YourCloud Pty Ltd: Business Intelligence and Software Launch Project

This project presents a comprehensive business intelligence analysis for YourCloud Pty Ltd, a cloud-based software organization. The project evaluates the viability of launching a new software, considering market dynamics and investment decisions. It utilizes Monte-Carlo simulation with visual DSS software to calculate Net Present Value (NPV) under various scenarios, including different market shares, costs, and initial investments. The analysis includes code snippets for NPV calculations, risk assessment, and sensitivity analysis. The project also demonstrates the use of Microsoft Power BI for data visualization and analysis, showcasing dashboards with sales data, labor costs, and funding information. Furthermore, it discusses the impact of smart, connected products on business competition, detailing the transformation of value chains and the role of business intelligence in this context. The project covers the application of business analytics, the integration of smart components, and the use of data for strategic decision-making.

Running head: BUSINESS INTELLIGENCE
Business Intelligence
Name of Student:
Name of University:
Author’s Note:

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Table of Contents
Part A...............................................................................................................................................2
Answer 1......................................................................................................................................2
Answer 2......................................................................................................................................3
Answer 3......................................................................................................................................6
Part B...............................................................................................................................................7
Demonstration 1...........................................................................................................................7
Demonstration 2.........................................................................................................................10
Part C.............................................................................................................................................12
How Smart, Connected Products Are Transforming Competition................................................12
Introduction....................................................................................................................................12
Discussion......................................................................................................................................12
Conclusion.....................................................................................................................................14
References......................................................................................................................................15

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*Columns
*Years 2018,2021
*Rows
Initial investment(0) = 1750000.00 '.2
Market at Introduction (0)= 420000
Market Growth = 0.15'.2
Market Share = 0.10'.2
Total market = Market at Introduction;Total market(-1)*1.15
Sales Volume = Total Market*Market Share
Estimated selling price = 55.00 '.2
COP = nor(25.00,5.00) '.2
Total Revenue = Sales Volume*Estimated selling Price '.2
COGS = Sales Volume*COP
Annual overhead cost = 210000
Cash Flow = Total Revenue-COGS-Annual Overhead cost
Rate = 0.12'.2
NPV(0) = *NPV cash flow;rate
Part A
YourCloud Pty Ltd is a cloud based software organization operating out of Brisbane,
Australia. The organization plans to launch a new software. The cloud based software market is a
highly dynamic market. In recent times due to some bad decisions, there have been some wrong
investments. Hence, the senior management want to analyze the market before investing in any
new software. Towards this end, they have collected some data on the basis of which decision
has to be taken. Monte-Carlo simulation using visual DSS software is used to take the decision.
Answer 1
In order to calculates the NPV the following code is used:

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The analysis from the available data shows that the net present value (NPV) of the new
software would be $5440551. This is much higher than the anticipated NPV of $2million. Thus,
the senior managers can take a decision in favour of launching the new software.
Answer 2
In order to calculates the NPV the following code is used:

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*Columns
*Years 2018,2021
*Rows
Initial investment(0) = UNI(100000.00,200000.00) '.2
Market at introduction (0)= 420000
Market Growth = 0.15'.2
Market Share = TRI(0.05,0.10,0.15)'.2
Total market = Market at introduction;Total market(-1)*1.15
Sales Volume = Total Market*Market Share
Estimated selling price = 55.00 '.2
COP = NOR(30.00,12.00) '.2
Total Revenue = Sales Volume*Estimated selling Price '.2
COGS = Sales Volume*COP
Overhead cost Annual = TRI(150000,215000,350000)
Cash Flow = Total Revenue-COGS-Overhead cost Annual
Rate = 0.12'.2
NPV(0) = *NPV cash flow;rate
In order to evaluate the impact of variations in market share, COP, overheads and initial
investment a risk assessment study needs to be done. The risk assessment needs to be evaluated
based on

