot Welding MachineMIG Welding MachinePlasma Cutter

Paint Booth

Crash Repair System

Paint Booth AccessoriesFilters for Pit, Generating Unit and Ceiling

HVLP - Painting Guns

Filter, Regulator and Lubricator

   

   

SCOPE IN AUTOMOBILE INDUSTRY-

Automobile engineering is a branch of vehicle engineering, incorporating elements of mechanical, electrical, electronic, software and safety engineering as applied to the design, manufacture and operation ofmotorcycles, automobiles,buses and trucks and their respective engineering subsystems.

The engineers of automobile engineering has to develop car bodies and buildups with aggregates like engines, clutches, gears and steerings. They have to design according the postulations of aerodynamics and stylistics, they have to construct and calculate according the postulations of functionality, safety, the dreasing of resources and the economy.

According the priority of the study the engineer gets activities as an engineer for development in body design, commercial and special vehicles or the design of motors and transmissions. The field of activity of a qualified engineer in automobile engineering contains development (construction, calculation and testing), priming of work, fabrication and observation of the functionality of vehicles for street and rails.

 

Education of Automobile engineering

 The course curriculum is designed to teach students all aspects of moving vehicles, their construction, repair and maintenance.To become an automobile engineer, one must have a BE or a B. Tech degree. Even a degree holder in Electrical or mechanical can go for specialization in automobile engineering in postgraduate level. Diploma holders can take AMIE examination to be at par with the degree holder. Employment Opportunity Automobile Engineering: Automobile manufacturing Industry, in maintenance and service station, private transport company and Defense Services. Self-employment is also possible in setting up automobile garages or maintenance workshop.To become a qualified automobile engineer one must have a graduate degree (B.E / B.Tech) or atleast a diploma in automobileautomobile Engineering is 10+2, with Physics, Chemistry, Mathematics and preferably Biology.basic eligibility criteria for a candidate aspiring to do a BE in Selection to all Engineering courses is on the basis of : (a.) merit / marks secured in the final exams of 10+2 (with science subjects) ; (b.) through the means of entrance exams (joint entrance exam / JEE for the IIT's and separate state level and national level exams for other institutions). Engineering courses are available at two levels. There are the degree and postgraduate degree courses offered by the engineering colleges and Institutes of Technology (IITs), and, the diploma courses available at polytechnics.Competitive examinations on an all-India basis for admission to a B Tech / B E course are also conducted by The Birla Institute of Technology, at Pilani, Rajasthan, and at Ranchi; the University of Roorkee, UP; Manipal Institute of Technology, Manipal; the Faculty of Engineering and Technology, Annamalai University, among others.  A national level exam called AIEEE is also conducted for admissions in the 20 NITs in India and many other prestigious colleges.Many colleges having the branch agricultural engineering give admission on the basis of this entrance exam.So one

can get admission in engineering college for pursuing automobile engineering by qualifying these exams. Scope of Automobile engineering

 As automobile industry is showing rapid growth in India, the country becomes a house to numerous well-established automobile companies. They offer excellent job opportunitiesto develop a career in Automobile Industry. Some of the popular car-producing companies that offer jobs in the automobile industry are- Suzuki, Toyota, Tata, Fiat, Honda, Mahindra & Mahindra, Ford, Hyundai and Skoda. Manufacturing of two-wheelers is dominated by the companies TVS, Bajaj Auto, LML, Kinetic, Yamaha and Hero Honda. The tractors are manufactured by the popular companies like Escorts, L&T, Mahindra & Mahindra, Punjab Tractors, John-Deere, New Holland and ITL-Renault.Automobile engineering is a part of mechanical engineering that has gained immense importance in recent years. As more and more automobile companies invest in India, the career scope for automobile engineering students has brightened. Many Automobile Organizations like TELCO, Bajaj Auto, L & T, Mahindra & Mahindra, Gabrial, Heldex India, Sai Service, Hero group etc, conduct campus interviews for the final year students from these automobile engineering colleges and recruit in advance. To know about the institutes providing courses in automobile engineering.Many job opportunities are available for the candidates with b.e. and ITI courses. Some of the automobile companies require IT specializations. The technical education is offered by plenty of engineering and polytechnic colleges in India. The eligible candidates are selected by the companies and then trained properly. Considering the wide scope of Automobile sector, it is not surprising that more and more candidates are dreaming to develop a career in Automobile Industry. Now, with so many foreign automobile companies like Volkswagen, Audi, Renault etc targeting India as a base for manufacturing cars, the scope for a career in Automobile Industry is rising rapidly. 

Aerospace engineering / Aeronautical Engineeringis one of the most challenging branch of engineering with a wide scope for growth. This field deals with the development of new technology in the field of aviation, space exploration and defence systems. It specialises in the designing, construction, development, testing, operation and maintenance of both commercial and military aircraft, spacecrafts and their components as well as satellites and missiles.As Aerospace engineering involves design and manufacture of very high technology systems, the job requires manual, technical as well as mechanical aptitude. Aeronautical engineer's usually work in teams under the  supervision of senior engineers, bringing together their skills and technical expertise. Though highly paid, the work is very demanding. An aeronautical engineer needs to be physically fit and fully dedicated to his work. One needs to be alert, have an eye for detail and should have a high level of mathematical precision to be successful.

The specialisations includes in areas like structural design, navigational guidance and control systems, instrumentation and communication or production methods or it can be in a particular product such as military aircrafts, passenger planes, helicopters, satellites, rockets etc. Engineers may work in areas like design, development, maintenance as well as in the managerial and teaching posts in institutes. They find a very good demand in airlines, aircraft manufacturing units, air turbine production plants or design development programmes for the aviation industry. 

EDUCATION OF AERONAUTICAL ENGINEERINGTo be an aeronautical engineer one should have a graduate degree (B.E/B.Tech.) or at least a diploma in Aeronautics. The degree and postgraduate degree courses are offered by the engineering colleges and Institutes of Technology (IITs), and the diploma courses are available at polytechnics. The basic eligibility criteria for a BE / B.Tech is 10+2 or equivalent examination, with Physics, Chemistry and Mathematics and must have a fairly high percentage of marks in the aggregate. One must also pass the qualifying exam JEE (Joint Entrance Exam) conducted by the IIT's. Selection : Selection to the graduate courses ( BE / B.Tech ) is based on merit i.e the marks secured in the final exams of 10+2 and through entrance exams. Entrance to the IIT's is through 'JEE' (Joint Entrance Exam) and for other institutions through their own separate entrance exams and other state level and national level exams. Most of the institutes conducting engineering courses in Aeronautics consider JEE score as the qualifying grade. There is also the Associate Membership Examination of the Institute of Engineers (AMIE), which enables working people in the private and public sector, or diploma holders to acquire a Bachelor's engineering degree through distance education by studying the syllabus and appearing for the Associate membership examination of the Institute of Engineers (AMIE) conducted by ASI ('The Aeronautical Society of India'). This degree is equivalent to aeronautical engineering degree. Those with a degree in electronics, mathematics or physics can also find opportunities in this area. Some Institutes offer postgraduate (M Tech) and Doctoral (Ph D) programmes in Aeronautics. The Madras Institute of Technology offers a three year Graduate Programme in Aeronautical Engineering for B Sc students, subject to their having passed Maths and Physics at the graduation stage. The Indian Institute of Science (IIS), Bangalore has M Tech and Ph D programmes in aeronautics. SCOPE OF AERONAUTICAL ENGINEERING

 Aeronautical Engineers work with one of the most technologically advanced branches of engineering. The main thrust in this area is on design and development of aircrafts to space and satellite research. Initially, candidates begin work as graduate engineer trainees or junior Engineers. Keeping in view their performance, academic background and aptitude, they are placed for training in the aircraft maintenance/overhaul or support section. On completion of training they are placed as assistant aircraft engineers or assistant technical officers. They have to clear departmental examinations for further promotions.  They may advance to administrative or executive positions or become consultants. Aeronautical engineers are assisted by aircraft mechanics in maintenance of aircraft frame, engine, electrical system and other ancillary fittings.

