Smart Connected Products and Sustainability

The provided document appears to be a compilation of research papers and articles related to smart connected products and sustainability. The assignment likely requires students to analyze and discuss the impact of these products on business strategies, consumer behavior, and environmental sustainability. It may also involve exploring the benefits and challenges associated with green supply chain management and product-service systems. The summary of this assignment would require students to critically evaluate the research papers and articles provided, identifying key themes and takeaways related to smart connected products and sustainability.

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......................................................................................................................................5
Overall interpretation:..................................................................................................................7
Part B...............................................................................................................................................8
Demonstration 1...........................................................................................................................8
Demonstration 2...........................................................................................................................9
Part C.............................................................................................................................................11
How Smart, Connected Products Are Transforming Competition................................................11
Introduction................................................................................................................................11
Discussion..................................................................................................................................12
Conclusion.................................................................................................................................13
References......................................................................................................................................15

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Part A
Our company “Cloud-Pty Limited” is a cloud-based software development organization in
Brisbane, Australia. Our company is planning to launch new responsive cloud-based software
application in the market. Recently, dynamic and competitive approach has developed some bad
software decisions of investment. These days, senior manager need a prominent analysis of every
new product launched in the market. My task is to provide advice to the senior management on
the feasibility of the new product.
Visual DSS software and Monte-Carlo simulation technique is utilized for proper decision
making about launching the software.
Answer 1
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 = 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 = 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

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According to the decision support model developed using visual DSS, The NPV value is
calculated as $5440551. The summarized NPV is greater than $2 million. Therefore, my
manager would make a wise decision if he/she launches the software in the market now. The
decision of launching the software is correct.
Answer 2
Monte Carlo Simulation Model
*Columns
*Years 2018,2021
*Rows
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
NPV(0) = *NPV cash flow;rate

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Model Output
In case of risk-analysis, I am asked to analyze the impact of variation in the market share, cost of
producing, overheads and initial investment on the NPV.
 Market share is most likely to be 10% ranging between 5% to 15%.
 Unit cost follows normal distribution with mean $30 and standard deviation $12.
 Overhead cost could be in the interval of $15000 and $35000. However, it is most likely
to be $215000 per year.
 Initial investment requirement is uniformly distributed from $1000000 to $2000000.
Cumulative probabilities report

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The decision is to be taken whether it would be a correct decision to launch the software when
20% or greater chance is that the present net value would be less than $1000000.
The calculated cumulative NPV for 20% chance is $3090358 that is greater than $1000000. Even
if we consider the chance for 10%, the cumulative NPV is found to be $2326111. Hence, it could
be interpreted that not only 20% but also 10% chance of risk displays the net present value
greater than $1 million (Wang et al. 2009).
It could be interpreted that the software could be launched easily in the market for the NPV $1
million with less than 10% risk.
Answer 3
Monte Carlo simulation Model
*Columns
*Years 2018,2021
*Rows

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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
NPV(0) = *NPV cash flow;rate
Model Output
(Rubinstein and Kroese 2016)
As CEO of my company received the analyzed outcomes, he became very concerned about the
assumptions hypothecated in the NPV model. However, CEO focuses on some uncertainties of
the model that are-
 Selling price is distributed between $65 and $45.
 Unit cost is normally distributed with average $25 and standard deviation $5.

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CEO asks to go ahead for launching the software if there was at least an 80% probability of the
NPV to be greater than $2,500000.
According to the analysed Visual DSS model, the cumulative NPV with more than 80%
probability is at least $6251925. It is far larger than $2500000. Hence, the software has
credibility to be launched in the market (Bhushan and Rai 2007).
Overall interpretation:
As per all the market value assumptions and uncertainties, the product clears all the check-points
and criterions. From the analysis of all the three questions, it could be interpreted that CEO
should accept the proposed production of the product. The reason is that, the product fulfils all
the aspects of decision criteria (Chiasson and Lovato 2001).

