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Product Description

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™.

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.

Hints

  • Note that overhead and initial investment both occur at the START of the respective year, and profit occurs at the END of the year.
  • You should use the correct NPV formula in Visual DSS. Use the ‘Help’ feature within the Visual DSS application and Visual DSS tutorial to learn more about the correct NPV formula, which is applicable to the scenario, described in Question 1.
  • Initial investment is a startup cost applicable to the first period only (i.e. 2018).
  • The NPV is only relevant for the first period (i.e. 2018) for decision-making – so only report it for that period. Using NPV (0) in Visual DSS will allow you to achieve this.

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.

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.

Product Description

NPV model

*Columns

*Years 2018,2021

*Rows

Initial investment needed(0) = 1750000.00 '.2

Market at time (0)= 420000

Market Growth = 0.15'.2

Market Share = 0.10'.2

Total market = Market at time;Total market(-1)*1.15

Sales Volume = Total 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

Model Output

The Net present value (NPV) that has been estimated based on the result of model is $5440551.00. From the NPV, it can be said that the claim regarding the NPV being above $2 million is correct. It is correct from the fact that the NPV has been calculated for the first period only that is for 2018 and NPV(0) has been used in Visual DSS to determine the NPV. The NPV that has been calculated is above $2 million as evident from the results.

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

Market at time (0)= 420000

Market Growth = 0.15'.2

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

Total market = Market at time;Total market(-1)*1.15

Sales Volume = Total 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

It has been determined that the senior management should accept the proposed production of product as the decision criteria of the company is not violated. The NPV that has been calculated at 20% or greater is more than $1,000,000 (1 million).

Monte Carlo simulation Model

*Columns

*Years 2018,2021

*Rows

Initial investment needed(0) = 1750000.00 '.2

Market at time (0)= 420000

Market Growth = 0.15'.2

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

Total market = Market at time;Total market(-1)*1.15

Sales Volume = Total 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

From the produced cumulative probabilities report and graph, it can be said that the CEO will accept the proposed production of the product. It is due to the fact that the NPV being calculated at 80% and less is greater than the decided value of $1,850,000. The NPV at less than 90% probability is calculated to be $6619214.  

Decision Support System (DSS) Analysis

USA has the most SalePrice (sum) of the DB9 according to the designed dashboard by selecting DB9 in slicer.

Power BI in validation of business assumptions

Power BI is used for validating business assumptions as it provides data visualization which helps to easily understand status of the business. Power BI is a powerful analytics tool that helps to analyse data in an effective manner. In this particular demonstration, Power BI is used to easily determine that USA has the most SalePrice (sum) of DB9. Hence, Power BI can be considered as an essential tool that is used for validating business assumptions.

The list is presented below to illustrate on the sectors that receive Research Fellowship funding:

  • Health care and social assistance
  • Renewable energy
  • Agriculture, forestry and fisheries
  • Electricity, gas, water and waste services
  • Great Barrier Reef
  • Professional, scientific and technical services

Potential issues with data validation based on fields

Data validation based on fields may raise potential issues such as if there are blank in the data of certain fields then the validation may give error or wrong results. Another issue that persists with validation based on fields is that the output data type may not be the same as that of source and the user have to changed it manually otherwise it will lead to erroneous data.

This report depicts the role of different smart and connected products in the business intelligence and also their usability effectiveness to drive any business towards massive commercial success. Due to frequent evolution of technology as well as products in intelligence and connected devices in business applications the entire business field is getting improved every day. The relationship between the Business Intelligence (BI) and all connected products are also illustrated in this report. With the features utilized by Business Intelligence (BI) the business enterprises can keep on improving their operational and functional strategies.

The new business capabilities and huge amount of information those are generally offered by the smart connected products helps to redefine the core functional activities of the company.  Both the cloud based operating system and software has become integral part of the new products. Porter and Heppelmann (2014), has stated that different new product developing principles are emerging based upon the manufacturing component and other frequently changing processes. Not only this but also it has been found that, in order to secure the business functions IT security is considered as an important part that has to be maintained. In order to gain competitive advantages, the functions must have the ability to unlock the full value data. Besides data unlock proper management, governance and data security analysis are the simultaneous functions considering.

Risk Analysis and Monte Carlo Simulation

If it is found that the all the individual sensor reading are valuable then, over the time through identification of readings for different products the enterprises can uncover various insights. The data gathered from individual sensors like temperature from the car engine, throttle position, consumption of fuel can reveal the way through which the performances are interrelated to the engineering specification of the cars (Joachimsthaler et al. 2015). The reasons for which the problems are occurring, rather the linking combination of the readings are helpful whenever the root causes are determined as difficult to reduce. The data those are generated from the sensor which can measure the rate of vibration and heat can also forecast the imminent failure days as well as weeks. The application field of big data analytics can combine mathematics, computer science and business analysis techniques as well.  

