According to Procter and Gamble Company annual report of the year 2013, a variety of raw materials used in product manufacturing is exposed to price fluctuations. This is attributed to the climatic conditions, supply conditions, and political and economic changes, among other fluctuating factors. The overheads are prone to modifications because of the variations in prices of goods, inputs in the form of raw materials, wages, depreciation and appreciation of the currency, and interest rates which determine the availability of credit (Todaro & Smith, 2006).
Therefore, a company must ensure its ability to control the fluctuations through strategic pricing actions, coming up with projects that save on costs, making proper sourcing decisions, hedging decisions, and improving the productivity that is consistent with the objective of control over fluctuations in order to be successful in this industry (Farnham, 2005). Credit and currency exposure is important to countries, such as Venezuela, Argentina, China, India, and Egypt, known for exercising currency controls (Bateman & Snell, 2011). It is vital for a business to sustain the necessary production and supply arrangements to ensure the successful management of disruptions. Consequently, there will be a need to execute, realize, and maintain plans during cost improvement to include outsourcing of projects as well as identifying, training, and ensuring that employees are held. The company announced its efficiency regarding the minimization of costs and production in research development areas and marketing.
Evidence for Future Options
To handle areas prone to frequent cost fluctuations of key inputs, the company has decided to use future options (Bateman & Snell, 2011). The options are the limited basis with maturities ranging from less than one year to five years mature swap contracts. Most costs of sold products are subject to a given range of variable in nature. The chief sources of variation in gross margin are the cost of raw materials, the results of pricing geographical mix, and the product. In this case, the costs are the major variants about marketing while the overheads take minor portions.
In the year 2012, Procter and Gamble Company introduced a plan to help them in production and cost minimization that would reduce the cost of overall production and supply process. The plan is deliberated to increase the rate of cost reductions by making an efficient decision in the management and manufacturing as well as other strategic processes to identify the way forward.
The company also has a post-employment sponsorship program, which includes cover pension, defined contribution and benefit plans, and post-employment benefit (OPEB) plans. The latter consists of medical and life insurance for retirees to a large extent. For financial reporting purposes, OPEB will require projections on the accrued benefit obligations, including variables such as mark down rates and the anticipated increase in salaries. Also, it will need employee-related factors such as earnings, retirement age, deaths, anticipated return on assets, and trend rates of healthcare cost (Bateman & Snell, 2011). These, among other hypotheses, influence the annual expenses while the obligations identified in the underlying plans’ assumptions are postponed.
In conclusion, the strategies initiated by the company, including the future long plans and the expected tactics are potentially viable. Probably, the success of the company will be reliant on the strategies.
References
Bateman, T. S., & Snell, S. (2011). Management: building competitive advantage (3rd ed.). Chicago: Irwin.
Farnham, P. G. (2005). Economics for Managers. Upper Saddle River, NJ: Pearson Prentice Hall.
Todaro, M. P., & Smith, S. C. (2006). Economic development (9th ed.). Boston: Pearson Addison Wesley.
During the world economic crisis between 2008 and 2010, the automotive industry was one of the worst affected and this is obvious in the low sales and massive layoffs that were characteristic to companies operating in the automobile markets (Lafontaine & Morton, 2010). Government intervention and different strategies applied by the players in the automobile market contributed to restoring the performance of the automotive industry (Mishkin, 2011). Nevertheless, the effects of the economic crisis make all industries unpredictable and make it necessary for business analysts to constantly perform forecasts and analysis on market performance. This paper is a forecast of the potential sales performance of AutoNation based on past trends from the last eleven years.
Trend
AutoNation is a major player in the automotive industry and currently has 2% of the market share. The trend in the automotive industry will be similar to that of AutoNation, which makes it possible to forecast the company’s performance in the next two years. Using the United States’ government’s monthly retail sales survey, the annual performance of the automotive industry was derived. Figure 1 below is a scatter plot that illustrates the sales performance of the automotive industry in the last eleven years. A generally horizontal trend line indicates that the automotive industry is remaining flat with some seasonal growths and deeps, which can be observed in the repeated peaks and valleys.
Figure 1: Sales in million of dollars
The economic recession from 2008 to 2010, the growth in automotive sales since 2010, and the seasonal buying behavior of consumers are some factors that have shaped the trend line.
Yearly Patterns
Figure 2: AutoNation Sales in Millions
Figure 2 above is the trend line for AutoNation sales in the last eleven years and a slight upward pattern can be observed. The peaks and valleys are seasonal and are seen to repeat annually in the same months. Seasonal influences such as low car sales during the summer (from June to September), increase in consumer spending after-tax rebates (from February to March), and end of year sales (from October to December) all have yearly effects on the trend line. Annually, a negative deep can be observed during the summer period when consumers spend most on other products, and a positive deep can be observed during the Christmas holidays and after-tax rebates when consumers spend on luxury products.
Linear Forecast
The results of the linear forecasts are presented in the Excel sheet attached to this document. Using linear regression, the results show that there will be continuous growth in the average sales of AutoNation’s products for the next two years. These sales will follow the same peak and valley patterns that were seen in the previous figures.
