“Forecasting is a waste of a manager’s time because no one can actually predict the future”.
I disagree with this statement. To my mind, forecasting is crucial for marketers nowadays. It is the basis for the policies and business strategies. The future of the market is shaped with the help of forecasting. It is correct that the future cannot be predicted, but at least the forecasting creates an opportunity to evaluate the possible outcomes and address them before something negative happens.
In our globalising world, the markets are becoming broader and bigger. The number of clients involved is large, and the outcomes of using the wrong strategy in the process of trading can result in a huge loss of income. A good manager should always monitor the changes in the market carefully and follow all the new tendencies in order to be able to develop a flexible marketing system that would respond to all the issues and address the problems as soon as they occur without creating damage and losing money.
Contemporary marketing is all about strategies and tactics. This is why simply waiting for visible changes in the market field to occur and then reacting to them and formulating a response is going to be highly non-profitable for the company and for the traders.
Monitoring the changes in the market, and the economic situation, in general, is what helps the suppliers establish the prices. In cases when the prices are too high and do not match the expectations of the potential customers, the companies may lose their clients. If the prices are too low, this leads to the inevitable loss of capitals. In both situations, the company ends up losing income, and it is the fault of the people that were supposed to be involved in the forecasting of the market.
The forecasting is the base element of the market analysis (Berry 2014). It allows the managers to collect the data for the planning of the future strategy. The analysis is done in order to determine the number of potential clients in the target market. The number of clients makes it possible for organisations to learn the approximate level of income and revenue in advance. Such an analysis could cover the periods of time lasting for several years.
Of course, the numbers received by means of such forecasting are estimates. It is true that no one can predict the future and count the precise data because markets are changeable. Any kinds of circumstances may occur and create a positive or negative impact on the further development of the markets and affect the possible number of future customers. Yet, the numbers received through the forecast give the managers of the company an approximate idea of the future, and knowing the future, even vaguely, is highly empowering. The forecast is normally based on the changes that happen in similar markets or on the latest and the most popular tendencies (Forecasting and Business Trends 2014).
The managers collect the data for the forecasts looking at what happened in a certain market within the last several years. They find patterns of growth or decrease and can predict future success or downfall.
The traders need to be aware of the developments within the markets and take things under control. In the contemporary world, the process of globalisation keeps going without people being in charge. This is why I think that the aspects of marketing that can be predicted and counted should be managed.
Reference List
Berry, T. (2014). What Is a Market Forecast? Web.
Forecasting and Business Trends. (2014). Business Studies. Web.
Formalized forecasting has massive benefits which involves the ability of the management of an organization to plan inventory levels, shift or add labor, decrease or increase production and determine whether or not to order raw material. It is also vital to mention that forecasting is a fundamental factor which affects the cost of raw materials and shipping cost such as petroleum increase, tax fluctuations and other overheads incurred by an organization. The margin or profitability of a firm is determined by the latter. On the same note, the overall productivity of a firm is also dictated by the ability to carry out thorough business forecasting.
The posterior demand of a product is a major consideration that is shaped by various types forecasting methods. This can equally enhance the process of evaluating the anticipated value of a product. Therefore, effective planning and shrewd utilization of the available resources can be put in place. In this case, forecasting limits inconveniences caused by delay of delivery of products to customers.
This is through the number of products required and the arte of producing them at the right time. It also factors in number of units to be produced, the inventory, cash flow, and amount of money that will be used for the process. Hence, augmenting distribution process and minimizing challenges associate with inventory are among the major attributes of business forecasting. The latter is also a vital business undertaking that makes it possible for firms to organizations to execute proper planning. The strategic supply chain management also requires effective business forecasting.
Prepare a weekly forecast for the next four weeks for each product. Briefly explain why you chose the methods you used.
In order to prepare a weekly forecast for roses, it is crucial to employ a simple method of time series. Using the latter approach, it is indeed possible to determine the forecast for the next four weeks. From the 14th week data, the sales result is 3.457. The latter is then multiplied by the number of weeks and then the value +48.28 is added. These forecasts will appear as demonstrated below. It will adopt the formula used for the 4 weeks within a period of four weeks. Hence, from the 15th to 18th week, the forecast is demonstrated below based on the data obtained from previous weeks.
