Business decision making is an important aspect of managerial activities which involves providing the basis for new projects acceptance or rejection, development of the most effective business lines, establishing sound pricing policy, making conclusions on the results of marketing surveys, and a range of other important processes in a company. This paper is devoted to obtaining some practical experience in the usage of the most common business decision making tools. It incorporates several scenarios faced by different companies that require application of business decision making knowledge and related software. The paper is based on several academic books on the related topics and utilizes Excel as the main software to solve the discussed problems.
The paper is aimed at providing the essence of practical implementation of the issues and aspects covered by business decision making course. The first part of the work leads to understanding primary and secondary sources of information and ways of collecting data in order to support chosen survey methodology and sampling frame. It also includes practical application with respect to composing a sample questionnaire for the chosen scenario. The second part is devoted to revealing data analysis techniques used for business purposes. The third part covers skills of production information in the required formats such as line and bar graphs, trend lines and formal business reports. This is followed by the application of business software for analysing available information in the fourth part. The final part of the paper deals with the project evaluation techniques such as net present value, payback period and internal rate of return.
Business decision making can be defined as the process of selecting the most attractive options in business activities based on the available information and the evaluation of possible positive and negative outcomes (www.businessdictionary.com). Effective business decisions are made after conducting extensive surveys on collected information and applying business software tools to analyse it. This process is of critical importance for any organization as it provides efficient allocation of available resources and allows to choose the most attractive business development projects.
Primary data is defined as the data collected directly for the specific research purposes and further used for this research (Hox & Boeije 2005). Thus, the researcher controls the whole process of designing the data collection method, place and population where the data is gathered, and questions to be answered while collecting it. Examples of primary data include interviews, questionnaires, field surveys, etc. Secondary data relates to the data collected for other purposes by other researcher or institution and then reused for specific research purposes. Such data cannot be controlled by the researcher but it is much easier and cheaper to obtain as compared to the primary data collection. Examples of secondary data include the World Bank country development information, statistical data on population, and other available databases. Company in the scenario will base its business decision upon primary data collected with the help of a questionnaire in London.
A survey can be referred to as an activity of collecting information in an organised and methodological manner and uses well-defined concepts, methods and procedures in order to present such information in a useful summary form (Statistics Canada 2010). There exist a wide range of survey methods which can be classified either as sample or census surveys. Sample surveys are based upon framed data from selected population. Samples can be constructed randomly, with aligned probabilities, and using statistical means. A census survey differs in a way that it is based upon information collected from the whole population. According to the ways of data collection survey methods incorporate self-enumeration, interview-assisted, computer-assisted, direct observation, and questionnaires. Interview-assisted surveys can be conducted by personal interviews or by telephone. Such survey method allows collecting more data than it is usually needed; in result more time is needed, but it is a low cost survey method.
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The scenario should better use personal interviews structured with the developed questionnaire and sampled randomly. Such method would cover a wide variety of potential customers in London but at the same time focus on the questions of interest for the company introducing new coffee product to the market. Additionally, interviews can possibly provide more valuable information about the market concerning possible competitors and customer preferences not covered by the questionnaire.
Questionnaire is designed to obtain necessary marketing information before making the business decision to introduce new coffee product to the London market. The new brand of coffee sachet needs to be financed by investors who are concerned about the market dynamics and possible financial outcomes of the project. The sample questionnaire with answers considered as correct or more favourable for the project and its investors as follows.
1. Do you drink at least one cup of coffee every day? (Yes)
2. What are your personal reasons for consuming coffee? (Taste, smell, price)
-Cannot wake up without coffee
-I do not drink coffee
3. Do you prefer drinking the same type of coffee for ages? (No)
4. Are you concerned about the origin of your coffee? (Yes)
5. Do you often try new brands or products that appear in the grocery store? (Yes)
6. What type of coffee do you prefer? (Sachet, instant)
7. Would you try a coffee from African continent imported with fair-trade? (Yes)
8. Propose your reasonable price for sachet coffee: __________
9. Specify you gender and age: ___________
Mean, median and mode calculations are based on frequencies determined in the table below.
|Amount Spent||Mid-point||No. of Orders||Total Amount Spent||New Frequency||New Cumulative Frequency|
Mean is the ratio of total amount spent sum to the number of orders total:
Mean = 6031.75 / 120 = 50.26.