Time on GDP Forecast
In the linear forecast, the ‘years’ and the ‘months’ were used as the dummy variables. The GDP recorded in the last eleven years can be used to predict the performance of the AutoNation since the GDP represents the total sales recorded in the country. The Excel document attached to this report provides details of the results achieved when the GDP was used to forecast the sales of AutoNation.
Conclusion
This report provided a two-year forecast for AutoNation using two different methods. The two methods used are valid and provide an idea of the potential performance of the company. However, it is recommended that the Linear Forecast method is used since it provides a lower forecast of the company’s performance.
References
Lafontaine, F. & Morton, F. S. (2010). Markets: State franchise laws, dealer terminations, and the auto crisis, Journal of Economic Perspectives, 24(3), pp. 233-50.
Mishkin, F. S. (2011). Over the cliff: From the subprime to the Global Financial Crisis, Journal of Economic Perspectives, 25(1), pp. 49-70.
Using forecasting techniques for predicting the changes in sales rates and, therefore, defining further strategies is a common pattern in modern business management (Simkin, Norman and Rose 318). The introduction of information technology tools has opened new horizons for marketers, yet also posed a range of threats in terms of the reliability of the data. Sales per click ratio, which is a comparatively recent addition to the existing marketing strategies, can be considered a rather flimsy factor in determining the company’s sales, yet it does provide sufficient data for analysis and offers ample opportunities in terms of defining the company’s performance. Although the tool in question still needs further testing, it should be incorporated into the Excellent Consulting Group’s strategy as one of the means of locating possible sales rates for the next month.
Therefore, it can be assumed that the number of monthly website hits can serve as the basis for making forecasts regarding further sales. However, as the analysis carried out above shows, the correlation between the number of times that people frequent the site in question and the number of sales that the company makes over a month is not necessarily in direct proportion to each other. The relations between the two are much more complicated, as the people, who occasionally find themselves on the company’s site, are comparatively numerous and do not affect the actual number of sales (Drolias 73).
Despite the positive tendency in the correlation between sales and clicks, one still has to admit that the variation between the key data is rather high, the standard deviation of the data set in question being approximately 73. Seeing that the measure tool in question allows for “a measure of dispersion that is defined in the same units as the random variable” (Leavenworth 141), it can be assumed that the data under analysis is far too scattered to allow for a clear and precise forecast in terms of the company’s further sales. On the one hand, the trend line displayed in the diagram points to the high possibility of a positive outcome. On the other hand, the inconsistency of the data shows that a sudden drop in the correlation may jeopardize the organization’s success.
Nevertheless, the number of clicks per site should still be incorporated into the analysis of the company’s performance in general and the success of its sales in particular. As the diagram shows, there is a positive trend in the correlation between the variables in question. Indeed, a closer look at the chart will show that the increase in hits is typically followed by an increase in the number of clicks and vice versa. For example, the consistent rise in the number of site visitors April to May (1,050 and 1,180) correlated with the rise in the number of sales that were carried out successfully (301 and 510 correspondingly). The specified tendency can be observed throughout the entire year with minor exceptions.
At this point, the ratio of clicks per site to the number of sales deserves to be mentioned. As it has been stressed above, the increase in clicks eventually leads to a rise in the number of sales; however, the extent, to which sales increase, is not consistent. According to the chart, the ratios observed from January to March varied from 1,917808219 to 3,488372093. In other words, assuming that the connection between the number of sales and the number of clicks per site is obvious would be quite hasty.
Nevertheless, the standard deviation of the specified ratio is rather low, which allows suggesting that the ratio of clicks to sales should still be trusted as a means of carrying out quite an accurate forecast of future sales (McCallum 6). Particularly, the standard deviation of the above-mentioned ratio makes approximately 0.45, which means that the data in question can be viewed as trustworthy.
To determine the efficacy of the tool in question, one should forecast the sales that the company is likely to make in the next three months. Using the formula that was generated with the help of a scatter plot, one will conclude that the organization is likely to experience a slight increase in its monthly income (Stokes 237). Specifically, the sales forecast will allow estimating the sales for three months, i.e., February, March, and April. Particularly, the formula shows that the sales rates are going to reach approximately 504, whereas a slight drop is expected in March (485). In April, however, the rates of the product sales are going to rise slightly, though failing to reach the February level (482). Though the specified results do not quite coincide with the actual ones, they still are rather close to the company’s data. Hence, it can be assumed that the tool in question can and should be used as one of the means for determining the company’s sales.
Although the tendency for the sales in the target company to rise along with the amount of clicks per month is rather weak, it is still positive, which allows suggesting that it should be used as the basis for making prognoses for future sales. One must bear in mind, though, that the current trend is rather weak and, therefore, needs substantial support, i.e., the risk management strategy (Hull 817) that can ensure that the organization will not go bankrupt in case of a failure.
Works Cited
Drolias, Basileios. Pay-per-click: The Complete Guide. New York, New York: Lulu.com, 2007. Print.