Following falling profits and pressure from rising competition, I would adopt the market based sales forecast for Gold Coast blossoms. This will be based on market research to determine how much to produce and to sell. The figures will be above value based sales forecast.
This method helps find breakeven sales figures by dividing annual overheads by profit gross margin (selling price-direct cost *100). I will be able to determine the forecast for the next four weeks. From the 14 week data, the sales result will appear as demonstrated below.
The sales result.
Works Cited
William, Stevenson. Operations Management with Student DVD and Powerweb, 11th Edition, Boston: McGraw-Hill Irwin Publishing, 2005.Print.
Forecasting is important in modern supply chain management, especially in companies that manufacture items on inventory rather than by order. To ensure that they produce the right level of materials that satisfies their customers, manufacturers rely on material forecasting.
This enables them to avoid producing an overcapacity of goods that will store in the market. Additionally, a manufacturer is to fulfill his customer’s demand and thus be able to forecast it to avoid financial catastrophe.
A forecast should be reviewed regularly by the management in order not to be static. The reason for this is to enable the inclusion of information on the future trends, external or internal environment to give a more accurate forecast.
Discussion
Having an accurate forecast is very important task in supply chain management. Since company depends on the investment in the inventory, forecast accuracy is very important to the bottom line. Safety –stock levels needed to reach targets can be reduced if accuracy across all range of SKUs can be improved.
For proper allocation of supply chain resources, forecast accuracy is central at primitive SKU level. A supply imbalance can be met if there is an inaccurate demand forecast (Burt et al, 2010).
The importance of forecasting within the form cannot be overstated; managers use forecast generation and sharing to guide the distribution of resources, provide target for organizational efforts, sales, and product development and integrate the operation’s management function with marketing (Burt et al, 2010).
The main output required from forecasting is the determination of how many products will be bought by customers. Acquisition of too much raw materials may result from an overly optimistic forecast while a too low forecast may not match customer’s demands and lead to lose of customers (Burt et al, 2010).
An anticipated demand profile is typically delivered over a number of months for the top level products. This will in turn drive procurement activities that utilize known lead time to enable the procurement of materials to meet the forecast requirements.
Actual forecast varies from company to company; however, the key concepts are utilized commonly in most businesses.
Statistical Forecasting
Many companies utilize forecasting software that tackles data from the ERP and extrapolates it into demand profile for the future.This data analysis method is supported by market intelligence to tune the demand profile into what is thought to be realistic (Chockalingam, 2009).
This approach follows data integrity, and thus it is very beneficial to the organization. The company bases its analysis on the non subjective transaction data as the forecast is configured in a way that its solution delivers a number of outputs depending on pre-configured criteria.
The measure of how close actual demand is to the forecast quantity is referred to as the forecast accuracy. The converse of error gives the forecast accuracy.
Accuracy (%) =1 – Error (%).
Non Statistical forecast
This forecast is determined by production plainness. It is defined from quantities which are based on the current demand (Chockalingam ,2009).
Conclusion
Forecasting is based on complex calculation; historical periods are used to determine future demands. This gives a planner a guide to future demand. It is impossible to attain accurate forecast. Therefore, planners’ knowledge of the future is important in determining the products future demand. The usefulness of an accurate forecast is that it enables the supplier to plan its strategy tactfully.
In addition, it allows for the limited flexibility to reschedule resources. Hence, the inability to maintain an accurate forecast can result in decrease of sale, customers, excess inventory which may result in loss of goods and other inefficiencies.
Other than that, the benefit of maintaining an accurate forecast is that it helps in capacity planning and setting strategic initiatives. This approach allows for the flexibility to change and err (Chockalingam ,2009).
References
Burt, D. N., Petcavage, S. D., & Pinkerton, R. L. (2010). Supply management (8th ed.). Boston: McGraw‐Hill.