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1. The market share of the new software would vary in the rage of 5 to 15% with a
probability of around 10%
2. The unit cost of the software is normally distributed with mean $30 and standard
deviation $12.
3. Overhead cost for the software would vary between $150,000 and $350,000 with the
maximum probability of the overhead cost being $215,000 per year.
4. The initial requirement for the production of the software would be between $1 and $2
million.
From the analysis it is found that given that the market share is 20% the NPV of the software
would be $2,908,313. Moreover, given a market share of 10% the NPV of the software would be
$2,244,173. In either case it is found that the NPV of the new software is higher than $1,000,000.
Since the NPV of the software is higher than the anticipated value of $1,000,000, hence the
management can launch the new software.

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*Columns
*Years 2018,2021
*Rows
Initial investment (0) = 1750000.00 '.2
Market at introduction (0)= 420000
Market Growth = 0.15'.2
Market Share = TRI(0.05,0.10,0.15)'.2
Total market = Market at introduction;Total market(-1)*1.15
Sales Volume = Total Market*Market Share
Estimated selling price = UNI(45.00,65.00) '.2
COP = NOR(25.00,5.00) '.2
Total Revenue = Sales Volume*Estimated selling Price '.2
COGS = Sales Volume*COP
Overhead cost Annual = 210000
Cash Flow = Total Revenue-COGS-Overhead cost Annual
Rate = 0.12'.2
NPV(0) = *NPV cash flow;rate
Answer 3
In order to calculates the NPV the following code is used:

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The CEO of the organization had requested certain extra assumptions to be considered before
he gave the signal to produce the software. The assumptions according to the CEO are:
1. The selling price of the software is in the range of $45 and $65.
2. The unit cost of the software is normally distributed with mean $25 and standard
deviation of $5
3. There should 80% probability that the NPV of the software would be more than
$2,500,000.
On the basis of the above assumption the re-evaluated model shows that there is 80%
probability that the NPV would be at least $6,258,513. Since the estimated NPV is higher than
the CEO’s requirements hence the software may be launched.
Part B
Demonstration 1

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Figure 1: Complete Car sales
The above figure represents the car sales of British car sales. From the analysis it is found
that the data contains information on the sales of cars of various models. The map represents the
labour costs across different countries. The diameter of the bubble represents the labour costs.
From the diameter of the bubble it can be envisaged that the labour costs for United Kingdom is
the highest. The analysis also provides us with the information that the organization has sold
495K spare parts. In addition, the delivery charges of various models show that there is variation
in the delivery charges across models. Moreover, information on the sales price of cars in
different countries from 2012 to 15 is also presented. From the information, it can be gathered
that the sum of the sales prices across 4 years is the highest for United Kingdom. Moreover, the
sum of the sales price for the organization is 31697940.

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Figure 2: Car Sales DB9
For the model DB9 the highest labour cost is from United States. Moreover, for the
model 44K spare parts have been sold by British Car Sales. In addition, it is found that sum of
the car sales for model DB9 is 5423860. For model DB9, the highest sales has been from United
States. In United States, total DB9 sales is 3609410.
Microsoft power BI is a very useful tool for analysis of data. From the analysis of the
present data it is seen that the tool can be used to visualise geographical information, yearly
trends very effectively. Moreover, the information’s in a dashboard using similar variables gets
related. Thus change in information in one visualising tool gets reflected in another visualising
tool. In the present circumstance, a second dashboard was created using the first dashboard as
reference. When the slicer tool is used, all visualisations change with reference to the slicer.
Thus, in the second dashboard all the information is related to “DB9” only. Thus, business data
can be analysed and investigated through the use of Microsoft power BI.

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Demonstration 2
Figure 3: Information on Funding Recipients
In the above dashboard information is analysed on the advance Queensland Funding
recipients. The information contains the funding committed based on program and sector.

