CASE STUDY REPORT

CASE STUDY REPORT

MARKS: The Case Study Report is worth 30% of assessment marks in this unit.

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FORMAT: Both Parts 1 and 2 should be typed and for Part 2 be in report format with the main tables, charts and results presented throughout the report to highlight your responses to the case study report questions. All other computer output should be put into labelled and referenced appendices.

General Details of the Case Study Report:

The Case Study Report assessment will require two written submissions based around a comprehensive business forecasting project. Your group will need to determine and find suitable empirical data which will be then be used as the basis of relevant business forecasts and associated recommendations.

This is, in general, a group assessment task although there will be a separate individual component (Part 1) within the group assessment framework. Groups will be of either three, four or five participants with members in each group required to be enrolled in the same tutorial. You may not do this case study report individually. The number of people in the group will not be a consideration for the awarding of marks in the case study report. Groups will be formed in the tutorials in Week 2 of the semester and should be finalised by Week 4.

The Case Study Report submissions will be divided into two distinct report submissions; Each report submission (Parts 1 and 2) will be worth 15% of the total Case Study Report mark.

Part 1 consists of some preliminary data identification, analysis and provision of basic forecasts and will be done individually by each member of the group. Each student is required to submit their response to Part 1 through Turnitin on the unit website by 11 pm, Sunday 22nd April.

Part 2 will involve the development of further forecasts for relevant business variables based on the empirical data and analysis in Part 1. This is to be done as a group and the analysis required may involve development of scenarios, strategies, and provision of suitable recommendations to the relevant business organisation. Each group is required to submit their Part 2 final report (one report only per group) through Turnitin on the unit website by 11 pm, Sunday 3rd June. For the final report submission, you a required to provide a cover sheet with the names and SID’s of each of the group members.

Important Instructions:

Students should have formed groups of 4 (minimum 3, maximum 5) in their tutorial groups. Any intra-group allocation of tasks (for Part 2) must be sorted within the group. If the contribution of all members of the group is not approximately equal, all members of the group should complete the relevant peer review statement form suggesting the % contribution of each student to the Case Study Report task (Part 2). These statements should be either attached to the Case Study Report (Part 2) submission or emailed to your tutor. The unit convenor will consider the peer review statements and other evidence and may potentially adjust individual student marks for Part 2.

If no peer review statements are received, then all members of the group will receive the same raw mark for the group component.

Assignments must be the work of the students whose names appear on the assignment. Plagiarism, copied or paraphrased assignments will be heavily penalised with potential loss of all marks and further disciplinary action.

Both Part 1 (individual) and Part 2 (group) reports are required to be submitted through Turnitin by the relevant dates indicated above. No extensions will be granted.

There will be a deduction of 10% of the total available marks made from the total awarded mark for each 24-hour period, or part thereof, that the submission is late (for example, 25 hours late in submission – 20%, 6 marks, penalty). This penalty does not apply for cases in which an application for Special Consideration is made and approved.

There are two major task options in the Case Study Report. Groups need only choose one of the two tasks A or B. They both involve approximately the same amount of work. Indicate your choice of task clearly on the cover sheet.

However, all students in a group must do the same Task. This requires consensus within the group on the choice of Task. Groups will need provide a document to their tutor in Week 5 indicating their choice of Task and evidence (all student names, SID’s and signatures) that all group members have agreed on the indicated Task.

Both major task options involve choosing appropriate data for the task. The choice of appropriate data needs to be justified and relevant data selection is part of the assessment process. In both cases, forecasts for the relevant target variable will be related to forecasts for variables potentially available through ABS and RBA websites. Often in forecasting and other research that available data will not perfectly match the situation. The forecaster/researcher needs to make a judgment on suitable data that approximate the exact variable (s) needed.

(i) Part 1 Report (Individual): Students should formulate responses to the relevant questions indicated in Part 1 (for the task undertaken by their group) in the form of a short preliminary report. Although presentation will be important and will be assessed strict report format (Executive Summary, Introduction, Body etc.) is not required. However, all workings (spreadsheets, computer output), detailed methodological descriptions and references should be contained in the appendices. The report for Part 1 should be around 1500 – 2000 words and no longer than 6 x 1½ spaced 12 point typed pages excluding cover page, executive summary, references, and appendices.

(ii) Part 2 Report (Group): The Part 2 report is required to be a management style reports with an executive summary, introduction, body, conclusions, and appendices. The executive summary should summarise the report in no more than a page and include the main profit forecasts and any key recommendations.