Hull, John C. Risk Management and Financial Institutions. New York City, New York: John Wiley & Sons, 2012. Print.
Leavenworth, James Bradfield. Introduction to the Economics of Financial Markets. New York City, New York: Oxford University Press, 2007. Print.
McCallum, Ethan Q. Bad Data Handbook: Cleaning Up the Data so You Can Get Back to Work. Sebastopol, California: O’Reilly Media, Inc., 2012. Print.
Simkin, Mark G., Carolyn S. Norman, and Jacob M. Rose. Core Concepts of Accounting Information Systems. New York City, New York: John Wiley & Sons, 2014. Print.
Economic, Technological and Operations Management Demand Forecast
Forecasting is very important in management. It is majorly concerned with the use of past and present trends in the market to determine what may happen in the future (Clements & Hendry, 1998). It helps firms, organizations and countries prepare in advance on how to deal with future uncertainties. Despite having many similarities, economic forecasting, technological forecasting and operations management demand forecasting also have significant differences. The main difference between the three types of forecasting is the element that is foreseen (Clements & Hendry, 1998). Precisely, technological forecasting deals with the anticipation of possible changes in technology and its effects on other operations (Henry, 1991).
Therefore, it solely deals with technological inventions and their impacts (Henry, 1991). On the other hand, economic forecasting deals with future economic possibilities (Marquez, 2002). This is usually done by countries, individual firms and other organizations.
Unlike technological and economic forecasting, operations management demand forecast deals with the resources that organizations may need for efficient production in the future and possible changes in consumer behaviour (Kumar & Suresh, 2009). It is mainly concerned with the type and amounts of good and services their customers may demand in the future (Kumar & Suresh, 2009).
Strategic Importance of Forecasting to Human Resources, Production Capacity and Supply Chain Management
Forecasting helps ensure that the process of production is efficient. Managers who carry out analyses of market trends always ensure that the use of resources in the production process is controlled. The raw materials are used according to the number of products likely to be demanded. In the long-run, there are very minimal wastages.
Forecasting also ensures that the supply chain does not break at some point due to sudden changes (Molnar, A2010). A firm that carries out its forecasting process properly always prepares in advance for changes even before they occur. As a result, the production and supply processes are continuous since they are not affected by the changes. Such readiness also ensures that the production capacity of the firm is not affected negatively. In fact, good predictions always help firms maintain or increase their production capacities depending on the projected demand.
Qualitative and Quantitative Methods of Forecasting
Qualitative forecasting
The Delphi Method involves interrogating a panel of experts about their opinions on probable events (Naik, 2004). However, the interrogation is not done in a boardroom. Instead, each of the experts is questioned independently. The questioning is then followed by a compilation of all their responses by an outsider, who then brings them more questions. This process repeats itself until a common stand is reached. Its main purpose is to prevent the experts from arriving at a consensus prior to the interview.
Advantage
This method completely eliminates the possibility of mob psychology and external pressure as the experts do not meet.
Disadvantage
The level of reliability is usually very low
The experts rarely reach a consensus.
Quantitative forecasting
The Simple Moving Method involves the analysis of trends in an adjustable period (Nikolopoulos, 2010). The length of the period does not change. Its main purpose is to quantify the performance of the business in given periods in order to use them in predicting future trends. This method is commonly used by mobile phone manufacturers in determining the number of devices people buy each year.
Advantage
It is more reliable since it compares trends over a uniform period. Its predictions are usually very accurate.
Disadvantage
It is not good at predicting sudden changes.
References
Clements, M., & Hendry, D. (1998). Forecasting economic time series. Cambridge: Cambridge University Press. Web.
Henry, B. (1991). Forecasting technological innovation. Dordrecht: Kluwer Academic. Web.
Kumar, S., & Suresh, N. (2009). Operations management. New Delhi: New Age International. Web.
Marquez, J. (2002). Understanding Economic Forecasts. International Journal of Forecasting, 18(3), 464-466. Web.
Molnar, A. (2010). Economic forecasting. New York: Nova Science Publishers. Web.
Naik, G. (2004). The structural qualitative method: a promising forecasting tool for developing country markets. International Journal of Forecasting, 20(3), 475- 485. Web.
Nikolopoulos, K. (2010). Forecasting with quantitative methods: the impact of special events in time series. Applied Economics, 42(8), 947-955. Web.
Exponential smoothing is one of the common forecasting models used by many organizations today. The exponential model generates an accurate forecast that can be applied in automated systems. Consequently, the method can be used in large-scale forecasting. Engemann and Henderson (2012) noted that exponential smoothing is a time series technique which is based on historical data of a forecast. It is mostly applicable in situations where there is reasonable continuity between the past and the future. Thus, the statistical technique is best suited for short term decision making that can easily be identified based on current and future trends.
In the long term, these assumptions can potentially become difficult and unreliable because variables changes significantly due to changes in technology and improved efficiency. For instance, I have spent the last 6 out of seven days working on linear regression computations. Next week, I forecast that I will spend 6 out of seven days in front of a computer working on linear regression Excel computation.