The management of Highline Financial Services has to determine the demand for the services of this organization during the next four quarters. Several challenges should be considered. In particular, there is an oscillating pattern of demand. However, the available data are not sufficient for calculating seasonal relatives. Therefore, it is difficult to apply such techniques as the centered moving average or simple average methods. Nevertheless, other tools can be useful for making intuitive predictions. Overall, this forecast can have significant implications for the senior executives of this company. In particular, this information is necessary for determining the number of workers who should be hired or dismissed. Furthermore, it is possible that business administrators will need to change some of the current practices to increase the demand for the services.
Forecasting methods
The changes in demand can be illustrated with the help of two bar charts because they can cast a light on the general trends affecting the company. In particular, one should focus on the quarterly changes in demand for during each year.
Service A.Service B.Service C.
Overall, these charts indicate that there are certain seasonal variations. For instance, the demand for Service A declines during the second quarter, but it reaches the peak in the third quarter (Patnaik, 2015). This tendency has been observed during each of the two years. The variations of demand for Season B follow a similar pattern during the two years. However, the causes of these variations are not explained.
Moreover, one should note that the demand for Service B diminished consistently (Patnaik, 2015, p. 133). For instance, one can reach this conclusion by comparing corresponding quarters in each year. At the same time, the customers of this company became more willing to use Service A. Nevertheless, the level of demand for Service C fluctuates. It is one of the exceptions that should not be overlooked.
In this case, one can apply the so-called naïve forecast. This approach is premised on the assumption that the last indicator in the time series should be viewed as the basis for the prediction (Patnaik, 2015, p. 85). Furthermore, it is supposed that the difference between the last two values will be used to estimate the change in demand (Patnaik, 2015, p. 85). The forecast involves the comparison of corresponding quarters in each year. The results can be presented in the table format.
Table One: Service A.
Service A
The first year
The second year
Forecast
The first quarter
60
72
84
The second quarter
45
51
57
The third quarter
100
112
124
The fourth quarter
75
85
95
Table Two: Service B.
Service B
The first year
The second year
Forecast
The first quarter
95
85
75
The second quarter
85
75
65
The third quarter
92
85
78
The fourth quarter
65
50
35
Table Three: Service C.
Service A
The first year
The second year
Forecast
The first quarter
93
102
111
The second quarter
90
75
60
The third quarter
110
110
110
The fourth quarter
90
100
110
This forecast is based on the additive approach to the estimation of demand. In this case, the person, who makes the prognosis, either adds or subtracts a certain quantity from the previous value (Patnaik, 2015). Admittedly, the naïve forecast is considered to be a very simplistic tool (Boyer & Rohit, 2009). This tool can yield valid results provided that the market trends do not change (Boyer & Rohit, 2009). Apart from that, the person has to assume that the changes of quantitative indicators will resemble an arithmetic progression, but it may follow a different pattern. However, this approach can be adopted if the time series is very short. Furthermore, this method is very time-efficient. So, this tool should not be disregarded.
The demand can also be estimated according to the multiplicative model. In this case, one should calculate the ratio of the second-year results to the first-year results. In turn, this coefficient will be applied to estimate the demand for the third year.
Service A.
Service A
The first year
The second year
Coefficient
Forecast
The first quarter
60
72
1.2
72× 1.2=86.4
The second quarter
45
51
1, 13
57×1.13= 57.8
The third quarter
100
112
1.12
112 ×1.12 = 125.44
The fourth quarter
75
85
1.33
85×1.13=96.33
Service B.
Service B
The first year
The second year
Coefficient
Calculation and forecast
The first quarter
95
85
1.12
85÷1.12=75.89
The second quarter
85
75
1.13
75 ÷1,13=66.37
The third quarter
92
85
1.082
85÷1.082= 78.5
The four quarter
65
50
1.3
50 ÷1.3 = 38.46
Service C.
Service C
The first year
The second year
Coefficient
Calculation and forecast
The first quarter
93
102
1.09
102× 1.09= 111.8
The second quarter
90
75
1.2
75 × 1.2= 62,5
The third quarter
110
110
1
110
The four quarter
90
100
1.11
111
Again, one should keep in mind that these estimates may not be accurate. The problem is that the results of the third year may not change according to the multiplicative model. It is one of the pitfalls that should be taken into account.