 

Abstract:One Industry which is full of potential today is that of Two Wheeler Industry in India. The Production capacity of major players increases because of the increase in demand which leads to increase in the after marking opportunities.

The objective of the report is to study the business environment of the automotive chains in the aftermarket and to build an entry strategy for the new players.

The report begins with an overview of the Global Automobile/ auto component industry and describes Indian Automobile Industry in detail. It specifies the two wheeler industry and then to corresponding automotive chains. It talks about auto component market in India following future growth of the industry. The basis of the information is Automobile Component Manufacturing Association (ACMA), Confederation of Indian Industries (CII) and Society of Indian Automobile Manufacturers (SIAM).

The report contains a competitive analysis of key Players such as Rolon, Ti Diamond along with their individual strengths & weaknesses. Primary Research is carried out

by Questionnaire technique. The responses are captured, analysed & a suitable entry strategy is made for M/S Rockman Industries Ltd. Apart from market trends, there are recommendations, suggestions and conclusion.

Objectives of the Study

In the last few years two wheeler markets in India has grown rapidly. More than 7 million units were sold in 2008 - 2009 out of which 6.2 million were motorcycles. In April 2009 it has increased by 10.7 % in comparison to 2008, which offers a huge opportunity for aftermarket OEM’s.

The Objectives are as under:

Study of automobile and automotive industry - especially for two wheelers In-depth study on 2-wheeler chains Studying the existing players in auto chain industry Understanding end-users needs and preferences Comparative analysis of existing key players product Evaluating market share of existing players A study on the distribution network of the existing players. Estimating the production of motorcycles & sales figures and estimating the overall market size.

This report gives an understanding of auto-component industry in India. It analyses indian

automobile industry, auto-component structure, domestic market size, turnover and recent

trends in two wheeler sector. Industry product range and export/import describes the

present demand and supply trends. With the help of future forecasting we have tried to

provide better understanding of future opportunities in the sector. Further we have talked

about auto chain trends and compeititon between major players.

Products Mentioned:

- Overall automobile sector

- two wheelers

- automotive chain

One Industry which is full of potential today is that of Two Wheeler Industry in India. The

Production capacity of major players increases because of the increase in demand which

leads to increase in the after marking opportunities. 

The objective of the report is to study the business environment of the automotive chains in

the aftermarket and to build an entry strategy for the new players.

The report begins with an overview of the Global Automobile/ auto component industry and

describes Indian Automobile Industry in detail. It specifies the two wheeler industry and then

to corresponding automotive chains. It talks about auto component market in India following

future growth of the industry. The basis of the information is Automobile Component

Manufacturing Association (ACMA), Confederation of Indian Industries (CII) and Society of

Indian Automobile Manufacturers (SIAM). 

The report contains a competitive analysis of key Players such as Rolon, Ti Diamond along

with their individual strengths & weaknesses. Primary Research is carried out by

Questionnaire technique. The responses are captured, analysed & a suitable entry strategy

is made for M/S Rockman Industries Ltd. Apart from market trends, there are

recommendations, suggestions and conclusion. 

Objectives of the Study

In the last few years two wheeler markets in India has grown rapidly. More than 7 million

units were sold in 2008 – 2009 out of which 6.2 million were motorcycles. In April 2009 it has

increased by 10.7 % in comparison to 2008, which offers a huge opportunity for aftermarket

OEM’s. 

The Objectives are as under: 

- Study of automobile and automotive industry – especially for two wheelers

- In-depth study on 2-wheeler chains

- Studying the existing players in auto chain industry

- Understanding end-users needs and preferences

- Comparative analysis of existing key players product

- Evaluating market share of existing players

- A study on the distribution network of the existing players. Estimating the production of

motorcycles & sales figures and estimating the overall market size.

Every other day, we have been hearing about some new launches, some low cost cars – all customized in a manner

such that the common man is not left behind. In 2009, the automobile industry is expected to see a growth rate of

around 9%, with the disclaimer that the auto industry in India has been hit badly by the ongoing global financial crisis. 

The automobile industry in India happens to be the ninth largest in the world. Following Japan, South Korea and

Thailand, in 2009, India emerged as the fourth largest exporter of automobiles. Several Indian automobile

manufacturers have spread their operations globally as well, asking for more investments in the Indian automobile

sector by the MNCs. 

Potential of the Automobile industry

In 2008, Hyundai Motors alone exported 240,000 cars made in India. Nissan Motors plans to export 250,000 vehicles

manufactured in its India plant by 2011. Similar plans are for General Motors. 

Turnover of Automobile Manufacturers(In USD Million)

Year In USD Million

2002-03 14,880

2003-04 16,544

2004-05 20,896

2005-06 27,011

2006-07 34,285

The figures show that the automobile sector in India has been growing robustly. The market shares of the different

types of vehicles will clearly depict the demand pattern in this sector. 

Domestic Market Share for 2008-09

Passenger Vehicles 15.96%

Commercial Vehicles 3.95%

Three Wheelers 3.6%

Two Wheelers 76.49%

Automobile Companies Audi Bajaj Auto BMW Chevrolet DaimlerChrysler (Mercedes) Fiat Ford General Motors Hindustan Motors New Car Launches Hero Honda Motors Hyundai Motors Mahindra & Mahindra Maruti Udyog San Motors Skoda Tata Motors Yamaha Motors Top Automobile Companies

Domestic Sales

The cumulative growth of the Passenger Vehicles segment during April 2007 – March 2008 was 12.17 percent. Passenger Cars grew by 11.79 percent, Utility Vehicles by 10.57 percent and Multi Purpose Vehicles by 21.39 percent in this period.

The Commercial Vehicles segment grew marginally at 4.07 percent. While Medium & Heavy Commercial Vehicles declined by 1.66 percent, Light Commercial Vehicles recorded a growth

of 12.29 percent.

Three Wheelers sales fell by 9.71 percent with sales of Goods Carriers declining drastically by 20.49 percent and Passenger Carriers declined by 2.13 percent during April- March 2008 compared to the last year.

Two Wheelers registered a negative growth rate of 7.92 percent during this period, with motorcycles and electric two wheelers segments declining by 11.90 percent and 44.93 percent respectively. However, Scooters and Mopeds segment grew by 11.64 percent and 16.63 percent respectively.

Exports

Automobile Exports registered a growth of 22.30 percent during the current financial year.

The growth was led by two wheelers segment which grew at 32.31 percent. Commercial vehicles and Passenger Vehicles exports grew by 19.10 percent and 9.37 percent respectively. Exports of Three Wheelers segment declined by 1.85 percent.

 

Qualitative data is subjective, rich, and in-depth information normally presented in the form of words. In undergraduate dissertations, the most common form of qualitative data is derived from semi-structured or unstructured interviews, although other sources can include observations, life histories and journals and documents of all kinds including newspapers.

Qualitative data from interviews can be analysed for content (content analysis) or for the language used (discourse analysis). Qualitative data is difficult to analyse and often opportunities to achieve high marks are lost because the data is treated casually and without rigour. Here we concentrate on the content analysis of data from interviews.

Theory

When using a quantitative methodology, you are normally testing theory through the testing of a hypothesis. In qualitative research, you are either exploring the application of a theory or model in a different context or are hoping for a theory or a model to emerge from the data. In other words, although you may have some ideas about your topic, you are also looking for ideas, concepts and attitudes often from experts or practitioners in the field.

Collecting and organising data

The means of collecting and recording data through interviews and the possible pitfalls are well documented elsewhere but in terms of subsequent analysis, it is essential that you have a complete and accurate record of what was said. Do not rely on your memory (it can be very selective!) and either tape record the conversation (preferably) or take copious notes. If you are taking notes, write them up straight after the interview so that you can elaborate and clarify. If you are using a tape recorder, transcribe the exact words onto paper.

