Master Thesis
Spring semester 2008
Supervisor: Gert-Olof Boström
Authors: Luis Barrios
Jonas Kenntoft
The Business Analysis
Process of New Product
Development
- a study of small and medium size enterprises
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New Product Development: Sales Forecasting and Risk Assessment in SMEs, Slides of Business Policy and Regulation
The importance of effective new product development (NPD) in competitive industries, focusing on sales forecasting and risk assessment in Small and Medium Enterprises (SMEs). The authors suggest that sales forecasting is the foundation for many other forecasts and discuss the challenges of predicting sales and making profit projections. The document also explores the concept of product cannibalization and its impact on sales. SMEs are identified as the main focus, and the document outlines the criteria for selecting companies for interview and the headings for data analysis.
What you will learn
- How is risk assessment conducted in SMEs?
- What is the impact of product cannibalization on sales in SMEs?
- What factors influence sales forecasting in SMEs?
- How does sales forecasting influence other forecasts in SMEs?
- What methods are used for sales forecasting in SMEs?
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Master Thesis Spring semester 2008 Supervisor: Gert-Olof Boström Authors: Luis Barrios Jonas Kenntoft
The Business Analysis
Process of New Product
Development
- a study of small and medium size enterprises
PREFACE
PREFACE
When we started this thesis journey we did not have any idea of where we would land. Now after 10 weeks we have landed this, and we are pleased with the result. It has been a lot of fun but also hard work involved in completing this thesis. Last but not the least we learned a lot about both our subject and how to conduct research on this level. During these 10 weeks we have gotten help from a lot of people but most of all from our supervisor Gert-Olof Boström. We would like to thank him for helping us completing this journey. We would also like to thank all the people that we have interviewed, without you there would not have been a thesis.
We hope that this thesis will get you interested in the many small and medium size enterprises in our world, and to not neglect their importance of the global economy.
Umeå, April 2008
Jonas Kenntoft Luis Barrios
TABLE OF CONTENT
4.6 ANALYSIS OF THE EMPIRICAL DATA ......... FEL! BOKMÄRKET ÄR
- INTRODUCTION........................................................................................ TABLE OF CONTENT
- 1.1 BACKGROUND....................................................................................
- 1.2 PROBLEM DISCUSSION.....................................................................
- 1.3 PROBLEM STATEMENT
- 1.4 PURPOSE AND RESEARCH QUESTIONS..........................................
- 1.5 DELIMITATIONS
- 1.6 DISPOSITION
- INTRODUCTION........................................................................................ TABLE OF CONTENT
- THEORY METHODOLOGY.....................................................................
- 2.1 CHOICE OF SUBJECT.........................................................................
- 2.2 THEORETICAL AND PRACTICAL PRECONCEPTIONS.....................
- 2.3 EPISTEMOLOGICAL CONSIDERATIONS...........................................
- 2.4 SCIENTIFIC APPROACH.....................................................................
- 2.5 RESEARCH APPROACH
- 2.6 CHOICE OF THEORIES.......................................................................
- 2.7 SECONDARY DATA COLLECTION
- 2.8 CRITICISM OF SECONDARY DATA COLLECTION
- THEORETICAL FRAMEWORK
- 3.1 SMALL AND MEDIUM SIZE ENTERPRISES
- 3.2 NEW PRODUCT DEVELOPMENT.....................................................
- 3.3 DEFINE CONCEPTS
- 3.4 NEW PRODUCT PLANNING PROCESS
- 3.5 BUSINESS ANALYSIS
- 3.6 CONCEPTUAL FRAMEWORK...........................................................
- PRACTICAL METHODOLOGY.............................................................
- 4.1 CHOICE OF DATA COLLECTION METHOD
- 4.2 CHOICE OF RESPONDENTS
- 4.3 ACCESS AND COLLECTION OF EMPIRICAL DATA
- INTERVIEWS 4.4 INTERVIEW GUIDE AND QUESTIONS USED DURING THE
- 4.5 CRITICISM OF PRIMARY SOURCES
- EMPIRICAL FINDINGS AND ANALYSIS INTE DEFINIERAT.
- 5.1 INTRODUCTION OF RESPONDENTS...............................................
