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COIS13013 Business Intelligence 2

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Question:

Part A

This part emphasises on the use of a DSS tool, and students are required to demonstrate proficiency with Visual DSS™ (a DSS development language/DSS generator).

Solve Questions 1, 2 and 3 using VISUAL DSS™.

Question 1

YourCloud Pty Ltd is a cloud-based software development company located in Brisbane. The company is planning to introduce new responsive cloud based software application into the market. In recent times, the dynamic market competition has created some bad software investment decisions. Now senior management require a thorough analysis of every new product that is introduced to market. As a senior business analyst, you have been appointed to advise the senior management on the feasibility of the new product.

An initial analysis conducted by other analysts for the product claim the anticipated net present value (NPV) for the new product line is over $2 million and they have recommended the manufacture of the product based on this assessment.

Your task is to use a decision support system (DSS) and report to the senior management on whether the claim of the NPV being over $2 million is correct or incorrect using the relevant information given in Table 1.

Table 1: Summarised product details

Cost of production: $25.00 per unit

Annual overhead cost: $210,000 (cloud hosting is outsourced)

Initial investment needed: $1,750,000

Estimated selling price: $55.00 per unit

Market at time of introduction: 420,000 units per year

Market growth: 15% per year

Market share: Most likely 10%

Assumed economically useful lifetime: 4 years, commencing 2018

Discount rate used to analyse new product proposals is 12%

You need to assume that the overhead and initial investment occurs at the START of the respective year, profit occurs at the END of the year and initial investment was only applicable to the first year.

Your task:

  1. Develop a decision support model using Visual DSS using the variables described above. Include comments within your Visual DSS model to explain the variables and your calculations.
  2. Based on the result of your model, what is the net present value (NPV)? Explain whether the claim regarding the NPV being above $2 million is correct or incorrect.

Question 2

You are now asked to analyse the variations on the impact of market share, cost of producing, overheads and initial investment on the NPV. You need to conduct a risk analysis based on the information below:

  1. a) Market share: could be as low as 5% or as high as 15%, but is most likely to be 10%. The distribution could be represented using a triangular distribution.
  2. b) Unit costs can be described by normal distribution – mean of $30.00 and standard deviation of $12.00.
  3. c) Overhead: could be as low as $150,000 per year or as high as $350,000 per year, but is most likely to be $215,000 per year. The distribution could be represented using a triangular distribution.
  4. d) Initial investment requirements can be uniformly distributed between $1,000,000 and $2,000,000.

The senior management decided on the following decision criteria:

Decision criteria: The company is unwilling to proceed if there is a 20% or greater chance that the net present value will be less than $1,000,000 (1 million).

Your task:

  1. You are required to use Visual DSS to run a Monte Carlo simulation (a Risk Analysis).
  2. Produce a cumulative probabilities report and graph for the above question. Based on results and the decision criteria, explain whether the senior management should accept or rejectthe proposed production of the product.

Question 3

When the above analysis reached the Chief Executive Officer (CEO) of your company, he became very concerned about the assumptions made in the model. His experience has taught him to consider the uncertainty associated with selling price and production costs more thoroughly. He required further analysis to be done by incorporating the following uncertainties to Question 1 model:

  • Selling price: uniformly distributed between $65and $45.
  • Unit costs: normally distributed, mean of $25.00, standard deviation of $5.00.

He applied different decision criteria and was willing to go ahead with the product proposal if there was at least an 80% chance the net present value would be greater than $1,850,000.

Your task:

  • You are required to use Visual DSS to run a Monte Carlo simulation (a Risk Analysis). Based on your results determine whether the CEO will proceed under these uncertainties.
  • Produce a cumulative probabilities report and graph for the question. Based on results and the decision criteria, will the CEO accept or rejectthe proposed production of the product?

Part B:

In this part, the students are required to demonstrate data validation by using Power BI.