However you record the data, you should end up with a hard copy of either exactly what was said (transcript of tape recording) or nearly exactly what was said (comprehensive notes). It may be that parts of the interview are irrelevant or are more in the nature of background material, in which case you need not put these into your transcript but do make sure that they are indeed unnecessary. You should indicate omissions in the text with short statements.

You should transcribe exactly what is said, with grammatical errors and so on. It does not look very authentic if all your respondents speak with perfect grammar and BBC English! You may also want to indicate other things that happen such as laughter.

Each transcript or set of notes should be clearly marked with the name of the interviewee, the date and place and any other relevant details and, where appropriate, cross-referenced to clearly labelled tapes. These transcripts and notes are not normally required to be included in your dissertation but they should be available to show your supervisor and the second marker if required.

You may wonder why you should go to all the bother of transcribing your audiotapes. It is certainly a time-consuming business, although much easier if you can get access to a transcription machine that enables you to start and stop the tape with your feet while carrying on typing. It is even easier if you have access to an audio-typist who will do this labour intensive part for you. The advantage of having the interviews etc in hard copy is that you can refer to them very quickly, make notes in the margins, re-organise them for analysis, make coding notations in the margins and so on. It is much slower in the long run to have to continually listen to the tapes. You can read much faster than the tape will play! It also has the advantage, especially if you do the transcription yourself, of ensuring that you are very familiar with the material.

Content analysis

Analysis of qualitative data is not simple, and although it does not require complicated statistical techniques of quantitative analysis, it is nonetheless difficult to handle the usually large amounts of data in a thorough, systematic and relevant manner. Marshall and Rossman offer this graphic description:

"Data analysis is the process of bringing order, structure and meaning to the mass of collected data. It is a messy, ambiguous, time-consuming, creative, and fascinating process. It does not proceed in a linear fashion; it is not neat. Qualitative data analysis is a search for general statements about relationships among categories of data."

Marshall and Rossman, 1990:111

Hitchcock and Hughes take this one step further:

"…the ways in which the researcher moves from a description of what is the case to an explanation of why what is the case is the case."

Hitchcock and Hughes 1995:295

Content analysis consists of reading and re-reading the transcripts looking for similarities and differences in order to find themes and to develop categories. Having the full transcript is essential to make sure that you do not leave out anything of importance by only selecting material that fits your own ideas. There are various ways that you can mark the text:

Coding paragraphs – This is where you mark each paragraph with a topic/theme/category with an appropriate word in the margin.

Highlighting paragraphs/sentences/phrases – This is where you use highlighter pens of different colours or different coloured pens to mark bits about the different themes. Using the example above, you could mark the bits relating to childcare and those relating to pay in a different colour,

and so on. The use of coloured pens will help you find the relevant bits you need when you are writing up.

With both the above methods you may find that your categories change and develop as you do the analysis. What is important is that you can see that by analysing the text in such a way, you pick up all the references to a given topic and don’t leave anything out. This increases the objectivity and reduces the risk of you only selecting bits that conform to your own preconceptions.

You then need to arrange the data so that all the pieces on one theme are together. There are several ways of doing this:

Cut and put in folders approach

Make several copies of each transcript (keeping the master safe) and cut up each one according to what is being discussed (your themes or categories). Then sort them into folders, one for each category, so that you have all together what each interviewee said about a given theme. You can then compare and look for similarities/differences/conclusions etc. Do not forget to mark each slip of paper with the respondent’s name, initials or some sort of code or you won’t be able to remember who said what. Several copies may be needed in case one paragraph contains more than one theme or category. This is time consuming and messy at first, but easier in the long run especially if you have a lot of data and categories.

Card index system

Each transcript must be marked with line numbers for cross-referencing purposes. You have a card for each theme or category and cross-reference each card with each transcript so that you can find what everyone has said about a certain topic. This is quicker initially but involves a lot of referring back to the original transcripts when you write up your results and is usually only suitable for small amounts of data.

Computer analysis

If you have access to a computer package that analyses qualitative data (e.g. NUDIST) then you can use this. These vary in the way they work but these are some of the basic common principles. You can upload your transcripts created in a compatible word-processing package and then the software allows you to mark different sections with various headings/themes. It will then sort all those sections marked with a particular heading and print them off together. This is the electronic version of the folders approach! It is also possible to use a word-processing package to cut and paste comments and to search for particular words.

There is a great danger of subjective interpretation. You must accurately reflect the views of the interviewees and be thorough and methodical. You need to become familiar with your data. You may find this a daunting and stressful task or you may really enjoy it – sometimes so much that you can delay getting down to the next stage which is interpreting and writing up!

Presenting qualitative data in your dissertation

This would normally follow the topics, themes and categories that you have developed in the analysis and these, in turn, are likely to have been themes that came out in the literature and may have formed the basis for your interview questions. It is usually a mistake to go through each interviewee in turn and what they said on each topic. This is cumbersome and does not give the scope to compare and contrast their ideas with the ideas of others.

Do not analyse the data on a question-by-question basis. You should summarise the key themes

that emerge from the data and may give selected quotes if these are particularly appropriate.

Note how a point is made and then illustrated with an appropriate quote. The quotes make the whole text much more interesting and enjoyable to read but be wary of including too many. Please note also the reference to literature (this one is an imaginary piece of literature) – you should evaluate your own findings in this way and refer to the literature where appropriate. Remember the two concepts of presenting and discussing your findings. By presenting we mean a factual description/summary of what you found. The discussion element is your interpretation of what these findings mean and how they confirm or contradict what you wrote about in your literature section.

If you are trying to test a model then this will have been explored in your literature review and your methodology section will explain how you intend to test it. Your methodology should include who was interviewed with a clear rationale for your choices to explain how this fits into your research questions, how you ensured that the data was unbiased and as accurate as possible, and how the data was analysed. If you have been able to present an adapted model appropriate to your particular context then this should come towards the end of your findings section.

It may be desirable to put a small number of transcripts in the appendices but discuss this with your supervisor. Remember you have to present accurately what was said and what you think it means.

In order to write up your methodology section, you are strongly recommended to do some reading in research textbooks on interview techniques and the analysis of qualitative data. There are some suggested texts in the Further Reading section at the end of this pack.

SALES FORECASTING

1. Forecasting methods: an overview 2. Direct extrapolation of sales 3. Causal approaches to sales forecasting 4. New product forecasting 5. Evaluating and selecting methods 6. Estimating prediction intervals 7. Implementation 8. Conclusions Overview Interesting and difficult sales forecasting problems are common. Will the 1998 Volkswagen Beetle be a success? Will the Philadelphia Convention Hall be profitable? How will our major competitors respond if we raise the price of our product by 10 per cent? What if we cut advertising by 20 per cent? Sales forecasting involves predicting the amount people will purchase, given the product features and the conditions of the sale. Sales forecasts help investors make decisions about investments in new ventures. They are vital to the efficient operation of the firm and can aid managers on such decisions as the size of a plant to build, the amount of inventory to carry, the number of

workers ,to hire, the amount of advertising to place, the proper price to charge, and the salaries to pay salespeople. Profitability depends on (1) having a relatively accurate forecast of sales and costs; (2) assessing the confidence one can place in the forecast; and (3) properly using the forecast in the plan. Marketing practitioners believe that sales forecasting is important. In Dalrymple”s (1975) survey of marketing executives in US companies, 93 per cent said that sales forecasting was “one of the most critical” or “a very important aspect of their company”s success.” Furthermore, formal marketing plans are often supported by forecasts (Dalrymple 1987). Given its importance to the profitability of the firm, it is surprising that basic marketing texts devote so little space to the topic. Armstrong, Brodie and Mclntyre (1987), in a content analysis of 53 marketing textbooks, fou9nd that forecasting was mentioned on less than 1 per cent of the pages. Research on forecasting has produced useful findings. These findings are summarized in the Forecasting Principles Project, which is described on the website forecastingprinciples.com. This entry draws upon that project in summarizing guidelines for sales forecasting. These forecasting guidelines should be of particular interest because few firms use them. I also describe some commonly used approaches that are detrimental to sales forecasting. After a brief overview of forecasting methods, I discuss the direct extrapolation of sales data, either through statistical data or simply judgmental. Next, I describe causal approaches to sales forecasting. Attention is then given to new product forecasting. This is followed by a discussion of how to select appropriate methods and by a description of methods to assess uncertainty. I conclude with suggestions for gaining acceptance of forecasting methods and of forecasts. 21. Forecasting methods: an overview Forecasting involves methods that derive primarily from judgmental sources versus those from statistical sources. These methods and their relationships are shown in the flow chart in Figure 1. Judgment and statistical procedures are often used together, and since 1985, much research has examined the integration of statistical and judgmental forecasts (Armstrong and Collopy 1998b). Going down the