Linear regression is used to forecast future values of a company such as demand metrics and other variables that predict economic climate. Linear regression can be employed in the casual model with several explanatory variables. This model is best suited where there is no time component. For instance, it can be used to forecast when a metal will melt under different conditions by developing a line of best fit based on historical performance to predict the future outcome.
Regression model can be used to forecast business continuity and risk management program. Linear regression is a forecasting model that uses the assumption of the underlying factor when it is possible to identify some variable that influences a given variable (Tammineedi, 2010). For instance, regression can be used in business continuity management (BCM) to forecast future sales based on weather conditions which can enable supermarkets to predict umbrella sales in a given period. It is also a seasonality model that can allow hotels to predict how sales will increase or decrease during summer and winter.
Reliability model is essential in business continuity management plan because it ensures that a firm can continue to operate smoothly when faced by challenges. Reliability models are is used by companies to analyze the probability that a system will failure within a given time. It enables managers to determine precisely how long a system will function before it failures. This allows managers to determine the cause and estimate the probability of recovery.
Reliability and risk assessment models offer a robust key performance indicator that can be used to compose assurance production strategies. Clas (2008) argues that these approaches can be used to identify the threat and undesired models that can jeopardize business operations. Reliability models can support identification of probability of scenarios, infrastructure, and logistical bottlenecks that might affect a business. An organization can rely on these models to maximize sufficient recovery period and disruption shortfall. This includes determining the minimum level at which each activity needs to be done on resumption to enable managers to minimize negative impact and loss of revenues.
One of the greatest challenges facing decision makers in BCM is to compare the best course of action that will yield maximum return. Nicoll and Owens (2013) argue that in most cases, the task can potentially prove to be too challenging. Challenges arise due to organizational complexity in decision alternatives. For instance, the decision maker may lack sufficient information to enable them to make proper decisions. The lack of information may stain the decision maker’s capacity to evaluate the consequences different cause of action. However, in reality, choices require critical evaluation of different courses of action effectively before making any decision.
Secondly, undetermined elements always encroach in decision making process thus making it difficult to predict the outcome. For instance, in most cases, the outcome is determined by other people who may not have been involved in the original decision. This led to the postponement of decisions making process. However, even when decisions are made, the decision maker failures to consider all possible implications a particular decision.
Statistical probability is mostly employed in the assessment of uncontrollable events using statistical probability. Statistical probability measures risk where the probability distribution is known. Moreover, it involves application of probability to identify the expected outcome. In this model, the decision maker does not have all the information in order to make sound decisions. The information that lacks in this model is quantified using probability.
Using probability enables the decision maker to shield themselves against adverse uncertainty. The decision-making process is treated as a gamble by determining the tradeoff value of an individual outcome against its probability (Jedynak, 2013). For instance, if we want to establish the possibility of a child choosing vanilla ice cream from other flavors, this data can be obtained from surveys. Presumably, if out of 100 children only 35% chose vanilla; the probability that a child at any given time will choose vanilla is 0.35.
References
Clas, E. (2008). Business Continuity Plans. (Cover story). Professional Safety, 53(9), 45-48. Web.
Engemann, K. & Henderson, D. (2012). Business continuity and risk management: essentials of organizational resilience. Brookfield, Connecticut, USA: Rothstein Associates Inc., Publisher. Web.
Jedynak, P. (2013). Business continuity management – perspective of management science. Contemporary Management Quarterly / Wspólczesne Zarzadzanie, 12(4), 85-96. Web.
Nicoll, S. R., & Owens, R. W. (2013). Emergency Response & Business Continuity. Professional Safety, 58(9), 50-55. Web.
Tammineedi, R. L. (2010). Business Continuity Management: A Standards-Based Approach. Information Security Journal: A Global Perspective, 19(1), 36-50. Web.
Forecasting principles are applied by companies to predict sales for a certain industry during a selected period with the focus on firms’ share in these sales depending on tendencies in the market. Dr. Pepper Snapple Group is a company that produces soft drinks, and it is one of the leaders in the beverage market of the United States. This company uses sales forecasts actively to determine production volumes for different periods and regions of the country (Lombardo, 2018). The purpose of this paper is to analyze the impacts of Dr. Pepper’s sales forecasts on other forecasts, explain the role of exogenous factors in forecasting, and assess prerequisites of good forecasts in the process.
Identifying the Sales Forecast
In 2017, Dr. Pepper increased its net sales to $6.7 billion that indicated a 3.9% rise in comparison with the previous year. A sales forecast predicted such increases because of focusing on developing an attractive product and package mix for customers and improving an advertising campaign. The company addressed the forecasted changes in sales with the focus on the 1% rise in the sales volume and the 3% rise in the bottler case sales volume in comparison to 2016 (“Dr. Pepper’s (DPS) Q4 earnings,” 2018). To predict certain outcomes based on forecasting, the company’s leaders chose a causal model to determine the number of bottles to produce depending on market trends and changes in customers’ demands.