The Introduction should outline the purpose of the study and the general background. The body should include a brief methodology description and outline key findings (eg. forecasts) and briefly how they were determined. Use plots, graphs, summary tables and other evidence to highlight key discussion points. Conclusions should contain key recommendations and limitations. All workings (spreadsheets, computer output), detailed methodological descriptions and references should be contained in the appendices. Although the assignment questions and hints have been framed using (a), (b) etc. you will lose marks if you respond to each question sequentially in (a), (b) etc format. It must be in the style of a coherent management report. It is feasible to have different members of the group work on different parts of the assignment; however, the final report must be logical, co-ordinated, and consistent in style and font. Different fonts and styles from contributions made by different students will be penalised.

Presentation/style of both reports and relevant findings will be important. This includes proper presentation of graphs, tables and charts used to highlight key points in the report.

The report for Part 2 should be around 5000 – 6000 words and no longer than 15 x 1½ spaced 12 point typed pages excluding cover page, executive summary, references, and appendices. It should include standard elements such as references if you have used external sources (Harvard reference style).

A: Forecasting task A: You can only select one of the forecasting tasks

Suppose you are employed by a firm which markets interior trim insurance to new car owners. The insurance can only be provided on new cars and is valid for a 12-month period after which the policy lapses and cannot be renewed. The insurance covers things like stain removal from upholstery and minor repairs from scratches and other blemishes. You have been asked to predict the likely potential market for this insurance service for a 12-month period. This insurance is a new product/service and will be launched in March 2018 with a comprehensive marketing campaign.

As the insurance is a new product/service, there is no historical data on insurance sales available. One approach being considered by the insurance company is to forecast the sales of relevant new vehicles and to assume a certain proportion of new cars sold will opt for the insurance. This will allow relevant forecasts for new insurance policy sales to be generated.

Currently the firm is considering either offering the same insurance policy for all passenger vehicles (excluding trucks and utilities) or offering separate insurance policies for normal passenger vehicles and SUV type vehicles. You have been assigned the task of analysing relevant time series and providing forecasts for the relevant period. Your group will also need to provide recommendations to management on possible outcomes associated with the proposed segmentation outlined above. In addition to providing forecasts, you are also required to outline relevant considerations due to changing economic circumstances and other environmental factors.

Part 1: Individual (15%) Due: 11 pm, Sunday 22nd April

Identify a relevant time series for the total number of new vehicles sold in Australia (for at least the last 10 years) which may be useful in generating the forecasts required by the insurance company. Justify your choice of time series.

(Data should be available on relevant Australian government websites such as the Reserve Bank (RBA) or Australian Bureau of Statistics (ABS). It may also be available on relevant governmental department websites or through relevant trade organisations and/or commercial data/research agencies)

Provide a line chart of the relevant time series for approximately the last 10 years.

Comment in general on the characteristics of the time series line chart. What systematic components are evident in the time series?

Outline the economic and environmental factors or circumstances which are likely to have influenced the characteristics or components of the time series identified in (ii)? Will these factors or circumstances apply to the relevant forecasting period?

Apply an appropriate method to smoothe (remove randomness) from the time series chosen in (a) to help further identify the relevant systematic components.

From the results of the above smoothing, provide a time series line chart comparing the original time series with the generated smoothed values.

Does the line chart generated in (b (i)) suggest any re-evaluation or modification (if at all) to your answer to (a.(ii))? Explain.

Using the results of the smoothing method you applied, provide numerical estimates for the underlying systematic components (for example, if a trend is observed provide a relevant trend equation and/or if seasonality is observed provide estimates of the seasonal relatives or indexes).

Provide at least one appropriate time series model for the time series you selected in (a) and provide relevant monthly forecasts for total new vehicle sales in Australia for April -18 to Mar-19. Justify your choice of model.

Part 2: Group (15%) Due: 11 pm, Sunday 3rd June

Remember the Part 2 Group Report should be in the style of a coherent management report and you should not answer the questions below as (a), (b) (c) etc. The questions are provided as a guide to the tasks that need to be completed within the report.

– Redo Part 1: (a) and (c) above (possibly using the responses of each of the individual members of the group to Part 1: (a) and (c)).

However, the group will need to consider the forecast problem brief which requires, in part, evaluation of different strategies involving segmentation of the new vehicle market into passenger and SUV vehicles. One forecast approach may be to forecast the relevant quantities separately. Alternatively, another approach may be to forecast total new vehicle sales and then apply a simple mechanism for allocating the total new sales forecast into the required quantities. The group will need to briefly explain which approach it has decided to use. The group response to Part1: (a) and (c) must be consistent with the approach decided.

Note: For the forecasts in Part 1:(c) the group will need to provide forecasts for Jul-18 to Jun-19 not just Apr-18 to May-19.

(b)Provide at least one regression model for forecasting the relevant time series. For this model provide monthly forecasts for Jul-18 to Jun-19 (a regression model with time and suitable dummies is acceptable. Groups are not expected to build complex regression models using other X variables)

(c) Compare the results of the time series and regression model approaches. Which of these models do you prefer? Justify your choice of model.