figure, there is an increasing amount of integration between judgmental and statistical proceduresEconometric models allow for extensive integration of judgmental planning and decision making. They can incorporate the effects of marketing mix variables as well as variables representing key aspects of the market and the environment. Econometric methods are appropriate when one needs to forecast what will happen using different assumptions about the environment or different strategies. Econometric methods are most useful when (1) strong causal relationships with sales are expected; (2) these causal relationships can be estimated; (3) large changes are expected to occur in the causal variables over the forecast horizon; and (4) these changes in the causal variables can be forecast or controlled, especially with respect to their direction. If any of these conditions does not hold (which is typical for short-range sales forecasts), then econometric methods should not be expected to improve accuracy. 2. Direct extrapolation of sales If one does not have substantial amounts of sales data; it may be preferable to make judgmental extrapolations. This assumes that the person has good knowledge about the product. For example, the characteristics of the product and market and future plans are all well-known. When one has ample sales data, it is often sufficient merely to extrapolate the trend. Extrapolation of the historical sales trend is common in firms (Mentzer and Kahn 1995). Extrapolation methods are used for short-term forecasts of demand for inventory and production decisions. When the data are for time intervals shorter than a year, it is generally advisable to use seasonal adjustments, given sufficient data. Seasonal adjustments typically represent the most important way to improve the accuracy of extrapolation. Dalrymple”s (1987) survey results were consistent with the principle that the use of seasonal factors reduces the forecast error. Seasonal adjustments which also led to substantial improvements in accuracy were found in the large-scale study of time series by Makridakis et al. (1984). If the historical series involve much uncertainty, the forecaster should use relatively simple models. Uncertainty in this case can be assessed by examining the variability about the long-term trend line. Schnaars (1984) presented evidence that the naïve forecast was one or me most

accurate procedures for industry sales forecasts. Uncertainty also calls for conservative forecasts. Being conservative means to stay near the historical average. Thus, it often helps to dampen the trend as the horizon increases (see Gardner and McKenzie 1985 for a description of one such procedure and for evidence of its effectiveness). 5One of the key issues in the extrapolation of sales is whether to use top-down or bottom-up approaches. By starting at the top (say the market for automobiles), and then allocating the forecast among the elements (e.g. sales of luxury cars or sales of the BMW 3-series) one typically benefits from having more reliable data, but the data are less relevant. In contrast, the bottom-up approach is more relevant and less reliable. "(For a more complete discussion on these issues, see Armstrong, 1985: 250-66 and MacGregor 1998.) Research on this topic has been done under the heading of “decomposition” or “segmentation.” Additive breakdowns tend to be fairly safe. Seldom do they harm forecast accuracy, and often they provide substantial improvements (Dangerfield and Morris 1992). 3. Causal approaches to sales forecasting Instead of extrapolating sales directly, one can forecast the factors that cause sales to vary. This begins with environmental factors such as population, gross national product (GNP) and the legal system. These affect the behavior of customers, competitors, suppliers, distributors and complementors (those organizations with whom you cooperate). Their actions lead to a market forecast. Their actions also provide inputs for the market share forecast. The product of the market forecast and the market share forecast yields the sales forecast. The breakdown of the problems into the elements of Figure 2 may aid one”s thinking about the sales forecasts. It is expected to improve accuracy (versus the extrapolation of sales) only if one has good information about each of the components and if there is a good understanding about how each relates to sales. If there is high uncertainty about any of the elements, it might be more accurate to extrapolate sales directly. Figure 2. Causal approach to sales forecastingEnvironment

CustomersMarket ForecastCompany Marketing MixMarket ShareSales ForecastSupplier(s)Distributor(s) andComplementor(s)Competition Marketing MixThe primary advantage of the indirect approach is that it can be more directly related to decision making. Adjustments can be made in the marketing mix to see how this would affect the forecast. Also, forecasts can be prepared to assess possible changes by other decision makers such as competitors or complementors. These forecasts can allow the firm to develop contingency plans, and these effects on 6sales can also be forecast. On the negative side, the causal approach is more expensive than sales extrapolation. Environment It is sometimes possible to obtain published forecasts of environmental factors from Tablebase, which is available on the Internet through various subscribing business research libraries. These forecasts may be adequate for many purposes. However, sometimes it is difficult to determine what methods were used to create the forecasts. In such cases, econometric models can improve the accuracy of environmental forecasts. They provide more accurate forecasts than those provided by extrapolation or by judgment when large changes are involved. Allen (1999) summarizes evidence on this. Important findings that aid econometric methods are to: (1) base the selection of causal variables upon forecasting theory and knowledge about the situation, rather than upon the statistical fit to historical data (also, tests of statistical significance play no role here); (2) use relatively simple models (e.g. do not use simultaneous equations; do not use models that cannot be specified as linear in the parameters); and (3) use variables only if the estimated relationship to sales is in the same direction as specified a priori. The last point is consistent with the principle of using causal not statistical reasoning.

Consistent with this viewpoint, leading indicators, a non causal approach to forecasting that has been widely accepted for decades, does not seem to improve the accuracy of forecasts (Diebold and Rudebusch 1991). Interestingly, there exists little evidence that more accurate forecasts of the environment (e.g. population, the economy, social trends, technological change) lead to better sales forecasts. This, of course, seems preposterous. I expect that the results have been obtained for studies where the conditions were not ideal for econometric methods. For example, if things continue to change as they have in the past, there is little reason to expect an econometric model to help with the forecast. However, improved environmental forecasts are expected when large changes are likely, such as the adoption of free trade policies, reductions in tariffs, economic depressions, natural disasters, and wars. Customers One should know the size of the potential market for the given product category (e.g. how many people in region X might be able to purchase an automobile), the ability of the potential market to purchase (e.g. income per capita and the price of the product), and the needs of the potential customers. Examination of each of these factors can help in forecasting demand for the category. Company The company sets its own marketing mix so there is typically little need to forecast these actions. However, sometimes the policies are not implemented according to plan because of changes in the market, actions by competitors or by retailers, or a lack of cooperation by those in the firm. Thus, it may be useful to forecast the actions that will actually be taken (e.g. if we provide a trade discount, how will this affect the average price paid by final consumers?) Intermediaries What actions will be taken by suppliers, distributors and complementors? One useful prediction model is to assume that their future decisions will be similar to those in the past, that is, the naive model. For existing markets, this model is often difficult to improve upon. When large changes are expected, however, the naive model is not appropriate. In such cases one can use structured judgment, extrapolate

from analogous situations, or use econometric models. 7Structure typically improves the accuracy of judgment, especially if it can realistically mirror the actual situation. Role playing is one such structured technique. It is useful when the outcome depends on the interaction among different parties and especially when the interaction involves conflict. Armstrong and Hutcherson (1989) asked subjects to role play the interactions between producers and distributors. In this disguised situation, Philco was trying to convince supermarkets to sell its appliances through a scheme whereby customers received discounts based on the volume of purchases at selected supermarkets. Short (less than one hour) role plays of the situation led to correct predictions of the supermarket managers” responses for 75 per cent of the 12 groups. In contrast, only one of 37 groups was correct when groups made predictions without benefit of formal techniques. (As it turned out, the decision itself was poor, but that is another story.) Econometric models offer an alternative, although much more expensive approach to forecasting the actions by intermediaries. This approach requires a substantial amount of information. For example, Montgomery (1975) described a model to predict whether a supermarket buying committee would put a new product on its shelves. This model, which used information about advertising, suppliers” reputation, margin and retail price, provided reasonable predictions for a hold-out sample. Competitors Can we improve upon the simple, “naïve,” forecast that competitors will continue to act as they have in the past? These forecasts are difficult because of the interaction that occurs among the key actors in the market. Because competitors have conflicting interests, they are unlikely to respond truthfully to an intentions survey. A small survey of marketing experts suggested that the most popular approach to forecasting competitors” actions is unaided expert opinion (Armstrong et al. 1987). Because the ,experts” are usually those in the company, however, this may introduce biases related to their desired outcomes. For example, brand managers are generally too optimistic about their brands. Here again, role playing would appear to