- 5.2 GENERAL BUSINESS ANALYSIS AND FORECASTING....................
- 5.3 SALES FORECASTING.......................................................................
- 5.4 PROFIT PROJECTIONS.....................................................................
- 5.5 COST ESTIMATION............................................................................
- 5.6 RISK ASSESSMENT
- 5.7 CANNIBALIZATION OF SALES
- CONCLUSIONS TABLE OF CONTENT
- BUSINESS ANALYSIS? 6.1 WHICH ARE THE IMPORTANT FACTORS FOR SMEs IN THEIR
- THE TWO SMEs STUDIED? 6.2 HOW DOES THE BUSINESS ANALYSIS PROCESS LOOK LIKE IN
- 6.3 PROBLEM STATEMENT
- 6.4 SUGGESTIONS FOR FUTURE RESEARCH
- CRITERIA OF TRUTH
- 7.1 VALIDITY............................................................................................
- 7.2 RELIABILITY
- LIST OF REFERENCES
- SCIENTIFIC ARTICLES
- BOOKS......................................................................................................
- ONLINE SOURCES...................................................................................
- RESPONDENTS........................................................................................
- APPENDIX A
- FIGURE 2.1 THE DYNAMICS OF INDUCTION AND DEDUCTION LIST OF FIGURES AND TABLES
- RESEARCH FIGURE 2.2 THE LOCATION OF INDUCTION AND DEDUCTION DURING
- RESEARCH APPROACHES TABLE 2.1 : DIFFERENCES BETWEEN QUANTITATIVE AND QUALITATIVE
- TABLE 3.1 : SMEs DEFINITION
- FIGURE 3.1 : NEW PRODUCT PLANNING PROCESS
- FIGURE 3.2 : INCLUDING CONCEPTS IN THE BUSINESS ANALYSIS STAGE
- FIGURE 3.3 : MODEL OF THEORIES
- FIGURE 5.1 : VÄSTERBOTTENS KURIRENS LUMPY DEMAND...........
- FIGURE 6.1 : FINAL MODEL – THE BUSINESS ANALYSIS PROCESS IN SMEs
INTRODUCTION
The most common obstacle to innovation in small medium size enterprises (SMEs) is according to De Toni and Nassimbeni (2003) the excessive cost of product development projects. The uncertainty of market acceptance is the second major difficulty. The innovative process in SMEs is also more informal and less structured, the base of managerial competencies is limited, the availability of financial resources is lower, and the attraction towards skilled labour weaker. De Toni and Nassimbeni (2003) also listed other problems SMEs have; they may for example have difficulties to recruit, train, and retain highly qualified competent personnel. Since they are often unable to match the wage opportunities, career development opportunities or job security given by large firms. SMEs also have a limited access to finance and supposed equity gaps are very often cited as barriers to innovations in small firms. (ibid) Therefore, SMEs are disadvantaged in financial support and the market for skilled labour, resources which is essential for successful innovation particularly during the early stages of product development. Developing successful new products requires systematic planning to coordinate the many decisions, activities, and functions necessary to move the new-product idea to commercial success. (De Toni and Nassimbeni, 2003)
Cravens and Piercy (2005) developed the new product development planning process. The process includes such stages as, need analysis, idea generation, screening and evaluation, business analysis, product and marketing strategy development, and finally testing and commercialization. The business analysis stage components are sales forecasts, cost estimation, profit projections, risks assessments and finally the possible cannibalization of sales. The business analysis components should be used to estimate the commercial performance of the new-product concept. Before going any further this assessment is crucial for moving on in the product development process. (Cravens and Piercy, 2005)
The business analysis stage have many important components, therefore we see it as a crucial part of the whole process. The emphasis of this thesis will be in the business analysis stage, because of its importance in the new product planning process. Cravens and Piercy (2005) said for example to stress the importance of the business analysis stage that “business analysis is the final assessment before deciding whether to develop the concept into a new product or not.”
1.2 Problem Discussion
According to Fader, Hardie, and Huang (2004) there has always been an interest shared by academics and practitioners in the matter of forecasting new product sales. However estimations are not straightforward, according to Shahrul-Yazid and Nooh (2007) the common traits of business analysis issues (e.g. sales projections) are their high uncertainty, high ambiguity and their risky characteristics.