(d) Suppose that the insurance premium presently considered for the marketing campaign is $410 for 12 months no matter the type of vehicle. The firm is however also considering another strategy which will use a split premium of $385 for normal passenger vehicles and $435 for SUV’s. From current data and market research studies the organisation is confident of about a 6 % penetration of passenger vehicles in the Australian market ie approximately 6% of all registered new passenger vehicles sold each month will opt for this type of insurance cover. Market research also indicates the penetration for SUV’s is likely to be higher at approximately 8%. Using the forecasts for new car sales and the data above, provide revenue projections (in $) for insurance policies sold for the nominated months for each type (normal passenger and SUV’s) and total passenger vehicles.

(e) Suppose that the firm has been in business in the USA since July 2017. Data obtained from the USA database has revealed that presently each month approximately 2% of the total number of vehicles who have a current interior insurance policy cover will have a claim for damage covered under the policy filed by their owners. The average cost of the repair in USA is approximately $475 ($AUD) per claim (over all vehicles both passenger and SUV) and is estimated to be approximately the same in Australia. Overheads are approximately $250,000 a month.

Further breakdown of the USA data reveals the claim % and average repair costs for the different segments (passenger, SUV) to be as below.

Normal Passenger SUV’s Total
Claim % per month 1.85% 2.75% 2%
Average cost of Repair $ 420 $ 515 $475

Provide a monthly profit prediction for the indicated months for each type (SUV, Passenger) and Total (combined SUV, Passenger). Be sure to explain any assumptions you have made and demonstrate how the profit predictions have been made.

(f) Indicate how a change in the values of assumed values in (e) might affect the profit forecasts. Provide forecasts for a few relevant scenarios (assume that overheads are fixed at $225,000 and the average cost of repairs given in the table above per month is not alterable). Justify your choice of scenarios.

(g) Make recommendations (based on the quantitative forecasting analysis only) on the proposed segmentation strategy of the insurance company.

(h) Indicate any limitations and qualifications to your forecasts and conclusions based on economic or other relevant circumstances. How will these factors impact on the forecasts, if at all?

(These may include specific assumptions made for your calculations above, broader assumptions about the applicability of the forecast methods you have used and market and environmental factors which may change over the forecast horizon)

B: Forecasting task B: (You can only select one of the forecasting tasks)

You have just been retained as an analyst for BRIC-MAKERS Australia, a manufacturer of clay bricks used in the construction industry. BRIC-MAKERS has recently undergone a change of management with the new management team employing a more evidence-based decision-making approach. The forecasting processes instituted by the previous management team were poor leading to waste and inefficiencies. The new management wants to have a more rigorous approach to forecasting and has employed you and your expert team to provide relevant forecasts for key sales indicators.

The Australian brick industry has two main market segments; residential and non-residential. Approximately 85% of all BRIC-MAKERS’ brick sales are for the residential sector with the balance derived from non-residential construction. BRIC-MAKERS operate only in NSW and do not sell to other states. Currently Bric-makers have an approximate 32% share of the brick market in NSW.
Residential dwelling types

The total new residential building construction market comprises two main segments, new house construction, and new other residential buildings.

New houses can be separated in to single level and multi-level (i.e. two level and split level) dwellings. Recent anecdotal evidence suggests new homebuilders are opting increasingly for multi-level dwellings (as land block sizes decline). The new manager anticipates the proportion of multi-level new dwellings will continue and potentially increase for 2018 and beyond.

The following table summarises the average number of bricks used, on average, in each residential building construction and conversion.

Type of dwelling Average number of bricks used per dwelling
New houses – single level 7,500
New houses – multi-level 14,000
New other residential buildings

(i.e. duplex, apartments etc).
18,500

Although historical sales data for BRIC-MAKERS is available, the new manager suggests the historical data is not reliable and is not a true record of past sales. This was due to poor accounting and recording systems employed by the previous management. The new manager suggests forecast of brick sales can be made indirectly by forecasting new building construction on residential dwellings and conversions and then applying BRIC-MAKERS historical market share of brick sales.

Your overall task (as a group) is to forecast BRIC-MAKER’s overall brick sales considering the different dwelling categories (indicated in the Table above) in the residential segment in NSW.

Part 1: Individual (15%) Due: 11 pm, Sunday 22nd April

Identify a relevant time series for the total new residential dwellings constructed (or to be constructed) in NSW (for at least the last 10 years) which will be useful in generating the necessary forecasts required by the BRIC-MAKERS. Justify your choice of time series.

Note: There are a few candidate time series which measure activity in the new residential construction sector from different viewpoints and with different time intervals. Be mindful of the forecast brief above when choosing your time series.