Analyzing the Impact of the Sales Forecast on Other Forecasts
For Dr. Pepper and other firms, sales forecasts directly influence setting the budget for a year. Thus, sales forecasts determine volumes or resources and materials to be ordered from suppliers and labor to be hired additionally. If sales forecasts are not appropriate, a firm can face significant losses and waste resources. Overhead costs depend on sales forecasts because different sales volumes influence expenses associated with supplies, advertising, taxes, and insurance among others (Merigo, Palacios-Marques, & Ribeiro-Navarrete, 2015). Cash receipts and disbursements also depend on sales forecasts because unachieved goals regarding sales influence the amount of cash available for a firm’s operations.
Exogenous Factors and Prerequisites of a Good Sales Forecast
To develop sales forecasts, managers should aware of such exogenous factors as the country’s GDP, market and industry trends, inflation and unemployment rates, and interest rates trends among others. If economic conditions in the country, which are analyzed concerning GDP, are negative, this aspect influences consumers’ buying capacity and sales. Market and industry trends accentuate consumers’ interest in the product and changes in prices. Inflation and unemployment rates are also important factors to impact consumers’ readiness to improve their purchasing of a certain product (Trapero, Kourentzes, & Fildes, 2015). Interest rates trends influence sales mostly internally. From this perspective, the prerequisites of an appropriate sales forecast include the analysis of economic conditions in the country, market and industry tendencies, a company’s previous market share, the position of competitors, advertising and promotion results, changes in set prices, and studies on market trends (Kolassa, 2016). All this information is important to forecast sales while paying attention to the factors that can influence alterations in consumer behavior.
Conclusion
Sales forecasts are important for firms to determine what volumes of products to manufacture and what revenues to expect. If these forecasts are formulated inappropriately or are not based on the analysis of previous sales, it is possible to expect increasing costs and losses. Therefore, it is necessary to understand how sales forecasts influence other aspects of a company’s operations and its sales strategy, recognize factors that can influence purchasing trends, and become aware of prerequisites of an effective forecast.
Merigo, J. M., Palacios-Marques, D., & Ribeiro-Navarrete, B. (2015). Aggregation systems for sales forecasting. Journal of Business Research, 68(11), 2299-2304.
Trapero, J. R., Kourentzes, N., & Fildes, R. (2015). On the identification of sales forecasting models in the presence of promotions. Journal of the Operational Research Society, 66(2), 299-307.
Different levels of understanding of market information necessitate varying approaches to sales forecasting, as an analyst depends on concrete numbers to procure reliable results. Access to historical sales and promotional response data allows identifying the applicable methods and utilizing them to the best of their extent. This process permits appraising and adopting an effective forecasting mechanism for various marketing mix allocation scenarios considering the outlined case.
Differences Between Forecasting Methods
There are various approaches to marketing forecasting, each possibly yielding different results. The average method, reliant on mean historical data, and the naive one, which considers only the last observation, should not produce similar numbers (Hyndman & Athanasopoulos, 2018). The seasonal naive method allows utilizing the previous time of the year’s numbers, and the drift method relies on “the average change seen in the historical data” (Hyndman & Athanasopoulos, 2018, p. 49). Thus, four of the most commonly used forecasting processes depend on earlier achieved results.
Evaluation Concerning the Outlined Scenario
Evaluating these methods becomes possible when relying on the outlined scenario, affected by the availability of historical sales and, more decisively, promotional response data. According to the research by Trapero, Kourentzes, and Fildes (2015), this factor necessitates manipulating the data achieved by either method and making expert modifications. Assuming that the previous marketing period also utilized promotions, the seasonal naive method and the drift method may allow achieving the most accurate pre-adjustment results. Conversely, the average and naive approach may output numbers that are too broad or too narrow correspondingly.
Choosing a Forecasting Method
Deciding between the seasonal naive and drift method requires further analyzing the differences in their applicability. The latter method could be more relevant, as Hyndman and Athanasopoulos (2018) state it allows a more flexible time-based approach. Thus, since the only implemented assumption is the company’s previous use of promotional materials, research indicates that the drift method may be more useful to estimate the level of sales.
Conclusion
Since the drift method allows considering both the past and present observations to formulate future results, this rationale may make it most useful in different marketing mix allocation scenarios. For example, the results may be used when creating a case within which the company’s promotional activity is increased, forecasting the effect this may have based on appropriately identified previous time brackets. From trade promotions to pricing strategies, the drift method allows accounting for all previously achieved results to decide these tactics’ planned effectivity.
References
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice (2nd ed.). Web.
Trapero, J. R., Kourentzes, N., & Fildes, R. (2015). On the identification of sales forecasting models in the presence of promotions. Journal of the Operational Research Society, 66(2), 299-307. Web.