Power BI is a business analytics service provided by Microsoft. It provides interactive visualizations with self-service business intelligence capabilities, where end users can create reports and dashboards by themselves, without having to depend on information technology staff or database administrators.

Download Microsoft Power BI Desktop from https://powerbi.microsoft.com/en-us/downloads/ 

Download Power BI sample files from https://github.com/apress/pro-power-bi-desktop Download, Zip

Demonstration 1

Open Microsoft Power BI Desktop. Use CarSales.xlsx as the Source Data. This is a worksheet of British car sales. Create the following on one Page Dashboard.

Visualisation

Fields

Matrix

CountryName, ReportingYear, SalePrice

Column

DeliveryCharge, Model

Map

CountryName, LaborCost

Card

SpareParts

Slicer

Model

Your task:

  • Save a screenshot of the Dashboard.
  • Select DB9 in the slicer and save a second screenshot of the Dashboard.
  • Answer the following two questions:
  • Which Country has the most SalePrice (sum) of the DB9?
  • How can you use Power BI to validate business assumptions in this demonstration?

Demonstration 2

Visit Queensland Government data. https://data.qld.gov.au/dataset 

Search for: Advance Queensland Funding Recipients https://data.qld.gov.au/dataset/advance-queensland-funding-recipients 

Select Committed Funding Recipients

https://data.qld.gov.au/dataset/advance-queensland-funding-recipients/resource/0f97b985-f5c7-49d2-8b0a-bc5dfbe070b9 

Download the CSV file. Open Microsoft Power BI Desktop. Select the CSV as a source.

Create the following on one Page Dashboard.

 

Visualisation

Fields

Pie Chart

Program, Count of Program

Table

Program, Funding_Committed

Stacked Bar Chart

Sector, Funding_Committed

Treemap

Program, Funding_Committed


Your task:

  • Save a screenshot of the Dashboard.
  • Select in the Treemap, Research Fellowship and save a 2nd screenshot of the Dashboard.
  • Answer the following two questions:
  • Which sectors receive Research Fellowship funding?
  • What potential issues are present in terms of data validation based on the fields?

Part C:

You are required to write a report to discuss how smart, connected products contribute to business analytics and transform companies by using business intelligence from a case study “How Smart, Connected Products Are Transforming Competition”.

 

Answer:

Part A: Visual DSS

1: NPV Model

Process Code:

*Columns

*Years 2018,2021

*Rows

Initial investment(0) = 1750000.00 '.2

Initial Market (0)= 420000

Market Growth = 0.15'.2

Market Share = 0.10'.2

Expected market = Initial Market;Expected market(-1)*1.15

Sales Volume = Expected Market*Market Share

Estimated selling price = 55.00 '.2

Cost of production = 25.00 '.2

Total Revenue = Sales Volume*Estimated selling Price '.2

Cost of Goods sold = Sales Volume*Cost of Production

Annual overhead cost = 210000

Cash Flow = Total Revenue-Cost of goods sold-Annual Overhead cost

Rate = 0.12'.2

NPV(0) = *NPV cash flow;rate

Process Output:

 

According to the given information, the organisation YourCloud Pty Ltd has already conducted the preliminary analysis by another analyst and as per the analysis the NPV of this newly proposed project would be over 2 million.

However, with the help of Visual DSS tool and given information, the analyst has designed a decision support model and performed a NPV analysis. According to this analysis, the NPV of this project would be 5440551, which is much more than 2 million value.

