Business Intelligence: Smart, Connected Products Transforming Competition

This article discusses the impact of smart, connected products on competition and how they are transforming the way companies use business intelligence. It covers the four phases of the product cloud, the three core elements of smart, connected products, and their role in manufacturing firms.

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

1BUSINESS INTELLIGENCE
Table of Contents
Part A...............................................................................................................................................2
Answer 1......................................................................................................................................2
Answer 2......................................................................................................................................3
Answer 3......................................................................................................................................5
Part B...............................................................................................................................................6
Demonstration 1...........................................................................................................................6
Demonstration 2...........................................................................................................................8
Part C...............................................................................................................................................9
How Smart, Connected Products Are Transforming Competition..................................................9
Introduction................................................................................................................................10
Discussion..................................................................................................................................10
Conclusion.................................................................................................................................12
References......................................................................................................................................13

2BUSINESS INTELLIGENCE
Part A
Answer 1
The coding for calculating the NPV is:
*Columns
*Years 2018,2021
*Rows
Initial investment needed(0) = 1750000.00 '.2
Market at time of introduction (0)= 420000
Market Growth = 0.15'.2
Market Share = 0.10'.2
Total market = Market at time of introduction;Total market(-1)*1.15
Sales Volume = Total Market*Market Share
Estimated selling price = 55.00 '.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

3BUSINESS INTELLIGENCE
From the analysis it is found that the NPV is higher than $2million.
Answer 2
The coding to calculate the NPV is as follows:
*Columns
*Years 2018,2021
*Rows
Initial investment needed(0) = UNI(100000.00,200000.00) '.2
Market at time of introduction (0)= 420000
Market Growth = 0.15'.2
Market Share = TRI(0.05,0.10,0.15)'.2
Total market = Market at time of introduction;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

4BUSINESS INTELLIGENCE
NPV(0) = *NPV cash flow;rate

5BUSINESS INTELLIGENCE
From analysis it is found that the risk for NPV being greater than $1 million is 20%.
Answer 3
The code for the NPV calculations is as follows:
*Columns
*Years 2018,2021
*Rows
Initial investment needed(0) = 1750000.00 '.2
Market at time of introduction (0)= 420000
Market Growth = 0.15'.2
Market Share = TRI(0.05,0.10,0.15)'.2
Total market = Market at time of introduction;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
NPV(0) = *NPV cash flow;rate

6BUSINESS INTELLIGENCE
The analysis shows that the NPV value is less than $2,500,000. Hence, it can be interpreted that
the software’s credibility enables it be launched.

7BUSINESS INTELLIGENCE
Part B
Demonstration 1
Figure 1: Complete Car sales
The analysis into the car sales data is presented in the above figure. From the analysis we
find that the information is about sales of different car models. in the top right hand corner, the
labour cost is analysed based on different countries. A cursory look at the image shows that the
highest labour cost is for United Kingdom followed by United states. In addition, the above
image also provides information that a total of 495K spare parts were sold by the organization.
The bottom left hand corner provides information on the delivery charge of different car models.
from the image it can be found that the delivery charge has a wide variation across models. The
top right hand corner provides information on the sales price across countries. From the
information it can be found that the maximum sum of the sales price is for United Kingdom. The
highest sum of the sales price for United Kingdom is 15725000.

8BUSINESS INTELLIGENCE
Figure 2: Car Sales DB9
The above image is derived from filtering of the data in Power BI. The data is presented
for model DB9. The total spare parts sold by the organization is 44K. In the top left hand corner,
it is found that the maximum sum of salesprice is for United States. The sum of salesprice for
United States is 3609410.
With the use of Power BI we have analysed information on the sales of cars, based on
models and their spare parts. The data contained detailed information on the sales of spare parts
across countries, models and year. In the above analysis strong use of filtering tool was used.
The filtering tool helped us to select a particular model. When a particular model was selected all
adjoining information related to the particular model (DB9) was derived. A business thrives on
information. In the present case the organization may need information on spare sales across
different countries, models, timeline. With the aid of Power BI the information was segregated.
Thus, the organization would be able to get detailed information on different spare parts based on
models. In other words, the organization would be able to validate any assumptions it makes for
the sales of its products.

9BUSINESS INTELLIGENCE
Demonstration 2
Figure 3: Information on Funding Recipients
The above figure presents information on the research funding’s at Queensland. The top
right hand corner image provides information on the count of the funding’s received by
department. Information on the quantity of funding’s received is also given.

10BUSINESS INTELLIGENCE
Figure 4: Research Funding’s on Research Fellowships
Funding’s for research fellowship is available for agriculture, forest and fisheries and arts
and recreational sectors. The government of Queensland has committed a total of 9300000
towards research fellowships.
The analysis of the second figure on Research funding’s provides information about the
government of Queensland’s commitment. However, the information provided by the images is
vague. In the top right hand corner, all the departments which receive funding’s are showed. In
addition, the lower left hand corner shows all the departments are shown, although the
department which received funding’s for research fellowships are in dark colour. In fact, the
information cannot be validated easily. The only information which is given is the total amount
of funding’s. In the above analysis the filtering of the data it seems did not work.
Part C
How Smart, Connected Products Are Transforming Competition
Introduction
As discussed by Harwood et al. (2014), the progress in information technology has been
seen with the transforming of products. “Smart, connected products” in general are depicted to
offer different types of the services which are seen with higher “product utilization, greater
reliability, new functionality and capabilities to transcend across the traditional product
boundaries”. In addition to this the smart connected products make use of the three core
elements, which are seen with “physical components, smart components and connectivity
components”. Electrical and mechanical parts constitute the physical components. The smart
components are considered with components such as microprocessors, control software, engine
control unit and sensors. The different aspects of the connectivity are included with the protocols
with the wireless or the wired connections with ports and antennae. The changing aspect of the
product is considered with the disrupting value chains, which in many cases forces the