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Part B
Demonstration 1
Figure 1: Complete Car sales
The above figure presents the car sales data. The analysis is based on different models.
From the analysis it is found that the highest car sales is from United Kingdom. The total car
sales is of 73139.53. the total car sales is the average of the last four years. The lower left hand
figure shows the delivery charge of based on models. Various models of cars are being
considered. From the analysis it can be said that there are variations in delivery charges for
different models. The total volume of sales of spare parts is 495K. The top right hand figure
presents the variations in labour costs by country. There are differences in labour costs across
countries. From the figure it can be seen that the highest labour cost is for United Kingdom.

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On selection of model DB9 it is found that the total spare parts sold in the last 4 years if
44K. For the model DB9 the maximum salesprice (sum) has been from United States. The sum
salesprice is 3609410. The total sum salesprice is 5423960.
In this analysis we analysed the car sales data. The data contained information on the
sales of different models of cars, there spare parts. The distribution of the data is across different
countries, and years. In the first model of analysis that was built the analysis considered the
complete data. The analysis considered the sum of the sales price for the years of the available
data. In the second model, the data for DB9 model was only selected. Power BI was able to
reduce the adjoining charts to model DB9 only. Thus in the second model the labour charge,
number of spare parts sold for model DB9 only is easily visualised. In addition, the total sales
price for model DB9 is also derived. Hence, Power BI was easily able to filter data according to
the requirement. In a business environment it is essential to have information as segregated as
possible. This can provide information on the performance of different models. Thus if the
organization would want to launch a new product then with the information available they can
get prior information about the performance of the product.
Demonstration 2

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The sectors which receive research fellowship funding are agriculture, forest and
fisheries, arts and recreational services, Electricity, gas and waste water services. The total
funding committed for research fellowships is 9300000.

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The difficulty in data validation in the research funding seems to be the filtering of data.
In the pie chart all the programs are shown, although only the research fellowship program
should have been highlighted. Further, the bar chart all the programs are shown, though the
funding by research fellowship is again highlighted. Thus the validation of the data based on
fields is not properly done.
Part C
How Smart, Connected Products Are Transforming Competition
Introduction
As stated by Brehm and Klein (2017), information technology has a significant role in
revolutionizing products. Smart, connected products offer several opportunities in various
categories of products. These are understood with higher product utilization, greater reliability,
new functionality and capabilities to transcend across the traditional product boundaries. Smart,
connected products comprise of three core elements- physical components, smart components
and connectivity components. Physical components include the electrical and mechanical parts.
The smart components are depicted with control software, engine control unit, sensors and
microprocessors. The connectivity aspect includes protocols with wired or wireless connections,
ports and antennae. The changing product nature are discerned with disrupting value chains
which compels the companies to retool and innovate in their internal strategies. Smart, connected
products allow for applying new set of strategies to create and capture phenomenal amount of
sensitive data. Some of the other benefits of smart connected products are considered with
redefining the relationships with the traditional business partners and defining the role companies
needs to have in expanding the industry boundaries. The important discourse of the study aims to
show how the smart, connected products contribute to the business analytics and transform the
companies to use business intelligence (Mani and Chouk 2017).

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Discussion
As discussed by (Chin, Tat and Sulaiman (2015), the implementation of the business
analytics can be segregated into four distinct phases of Product cloud. The first phase of product
cloud refers to smart product applications for software applications running on remote servers
which manages the monitoring, controlling and optimizing the product functions. The second
phase of rules/analytics engine defines the rules between the business logic and big data
analytical capabilities which are seen to populate the algorithms involve in the product
operations revealing about new products insights. The third phase of application development
and execution environment enables rapidly creating applications for smart, connected business
applications with the use of run-time tools data access and visualization. The fourth phase is
associated to big data database system which enables normalization of historic product data and
real time data. The four elements of the product cloud phase relate to network communication
protocol which enables the communication between the cloud and product. The product
hardware includes the embedded sensors, processors, connectivity ports which supplements the
electrical and traditional mechanical components. In addition to this, smart, connected products
helps in transforming the competition by implementing the tools responsible for managing the
user authentication, system access and secure the product connectivity. The application of smart,
connected products also acts as a gateway gathering information from various types of external
sources like weather, commodity, traffic, energy prices, social media and geo-mapping which
addresses product capabilities. Additionally, smart connected products integrate the data with the
core enterprise business systems like PLM, CRM and ERP (Fahimnia, Sarkis and Davarzani
2015).
As discussed by (Porter and Heppelmann (2015), the implementation of the smart,
connected products have a pivotal role across the manufacturing sectors. In several types of the
heavy machinery manufactured by Schindler’s, the PORT technology minimizes the elevator
waiting times by more than 50%. This is done by predicting the elevator demand patterns and
calculating the fastest time to the destination and assign the appropriate elevator to move
passengers quickly. In the energy sectors the ABB’s smart technologies ensures huge amount of
the real time data in terms of generating, distributing and transforming such changes in
temperatures for secondary substations. The application of the smart, connected products
provides the opportunities to the companies to build new technology infrastructure, which will