The second part of the Business Report for Balogne Pty. Ltd addresses the issues of financial forecasting and the sphere of business and technology. Microsoft Excel has been applied to the creation of four graphs and one table demonstrating the company’s predicted performance based on such key indicators as earnings, earnings by product, variable costs, and sell prices. It has been explained how certain market influences, such as inflation/deflation, can affect the calculations of predictions. Further, contemporary business issues have been addressed, including knowledge management, business intelligence, Five Forces, and security.
Introduction
Upon reflecting on the role of business modeling and process modeling in decision-making, the use of Microsoft Excel for calculations, and the adoption of Web 2.0 applications for internal and external communications, the business analyst of Balogne Pty. Ltd, a company that produces several types of sugar products, turns to forecasting and strategic business planning.
For the newly appointed General Manager, it is important to receive predictions as per the company’s expected performance and factors that may affect the operation in the nearest future. Three areas will be addressed as part of these business analytics efforts: forecasting financial performance (with the use of graphs demonstrating key indicators of financial dynamics), accumulating predicted financial data (with the use of a pivot table), and investigating contemporary issues related to technology and business, including knowledge management, business intelligence, market forces, and security.
Forecast Graphs
In financial planning, strategic decisions should be based on future projections; even though such projections’ accuracy may be questionable (due to the constantly changing market trends and influences), there is still a need for Balogne Pty. Ltd to build forecasts as per such crucial financial indicators as revenues, costs, and earnings and such considerations as the dynamics of these indicators among the company’s products. The first prediction to be designed is the prediction of earnings; it should be based on the current indicators, inflation/deflation rates, and volume sold. The result of the prediction is presented below (Figure 1).
Figure 1: Yearly USD earnings forecast by product.
Earnings are calculated as the difference between revenue and cost: revenue, in turn, is the product of multiplying volume sold by the selling price, and the cost is the product of volume sold and variable cost plus fixed cost (overhead).
In 2016 and 2017, costs and prices are affected by the inflation/deflation rate, and there is growth/loss dynamics for volume sold, too. By considering all these factors and inputting data correctly into relevant software, such as a Microsoft Excel spreadsheet, a business analyst will be able to predict earnings and provide the General Manager with the information needed for strategic decision making. Further, looking into the dynamics of earnings distribution among different products is needed to improve product policies of Balogne Pty. Ltd (Figure 2).
Figure 2: Estimated percentage of total earnings by-product (2015 to 2017).
As can be seen from the graph above, golden syrup is accountable for the smallest portion of earnings. White sugar sales generate the largest portion of earnings, and these earnings are expected to grow in 2017 (Figure 1). The product accountable for the second-largest portion of earnings is brown sugar (Figure 2), but it has been demonstrating a consistent decline in earnings generation within the three years (Figure 1).
However, as it has been mentioned, this information is based on certain predicted financial rates, and it should not be overlooked that these rate’s dynamics may be significantly different from the predictions. The use of such software as Microsoft Excel provides convenient opportunities for incorporating these considerations into a business analysis spreadsheet.
By modifying the rate in one cell (which contains a fixed inflation/deflation rate), a user can see the changes in numbers in all the cells that refer to inflation/deflation, i.e. the numbers that depend on the rate; also, the graph immediately changes and displays new numbers based on the newly determined rate. For example, variable cost per unit dynamics can be explored for the situation in which the inflation/deflation rate for 2016 and 2017 is not -5 percent and 20 percent respectively but 15 percent and 25 percent respectively (Figure 3).
Figure 3: Variable cost per unit dynamics at an alternative inflation/deflation forecast.
When modifying the variable cost per unit dynamics, a user immediately modifies the total annual cost indicator and the total earnings level. However, it should be considered that the growth of variable cost per unit cannot pass unnoticed for the market, as it will affect the selling price per unit indicator. According to a new prediction, the latter rate in 2016 and 2017 is 25 percent and 35 percent respectively instead of 5 percent and 10 percent (Figure 4).
Figure 4: Sell price per unit dynamics at an alternative inflation/deflation forecast.
Along with the fixed cost, both indicators presented in the two graphs above will affect total earnings. Therefore, their dynamics should be monitored by a business analyst. Particular attention should be paid to the role of external market influences on the variable cost and sell price.
Pivot Table
Pivot tables can be successfully used for analysis in various spheres, and Microsoft Excel provides proper technologies for that. It has been claimed by researchers that a pivot table is a “highly flexible contingency table” (Dierenfeld & Merceron 2012, p. 116), which means that the format of a table allows modifying previously inputted data using formulas and connections established among different cells. A pivot table for Balogne Pty. Ltd is presented below (Table 1).
Table 1: Pivot Table of Balogne Pty. Ltd’s Earnings from 2015 to 2017 by Product.
