Operational Competencies at Harangue Ltd - Case Study Example

?Business Statistics: Harangue Ltd. Report Executive Summary The report is analysis of operational competencies at Harangue Ltd, Adelaide office; an Australian publicly listed company. As the newly appointed Project Analysis Team lead, it is my good fortune to present findings to the Company’s investigation of competitive advantage in three core divisions: 1) the information technology unit; 2) the telemarketing program; and 3) accounting controller activities. The indices and graphical illustration of the organisation’s findings attribute value added to forecasting of company advancement in the Australian market, and at the global level. Special focus is given to new customer growth. The Report is a compendium of ‘how we are doing’, and outlines time series data on quality, customer provision and retention and fiscal accountability. Moreover, the trend report examines both long term planning of organisational strategy in the three operational areas, and extends to Harangue Ltd. future interests in building customer equity across the value chain. Three (3) distinct and unrelated issues faced the company at the time that the data collection activities were conducted; and serve as basis to strategic Action Items in the report: Action Item 1: Information Technology Unit Harangue has for some time been concerned about the quality of its internet service. The information technology unit identified a method of determining satisfaction with the system that is both simple and able to withstand any attack on its method. Due to this fact, the company undertook a review of the service. Preliminary findings indicate that received and collected data provide evidence that the service is not up to standard. Action Item 2: Telemarketing Program Harangue, like many other companies in Australia, is fighting to maintain its market share. As part of the strategic direction to maintain this market share Harangue has instituted a cold call telemarketing program. Data collection on program performance targeted customer development in “sign up” respective to projected forecast of those figures. Action Item 3: Accounting Department Review of the accounting department controller procedure in audit and reporting established data on the total number of customers on the organisation’s books. Analysis of this data set attributes to new information on finance in the trend analysis segment of the strategic forecast. Strategy Methodological consideration in the report targets investigation of internal operational activities in the Information Technology Unit; Telemarketing Program; and Accounting Department at Harangue Ltd, Adelaide. Methodological implementation is measure to Harangue Ltd value statement. The study evaluates current operational competencies as the rationale of the data collection in the study. Methodological assumptions to evaluation of results are discussed in each analysis; with compilation of quantitative outcomes to the three data sets in Excel (Appendix A). Calculation of reporting frequencies, as well as the mean, median, standard deviation, lower and upper quartile of operational ‘value’ is summated in graphical statistical narrative. Additional description is provided with the illustrations where required. Quantitative findings to all three division studies are derived from separate mining queries. Strategic interest in statistical data defined codification of any qualitative data in a nominal ‘coding’ structure for quantitative measure, analysis and reporting. Sampling in the study is of the ‘convenience’ sort in terms of given operational variables, grounded in organisational processes. The sample population is further determined by distinct functions with departments in correspondence with operational strategies in each unit. Hypothesis Forecasting of operational competencies by three (3) Harangue Ltd units in the report will lead to better control of the ‘total’ strategic approach to the Company’s value chain; and ultimately future sustainability and profitability. Null hypothesis indicates that audit of information in the three organisational sections will be insufficient to project an adequate representation of strategic points of internal leverage requisite to the imposition of competitive advantage in the Australian and global market(s). Analysis Analysis of the findings to the current Harangue Ltd, Adelaide study provides information on the state of operational competencies in alignment with business strategy. Methodological design, data collection, methodological assumptions and sampling preface the analysis to each Section’s Action Item. Computations are provided (Appendix B, C & D). Business modeling applications used in Excel dissemination of the findings to the three (2) unit investigation are published in graphical illustration in each department audit in the Report. Hypothesis testing of each Section was conducted in: 1) One-sample Test, 2) Simple Linear Regression analysis, and 3) Trend illustration of time-series forecasting. Numerical descriptive measures implemented in coding and sorting data to the study are retained in sampling distributions in the three (3) Indices are correspondent to the three Sections: 1) Internet Data Index; 2) New Customers; and 3) Total Customers (Appendix A). Confidence Intervals to testing of the three (3) Action Items may vary in terms of reliability (Appendix E). The data drawn from the