 In order to understand the highlighted patterns, the big data analytics has eventually employed new additional techniques. Considering all these aspects it is found that, data from the smart and connected products, internal and external unstructured data are huge challenge to the enterprises. According to Porter and Heppelmann (2014), these factors can be arranged in an array which is comprises of sensor reading, location, sales history, warranty details, temperature etc. Wide range of data formats management and with the traditional data aggregation approach in terms of database and spreadsheet tables are not at all beneficial. One of the emerging solutions is “Data Lake” that can store data stream in the native format. The previous data and the new data can be studied with the help of the analytics tools that has four different categories such as descriptive, diagnostic, predictive as well as prescriptive.

The industry boundaries can be broadened and the existing products can be transformed with the help of the smart and connected products. The products those are separate as well as distinct can become part of the optimized systems for relating the products and components of the system. The companies those have been industry leaders from past few decades helps to shift the company boundaries and also play active role (Porter and Heppelmann 2014). The emergence of the products and systems highlights two different types of strategies and choice regarding the scope of the company. The first choice is about whether the company should spread their products or not and the second one is in order to build up connection between the products and information whether the company should provide a platform or not. It is expected that with the help of one of these components all the functional and operational parts can be eventually controlled.

Data Validation using Power BI

In order to gain big data opportunities the enterprises may tempted to enter into the relevant products.  However, sudden entry to new products includes high level risks and many other operational abilities as well. Thus, before entering to such product the company ought to identify a lucid position (Joachimsthaler et al. 2015). Expansion of product scope is beneficial and attractive. In order to optimize the systems it gives opportunities to improve performance along with co-designing capabilities. The company should stick to its knitting and deliver open connectivity if their optimization is not dependent on individual product design approach. These opportunities will provide advanced IT and technology driven environment to the company whenever IT played itself out. The companies whose products are central among the overall products will hold the best of the position for entering to the related products. The manufacturers who produce lesser number of critical machines are less capable to attract the consumers which are helpful to take the system in a broader environment.

Conclusion

From the overall discussion it can be concluded that, big data analytics and Business Intelligence (BI) play role to gain business success. Both in terms of commercial success and competitive advantages big data analytics and business intelligence are helpful. It helps to build successful and secured relationship between the product and the all connected devices. The business organizations can utilize the feature of these technologies to obtain the revolutionary changes in the field of technology and its operation. With the help of advanced technologies the business can drive its operation and other functions towards massive success. Apart from this, it is also found that with the help of technologies the companies can rapidly transform their application strategies. In order to implement such business strategies smart and connected products are also beneficial because it provides the characteristics offers by the business intelligence. Besides these, the other benefits that the BI offers include faster reporting, analyzing and planning ability. Besides improve data quality it also offers improved consumer’s satisfaction, better business decision making capability, that are also elaborated in this report.

Fan, S., Lau, R.Y. and Zhao, J.L., 2015. Demystifying big data analytics for business intelligence through the lens of marketing mix. Big Data Research, 2(1), pp.28-32.

Joachimsthaler, E., Chaudhuri, A., Kalthoff, M., Burgess-Webb, A. and Bharadwaj, A., 2015. How smart, connected products are transforming competition. Harvard business review, 93(1), p.4.

Larson, D. and Chang, V., 2016. A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 36(5), pp.700-710.

Laursen, G.H. and Thorlund, J., 2016. Business analytics for managers: Taking business intelligence beyond reporting. John Wiley & Sons.

Mitri, M. and Palocsay, S., 2015. Toward a model undergraduate curriculum for the emerging business intelligence and analytics discipline. Communications of the Association for Information Systems, 37(1), p.31.

Porter, M.E. and Heppelmann, J.E., 2014. How smart, connected products are transforming competition. Harvard Business Review, 92(11), pp.64-88.

Sallam, R.L., Tapadinhas, J., Parenteau, J., Yuen, D. and Hostmann, B., 2014. Magic quadrant for business intelligence and analytics platforms. Gartner RAS core research notes. Gartner, Stamford, CT.

Sharda, R., Delen, D., Turban, E., Aronson, J. and Liang, T.P., 2014. Businesss Intelligence and Analytics: Systems for Decision Support-(Required). London: Prentice Hall.

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[Accessed 26 February 2024].

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