be relevant. Although no direct experimental evidence is available on its value in forecasting competitor”s actions, role playing has proven to be accurate in forecasting the decision made in conflict situations (Armstrong 1999). Market share Can we do better than the naive model of no change? For existing markets that are not undergoing major change, the naive model is reasonably accurate (Brodie et al. 1999). This is true even when one has excellent data about the competitors (Alsem et al. 1989). However, causal models should improve forecasts when large changes are made, such as when price reductions are advertised. Causal models should also help when a firm”s sales have been artificially limited due to production capacity, tariffs, or quotas. Furthermore, contingent forecasts are important. Firms can benefit by obtaining good forecasts of how its policies (e.g. a major price reduction) would affect its market share. 4. New product forecasting New product forecasting is of particular interest in view of its importance to decision making. In addition, large errors are typically made in such forecasts. Tull (1967) estimated the mean absolute percentage error for new product sales to be about 65 per cent. Not surprisingly then, pretest market models have gained wide acceptance among business firms; Shocker and Hall (1986) provide an evaluation of some of these models. 8The choice of a forecasting model to estimate customer response depends on the stage of the product life-cycle. As one moves through the concept phase to the prototype, test market, introductory, growth, maturation, and declining stages, the relative value of the alternative forecasting methods changes. In general, the movement is from purely judgmental approaches to quantitative models that use judgment as inputs. For example, intentions and expert opinions are vital in the concept and prototype stages. Later, expert judgment is useful as an input to quantitative models. Extrapolation methods may be useful in the early stages if it is possible to find analogous products (Claycamp and Liddy 1969). In later stages, extrapolation methods become more useful and less expensive as one can work directly with time-series data on sales or orders. Econometric and segmentation methods become more

useful after a sufficient amount of actual sales data are obtained. When the new product is in the concept phase, a heavy reliance is usually placed on intentions surveys. Intentions to purchase new products are complicated because potential customers may not be sufficiently familiar with the proposed product and because the various features of the product affect one another (e.g. price, quality, and distribution channel). This suggests the need to prepare a good description of the proposed product. This often involves expensive prototypes, visual aids, product clinics, or laboratory tests. However, brief descriptions are sometimes as accurate as elaborate descriptions as found in Armstrong and Overton”s (1970) study of a new form of urban mass transportation. In the typical intentions study, potential consumers are provided with a description of the product and the conditions of sale, and then are asked about their intentions to purchase. Eleven-point rating scales are recommended. The scale should have verbal designations such as 0 = No chance, almost no chance (1 in 100) to 10 = Certain, practically certain (99 in 100). It is best to state the question broadly about one”s “expectations” or “probabilities” to purchase, rather than the narrower question of intentions. This distinction was raised early on by Juster (1966) and its importance has been shown in empirical studies by Day et al. (1991). Intentions surveys are useful when all of the following conditions hold: (1) the event is important; (2) responses can be obtained; (3) the respondent has a plan; (4) the respondent reports correctly; (5) the respondent can fulfill the plan; and (6) events are unlikely to change the plan. These conditions imply that intentions are more useful for short-term forecasts of business-to-business sales. The technology of intentions surveys has improved greatly over the past half century. Useful methods have been developed for selecting samples, compensating for nonresponse bias, and reducing response error. Dillman (1978) provides excellent advice that can be used for designing intentions surveys. Improvements in this technology have been demonstrated by studies on voter intentions (Perry 1979). Response error is probably the most important component of total error (Sudman and Bradburn 1982). Still, the correspondence between intentions and sales is often not close. Morwitz

(1999) provides a review of the evidence on intentions to purchase. As an alternative to asking potential customers about their intentions to purchase, one can ask experts to predict how consumers will respond. For example, Wotruba and Thurlow (1976) discuss how opinions from members of the sales force can be used to forecast sales. One could ask distributors or marketing executives to make sales forecasts. Expert opinions studies differ from intentions surveys. When an expert is asked to predict the behavior of a market, there is no need to claim that this is a representative expert. Quite the contrary, the expert may be exceptional. When using experts to forecast, one needs few experts, typically only between five and twenty (Hogarth 19,78; Ashton 1985). Experts are especially useful at diagnosing the current situation, which we might call “nowcasting.” Surprisingly, however, when the task involves forecasting change, experts with modest domain expertise 9(about the item to be forecast) are just as accurate as those with high expertise (Armstrong 1985: 91-6 reviews the evidence). This means that it is not necessary to purchase expensive expert advice. Unfortunately, experts are often subject to biases. Salespeople may try to forecast on the low side if the forecasts will be used to set quotas. Marketing executives may forecast high in their belief that this will motivate the sales force. If possible, avoid experts who would have obvious reasons to be biased (Tyebjee 1987). Another strategy is to include a heterogeneous group of experts in the hopes that their differing biases may cancel one another. Little is known about the relative accuracy of expert opinions versus consumer intentions. However, Sewall (1981) found that each approach contributes useful information such that a combined forecast is more accurate than either one alone. Producers often consider several alternative designs for the new product. In such cases, potential customers can be presented with a series of perhaps twenty or so alternative offerings. For example, various features of a personal computer, such as price, weight, battery life, screen clarity and memory might vary according to rules for experimental design (the basic ideas being that each feature should vary substantially and that the variations among the features should not correlate with one another). The

customer is forced to make trade-offs among various features. This is called “conjoint analysis” because the consumers consider the product features jointly. This procedure is widely used by firms (Wittink and Bergestuen 1998). An example of a successful application is the design of a new Marriott hotel chain (Wind et al. 1989). The use of conjoint analysis to forecast new product demand can be expensive because it requires large samples of potential buyers, the potential buyers may be difficult to locate, and the questionnaires are not easy to complete. Respondents must, of course, understand the concepts that they are being asked to evaluate. Although conjoint analysis rests on good theoretical foundations, little validation research exists in which its accuracy is compared with the accuracy of alternative techniques such as Delphi or judgmental forecasting procedures. Expert judgments can be used in a manner analogous to the use of consumers” intentions for conjoint analysis. That is, the experts could be asked to make predictions about situations involving alternative product design and alternative marketing plans. These predictions would then be related to the situations by regression analysis. Following the philosophy for naming conjoint analysis, this could be called exjoint analysis. It is advantageous to conjoint analysis in that few experts are needed (probably between five and twenty). In addition, it can incorporate policy variables that might be difficult for consumers to assess. Once a new product is on the market, it is possible to use extrapolation methods. Much attention has been given to the selection of the proper functional form to extrapolate early sales. The diffusion literature uses an S-shaped curve to predict new product sales. That is, growth builds up slowly at first, becomes rapid as word-of-mouth and observation of use spread, then slows again as it approaches a saturation level. A substantial literature exists on diffusion models. Despite this, the number of comparative validation studies is small and the benefits of choosing the best functional form seem to be modest (research on this is reviewed by Meade 1999). 5. Evaluating and selecting methods Assume that you were asked to predict annual sales of consumer products such as stoves,