As earlier stated, new product introductions are common and an essential part for many businesses. Many of these new products fail, for example exit from the market soon after product launch. These product failures are costly, as the development costs and marketing costs during the launch period can no longer be recovered. (Hitsch, 2006) A possible explanation for these facts could be related to what has been stated earlier by Cross and Sivaloganathan (2007), De Toni and Nassimbeni (2003) and Lin, et al. (2006) who all argued that (NPD) is a very complex process and that firms are often uncertain about the demand and profitability of their new products, and therefore they launch products that could fail. For Hitsch (2006) this raised the question of how firms should optimally learn about the true
INTRODUCTION
profitability of their products, and how the decision to launch or scrap a product is affected by demand uncertainty. Hitsch (2006) stated in his article that many firms use the market as a laboratory to attain information from observed sales to know the true demand for their products. Cravens and Piercy (2005) claimed that obtaining an accurate financial projection depends on the quality of the sales and cost forecast. Hitsch (2006) points out that if uncertainty is reduced it will result in the form of increased expected profits.
Business analysis issues are about predicting the success of a product or technology in the market, predicting reaction of competitors, suppliers and customers, and predicting how a product plan and strategy will work out based on limited information about the future and market dynamics (Shahrul-Yazid and Nooh, 2007). This limited information about the future and past experience is only able to give some guidance to a limited extent. Shahrul-Yazid and Nooh (2007) stated that this explained why we see more intuition than empirical analysis employed in making decisions when dealing with NPD business analysis issues. According to Fader, et al. (2004) this intuition phenomena enables the business analysis stage to anticipate the complete set of market dynamics that surround a new product launch.
Shahrul-Yazid and Nooh (2007) mentioned two driving concepts of the new product planning area in their research. The first one is time-to-market , if there is too much scrutiny then the time-to-market can be compromised. The second is evaluation accuracy , if you speed things too much you will not know what you have and you will not know if what you are offering is demanded. Therefore launching a product too fast will lose certain evaluation criteria. Corporations need to decide between “evaluation accuracy” versus “speed-to-market”. Corporations have to decide how much scrutiny or evaluation accuracy, for example the accuracy of revenue estimation analysis that they have concede in order to save time to be ahead in developing and launching a new product. Making that decision is a delicate balancing act. There is a risk to the product launch schedule if the development of the product is delayed or slowed down by excessive scrutiny of evaluation accuracy. (Shahrul-Yazid and Nooh, 2007)
Fader, et al. (2004) mentioned in their research the difficulty to get an accurate read on a new products long-term potential based on only a few initial weeks or test-market sales data. Common problems that were found include the artificial skew of initial sales level caused by significant promotional activity. Also the fact that early buyers may not exhibit typical purchasing rates due to the promotional activity and repeat-purchasing patterns might be difficult to sort out from the large amounts of first purchase data. Fader, et al. (2004) therefore suggested the essence of relying on formal models of new product sales so that practitioners can understand the underlying factors of sales forecast and in the end produce a valid sales forecast.
Making new product development projects successful has been a major challenge for the companies in the past, and will definitely continue to be a major challenge for tomorrow’s companies. (Shahrul-Yazid and Nooh, 2007)
To be able to stay competitive in today's business environment corporations need to be innovative, but all corporations do not have the same essential factors for successful innovation. SMEs have a more difficult task when it comes to innovation with their restrains related to limited competencies and the lack of financial resources. Due to these restrains,
INTRODUCTION
1.6 Disposition
2. Theoretical methodology
The chapter starts with a short presentation of the choice of subject and followed by an explanation of the chosen approaches used in the research. This chapter is presented to create an understanding of the different methodological choices and to increase the transparency of this study.
3. Theoretical framework
In this chapter an overview of previous research related to our research will be presented. There are a number of theories and concepts which we found relevant for this study which will be presented and introduced. The chapter will conclude with a conceptual framework where we grasp and arrange the previous research in a graphic model.