(Data should be available on relevant Australian government websites such as the Reserve Bank (RBA) or Australian Bureau of Statistics (ABS). It may also be available on relevant governmental department websites or through relevant trade organizations and/or commercial data/research agencies)

Provide a line chart of the relevant time series for approximately the last 10 years.

Comment in general on the characteristics of the time series line chart. What systematic components are evident in the time series?

Outline the economic and environmental factors or circumstances which are likely to have influenced the characteristics or components of the time series identified in (ii)? Will these factors or circumstances apply to the relevant forecasting period?

Apply an appropriate method to smoothe (remove randomness) from the time series chosen in (a) to help further identify the relevant systematic components.

From the results of the above smoothing, provide a time series line chart comparing the original time series with the generated smoothed values.

Does the line chart generated in (b (i)) suggest any re-evaluation or modification (if at all) to your answer to (a.(ii))? Explain.

Using the results of the smoothing method you applied, provide numerical estimates for the underlying systematic components (for example, if a trend is observed provide a relevant trend equation and/or if seasonality is observed provide estimates of the seasonal relatives or indexes).

Provide at least one appropriate time series model for the time series you selected in (a) and provide relevant monthly forecasts for total new residential dwellings in Australia for April -18 to Mar-19. Justify your choice of model.

Part 2: Group (15%) Due: 11 pm, Sunday 3rd June

Remember the Part 2 Group Report should be in the style of a coherent management report and you should not answer the questions below as (a), (b) (c) etc. The questions are provided as a guide to the tasks that need to be completed within the report.

– Redo Part 1: (a) and (c) above (possibly using the responses of each of the individual members of the group to Part 1: (a) and (c)).

However, the group will need to consider the information contained in the original forecast brief. The average number of bricks used per dwelling depends on the type of residential dwelling. One forecast approach may be to forecast the relevant categories separately. Alternatively, another approach may be to total new residential dwellings and then apply a simple mechanism for allocating the total new dwellings forecast into the required category forecasts. The group will need to briefly explain which approach it has decided to use. The group response to Part1: (a) and (c) must be consistent with the approach decided.

Note: For the forecasts required in Part 1:(c) the group will need to provide forecasts for Jul-18 to Jun-19 not just Apr-18 to May-19.

Provide at least one regression model for forecasting the relevant time series. For this model provide monthly forecasts for Jul-18 to Jun-19 (a regression model with time and suitable dummies is acceptable. Groups are not expected to build complex regression models using other X variables)

Compare the results of the time series and regression model approaches. Which of these models do you prefer? Justify your choice of model.

Suppose the price of bricks presently is $800 per 1000 bricks. The variable costs involved in producing bricks (per 1000) are as indicated below. Overhead costs are approximately $250,000 per month.

Costs Per 1000 bricks

Raw materials $100.00
Wages $100.00
Electricity/Fuel $65.00
Freight/Cartage $75.00
Repairs/Maintenance $25.00
Other $15.00

Using the forecasts for new residential dwellings generated previously and the above price and cost data, provide forecasts for the total number of bricks sold, revenue and costs for the nominated months (Jul-18 to Jun-19)

Be sure to explain any assumptions you have made and demonstrate how the profit predictions have been made.

Suppose BRIC-MAKERS is considering changing the price of its bricks. Management is considering three different prices of $710, $850, and $920 (per 1000 bricks) for all types of residential dwellings. However, market research indicates changes to brick prices will most likely impact on BRIC-MAKERS market share. The research determined market share was related to brick prices by the following equation.

Market share (as a %) = 32 – [0.027 * (P – 800)] where P = price per 1000 bricks

Assuming all other variables and factors remain the same as previously, what are the new monthly forecasts for total bricks sales, revenues, and profits for the relevant period if a price change is applied uniformly across all three categories of dwelling (Jul-18 to Jun-19)

Suppose the suggested prices can vary between new dwellings (single and multi-level combined) and new other residential buildings. Which of the price combinations will lead to the greatest profit for BRIC-MAKERS over the entire forecast period (Jul-18 to Jun-19)?

Indicate how a change in the cost values assumed values in (d) might affect the profit forecasts. Provide forecasts for the relevant period (Jul-18 to Jun-19) for a few relevant and realistic scenarios where variable cost components may change (assume that overheads are fixed at $250,000). Justify your choice of scenarios.

Make recommendations (based on the quantitative forecasting analysis only) on BRIC-MAKERS proposed pricing strategies (both uniform and differential pricing across new dwelling categories).

Indicate any limitations and qualifications to your forecasts and conclusions based on economic or other relevant circumstances. How will these factors impact on the forecasts, if at all?

(These may include specific assumptions made for your calculations above, broader assumptions about the applicability of the forecast methods you have used and market and environmental factors which may change over the forecast horizon)