refrigerators, fans and wine for the next five years., What forecasting method would you use? As indicated above, the selection should be guided by the stage in the product life-cycle and by the 10availability of data. But general guidelines cannot provide a complete answer. Because each situation differs, you should consider more than one method. Given that you use more than one method to forecast, how should you pick the best method? One of the most widely used approaches suggests that you select the one that has performed best in the recent past. This raises the issue of what criteria should be used to identify the best method. Statisticians have relied upon sophisticated procedures for analyzing how well models fit historical data. However, this has been of little value for the selection of forecasting methods. Forecasters should ignore measures of fit (such as RZ or the standard error of the estimate of the model) because they have little relationship to forecast accuracy. Instead, one should rely on ex ante forecasts from realistic simulations of the actual situation faced by the forecaster. By ex ante, we mean that the forecaster has only that information that would be available at the time of an actual forecast. Traditional error measures, such as mean square error, do not provide a reliable basis for comparison of methods (for empirical evidence on this, see Armstrong and Collopy 1992). The Median Absolute Percentage Error (MdAPE) is more appropriate because it is invariant to scale and is not overly influenced by outliers. For comparisons using a small set of series, it is desirable, also, to control for degree of difficulty in forecasting. One measure that does this is the Median Relative Absolute Error (MdRAE), which compares the error for a given model against errors for the naive, no change forecast (Armstrong and Collopy 1992). One can avoid the complexities of selection by simply combining forecasts. Considerable research suggests that, lacking well-structured domain knowledge, equally-weighted averages are as accurate as any other weighting scheme (Clemen 1989). This produces consistent, though modest improvements in accuracy, and it reduces the likelihood of large errors. Combining seems to be

especially useful when the methods are substantially different. For example, Blattberg and Hoch (1990) obtained improved sales forecasts by equally weighting managers” judgmental forecasts and forecasts from a quantitative model. The selection and weighting of forecasting methods can be improved by using domain knowledge (about the item to be forecast) as shown in research on rule-based forecasting (Collopy and Armstrong 1992). Domain knowledge can be structured, especially with respect to trend expectations. These, along with a consideration of the features of the data (e.g. discontinuities), enable improvements in the weightings assigned to various extrapolations. 6. Estimating prediction intervals In addition to improving accuracy, forecasting is also concerned with assessing uncertainty. Although statisticians have given much attention to this problem, their efforts generally rely upon fits to historical data to infer forecast uncertainty. Here also, you should simulate the actual forecasting procedure as closely as possible, and use the distribution of the resulting ex ante forecasts to assess uncertainty. So, if you need to make two-year-ahead forecasts, save enough data to be able to have a number of two-year ahead ex ante forecasts. The prediction intervals from quantitative forecasts tend to be too narrow. Some empirical studies have shown that the percentage of actual values that fall outside the 95 per cent prediction intervals is substantially greater than 5 per cent, and sometimes greater than 50 per cent (Makridakis et al. 1987). This occurs because the estimates ignore various sources of uncertainty. For example, discontinuities might occur over the forecast horizon. In addition, forecast errors in time series are usually asymmetric, so this makes it difficult to estimate prediction intervals. The most sensible procedure is to transform the forecast and actual values to logs, then calculate the prediction intervals using logged differences. 11Interestingly, researchers and practitioners do not follow this advice except where the original forecasting model has been formulated in logs. When the trend extrapolation is contrary to the managers” expectations, the errors are asymmetrical in logs. Evidence on the issue of asymmetrical errors is provided in Armstrong and

Collopy (1998a). In such cases, one might use asymmetrical prediction intervals. Notice that this discussion takes no account of asymmetric economic loss functions. For example, the cost of a forecast that is too low by 50 units (lost sales) may differ from the cost if it is too high by 50 units (excess inventory). But this is a problem for the planner, not the forecaster. Judgmental forecasts are also too narrow. That is, experts are typically overconfident (Arkes 1999). To a large extent, this is because forecasters do not get good feedback on their predictions. When they do, such as happens for weather forecasters, they can be well calibrated. When forecasters say that there is a 60 per cent chance of rain, it rains 60 per cent of the time. This suggests that marketing forecasters should try to ensure that they receive feedback on the accuracy of their forecasts. The feedback should be relatively frequent and it should summarize accuracy in a meaningful fashion. Another procedure that helps to avoid overconfidence is for the forecaster to make a written list of all of the reasons why the forecast might be wrong. 7. Implementation There are two key implementation problems. First, how can you gain acceptance of new forecasting methods, and second, how can you gain acceptance of the forecasts, themselves? Acceptance of forecasting methods The diffusion rate for new methods is slow. Exponential smoothing, one ofthe major developments for production and inventory control forecasting, was developed in the late 1950s, yet it is only recently that the adoption rate has been substantial (Mentzer and Kahn 1995). Adoption is probably slow because there are many steps involved in the diffusion of the method. Here is the traditional procedure. Techniques are first developed. Some time later they are tested. At each stage they are reported in the literature. They are later passed along via courses, textbooks, and consultants, eventually reaching the manager who can use them. Even then they may be resisted, perhaps because the procedures are too complex for the users. The future is promising, however. The latest methods can be fully disclosed on websites and they can be incorporated into expert systems and software packages. For example, the

complete set of rules for rule-based forecasting is kept available and up-to-date and can be accessed through the forecasting principles site (forecastingprinciples.com). Acceptance of forecasts Forecasts are especially useful for situations that are subject to significant changes. Often, these involve bad news. For example, Griffith and Wellman (1979), in a follow-up study on the demand for hospital beds, found that the forecasts from consultants were typically ignored when they indicated a need that was less than that desired by the hospital administrators. Firms often confuse forecasting with planning, and they may use the forecast as a tool to motivate people. That is, they use a “forecast” to drive behavior, rather than making a forecast conditional on behavior. (One wonders if they also change their thermometers in order to influence the weather.) One 12way to avoid this problem is to gain agreement on what forecasting procedures to use prior to presenting the forecasts. Another way to gain acceptance of forecasts is to ask decision makers to decide in advance what decisions they will make, given different possible forecasts. Do the decisions differ? These prior agreements on process and on decisions can greatly enhance the value of the forecasts, but they are difficult to achieve ,in many organizations. The use of scenarios offers an aid to this process. Scenarios involve writing detailed stories of how decision makers would handle situations that involve alternative states of the future. Decision makers project themselves into the situation and they write the stories in the past tense. (More detailed instructions for writing scenarios are summarized in Gregory 1999.) Scenarios are effective in getting forecasters to accept the possibility that certain events might occur. 8. Conclusions Extrapolations of sales are inexpensive and often adequate for the decisions that need to be made. In situations where large changes are expected or where one would like to examine alternative strategies, causal approaches are recommended. Some of the more important findings about sales forecasting methods can be summarized as follows: • Methods should be selected on the basis of empirically-tested theories, not

statistically based theories. • Domain knowledge should be used. • When possible, forecasting methods should use behavioral data, rather than judgments or intentions to predict behavior. • When using judgment, a heavy reliance should be placed on structured procedures such as Delphi, role playing, and conjoint analysis. • Overconfidence occurs with quantitative and judgmental methods. In addition to ensuring good feedback, forecasters should explicitly list all the things that might be wrong about their forecast. • When making forecasts in highly uncertain situations, be conservative. For example, the trend should be dampened over the forecast horizon. • Complex models have not proven to be more accurate than relatively simple models. Given their added cost and the reduced understanding among users, highly complex procedures cannot be justified at the present time. The sales forecast should be free of political considerations in a firm. To help ensure this, emphasis should be on agreeing about the forecasting methods, rather than the forecasts. Also, for important forecasts, decisions on their use should be made before the forecasts are provided. Scenarios are helpful in guiding this process.

 

Sales forecasts are common and essential tools used for business planning,

marketing, and general management decision making. A sales forecast is a

projection of the expected customer demand for products or services at a

specific company, for a specific time horizon, and with certain underlying

assumptions.

A separate but related projection is the market forecast, which is an

attempt to gauge the size of the entire market for a certain class of goods or

services from all companies serving that market. Sales and market forecasts

are often prepared using different methods and for different purposes, but

sales forecasts in particular are often dependent at least somewhat on

market forecasts. Although the focus of this discussion will be on sales

forecasting, a brief summary of market forecasting will help provide

context.

