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
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
*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
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
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.
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.
Initial Dashboard
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.
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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