IT Unit is perhaps most reliable, in that abductive machine logic presorts the data for index. In Action Item 1, the data collection method provided an index rating for each of the sample points collected. For instance, Harangue valuation of internet service providers mentioned in policy cites that a vendor should achieve an index value of no less than 0.99. Not a reliability measure of the test itself, standard deviation in performance measure of the sample is more demanding than proposed by classical theories of statistical reliability in testing of performance. Action Item 1: Information Technology Unit Research into accounting methods recommended by senior management resulted in data contained in the file ‘Total Customers’ in an effort to examine certain questions about: 1. Prediction of the total number of customers expected for Harangue for each of the next four months; 2. Applicability of the model for the prediction of the total number of customers Harangue is likely to have in one year’s time; 3. Differences that might be found in evaluation, and respective to Question 2. Methodological Assumptions Assumptions to the IT unit evaluation of departmental concerns used the ‘Internet Data Index’, and any supporting calculations or academic opinions foundational to administration of those values. Sampling Information Technology Unit - deliberation of activity was provided in the data file ‘Internet Data Index’. Analysis Results to the 1) One-sample Test, 2) Simple Linear Regression analysis, and 3) Trend analyses are found in graphical representation of the findings (Appendix F). Action Item 2: Telemarketing Program Until recently, Harangue Ltd. section heads would convene and, after examining cold call rates for the previous three months, provide a subjective forecast of the number of new customers expected in the coming months (often the group would provide an estimate beyond the one month horizon). Due to the fact that managers of other units in the organisation, have found forecasts during the past year have been particularly inaccurate because in some months higher than anticipated time had been allocated to cold calling activities, the Telemarketing Program was asked to provide more accurate insight into customer development. Joseph Banks instigated a data collection process and summarised the results for three (3) months in spreadsheet form (i.e. New Customers). While this is a limited data set for determination of future projection, the preliminary investigation provides a replicable model for further query. Methodological Assumptions The Telemarketing Program’s tri-partite data collection strategy underscored elements of ‘customer reach’ in development of a new data set on division performance through: 1. Development of an appropriate model that would allow the prediction of the total number of customers expected for Harangue for each of the next four months; 2. Resource driven logic to data collection. Sufficient reporting of demand in other sections that would make forecast of each month for number of new customers expected to be ‘signed up’. 3. Provision of information adequate for prediction of the total number of customers Harangue is likely to have in one year’s time. Note: A new model of data collection, reliability of the Telemarketing Program study data is provisional. Further testing for future reporting on the division should look at the following three (3) factors: 1. Sample Population - appropriateness for section heads to forecast the number of new customers based on the previous three months. 2. Variables – additional and/or alternate data collection. 3. Reliability - accuracy of estimates of based on the anticipation that the number of cold calls to be made in one forthcoming month (currently 67,000). Sampling Telemarketing Program – the program instigated a data collection process reporting ‘New Customers’. Analysis Results to the 1) One-sample Test, 2) Simple Linear Regression analysis, and 3) Trend analyses are found in graphical representation of the findings (Appendix G). Action Item 3: Accounting Department Evaluation of concerns in the IT Unit followed senior management request for reporting in this division. Existing data retained in the organisation’s network data file ‘Internet Data Index’ was run and submitted for the analysis. Methodological Assumptions Audit of new customers in accounting must follow current Australian rules to accounting practice. Measure of new customers is purely a nominal activity and in no way reflects actual accounting compliance to computation and reporting. Public reporting of accounts may differ from the current Report’s statistical forecast of performance. Such differences are mentioned in footnotes to audit in all quarterly and annual financial reporting to shareholders. Sampling Accounting Department – new customer data derived from audit reporting. Analysis Results to the 1) One-sample Test, 2) Simple Linear Regression analysis, and 3) Trend analyses are found in graphical representation of the findings (Appendix H). Summary of the Results Results to the hypothesis testing of the three (3) Indices from the Information Technology Unit, Telemarketing Program and Accounting Department at Harangue Ltd, Adelaide in the one-sample or T-test, linear regression and time-series forecasting or trend analysis places emphasis on continued attention to advancement of competencies across Company’ divisions. One Sample Test The values