2: Risk Analysis Using Monte Carlo Simulation

Process Code:

*Columns

*Years 2018,2021

*RowsCOIS13013 Business Intelligence: Use of a DSS tool in Cloud Pty Ltd

Initial investment(0) = UNI(100000.00,200000.00) '.2

Initial Market (0)= 420000

Market Growth = 0.15'.2

Market Share = TRI(0.05,0.10,0.15)'.2

Expected market = Initial Market;Expected market(-1)*1.15

Sales Volume = Expected market*Market Share

Estimated selling price = 55.00 '.2

Cost of production = NOR(30.00,12.00) '.2

Total Revenue = Sales Volume*Estimated selling Price '.2

Cost of Goods sold = Sales Volume*Cost of Production

Annual overhead cost = TRI(150000,215000,350000)

Cash Flow = Total Revenue-Cost of goods sold-Annual Overhead cost

Rate = 0.12'.2

NPV(0) = *NPV cash flow;rate

Process Output:

 

Once the NPV analysis is done, the organisation has asked to analyze the variations on the impact of market share, cost of producing, overheads and initial investment on the NPV. Accordingly, the analyst has performed a detailed risk analysis using Monte Carlo technique and found the above mentioned result.

According to the given information, the Company is unwilling to proceed if there is a 20% or greater chance that the net present value will be less than $1,000,000 (1 million). However, the above result has shown that there is a 10% or less chance that the NPV would be more than 2 million and 20% or less chance that the NPV would be around 3 million. Hence, the decision criteria set by the company holds true and thus they can proceed with the development of this prdoduct.

 

3: Risk analysis using Monte Carlo Simulation

Process Code:

*Columns

*Years 2018,2021

*Rows

Initial investment(0) = 1750000.00 '.2

Initial Market (0)= 420000

Market Growth = 0.15'.2

Market Share = TRI(0.05,0.10,0.15)'.2

Expected market = Initial Market;Expected market(-1)*1.15

Sales Volume = Expected market*Market Share

Estimated selling price = UNI(45.00,65.00) '.2

Cost of production = NOR(25.00,5.00) '.2

Total Revenue = Sales Volume*Estimated selling Price '.2

Cost of Goods sold = Sales Volume*Cost of Production

Annual overhead cost = 210000

Cash Flow = Total Revenue-Cost of goods sold-Annual Overhead cost

Rate = 0.12'.2

NPV(0) = *NPV cash flow;rate

Process Output:

Again, as per the given information the Chief Executive Officer (CEO) wanted to perform some further analysis before proceeding with this new product. Accordingly, he proposed some further criteria’s and all are incorporated in the final model. The results have been shown in the above figures.

Further, he also applied different decision criteria and was willing to go ahead with the product proposal if there was at least an 80% chance the net present value would be greater than $1,850,000. Now, the results has shown that there is a less than 80% chance that the NPV would be more than 6 million and less than 90% chance that the NPV would be around 7 million. Hence, incorporating these criteria’s implemented by the CEO, the project has shown positive figures. In other words, the company can go ahead with this proposed product.  

Part B: Power BI Demonstration

Demonstration 1

Initial Dashboard

 

Dashboard for DB9

 

According to this dashboard, USA has the highest sales price (sum) of the DB9. It is seen that Dashboards are supposed to provide a general overview of the content one would like to see. If anyone wanted to get into further detail they would click on a particular visual which then leads them to the origin of that visual. Slicers are meant to be fully interactive in the report view, not the dashboard view. It will thus help to concentrate on a specific aspect other than the entire dashboard.

Demonstration 2

Initial Dashboard

 

Dashboard for Research Fellowship funding

 

According to this dashboard, agriculture forestry and fishing, electricity, water, gas and waste services, healthcare and social assistance, arts and recreational services, etc are the major ones who received research fellowships. Considering this field, it can be said that the visualisation is shown as compact form, which is unable to display the entire results. This is because of large number of data set.

Part C: Report

According to the case study, "How Smart, Connected Products Are Transforming Competition", the main focus was to explore the applicability of business intelligence inside to the firm, looking in its element at how vivid, associated items influence contention, industry structure, industry limits, and technique. According to this, it can be said that the core competencies while developing any product, the power of synchronisation amid them is expanding with rapid enhancement of business intelligence. Altogether new capacities are developing, including those to deal with the stunning amounts of information now accessible (Hou et al. 2016). The greater part of this has significant ramifications for the exemplary hierarchical structure of producers. The great positive is maybe the most generous change in the assembling firm since a long time.