Earnings Forecast by Product
Brown Sugar
2015
$6 447 500
2016
$5 673 800
2017
$4 198 612
Product earnings
$16 319 912
Caster Sugar
2015
$1 465 800
2016
$1 377 852
2017
$1 240 067
Product earnings
$4 083 719
Golden Syrup
2015
$407 000
2016
$431 420
2017
$457 305
Product earnings
$1 295 725
Icing Sugar
2015
$598 000
2016
$663 780
2017
$1 008 946
Product earnings
$2 270 726
Raw Sugar
2015
$3 553 000
2016
$4 050 420
2017
$5 063 025
Product earnings
$12 666 445
Sugar Cubes
2015
$329 400
2016
$467 748
2017
$725 009
Product earnings
$1 522 157
Treacle
2015
$713 000
2016
$470 580
2017
$414 110
Product earnings
$1 597 690
White Sugar
2015
$2 042 500
2016
$2 042 500
2017
$2 369 300
Product earnings
$6 454 300
Total 2015
$15 556 200
Total 2016
$15 178 100
Total 2017
$15 476 374
Total Product Earnings
$46 210 674
The table allows accumulating the earnings indicators within three years for each of the eight products. Also, it can be used for the creation of graphs, such as graphs presented in the previous section (see Forecast Graphs) to visualize the predictions and dynamics of the key indicators of the company’s financial performance. In case the predictions change (e.g. by the changing inflation/deflation), the data in the table can be modified for each product, and the final indicators, i.e. total earnings, will be recalculated by the software.
Investigation of Contemporary Issues: Technology and Business
Further analysis of Balogne Pty Ltd’s current and expected performance will encompass the issues of knowledge management, business intelligence, Five Forces, and security. Concerning knowledge management, scholars pay much attention to the way the work of knowledge workers can be improved, optimized, and made more efficient, and the definition of a knowledge work that is widely adopted by scholars today includes “both the utilization and creating of abstract/theoretical knowledge” (Hislop 2013, p. 71). Also, an important aspect of knowledge work is that it is intellectual and mostly non-routine.
Therefore, it may present challenges for managing, monitoring, and evaluating because the product and value of intellectual work are hard to measure. Many business management strategies have been proposed, and a particular aspect of a company’s performance addressed by them is a situation with senior workers in Balogne Pty. Ltd. Senior workers are used to certain practices and procedures that may be unfamiliar or poorly understood by new workers, and the former do not always document information related to these practices and procedures because it is a routine for them. As a result, business analysts are not supplied with complete information on the company’s internal operation, and the effectiveness of analytics is undermined.
A way to address this complication is to establish a teaching and learning environment. Senior workers should be encouraged to share various aspects of their experience with new workers. As a result, the process of new workers’ improvement will be accelerated, and all the necessary documentation of processes in which senior workers are involved will be maintained because they will be asked to properly reflect on the practices they teach.
Also, additional training provided by senior employees will contribute to the knowledge management efforts of the company because relevant knowledge will be accumulated and exchanged. This practice will help the company pursue the purpose of business intelligence, i.e. ‘to support better business decision making’ (Business intelligence n.d., para. 1) using collecting, storing, retrieving, exchanging, and analyzing information.
In analyzing Balogne Pty. Ltd’s supplier power, the Five Forces model designed by Porter can be applied (Dobbs 2014, p. 32). The model encompasses four kinds of external influences—two bargaining powers (of suppliers and buyers) and two threats (of new entrants and substitutes)—and one internal, ongoing influence: industry rivalry, i.e. the competition itself. For a company, it is important to recognize all five influences and adjust its operation to the changing conditions of the market. It is particularly relevant for Balogne Pty. Ltd because the company is currently growing, which means that its enlarging operation is more likely to face the impact of industry trends and possible external changes. Future sales can be particularly affected by the two threats recognized in Porter’s model.
Recent research confirms that sugar is a much more harmful substance for humans than it was previously believed (Groopman 2017); therefore, it can be expected that, in the nearest future, many new players will appear in the industry that will offer sugar substitutes, and many people may be willing to buy those substitutes instead of sugar and sugar products produced by Balogne Pty. Ltd.
Also, the company should not overlook the bargaining power of its suppliers, i.e. farmers, on future sales. In case suppliers face difficulties due to the decreasing demand for sugar, they may renegotiate the existing conditions of collaborating with the company. Depending on the extent of amending the current agreements, the company may face the need to reorganize its operation and its production facilities. Finally, there is a factor within the industry.
The company’s competitors can manage to adapt to the arising conditions of decreasing demand for sugar, i.e. by offering substitutes, and this will mean that these competitors will address the same needs of the same customers as those they addressed before but in a new way. In the case of Balogne Pty. Ltd fails to make this shift in the nearest future, its indicators of successful financial performance will be damaged.
Finally, in terms of security, the new General Manager needs to take into consideration different threats, both external and internal. In this context, security is understood as the company’s ability to collect, store, and retrieve information regarding its suppliers, customers, and internal operation safely. Two internal threats to proper security are poorly designed information systems and poorly trained employees. To address the former threat, the General Manager should ensure that the software used by the company for any operations with information is of good quality, regularly updated, and adjusted to the company’s needs.
The processes of selecting and operating information systems should be coordinated by the management so that the risk of compromising important (and often confidential information) is minimized. Concerning the latter threat—poorly trained employees—it should be recognized that important data can be compromised due to the incorrect use of information systems or data collection instruments (McBride, Carter & Warkentin 2012, p. 1).