Median is the most frequently observed value. It is found as the value of sales price that is observed at 0.50*(n+1)th position of the total number of orders. Thus, median price is observed at 0.50*(120+1)th position with the amount spent of 40-50 or a mid-point of 45.00 with cumulative frequency of 0.483. Mode is the most frequently occurring value (Newbold, Carlson & Thorne 2015). In the scenario, the mode is attained at new frequency of 0.142 with the value of 50-60 or 55 as the mid-point price.
Based on the conducted analysis, it can be recommended to the store owner to keep sales price between 45 that is the median value and 55 that is the mode or most frequently spent value. Such conclusion is also supported by the mean value of 50.26 on the stores sold orders. Also, the above table provides basis for calculating additional statistical measures on the stores sales.
Range is the dispersion between the lowest and highest amounts spent in sales and constitutes 100 – 0.5 = 99.5. Also, the range can be defined as the difference between the highest and the lowest mid-points and amounts to 95 – 5.25 = 89.75. Standard deviation is found as the square root of individual values differences from the mean value (van der Wijst 2013). Using Excel formula for standard deviation, one obtains the value of 30.235 for this scenario. Lower quartile (25th percentile) is found at the cumulative frequency value of 0.233 and equals to 20-30 sales amount or mid-point price of 25.00. Upper quartile (75th percentile) is found at the cumulative frequency value of 50-60 sales amount of 50-60 or mid-point price of 55.00. Interquartile range is the difference between the mid-points at upper and lower quartiles and constitutes 55 – 25 = 30.00 sales price.
Analysis of correlation between temperature and sales volume is based on the following table.
|N||Temperature (X)||Sales (Y)||X^2||Y^2||X*Y|
As provided by Reitano (2010), correlation coefficient is found with the formula:
Thus, for the scenario correlation coefficient r constitutes 0.987. This indicates a very high correlation between temperature and sales volume in the store. Respectively, the owner can expect stronger sales figures during days when temperature is high and bad sales volume on cold days.
To sum up the conducted analysis, the store is able to produce the highest sales at prices ranging between lower and upper quartiles of 25 to 55 with the mathematically expected mean sales amount per order, which is 50.26. Sales amounts possess high deviation from the mean indicating that the store often changes prices which is not a good practice. In order to facilitate sales, the owner can set the prices between the median and mode values. Additionally, the higher temperature inside the store will enhance sales volume due to significant correlation revealed in the analysis.
Based on the financial results of the company, the following graphs were constructed.
Trend lines with forecasting for the next three years are provided on the graph below.
Presentation of the graphs and recommendations to CEO
The company has overall positive trend in its sales and final profit volumes. The line graph indicates that Graham Consultants Limited had generated the highest volume of sales revenue in 2004, 2005 and 2010. Also, during these years the final profit was nearly equal to its total costs and constituted about half of the generated sales as can be clearly seen on the bar graph. However, in all other years the final profit was considerably lower than half of its sales with the worst results obtained in 2000 and 2008. It can be recommended for the company to critically analyse the performance and apply policies that led to strong financial outcomes in 2004 and 2005 and a positive trend in 2010. It is projected on the trend line graph that the company was able to generate about 325,000 in sales by 2013 that with the all costs trend not overcoming 175,000 could provide the company with 150,000 in final profit.