4. Practical methodology
In this chapter we will present how we have collected our data required for this study. This chapter will cover such areas as choice collection method and choice of respondents, interview issues and collection of empirical data. Finally, we will conclude with discussing
4.5 CRITICISM OF PRIMARY SOURCES
5. Empirical findings and analysis
In this chapter we will present the empirical data collected from the interviews. We have also chosen to include the analysis in this chapter. The chapter will start with a short introduction to the companies. Then each subject has been divided into their own section. Each subject has the empirical data collected and the analysis included in each section. The analysis of each sector will provide a thorough analysis of the empirical findings and is interpreted with the help of the theoretical framework developed and the conceptual framework.
6. Conclusion and discussion
In this chapter we will start by presenting a model we have constructed. This model will be used to answer the research questions and problem statement which will thereby fulfil the overall purpose. Finally we will suggest some ideas concerning future research.
7. Criteria of truth
The following chapter and the last chapter of this thesis we will consider the scientific relevance of the study. This will be performed by looking at the validity and the reliability of the study.
THEORY METHODOLOGY
2. THEORY METHODOLOGY
In this chapter the methodology related to the theory is presented. The chapter starts with a short presentation of the choice of subject and followed by an explanation of the chosen approaches used in the research. This chapter is presented to create an understanding of the different methodological choices and to increase the transparency of this study.
2.1 Choice of subject
The discussion of finding a suitable subject for us was a short process. We both have an interest for product development and since we both study the Master’s Programme in Marketing we decided to look into the new product planning process. The specific choice of researching the business analysis stage in the planning process came to us from working with the marketing strategy simulation program MarkStrat. When using that simulation program we found that the most difficult part for our team was to predict and estimate product sales, product volume and make profit projections. Therefore we decided to research more in depth about those complicated and complex factors surrounding business analysis. We both had a strong interest in this subject and we both wanted to learn more about these specific processes, therefore the motivational and learning value issues were satisfied to a great extent.
The choice of focussing on the business analysis stage of the new product planning process came from the fact that it is close related and in our scope of our education. It was also what we could find within our scope of education the least studied one.
The idea of concentrating on SMEs came from the article written by De Toni and Nassimbeni (2003) that said; SMEs are disadvantaged in resources (e.g., financial support and skilled labour) which are essential for successful innovation. These resources are particularly important in the early stages of product development such as the planning process, in particular the business analysis stage. SMEs are consequently more challenged than larger corporations when it comes to NPD. We decided that SMEs should be our aim for this study and to research how they carry out their business analysis stage successfully with their limited resources.
2.2 Theoretical and practical preconceptions
There is a big concern for any researcher about influences that might compromise the objectivity of the research in development. We also shared that concern and especially about those influences coming from our own theoretical and practical preconceptions, because one might not notice them which are even more harmful. Preconception is the authors’ previous understanding which derives from the authors’ education, environment and personal experiences. (Ingeman and Bjerke, 1994) These preconceptions can influence the way data is collected and interpreted during the writing of the thesis. The preconceptions will also influence the approach to the research and the final result (ibid).
In order to keep our research as far away as possible from our own theoretical and practical preconceptions we had first to recognize them to be able to know where they could jeopardize most our research. Among our own preconceptions we highlighted estimation models, validity of estimations, and regression techniques. Summarizing and putting all together we began the research with a previous knowledge of estimation models, regression techniques but lots of doubts about the validity of such models within the business analysis process.
THEORY METHODOLOGY
Figure 2.1: The dynamics of induction and deduction
Source: Adopted from Ghauri and Grønhaug (2002)
It is important to mention that the process for induction goes from facts to propositions and later from prepositions to theories or laws. This means that even if the researcher does not end up in a theory or law, but only end up in propositions he or she could have used the inductive approach.
Another complementary opinion about induction and deduction is presented by Cooper and Schindler (2003); deduction is regarded as a form of inference that claims to be conclusive but must fulfil two requirements; truth and validity. Meanwhile induction is defined as drawing a conclusion from one or more facts or pieces of evidence.
The most important support from Cooper and Schindler (2003), deals with the thin line between induction and deduction that can confuse a researcher. The manner in which the inductive and deductive approach works can be seen in a simpler way with the help of following model.