It is possible to consider such a technique as moving average. A manager should use the following formula:
(Value1+ Value2 + Value3 +..+ Value n)÷n
In this case, one can compute the four-period moving average for each of the services offered by Highline Financial Services. In this case, the manager should pay attention to the most recent data because this information accurately reflects the latest trends in the industry (Chiulli, 1999). So, one should focus on the quarters of the second year.
Service A
(72+51+112+85) ÷4=80
Service B
(85+75+85+50) ÷ 4 =73.75
Service C
(102+75+110+100) ÷ 4 = 96.75
This method of forecasting will enable the management to estimate the level of demand for the first quarter of the third year. Admittedly, this approach does not take into account the possibility of seasonal variations. However, it is possible that this regularity may not exist. One should access a larger sample of data to show that the changes in demand can be explained by some seasonal or temporal changes. The problem is that random results can be confused with patterns and regularities if one looks at short time intervals (Chase, 2013). This possibility should also be considered by the management.
One can argue that the application of different forecasting techniques is helpful for estimating the range of demand during a given period such as 12 months. This knowledge is useful for determining the optimal number of employees who should be hired by Highline Financial Services in the future.
General trends and their implications
Although the forecasting methods can yield different results, they can illustrate some important tendencies. In particular, the demand for Service A has increased over the last two years, and it is possible that this trend will continue in the future. Additionally, the demand for Service B diminishes. In turn, it is difficult to make predictions about Service C. Thus, the management should spend more time to improve its operations. This argument is particularly relevant to Service B that does not appeal to many customers. However, it is possible that this trend cannot be attributed to internal weaknesses of the company.
For instance, this tendency can be explained by the changing needs of customers. In this case, the management should reduce the number of employees who work in this particular area. For instance, they can provide Services A and C. However, these recommendations can be elaborated if the managers collect data for a longer period. In this way, they can increase the precision of their forecasts.
Conclusion
Overall, this case suggests that business administrators should be skilled in using various forecasting methods. They need to understand the strengths and weaknesses of these tools. Furthermore, they should determine if the available data are sufficient for identifying certain patterns or regularities. For instance, it is possible to mention the existence of seasonal variations. In turn, the estimations included in this paper are based on the application of naïve forecasts, multiplicative models, and moving average.
These approaches are helpful for determining if customers will use the services of the company. Finally, one can argue that Service B should be the primary concern to the management. Moreover, business administrators should consider the results of forecasts to determine the optimal size of the workforce. This task is essential for reducing operational costs.
Reference List
Boyer, K., & Rohit, V. (2009). Operations and Supply Chain Management for the 21st Century. New York, NY: Cengage Learning.
Chase, C. (2013). Demand-Driven Forecasting: A Structured Approach to Forecasting. New York, NY: John Wiley & Sons.
Chiulli, R. (1999). Quantitative Analysis: An Introduction. New York, NY: CRC Press.
Patnaik, S. (2015). Operations Management. New York, NY: Lulu Press.
This paper is devoted to the study of judgmental forecasting. It contains general data about this type of forecasting, cases of its application, examples of its most effective use, and its advantages and disadvantages. As an example of judgmental forecasting, the Delphi method is proposed, which involves identifying a consistent assessment of an expert group through an independent, anonymous survey of experts in several rounds, which involves reporting the previous round’s results to the experts. Resources such as the book Forecasting: Principles and practice by Hyndman and Athanasopoulos, and Sniezek’s article “A comparison of techniques for judgmental forecasting by groups with common information” are used to support the presented data.