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Figure 4: Research Funding’s on Research Fellowships
The above dashboard presents information on advance funding for research fellowships.
The total advance for research fellowships is 9300000. The maximum amount advanced for
research fellowships is for health care and social assistance.
The information on advances by Government of Queensland has been analysed by the
use of Microsoft Power BI. In the first dashboard we have created a pie chart, tree map, a table
and stacked bar chart. However, the link between the four charts is very poor. When the second
dashboard is created we select “research fellowships.” In the tree map we select the given
variable. However, the pie chart is not able to reflect the value of “research fellowships,” only
the instruments become lighter. Further, in the stacked bar chart we find that the values of
“research fellowships” are deeper as compared to other instruments. Thus, in such
circumstances, where there is a poor link between the charts, data validation is poor.

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Part C
How Smart, Connected Products Are Transforming Competition
Introduction
The advancement in the IT has been identified with the various types of the revolution
brought by transformation. “Smart, connected products” is seen to present the various types of
the amenities which are identified with the improved “product utilization, greater reliability, new
functionality and capabilities to transcend across the traditional product boundaries”. The smart
connected products have been able to make use of three main elements such as “physical
components, smart components and connectivity components”. The different types of the
electrical and mechanical parts comprise of the primary physical components. The smart
mechanisms are seen with the various components ranging from “microprocessors, control
software, engine control unit and sensors”. The connectivity implementation is considered with
the various types of the protocols considered with the wired and wireless connections used with
devices such as ports and antennae. The varying nature of the product is seen to be considered
with the disrupting value chains, which force the companies in retooling and innovating the
internal strategies. “Smart, connected products” are seen to use massive amount of data. The
benefits of these are discerned with refining the relationships with the expansion brought in the
international boundaries. The different types of the significant discussion of the study has
included the way “smart, connected products” has been able to transform the way company use
BI (Business Intelligence) (Harwood et al. 2014).
Discussion
The business analytic acceptance process is segregated into four segregations within the
product cloud. In the first phase the product cloud is considered with the application of the smart
products concepts which are based on the remote servers and manages the “monitoring,
controlling and optimizing the product functions”. The second phase includes the analytics
engines which identifies the directions between the big data analytics and business logic. This

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has been also seen with the consideration of the algorithms involved in the operations of the
products and depicted with the new products insight. The application of the development and
execution environment considers the rapid creation of the applications for the “smart, connected
business applications with the use of run-time tools data access and visualization” in the third
phase. The fourth stage has been depicted with the elements of the big data which has ensured
normalization pertaining to the real time data and product data (Lefeuvre et al. 2016). The main
elements have been considered with the communication protocol for the network in the clouds
and product. The product hardware reflects the embedding “sensors, processors and connectivity
ports” which are identified to complements the electrical and traditional mechanical components.
The “smart connected products” are seen to benefit in the transformation of the competition by
executing the main tools responsible for ensuring authentication done by the users, access to the
system and protect the product connectivity. The technology is further seen to be considered as
the gateway for the different types of the information collected from the various types of the
external foundations like “weather, commodity, traffic, energy prices, social media and geo-
mapping”. These are seen to depict the capabilities of the product. It has been further seen with
the various types of the “Smart connected products” are able to integrate the factors critical to the
business. These include the products such as “PLM, CRM and ERP” (Kim et al. 2014).
The discourse of the “smart, connected people”, have signified on its significant role in
the firms involved in the manufacturing process. In various cases, the heavy equipment made by
Schindler’s, the use of PORT technology minimises the waiting time among the elevators by
fifty percent. The predictions are further seen to be based on the elevator demand pattern and the
evaluation of the “fastest to the destination and assign the relevant elevator to fasten passenger
movement”. Smart technology introduced by ABB ensures that the distributing and
transforming changes pertaining to the temperature for the secondary substations are considered
with the using real time information. The application of “smart, connected products” brings in
several opportunities for the organization for building the infrastructure and new technology
which comprises of several layers of “technology stack”. This enables modified software and
hardware applications and implements the network communications with the embedded
operating system into the product itself (Mohelska and Sokolova 2016).