Figure 2.2: The location of induction and deduction during research Induction
Deduction
Deduction
Source: Adopted from Cooper and Schindler (2003)
In the model it is observed that with deduction the hypothesis is tested to confirm if it is capable of explaining the facts. On the other side for induction it is noticed that its conclusion is only a hypothesis.
Laws and theories
Facts acquired through observation
Explanations and predictions theories
INDUCTION DEDUCTION
Fact 1
Fact 2
Why?
Hypothesis
THEORY METHODOLOGY
Finally as a summary it can be said that deductive approach is about testing and assessing existing theory, but inductive approach is about where theory, laws, propositions or hypotheses are generated from the practice.
Since the main objective in our research will not be testing any particular theory the deductive approach will be set aside and instead the inductive method will take the principal role, with this approach we are going to be able to test the data collected analyze it and pursue a new knowledge contribution.
2.5 Research approach: Qualitative
When reading about the different research approaches it becomes more and more obvious about our approach. There are two different approaches quantitative and qualitative. Quantitative approach can be regarded as an approach that emphasizes the quantification of data. Whereas the qualitative approach usually emphasizes the importance of words in the data. It is also important to mention that qualitative research is more focussed on theory creation and not on assessment of existing theories (Bryman and Bell, 2005). In Table 2. Bryman and Bell (2005) show the fundamental differences between quantitative and qualitative.
Table 2.1: Differences between quantitative and qualitative research approaches Quantitative Qualitative Principal direction when it comes to which part the theory should play in relation to the research
Deductive, assessment of theories
Inductive, theory creation
Knowledge theoretical direction
Natural science model, above all positivism
Interpretivistic approach
Ontological direction Objectivism Constructivism
Source: Adopted from Bryman and Bell (2005) p. 40
With the importance of the subjective in-depth view of the business analysis stage among SMEs the qualitative approach is therefore vital. The choice of qualitative research is based on our stated purpose.
The data that we collected cannot be generalized since it is not covering the whole population but it will give us an opportunity to gain an understanding of what the population that is covered thinks and decides about how they carry out their business analysis. One thing that comes up when working with qualitative research is the problem to connect information from different sources since they could be too specific. (Saunders, Thornhill and Lewis, 2000)
2.6 Choice of theories
The numerous theories needed for the research can be classified in basically two types: one will cover the explanatory needs for the readers of the research. This type of theory will consist of basic background concepts to help readers to “perform” in the same frequency as the authors. We can mention as example of this first theory, the basics supplied about small and medium size enterprises, and the general theory of the new product development process.
THEORETICAL FRAMEWORK
3. THEORETICAL FRAMEWORK
In this chapter an overview of previous research related to our research will be presented. There are a number of theories and concepts which we found relevant for this study which will be presented and introduced. The outline of the chapter is to start with the definition of SMEs and of new product development, then to introduce and define the concepts used in the research, and finally the introduction of business analysis theory. The chapter will conclude with a conceptual framework where we grasp and arrange the previous research in a graphic model.
3.1 Small and medium size enterprises
Different organizations have developed different definitions for firm size. The following definitions have been used for this study: According to the European Commission the definition of small and medium size enterprises (shown in Table 3.1) is made up of enterprises which employ fewer than 250 persons and which have an annual turnover not exceeding EURO 50 million, and/or an annual balance sheet total not exceeding EURO 43 million.
TABLE 3.1 : SMEs DEFINITION
Enterprise category Headcount Turnover or Balance sheet total
medium-sized < 250 ≤ € 50 million ≤ € 43 million
Small < 50 ≤ € 10 million ≤ € 10 million
Micro < 10 ≤ € 2 million ≤ € 2 million
Source: Official Journal of the European Union 20th^ of May 2003
According to the European Commission the micro, small and medium size enterprises are socially and economically important, since they represent 99 % of all enterprises in the European Union and provide around 65 million jobs and contribute to entrepreneurship and innovation.
The relevance of innovation orientation to smaller businesses is a reflection of the transformation of modern market environments in which new product development and differentiation have become important aspects of the business development of many firms (Appiah-Adu and Singh, 1998). However SMEs do not conform to the conventional marketing characteristics of marketing textbook theories (Tomes and Phillips, 2003).