11BUSINESS INTELLIGENCE
companies to retool and innovate their internal strategies. “Smart, connected products” make use
of the strategies to create and capture a massive amount of data. The various types of the other
benefits have been depicted with redefining relationships with the traditional business and
defining the responsibility of the companies in expanding across internal boundaries. The main
discussion of the study has depicted the way, “smart, connected products” are able to contribute
to the transform the companies the way in which they use business intelligence (Verhoef et al.
2017).
Discussion
The adoption of business analytics can be divided in four distinct phases of the product
cloud. The first phase of the product cloud refers to the smart product application for the
software applications based on remote servers which “manages the monitoring, controlling and
optimizing the product functions”. The second phase for the analytics engines identified the rules
between the business logic and big data analytical capabilities which is seen to inhabit the
algorithms involved in the operations of the product operations depicting about various aspects
of new products insights. The third phase of application development and execution environment
allows for rapidly creating applications for “smart, connected business applications with the use
of run-time tools data access and visualization”. The fourth stage considers the big data elements
which allows for normalization of the historic product data and real time information. The four
main elements are considered with the network communication protocol which enables the
communication among the product and clouds. The product hardware considers “embedding
sensors, processors, connectivity ports which supplements the electrical and traditional
mechanical components”. The “smart connected products”, assists in transforming the
competition by implementation of the tools responsible for maintaining “user authentication,
system access and secure the product connectivity”. The technology also acts as a gateway for
collecting information from the different types of the external sources such as” weather,
commodity, traffic, energy prices, social media and geo-mapping” which depicts the capabilities
of a product. “Smart connected products” also integrate the data with the critical business
systems such as “PLM, CRM and ERP” (Schmidt et al. 2015).
The discussions made by smart, connected people has stated about the important role in
the manufacturing firms. In several situations the heavy machinery manufactured by Schindler’s,

12BUSINESS INTELLIGENCE
the application of PORT technology reduces the waiting time in the elevators by 50%. The
predictions are made with the elevator demand patterns and evaluating the fastest to the
destination and assign the relevant elevator to fasten passenger movement. ABB’s smart
technologies ensures distributing and transforming changes in temperatures for secondary
substations as per the use of real time data. The implementation of “smart, connected products”
introduces several opportunities for the organizations to build new technology and infrastructure
which will comprise of several layers often known as “technology stack”. This consideration
allows for modified hardware, “software applications”, “implement network communications
and include an embedded operating system in the product itself”(Vorderer, Krömer and
Schneider 2016).
The “smart, connected products” enables the companies in forming new relationships
with the customers which needs essential marketing practice and new skill sets. The companies
analysing the product usage are able to get new insights about the procedure for better
positioning and creating value for the customers which allows for better positioning of the
offerings by making effective use of communication. The various types of the other forms of
the product and service bundles can deliver higher value to the individual segments and assign
price to those bundles for capturing a higher value. The approach is conducive in situations when
the products are able to quickly and efficiently tailored at a lower marginal cost with the use of
an appropriate software. John Deere manufacturer of multiple engines was seen to apply
different levels of horsepower rating based on same engine using the software (Kim et al. 2014).
“Smart, connected products” increases the range of the potential product features and
capability. In different occasions the companies are tempted to add several new features for
operating with low marginal cost and adding more sensors to the new software applications
which has large fixed cost associated to infrastructural development and product cloud. In case
of repairs company like Tesla “autonomously call for the corrective software downloads” and
provide the option for valet pick service to the Tesla facility (Leroy, Chen and Rindflesch 2014).
Cloud systems are seen to offer a competitive advantage by allowing the scope for
optimising and control the design of all the partss in the system. The Babolat’s paly pure
technology allows for the sensors and connectivity network in the racket handle which allows the

13BUSINESS INTELLIGENCE
users to track, analyse the ball impact, locations, “ball spin and ball speed” (Lefeuvre et al.
2016).
Conclusion
The discussions have analysed the three main elements of the system which has been
considered with “physical components, smart components and connectivity components”. In
addition to this, the first product cloud phase is considered with smart product applications,
similarly the second phase is observed with rules/analytics engine. The third phase is depicted
with application platform and the fourth stage with the assimilation of the product data database.

14BUSINESS INTELLIGENCE
References
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.
Leroy, G., Chen, H. and Rindflesch, T. C. (2014) ‘Smart and Connected Health [Guest editors’
introduction]’, IEEE Intelligent Systems, 29(3), pp. 2–5. doi: 10.1109/MIS.2014.52.
Schmidt, R., M�hring, M., H�rting, R. C., Reichstein, C., Neumaier, P. and Jozinović, P.
(2015) ‘Industry 4.0 - Potentials for creating smart products: Empirical research results’, in
Lecture Notes in Business Information Processing, pp. 16–27. doi: 10.1007/978-3-319-19027-
3_2.
Verhoef, P. C., Stephen, A. T., Kannan, P. K., Luo, X., Abhishek, V., Andrews, M., Bart, Y.,
Datta, H., Fong, N., Hoffman, D. L., Hu, M. M., Novak, T., Rand, W. and Zhang, Y. (2017)
‘Consumer Connectivity in a Complex, Technology-enabled, and Mobile-oriented World with
Smart Products’, Journal of Interactive Marketing, 40, pp. 1–8. doi:
10.1016/j.intmar.2017.06.001.

15BUSINESS INTELLIGENCE
Vorderer, P., Krömer, N. and Schneider, F. M. (2016) ‘Permanently online - Permanently
connected: Explorations into university students’ use of social media and mobile smart devices’,
Computers in Human Behavior, 63, pp. 694–703. doi: 10.1016/j.chb.2016.05.085.