However, employees should be trained not only on the technical aspects of working with information (i.e. on how to use particular software) but also on corporate ethics, e.g. it should be generally understood and respected that certain types of information should remain confidential so that the company does not let down its suppliers, customers, or employees.
Conclusions and Recommendations
Balogne Pty. Ltd is expected to demonstrate moderately successful financial performance, as its earnings growth for most products, although the earnings for some popular products decrease. However, an important threat is recognized: the demand for sugar can decline significantly in the nearest future, which should prompt the company to look for alternative ways of addressing its customers’ needs.
Improved knowledge management strategies will allow pursuing the goals of business intelligence, and increased attention to the selection and operation of information systems and technology- and ethics-related training for employees will allow improving the company’s security. Overall, it is assessed that the company, with its current performance, can overcome these challenges, and a properly designed business strategy will help address the issues outlined above.
Reference List
Business intelligence n.d. Web.
Dierenfeld, H & Merceron, A 2012, ‘Learning analytics with Excel pivot tables’, in Moodle research conference proceedings, Beuth University of Applied Sciences, Berlin, pp. 115-121.
Dobbs, ME 2014, ‘Guidelines for applying Porter’s five forces framework: a set of industry analysis templates’, Competitiveness Review, vol. 24, no.1, pp. 32-45.
Hislop, D 2013, Knowledge management in organizations: a critical introduction, 10th edn, Oxford University Press, Oxford, UK.
McBride, M, Carter, L & Warkentin, M 2012, Exploring the role of individual employee characteristics and personality on employee compliance with cybersecurity policies. Web.
The questions that may arise during the city council meeting related to a new criminal justice facility. These questions can be subdivided into political, tax, demographic, and administrative issues that should be considered to understand how each factor will influence the tax structure and vice versa (Menifield 135). The answers to these questions will help to assess the revenue structure and determine the revenue management strategy.
Question List
Political Questions
What are the opinions of the dominant political parties towards the new project? Do they agree or disagree that a new facility may be needed?
What is the dominant political culture mixed or unified? The attitudes of which political parties matter the most?
Are there any political reasons not to raise taxes in the present circumstances?
Tax Questions
Are there any other sources of revenues that would help to avoid raising taxes?
Should the funds be earmarked from the current budget, or is additional profit required to cover the needs of the new facility?
Is there a legal way to raise taxes?
Demographic Questions
Will the increased taxes have an adverse economic impact on the population and business?
What demographic groups, industries, and organizations may benefit from the implementation of the proposed project?
Administrative Questions
Who will be in charge of ensuring the efficiency of the new facility constructions and operation?
Are there any additional administrative costs associated with building the new jail? If yes, what are they, and how will these expenses be covered?
Conclusion
The revenue management plan is a laborious undertaking that has an impact on various stakeholders, both local and federal. While discussing the plan, it is vital to address all the possible issues that may facilitate or stall the project. The city council is to review all the revenue streams and expenditures to find the funds for a wide variety of projects. Sometimes, a project may be opposed due to subtle reasons, such as government elections, which make increasing taxes impossible. In short, the revenue management plan elaboration is a complicated matter that requires the attention of highly qualified specialists.
Work Cited
Menifield, Charles. The Basics of Public Budgeting and Financial Management: A Handbook for Academics and Practitioners. 2nd ed., University Press of America, 2015.
Forecasting is essential for business as it allows identifying specific factors that will influence the operations. These techniques can help predict the supply, demand, and other indicators for products and services. Flostrand (2017) states that managers can “rely on their own intuition and judgment or resort to the insights of others” (para 1). This is because they often do not possess quantifiable data that can be used to resolve an issue. Considering this, the Delphi technique is crucial as it helps create forecasts for uncertainties relying on the opinions of experts. The process involves polling the specialists on their opinion regarding a problem, collecting the responses, giving the answers to other experts, and repeating the process until a consensus is reached (Flostrand, 2017). Therefore, the Delphi technique is useful in cases where the data amount is limited, and the opinion of specialists is the only option for performing a forecast.
Both in daily lives and business, the extensive use of computers offers a new way of forecasting. Weingärtner, Bräscher, and Westphall (2015) state that Cloud services provide a lot of functions that can be used by businesses to enhance their forecasting techniques. Thus, surveys and various market researches can be performed in this way, making it easier for both the researchers and the respondents. Furthermore, the computers allow utilizing techniques of data analysis that would enable receiving results of forecasts more quickly.
Different tasks may require a variety of forecasting techniques to carry out the process correctly. For example, Kahn (2014), states that forecasting for a new product should differ from the methods used to determine the values of existing commodities. The process of choosing the approach should include evaluating the data, plan, measurement, forecast, and analytics. In cases of new products, only a small number of data is available which requires qualitative methods of forecasting.
Weingärtner, R., Bräscher, G. B., & Westphall, C. B. (2015). Cloud resource management: A survey on forecasting and profiling models. Journal of Network and Computer Applications, 47, 99–106. Web.