According to Tomes and Phillips (2003) it is well documented that SMEs have distinctive characteristics that differentiate them from conventional marketing in larger firms. These characteristics may be determined by the inherent characteristics and behaviours of the entrepreneur or owner/manager; and they may be determined by the inherent size and stage of development of the enterprise. Such limitations can be summarised to be:
limited resources (such as finance, time, and marketing knowledge); lack of specialist expertise (owner-managers tend to be generalists rather than specialists); and limited impact in the marketplace
THEORETICAL FRAMEWORK
Appiah-Adu and Singh (1998) stated that small- and medium size enterprises are usually characterised by a relatively simple organisational structures and more unified cultures. In addition, small- and medium-sized businesses are characterized by a limited range of products and customers, thus, minimising the requirement for formal procedures developed to gather and process customer or market information for decision making. Additionally smaller firms are also often characterised by informal and short-term decision-making tactics (De Toni and Nassimbeni, 2003: Appiah-Adu and Singh, 1998). For our research it would be interesting to investigate what Appiah-Adu and Singh (1998) argued about the requirements for formal procedures in NPD that would be minimized in small size enterprises.
Lack of planning and under capitalization has frequently been advanced in the relevant literature, as the most critical determinants of small firm success or failure (Appiah-Adu and Singh, 1998). Having the planning determinant in mind it would therefore be interesting to see how accurate and how the planning process is performed.
Appiah-Adu and Singh (1998) reviewed the marketing management literature and found that an overwhelming majority of product planning process studies have been based on large firms. In spite of the important contribution that smaller businesses make to the economic development and growth in many countries, there is a scarcity of new product planning process related empirical research based on SMEs (ibid). The fact that large multinational firms dominates as the basis of many analyses and studies (Tomes and Phillips, 2003: De Toni and Nassimbeni, 2003: Appiah-Adu and Singh, 1998) proves that there is not that much interest in studying this field. Consequently, this gap gives us an opportunity for this study to focus on small- and medium-sized firms in the Swedish business environment which also coheres with the purpose and research questions of this study.
3.2 New product development
Before one can start to discuss new product planning process, one should first determine what defines a new product. In this part a definition will be argued resulting in the development of a definition and a path for this thesis. The following argument define “new product”, which is the final objective for a new product planning process.
According to Rudder (2003) practitioners and researchers used the term new product development to describe a range of product developments. However we think there is little agreement as to what actually is a “new product”. Rudder (2003) continued with believing that a broad definition is the most useful and should include either the development or introduction of a product new to the manufacturer or the introduction of an old product into a new market. The types of introduction innovations that can be undertaken are:
Brand reformulations: When a brand product have been largely unchanged over the past years. The company can reformulate the brand strategy. Line extensions: When a company decides to extend a successful product through for example a new flavour or colour New markets: Find new market for old ideas. New products: Find new product ideas for new markets or old markets. Imitation products: Produce a product similar to an existing one.
To quote Rudder (2003) who stated “only 10 per cent of the entire new products introduced over the last five years were truly innovative or new to the world”. If these numbers are true, then in actual practice the introduction of “new” products is rather rare to the world, this
THEORETICAL FRAMEWORK
Planning not only takes what comes out of the forecasts because when finished it can give such important feedback for forecasting in accordance with Waddell and Sohal (1994) when stated that forecasts may need to be adjusted to reflect the impact of planning actions.
Accuracy of forecasting
The first question at this point is why is forecast accuracy important? The answer seems very logical according to Herbig and Milewicz (1994), when forecasts of future economic activities are accurate associated with specific courses of action, they can correctly guide corporate strategy in an uncertain environment, but when they are inaccurate they can bankrupt any organization.
Herbig and Milewicz (1994) also stated that forecasts are almost always wrong. The only real question is, how much? We rather disagree with him and share the point of view of Waddell and Sohal (1994) where one has to be aware of forecasts has to be seen as a guide and not as the ultimate rigid policy. Herbig and Milewicz (1994) continued stating: “Why then should anyone forecast bad numbers? A forecast is better than no forecast” like saying the worse is to have nothing at all, we rather be conscious of forecast limitations.