of the quartiles in the one sample T-tests support a null hypothesis by proxy. No predicted mean is provided by the senior management. Due to the fact that ‘predicted’ standard deviation from the mean in IT Unit measure (0.99) varies from classical formulae, the test is ‘insignificant’. The distinct classes of data reported in the study are not vital statistical data for testing of coefficient of variation. With no secondary variables from which to measure, the singular quartile class of data does not reflect pairs from which to draw covariance solution from. Linear Regression The density of similar distribution of performance data indicates little radical deviation from the median average in performance in all three Sections. Action item insights provide some tertiary information about data collection factors which may have affected projected real valuation in the analysis. Time Series Forecasting Trend Illustration of growth projection of operational competencies in the IT Unit and Accounting Department time-series forecasting of growth shows consistent distribution of performance. Telemarketing Program trend forecasting reveals inconsistencies between Cold Calls and New Customers during the short period analysed. Future research into capacity building in this operational efficiency must be met by longitudinal accumulation of data over a longer period of time; and in assessment of dependent variables that might be impacting performance in cold calling in both employee performance, and externalities (i.e. customer response). Conclusions& Recommendations Summation of the report made by the Project Analysis Team on Harangue Ltd, Adelaide office operational competencies resulted in the recommendation of further study of performance in the Company’s divisions toward lean and agile strategy and future growth. Statistical analysis of data the report represent sampling information in mean and median record of less than 6 months. Statistical effect is weak; both in terms of density and time-series valuation and therefore, insufficient data for ordinal reporting. It is recommended that forthcoming valuation of those competencies might be improved in the interest of capacity building. Assets in technology and customer equity constitute the target for future development. Appendices Appendix A.1 Internet Data Index Month Year Total Customers Jan 1 75327 Feb 1 77116 Mar 1 79341 Apr 1 80983 May 1 82326 Jun 1 82879 Jul 1 84006 Aug 1 85119 Sep 1 86182 Oct 1 87418 Nov 1 88063 Dec 1 89444 Jan 2 90507 Feb 2 91927 Mar 2 93878 Apr 2 94784 May 2 96109 Jun 2 97189 Jul 2 97899 Aug 2 99208 Sep 2 100537 Oct 2 102028 Nov 2 103977 Dec 2 106375 Appendix A.1 Information Technology Unit - Internet Data Index Appendix A.2 New Customer Index Cold Calls New Customers 73440 5357 87480 6177 60360 4795 83700 5692 67860 4312 55260 3421 42240 2624 69240 4087 70080 4934 48180 2546 49800 3591 58860 4271 86100 5836 80940 5201 57900 3775 59100 3592 67020 4566 50400 2974 84720 5673 56400 3554 65400 4399 89880 6143 74400 4827 63300 5418 Appendix A.2. Telemarketing Program – New Customer Index Appendix A.3 Total Customer Index Month Year Total Customers Jan 1 75327 Feb 1 77116 Mar 1 79341 Apr 1 80983 May 1 82326 Jun 1 82879 Jul 1 84006 Aug 1 85119 Sep 1 86182 Oct 1 87418 Nov 1 88063 Dec 1 89444 Jan 2 90507 Feb 2 91927 Mar 2 93878 Apr 2 94784 May 2 96109 Jun 2 97189 Jul 2 97899 Aug 2 99208 Sep 2 100537 Oct 2 102028 Nov 2 103977 Dec 2 106375 Appendix A.3. Accounting Department – Total Customer Index Appendix B One-sample Test The data correspondent to each index reporting by the three sections is relevant to a ‘one-sample’ measure of value. The t-test compares sampling distribution of means with that specified by the null hypothesis (m). A t-test calculates sample mean in relation to standard error computed in distance in distribution of population mean (m). The t-score is achieved by dividing the difference between the sample mean, and m by the standard error of the mean. Testing of the single data set is found in the formula: Appendix C Simple Linear Regression Distribution of median response is delineated according to reported frequencies in a data set. Regression is measured in calculation of the most consistently reported nominal value in a population over time, represented in: y = b0 + b1x Solve for b0 and b1 = Median Frequency Appendix D Trend Forecasting Projection of future performance is based on the one sample test, and linear regression analyses in metric evaluation of error and growth. The two most commonly used are the mean absolute deviation (MAD) and mean absolute percent deviation (MAPD). Cumulative error, and average error or bias (E) measure risk in projection of demand: MAD= (Sum |Dt - Ft|) n Where: t = the period number Dt = the demand in period t Ft = the forecast for period t n = the total number of periods | | = the absolute value MAD < forecast accuracy MAPD= (Sum |Dt - Ft|) Sum(Dt) Absolute error is percentage of demand rather than per cycle Appendix E Confidence Levels Appendix F Graphical Illustration - Information Technology Unit Insert => Appendix F.3. Information Technology Unit - Time Series Forecasting (Trend Analysis). Appendix G Graphical Illustration – Telemarketing Program Appendix G.3. Telemarketing Program - Time Series Forecasting (Trend Analysis). Appendix H Graphical Illustration – Accounting Department Insert=> Appendix H.3. Account Department - Time Series Forecasting (Trend Analysis). Read More