A special term in studying sales and market forecasts is the word

"potential." This refers to the highest possible level of purchasing, whether

at the company level or at the industry or market level. In practice, full

potential is almost never reached, so actual sales are typically somewhat

less than potential. Hence, forecasts of potential must be distinguished from

forecasts that attempt to predict sales realized.

MARKET FORECASTING

Assessing market potential involves observing and quantifying relationships

among different social and economic factors that affect purchasing

behaviors. Analysts at the industry level look for causal factors that, when

linked together, explain changes (upward or downward) in demand for a

given set of products or services. This may be done on the local level, the

national level, or even the international level. The economic and social

variables that are deemed most important—those that historically have

shown the most influence on demand—are then incorporated into some type

of formula or mathematical model that attempts to predict future

purchasing activity based on expected changes in the causal factors.

The simplest example would be to consider the influence of widely observed

macroeconomic indicators such as gross domestic product (GDP) and

employment rates. A simplistic model of market growth might indicate that

based on time-series data from the past decade the restaurant market tends

to grow at one and one-tenth times the rate of GDP when the national

unemployment rate is less than 7 percent, and at four-fifths of the GDP

growth rate when unemployment is greater than 7 percent.

Suppose an analyst wishes to create a two-year forecast for the national

restaurant business. Using published estimates from government or private

sector economists, the analyst might learn that next year's GDP is expected

to grow at 2.9 percent and unemployment is expected to register at 6.7

percent. The following year, however, GDP growth is expected to slow to

1.9 percent and unemployment is expected to rise to 7.6 percent. Using the

simple model outlined, the forecast for next year's restaurant sales growth

would be based on the first condition observed, namely that market growth

is somewhat (10 percent) higher than GDP growth when unemployment is

relatively low. In other words, the first year's forecast would be 1.1 × 2.9,

or 3.19 percent restaurant market growth. In the second year, the second

condition would come into play—market growth is slower than GDP growth

—since unemployment is expected to surpass 7 percent. Thus the forecast

would be 0.8 X 1.9 percent, or 1.52 percent growth in demand for

restaurant services.

While this example illustrates the basic process of forecasting, serious

market forecasts would of course consider many more factors than GDP and

unemployment. For instance, more sophisticated models might look at the

changing demographics of the customer base (size, average income, and

other attributes), the rate of inflation, changes in interest rates, and

changes in related markets that could affect the market under

consideration. Consequently, the formulas for obtaining market forecasts

are considerably more complex. But, as in this example, many market

forecasts do rely on economic or demographic data from government or

other sources; the forecaster often doesn't need to come up with from

scratch his or her own projections for, say, GDP and population growth.

Many market forecasts also rely on published indexes, ratios, and averages

for various economic and social factors that have been compiled in

databases or in reference books.

SALES POTENTIAL AND FORECASTING

Sales forecasting is an attempt to predict what share of the market

potential identified in a market forecast a particular company expects to

have. For very small companies that serve only a fraction of the total

market, the company forecast may not even explicitly consider the market

forecast or share, although implicitly, of course, the company's sales are

subsumed under the total market size. In the other extreme, a monopoly's

sales forecast is essentially the same as the market forecast.

Forecasts of different kinds are often prepared at different levels of a

corporate enterprise. Managers of different stripes use forecasts for a

variety of purposes, including marketing planning, resource\investment

allocation, production scheduling, and labor recruitment. In some cases the

uses are simply informational, but in many cases forecasts are the basis for

major decisions like:

what product lines to pursue

how much to spend on production and in what ways

how aggressively to advertise or promote the products

how best to get the products to market in order to fulfill the projected

demand

Yet sales forecasts are conditional in that they are only estimations and are

highly interdependent with corporate strategy and actions. Some forecasts

are developed before strategies and action plans are formulated; others are

created to gauge the anticipated effects of an existing strategy.

A sales forecast may cause management to adjust some of its assumptions

or decisions about production and marketing if the forecast indicates that

(1) the current production capacity is grossly inadequate or excessive and

(2) sales and marketing efforts are inconsistent with the expected outcomes.

Management therefore has the opportunity to examine a series of alternate

plans for changes in resource commitments (such as plant capacity,

promotional programs, and market activities), changes in prices, or changes

in production scheduling. Indeed, when a company is evaluating different

courses of action it may develop separate forecasts for each option in order

to assess the implications of each.

THE HISTORICAL PERSPECTIVE.

As a starting point, management analyzes previous sales experience by

product lines, territories, classes of customers, or other relevant categories.

This analysis is often on a detailed level, such as on a month-by-month,

quarterly, or seasonal basis, in addition to looking at overall annual trends.

Such detailed views will allow management to look for seasonality in new

forecasts or even to devise strategies to improve sales during slow seasons.

Management needs to consider a time line long enough to detect significant

patterns in its sales history. This period is typically five to ten years. If the

company's experience with a particular product class is shorter,

management might also examine discernible experiences of similar

companies. The longer the view, the better management is able to detect

patterns that follow cycles. Patterns that repeat themselves, no matter how

erratically, are considered "normal," while variations from these patterns

are "deviant." Some of these deviations may have resulted from temporary

or fluke conditions, such as bad weather or uncommon events. Depending

on the circumstances, figures may need to be normalized to remove the

influence of such factors.

MARKET POSITION.

Forecasting may also consider how the company rates against its

competitors in terms of market share, research and development, quality,

pricing and sales financing policies, and overall public image. In addition,

forecasters may evaluate the quality and size of the customer base to

determine brand loyalty, response to promotions, economic viability, and

credit worthiness.

PRICE INDEX.

If prices for products have changed significantly over the years, changes in

dollar volume of sales may not correlate well with unit sales. To adjust for

such discrepancies, a price index may be developed showing the relative

prices of goods for a given year versus some reference year. Perhaps the

simplest case would be if the company's prices moved exactly at the rate of

inflation, in which case it could use the historical tables from the Consumer

Price Index or Producer Price Index, depending on what market it serves, to

adjust its figures. Using this information, the company can establish more

stable projections that are not unduly skewed by price fluctuations or

inflation trends.

GENERAL ECONOMIC CONDITIONS AND SECULAR TRENDS.

The condition of the overall economy often influences the rate of growth (or

decline) for particular markets and firms. Sales forecasters may consider

any number of macroeconomic trends that have been shown to correlate

with company sales, including GDP and inflation. General indicators like

these can be essential in interpreting a sales forecast or recent sales

history, as they will show, for example, whether the company's dollar sales

are rising faster than the rate of inflation or whether the company is

growing more rapidly than the economy on average.

Similarly, the company may consider its performance relative to its

industry, the secular trend. While the secular trend represents the average

for the industry, it may not be "normal" for a particular company. A

comparison of the company's trends to the industry pattern may highlight

that the company is serving a specialized market within the broader

industry or that the company isn't keeping up well with its competitors. The

forecast of such patterns may lead management to alter its strategies if

such trends are unfavorable, or to concentrate more on a strategy that

appears to be working well.

PRODUCT TRENDS.

Forecasters may also analyze sales trends of individual products. This may

include the use of price indexes. Such trends are important for

understanding product life cycles and separating the performance of similar

products (e.g., two different lines of shampoo from the same company) to

evaluate strengths and weaknesses.

DEVELOPING A SALES FORECAST

Forecasting involves more uncertainties than most other management

activities. For instance, while management exerts a good deal of control

over expenditures, it has little ability to direct the buying habits of its

customers. Thus, even while sales trends depend on the vagaries of the

marketplace, management must make a reasonable estimate of what the

future holds in order to plan corporate affairs effectively.

The process managers or analysts go through to create a sales forecast is

similar to this:

1. Determine the purposes of the forecast (e.g., for purchasing, strategic

planning, etc.).

2. Divide the company's products into homogeneous (or at least

relevant) categories.

3. Determine the major factors affecting the sales of each product group

and their relative importance.