What is Judgmental Forecasting
In cases of the extreme complexity of the problem, its novelty, insufficiency of available information, or the impossibility of mathematical formalization of the solution process, one has to turn to competent experts. The solution of problems by experts, their commenting, the formation of quantitative estimates, and the processing of the latter by formal methods is called the method of judgmental forecasting. Hyndman and Athanasopoulos (2018) assert that this method involves a set of logical procedures aimed at obtaining from experts the data necessary for the preparation and selection of rational decisions. In the theoretical aspect, the legitimacy of using judgmental forecasting is confirmed because methodologically correctly obtained expert judgments meet two criteria for the reliability of any new knowledge: accuracy and reproducibility of the result. Therefore, judgmental forecasting is created by experts based on their experience and assessments.
Judgmental forecasting helps to formalize the procedures for collecting, summarizing, and analyzing expert opinions to transform them into the most convenient form for making an informed decision. Moreover, this method of forecasting is continuously developed and improved. Currently, judgmental forecasting, in combination with other mathematical and statistical methods, can minimize the risk of making the wrong decision.
Where is Judgmental Forecasting used Most Effectively?
According to Hyndman and Athanasopoulos (2018), judgmental forecasting is applied in certain cases. Firstly, when an object or some phenomenon cannot be described mathematically. Secondly, it is used when there is no reliable, accurate, statistical information about the object that allows the use of objective methods. Thirdly, when there are no means for mathematical processing. For example, if there is no computer equipment, software, or qualified personnel. Fourth, it is a suitable method for an emergency situation when a quick decision is needed. Thus, there are four main cases in which judgmental forecasting is used.
Examples of how Judgmental Forecasting is Used Most Effectively
One example of the most effective use of judgmental forecasting may be the case of central banks. Hyndman and Athanasopoulos (2018) note that they involve judgment in forecasting the current level of economic activity, a procedure known as nowcasting because GDP data is only available quarterly. Moreover, this type of forecasting model is especially useful in such fields as research and development. Focus groups and expert groups can provide information that no computer model can provide. For example, by asking a group of people about what they are looking for in a product, companies can gauge their direction in developing specific product features.
Advantages and Disadvantages of Judgmental Forecasting
Like any other phenomenon, judgmental forecasting has both advantages and disadvantages.
Advantages
The undoubted advantage of judgmental forecasting is that it is universal and applicable to various forecasting objects. Moreover, it is relatively simple from a methodological point of view and does not place high demands on the quality of the initial basic information. Sniezek (1990) notes that the scope of statistical forecasting methods is limited to short- and medium-term forecasts. On the contrary, judgmental forecasting will allow to make long-term forecasts.
Disadvantages
Judgmental forecasting has several disadvantages. First, Hyndman and Athanasopoulos (2018) affirm that it can be inconsistent. Unlike statistical forecasting, which can be generated using the same mathematical formulas, judgmental forecasting relies heavily on human cognition and is subject to its limitations. For example, a limited attention span can lead to important information being missed, or a misunderstanding of cause and effect relationships can lead to erroneous conclusions. Moreover, human judgment may vary due to psychological factors. Second, judgment can be clouded by personal or political agendas when goals and projections are not separated. Even when goals and projections are separated, judgment can be clouded by optimism or wishful thinking. Third, there can be anchoring effects in judgmental forecasting where subsequent forecasts tend to converge or be close to the original familiar reference point. Anchoring can lead to conservatism and underestimation of new and more relevant information and thus create a systematic bias. Therefore, whenever possible, the judgmental results obtained by the methods of expert assessments should be supported by other forecasting methods.
The Delphi Method of Judgmental Forecasting
One of the methods for obtaining an individual opinion of experts is the Delphi method. Its essential features are anonymity, multi-level, and correspondence. According to Sniezek (1990), the Delphi method is effective because it allows to consider the opinions of all people who are relevant to any issue through a consistent combination of considerations, suggestions, and conclusions, and then come to a specific agreement. The advantage of the Delphi method is the use of feedback during the survey, which significantly increases the objectivity of expert assessments. Thus, using the Delphi method, expert opinions are determined by periodically weighing their views, taking into account the answers and arguments of colleagues.
References
Hyndman, R.J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice (2nd ed.). OText.
Sniezek, J.A. (1990). A comparison of techniques for judgmental forecasting by groups with common information. Group & Organization Studies, 15(1), 5-19.