It is to be noted that irrespective of any products and its associated items, from home apparatuses to modern gear, application of business intelligence considered three centre components: physical segments, savvy segments and connectivity components. Insightful, associated items require a radical new supporting innovation foundation (Diamond and Mattia, 2017). This "innovation stack" gives a portal to information trade between the item and the client and incorporates information from business frameworks, outer sources, and other related items. The innovation stack likewise fills in as the stage for information stockpiling and investigation, runs applications, and protections access to items and the information streaming to and from them.

This foundation empowers phenomenal new item abilities. To begin with, items can screen and give their very own account condition and condition, creating already inaccessible bits of knowledge into their execution and utilize. Second, complex item activities can be controlled by the clients, through various remote-get to choices (Vercellis, 2009). That gives clients the extraordinary capacity to alter the capacity, execution, and interface of items and to work them in unsafe or difficult to-achieve situations.

At the same time, the mix of checking information and remote-control capacity makes new open doors for streamlining. Calculations can considerably enhance item execution, usage, and uptime, and how items work with related items in more extensive frameworks, for example, brilliant structures and shrewd ranches (Watson and Wixom, 2007). Fourth, the blend of observing information, remote control, and advancement calculations permits self-sufficiency. Items can learn, adjust to the earth and to client inclinations, benefit themselves, and work without anyone else.

As the capacity to open the full estimation of information turns into a key wellspring of upper hand, the administration, administration, investigation, and security of that information is forming into a noteworthy new business work. While singular sensor readings are significant, organizations regularly can uncover intense bits of knowledge by recognizing designs in a huge number of readings from numerous items after some time (Chaudhuri et al. 2011). For instance, data from unique individual sensors, for example, an auto's motor temperature, throttle position, and fuel utilization, can uncover how execution corresponds with the auto's building determinations. Connecting mixes of readings to the event of issues can be helpful, and notwithstanding when the main driver of an issue is difficult to find, those examples can be followed up on. Information from sensors that measure warmth and vibration, for instance, can anticipate an approaching bearing disappointment days or weeks ahead of time. Catching such experiences is the area of enormous information examination, which mix arithmetic, software engineering, and business investigation strategies.

Huge information examination utilize a group of new methods to comprehend those examples. A test is that the information from brilliant, associated items and related inner and outside information are regularly unstructured. They might be in a variety of arrangements, for example, sensor readings, areas, temperatures, and deals and guarantee history. Regular ways to deal with information accumulation and investigation, for example, spreadsheets and database tables, are ill-suited to dealing with a wide assortment of information designs. The rising arrangement is an "information lake," an archive in which divergent information streams can be put away in their local organizations. From that point, the information can be considered with an arrangement of new information examination instruments. Those apparatuses fall into four classifications: elucidating, analytic, prescient, and prescriptive.

 

References:

Chaudhuri, S., Dayal, U. and Narasayya, V., 2011. An overview of business intelligence technology. Communications of the ACM, 54(8), pp.88-98.

Diamond, M. and Mattia, A., 2017. Data Visualization: An Exploratory Study into the Software Tools Used by Businesses. Journal of Instructional Pedagogies, 18.

Hou, Z., Zhang, H., Zhang, H. and Zhang, D., 2016. Visual analytics for software engineering data. In Perspectives on Data Science for Software Engineering (pp. 77-80).

Power, D.J., Sharda, R. and Burstein, F., 2015. Decision support systems. John Wiley & Sons, Ltd.

Rubinstein, R.Y. and Kroese, D.P., 2016. Simulation and the Monte Carlo method (Vol. 10). John Wiley & Sons.

Turban, E., Sharda, R. and Delen, D., 2011. Decision support and business intelligence systems. Pearson Education India.

Vercellis, C., 2009. Business intelligence (pp. 1-19). John Wiley & Sons, Ltd.

Watson, H.J. and Wixom, B.H., 2007. The current state of business intelligence. Computer, 40(9).

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