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The “smart, connected products” are further seen to allow for the relationships among the
customers which are integral to the new set of skills. The analysis done by the companies allows
to get new insights on creating value for the customers and better positioning in market by
effectively using the communication. The products and the service bundles will be able to deliver
a increased value to the individual segments and allocate the price for identifying a higher value.
This approach is conducive in situations when the products are able to efficiently and quickly
tailored as per the low cost per margin with the use of relevant software. John Deere the maker
of multiple engines applied various horse power rating by using same software (Fahimnia, Sarkis
and Davarzani 2015).
“Smart, connected products” increased the variety of the capability and the variety in the
product. In several instances the firms are tempted to add new characteristics for operating with
lesser marginal cost and accumulation of more sensors in the new software which has significant
amount of fixed cost related to the infrastructural expansion and product cloud. In circumstance
of any issues, Tesla “autonomously call for the corrective software downloads” and provides
option for valet pick service to the Tesla (Staff 2014).
The cloud structures suggest the competitive benefit by the controlling and optimising the
design of all portions in the system. In addition to this, “Babolat’s play pure” technical aspects
permit the sensors and connectivity network in the racket handle in the users to analyse “ball
impact, locations, ball spin and ball speed” (Porter and Heppelmann 2014).
Conclusion
The important discussions have interpreted the main conapts which are considered with
the “physical components, smart components and connectivity components”. In “addition to
this”, the first phase is taken into consideration with the product applications. In the second
phase the analytics engine is seen to be evident. The third phase comprises of the application and
the fourth stage includes the “assimilation of the product data database”.

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References
Fahimnia, B., Sarkis, J. and Davarzani, H. (2015) ‘Green supply chain management: A review
and bibliometric analysis’, International Journal of Production Economics, pp. 101–114. doi:
10.1016/j.ijpe.2015.01.003.
Harwood, J., Dooley, J. J., Scott, A. J. and Joiner, R. (2014) ‘Constantly connected - The effects
of smart-devices on mental health’, Computers in Human Behavior, 34, pp. 267–272. doi:
10.1016/j.chb.2014.02.006.
Kim, S., Hong, J. Y., Kim, S., Kim, S. H., Kim, J. H. and Chun, J. (2014) ‘Restful Design and
Implementation of Smart Appliances for Smart Home’, in Proceedings - 2014 IEEE International
Conference on Ubiquitous Intelligence and Computing, 2014 IEEE International Conference on
Autonomic and Trusted Computing, 2014 IEEE International Conference on Scalable
Computing and Communications and Associated Sy, pp. 717–722. doi: 10.1109/UIC-ATC-
ScalCom.2014.64.
Lefeuvre, K., Berger, A., Kurze, A., Totzauer, S., Storz, M. and Bischof, A. (2016) ‘Smart
Connected Sensations: Co-Creating Smart Connected Applications Through Distributed
Serendipity’, Proceedings of the 9th Nordic Conference on Human-Computer Interaction, 23–
27–Octo, p. 88:1--88:4. doi: 10.1145/2971485.2996723.
Mohelska, H. and Sokolova, M. (2016) ‘Smart, connected products change a company’s business
strategy orientation’, Applied Economics, 48(47), pp. 4502–4509. doi:
10.1080/00036846.2016.1158924.
Porter, M. E. and Heppelmann, J. E. (2014) ‘How Smart, Connected Product Are Transforming
Competition’, Harvard Business Review, (November), pp. 64–89. doi:
10.1017/CBO9781107415324.004.
Staff, H. B. R. (2014) ‘Strategic Choices in Building the Smart, Connected Mine’,
Https://Hbr.Org/2014/11/Strategic-Choices-in-Building-the-Smart-Connected-Mine, (November
2014), pp. 1–33. Available at: https://hbr.org/2014/11/strategic-choices-in-building-the-smart-
connected-mine.

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