Business report to the Regional Managers
Sales revenue represents the total amount of all orders performed to customers and the sum of all received and receivable bills from them. It is generated as the price times quantity sold per each product. All costs of the company include both direct and indirect costs. Direct costs refer to expenses directly allocated to the production process or provided services and include such components as direct materials, direct labour and other direct costs (Epstein 2014). Indirect costs are not linked directly with the produced sales and include general, administrative and sales costs as well as production overhead expenses. Final profit is the result of deducting all costs (both direct and indirect) from the sales revenue. In each graph above sales, all costs and final profit are shown separately for the respective years. This means that in 2010 the company was able to generate 295,000 in sales revenue but after deduction of all costs amounting to 155,000 only 145,000 was left to the company as the final profit. The overall trend in the firms performance is positive as it produced a positive financial outcome (that is net profit and not net loss) during the past ten years. Still, in some years all costs cut off almost all sales revenue leaving very small final profit for the organization. Forecasting figures for the next three years can provide a basis for budgeting needs and cost control policy. The firm has positive forecasting trends for the nearest future with growing sales and final profit volumes.
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The critical path of the project is the longest path of processes needed for the project (Pinto, 2015). In the scenario this is the path A-B-D-E-F-G-K-L. Total time length of the critical path also determines the projects duration. Thus, summing up the days for activities on the critical path, one obtains the project duration of 99 days or slightly more than 3 months.
Payback period of a project is the number of years needed to provide the return of the initial investment (Brigham & Ehrhardt 2014). First, one determines the number of whole years during which cumulative cash flows still remains negative. Second, the remaining negative cumulative cash flow is divided by the cash flow in the next period (where it already becomes positive) to determine the proportion of the following year needed to fully cover the investment. Thus, using Excel software one obtains a payback period for the Alistair project equal to 3.58 years and for the Bromley project equal to 2.60 years.
Net present value is the discounted value of projects future cash flows less its initial investment amount (Brigham & Ehrhardt 2014). The discounting rate is usually chosen to be the minimum required rate of a company and can be equal to the cost of its capital, interest rate on debt financing, return on equity or other similar value. With the NPV formula in Excel, one obtains net present value of Alistair project amounting to $4,556.72 and that of Bromley project equal to $4,515.23.
Table of Contents
Internal rate of return relates to the value of interest rate that equalizes discounted future cash flows on a project to its initial investment amount (Brigham & Ehrhard 2014). This rate is preferred to be as high as possible but at least equal to the rate of return required by the company on investments and used as discounting rate for determining the net present value amounts. Excel function IRR provides solutions to the projects internal rate of returns that equal to 10.66 % and 11.08 % for Alistair and Bromley projects respectively. Calculations on payback periods, net present values and internal rates of return on the two projects are presented in the table below.
Report to the Board members
Choice of the most attractive project should be based not only on its profitability but also on the payback period, net present value and internal rate of return criteria. Analysing Alistair and Bromley projects, it could be suggested to choose Bromley as the projects are mutually exclusive even though Alistair provides higher net present value estimation at 10 per cent discount rate. One can note that Bromley has much more attractive payback period which is shorter by one whole year and, thus, projected future cash flows of this alternative are more certain than those of Alistair. Additionally, Bromley has higher internal rate of return and, consequently, has better chances to increase the overall value of the company and the wealth of its investors.
To conclude, different scenarios discussed in the paper provide a strong practical insight into tools used for business decision making. Thus, a business decision can be made based on the results of a marketing survey that utilizes either primary or secondary data and can be constructed in a form of a questionnaire. Also, a manager can analyse statistical data available from the past performance records to determine the mean values, standard deviations, and correlation within various controlled and uncontrolled variables. Additionally, past statistics can be visualized in the form of line and bar graphs providing a clear view of the past performance results. Such analysis can be extended with the trend lines added to make projections for the future and use them for budgeting and controlling processes. An important role of a business decision making relates to dealing with projects. Respectively, the paper demonstrated the construction of the network diagram for a selected project with an estimation of the projects duration upon the defined critical path. Besides, a company might face the need to choose between mutually exclusive projects. Such choice can be made using payback period, net present value and internal rate of return criteria.