4. Choose one or more forecasting methods based on the kind of data

available and the sophistication needed in the forecast.

5. Gather all necessary data.

6. Analyze the data.

7. Check and cross-check any adjustments to the data (e.g., price

indexing or seasonal adjustments).

8. Make assumptions regarding any effects of the various factors that

can't be measured or forecast.

9. Convert deductions and assumptions into specific product and

territorial forecasts and quotas.

10. Apply forecasts to company operations.

11. Periodically review performance and revise forecasts.

While forecasting is still neither effortless nor flawless, the gap between

forecasts and reality has steadily narrowed over time. There are several

ways that a company can improve the likelihood of creating an accurate

sales forecast and using it effectively:

using more than one forecasting technique

abandoning or modifying a specific technique when it has proven

unreliable for the company's needs

remembering that forecasts are highly conditional

carefully monitoring market developments for changes that contradict

the underlying assumptions of the forecast

conducting periodic reviews and making changes when necessary

OVERVIEW OF FORECASTING APPROACHES

A variety of approaches can be used to surmise the future growth of sales.

Some are highly dependent on statistics and mathematical relationships,

while others are more inferential or speculative. The choice of approach

depends on how accurate or precise the forecast needs to be, how long of a

period it's for, the availability of past or supporting data, the funding

available for forecasting activities, and other considerations.

CAUSAL APPROACH.

In the causal approach, forecasters identify the underlying variables that

have a causal influence on future sales. For instance, new computer sales

generally have a direct influence on software application sales. In the

consumer software market, other causal factors might be population

growth, the expansion of computer-based activities such as electronic

commerce, and trends in work and school practices like working at home

and computer-based education initiatives. Still other more general factors

might be the growth of personal income, employment levels, patterns in

international trade (new market opportunities or new competitive threats),

and so forth.

To first assess the market trends, the task of the forecaster is to establish (if

it has not already been reliably by others) how these factors relate to one

another and to sales of software. Some will have a direct (positive)

relationship with sales, while others will have an inverse or negative

relationship. In statistical parlance, the forecaster is identifying a set of

correlations. Upon further examination, some of these correlations will

appear causal (population growth causes higher sales), while others will be

indirect or coincidental (inflation growth may cause both rising interest

rates and rising sales, but rising interest rates may have nothing to do with

rising sales). The sum of all this information is a formula or model that,

given a certain set of conditions characterizing the underlying factors, will

indicate the future behavior of sales.

On the next level the forecaster must assess the company's position in the

industry and how that is likely to change over the forecast period. If no

change at all were expected (i.e., it retains the exact same share of the

market over the period) the company's sales would grow at the exact same

rate as the broader market. Since this is uncommon, however, the

forecaster must surmise whether the firm's recent or intended actions—as

well as those of its competitors—will result in rising or falling market share.

Again, there are a variety of causal variables to be considered, such as

advertising expenditures, promotional efforts, new product introductions,

and technological changes, to name a few.

Eventually, through data analysis, model construction, and statistical

methods, the forecaster will arrive at a causal model of company sales

based on external factors and internal actions. When changes in those

factors occur (or are expected to), the implications for the company's sales

can be determined by recalculating the forecast using the same model but

different inputs. As this description suggests, causal approaches to

forecasting tend to be complex analyses of a wide array of potential

influences on sales.

A regression analysis   is a specific forecasting tool that identifies a

statistical relationship between sales, the dependent variable in the

analysis, and one or more influencing factors, which are termed the

independent variables. When just one independent variable is considered

(say, population growth), it is called a linear regression, and the results can

be shown as a line graph predicting future values of sales based on changes

in the independent variable. When more than one independent variable is

considered, it is called a multiple regression and can't be represented with

a simple line graph. Regression analysis is related to correlation analysis,

where the latter is concerned with the strength of relationships between the

independent and dependent variables.

Another causal model is life-cycle analysis. Here product sales growth rates

are forecast based on analysts' projections of the phases of product

acceptance by various segments of the market—innovators, early adapters,

early majority, late majority, and laggards. Typically, this method is used to

forecast new product sales. Analysts' minimum data requirements are the

annual sales of the product being considered or of a similar product. It is

often necessary to do market surveys to establish the cause-and-effect

relationships that signal the different phases of the product life cycle.

NONCAUSAL APPROACHES.

The most common noncausal approaches are time-series models, in which

patterns are extrapolated from standardized historical data in order to

reach a future projection. (Elaborate time series may also be used in causal

models as well.) Analysts plot these patterns in order to project future sales.

Because no attempt is made to identify and evaluate the underlying causes

of sales patterns, the analyst implicitly assumes that the underlying causes

will continue to influence future sales in roughly the same manner as in the

past. Consequently, while it is easier to use and understand, this approach

tends to be relatively simple and may not produce as reliable results as

other methods.

One common method of forecasting based on time series is the use of

moving averages. There are a number of specific methods that incorporate

moving averages. All of these assume in some way that future sales will

reflect an average of past performances rather than, for example, following

a linear percentage increase trend. Moving average methods minimize the

impact of random outcomes that could skew a forecast.

Exponential smoothing is a similar time-series technique. Rather than

relying on equally weighed historical averages, however, exponential

smoothing adds weight exponentially to the most recent values in the series.

This assumes that the most recent figures are the best indicators of current

trends and market forces, whereas older figures may represent an

inaccurate or out-of-date picture of the sales trend.

Any sophisticated time-series technique also includes some provision for

filtering out random noise or chance occurrences in the data that aren't part

of the underlying sales trend. In a mathematical formula this takes the form

of an error or noise term that is calculated into the forecast.

QUALITATIVE AND JUDGMENTAL APPROACHES.

A number of approaches rely on the informed opinions of various

individuals, who may consider past trends, causal factors, their personal

observations, or any number of other factors to arrive at a forecast. Usually

this involves asking a number of knowledgeable people from inside or

outside the company what they expect will happen during the forecast

period. The forecasters may be customers (intention-to-buy survey), sales

staff, or outside industry experts who are familiar with the company and its

competitors.

Aside from relatively informal internal surveys, perhaps the most widely

known judgmental approach is the Delphi technique, which convenes a

panel of (usually) outside experts who each come up with independent

forecasts and then revise their projections until they reach a consensus

position. Another important judgmental method is the program evaluation

and review technique (PERT), in which optimistic, pessimistic, and most

likely scenarios are developed (usually by one or more experts) and then

weighed to produce an average expected scenario. A third general

qualitative technique is called the probability assessment method (PAM), in

which relevant internal staff members are asked to rate the probability of

achieving a certain range or ranges of sales volume. The probabilities (given

in percentages) are then translated into a cumulative probability curve that

can be further analyzed to arrive at a forecast.

An intention-to-buy survey measures a target market's plans to buy a

product within a given time period. Market analysts frequently conduct

such surveys before introducing a new product or service. If it isn't a

product they already purchase, respondents are given a neutral and

reasonably detailed description of the product with the hope they will

provide honest answers. When surveying the general public, care must be

taken to ensure respondents don't provide unrealistically positive feedback

on new product ideas, otherwise the results will be meaningless.

Such qualitative or judgmental methods are often preferred when (1) the

variables influencing buying habits are changing or hard to determine, (2)

enough data isn't available to support a statistical approach, (3) quantitative

methods have given poor results in this forecasting situation, (4) the

planning horizon is too far into the future for normal statistical methods to

be useful, or (5) there is a need to consider technological breakthroughs

which may only be in the early stages of development but will have impact

during the forecasting period.

DIRECT VERSUS INDIRECT.

When forecasters first consider the broader market and then winnow it

down to the company level, it is known as the indirect approach. When they

only work with company data, it is called the direct approach. While indirect

obviously lends itself more to causal analysis and direct more to noncausal,

in theory direct and indirect approaches can be used in both causal and

noncausal models. While for many sales forecasters the direct approach is

most practical, it can be a revealing exercise to go through the indirect

approach, since it requires that the forecaster consider the entire market

potential for a product.