A common approach for investigating the accuracy of business forecasts is to compare forecasts with time series models. Business managers should provide superior forecasts to time series models as they can incorporate contextual information such as market conditions and business strategies. (Cassar and Gibson, 2007)
After knowing the importance of accuracy for forecasting it is necessary to control the degree of accuracy obtained, and to do so testing is mandatory According to Waddell and Sohal (1994) testing of forecast accuracy uses indirect processes like laboratory tests of constructed expectations or direct like survey-based processes. The next question is which process is better? One has to consider that indirect processes may present disadvantages as lack of domain knowledge (Cassar and Gibson, 2007); and second the presence of goal-setting pressures (ibid).
Later on the process then we can say how well or how bad are our forecasts but we can not stop here, we must try to improve them using more than one forecasting method or forecast and then combining their predictions. This has proved to be an extremely effective way of increasing forecasting accuracy and decreasing the variance in errors according to Waddell and Sohal (1994). The theories agree on the importance of accuracy and that is why we wish to include it in our research as well.
Forecasting techniques
When talking about techniques for forecasting we must distinguish two possible paths the formal and the informal techniques. If formal, another two paths enter in the equation these methods of forecasting are the qualitative and quantitative. (Waddell and Sohal, 1994) The path taken by the forecaster depends upon the availability and type of data. According to Waddell and Sohal (1994) for a forecast method to be called quantitative, that is “a method that relies on mathematical models and assumes that past data and other relevant factors can be combined into reliable predictions of the future”, historical quantitative data must be available; if not the method is called qualitative, technological or judgmental / subjective and depend upon managerial judgment and experience. [0]This means that different persons can obtain different results from the same information. (Waddell and Sohal, 1994) In a way
THEORETICAL FRAMEWORK
similar to informal methods, those are basically intuitive and depend on individual experience and abilities. These methods are used when there is insufficient time or data to use more formal means. As said by Waddell and Sohal (1994) this is confirmed when companies that posses large amount of quantitative data take some decisions base on time limitation.
Among the qualitative methods we can find the Delphi method which consists of three to six rounds of answers to progressively refined questions that are taken anonymously from an expert panel. (Waddell and Sohal, 1994) The scope of this method is turn pinpoint numbers into ranges on the project timeline supplemented by reliable corporate data, and apply the program evaluation review technique (Wheatley, 2004). We must however remember that no technique is the absolute and final word and we need to let the people responsible make the decisions that need to be made.
Waddell and Sohal (1994) mentioned another qualitative method, market surveys which are statistically designed surveys using consumers, prospective consumers or expert observers. The purposes of these are to gather information on market conditions.
Among quantitative methods there are; auto projection , which is, patterns of past demand projected into the future. Within this method we found moving average the simplest auto- projection method where the average demand for instance, is the arithmetic average of demand from a number of past periods. This method is some times made more sensitive adding a weighting factor related with each period (Waddell and Sohal, 1994)
Another quantitative method is the causal method which develops cause and effect relationships between variables. According to Waddell and Sohal (1994) “Causal methods help in predicting turning points in time series data and therefore are most useful in medium- to-long range forecasts.” Techniques and tools of forecasts are an important factor in forecasting method and will also be included in our further study of SMEs’ business analysis process.
Considerations of forecasting
According to Wheatley (2004) “probabilistic techniques create resource ranges, but they are not the whole answer” and consider them to be the first consideration any forecaster must have in mind.
We have to agree with the affirmation of Cassar and Gibson (2007) about smaller firms tending to have a higher degree of subjectivity in the forecasting process, and used simpler, less quantitative and more qualitative forecasting techniques. It was in concordance with Waddell and Sohal (1994) which paper presents a relationship between information contained in the data available, time and resources allowed for preparing the forecast.
“The less formalized forecasting approaches, lower levels of resources available, and less sophisticated processes for information gathering and analysis may lead to small business managers exhibiting less rational forecasting behaviour than those in larger firms” (Cassar, and Gibson, 2007) this affirmation is in direct relationship with the previous point where we found the mutual support findings between Waddell and Sohal (1994) and Cassar and Gibson, (2007) the quality of the forecast will be directly related with its rationality.