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comprise of series of layers often known as “technology stack”. This allows the companies to
include modified hardware, “software applications”, implement network communications and
include an embedded operating system in the product itself (Porter and Heppelmann 2014).
The smart, connected products allows the companies in forming new relationships with
the customers which require the marketing practices and new skill sets. The companies
accumulating and analysing product usage are able to gain new insights on how the products
allow better positioning, create value for customers and enable better positioning of the offerings
by making use of effective communication (Staff 2014). The use of data analytics allows the
forms to segment their markets in a more sophisticated way. Some of the other forms of the
product and service bundles deliver higher value to the individual segments and assign price to
those bundles for capturing greater value. This approach is ideal during situations when the
products may be quickly and efficiently tailored at a low marginal cost with appropriate
software. For instance, John Deere manufactured multiple engines with the application of
different levels of horsepower rating as per the same engine using the software alone (Porter and
Heppelmann 2015).
Smart, connected products substantially increases the range of the potential product
capabilities and features. In many situations are tempted to add several new features especially
with the low marginal cost for adding more sensors and new software applications which have
large fixed cost of infrastructural development and product cloud. Company such as Tesla when
in need of repairs are able to autonomously call for the corrective software downloads and when
necessary notify the customer with an invitation for a valet to pick up the car and deliver the
vehicle to the Tesla facility (Bugeja, Jacobsson and Davidsson 2017).
Cloud system are often seen to create a competitive advantage by allowing the companies
to optimize and control the design of all parts of the systems which are relative to one another.
The company is able to maintain the control over technology and data and provide direction of
development of the product and product cloud. Babolat’s play pure drive product system can put
the sensors and connectivity network in the racket handle, which allows the users to track,
analyse ball impact locations, ball spin and ball speed (Mohelska and Sokolova 2016).

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Conclusion
The discourse of the study has been able to analyse the three core elements of the system
which is seen with physical components, smart components and connectivity components. The
concept of the business analytics has been segregated into four distinct phases. The first product
cloud phase refers to smart product applications; the second phase is identified with the
rules/analytics engine. The third phase is referred as the application platform and fourth stage as
product data database.

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References
Bhushan, N. and Rai, K., 2007. Strategic decision making: applying the analytic hierarchy
process. Springer Science & Business Media.
Brehm, L. and Klein, B. (2017) ‘Applying the research on product-service systems to smart and
connected products’, in Lecture Notes in Business Information Processing, pp. 311–319. doi:
10.1007/978-3-319-52464-1_28.
Bugeja, J., Jacobsson, A. and Davidsson, P. (2017) ‘On privacy and security challenges in smart
connected homes’, in Proceedings - 2016 European Intelligence and Security Informatics
Conference, EISIC 2016, pp. 172–175. doi: 10.1109/EISIC.2016.044.
Chiasson, M.W. and Lovato, C.Y., 2001. Factors influencing the formation of a user's
perceptions and use of a DSS software innovation. ACM SIGMIS Database: the DATABASE for
Advances in Information Systems, 32(3), pp.16-35.
Chin, T. A., Tat, H. H. and Sulaiman, Z. (2015) ‘Green supply chain management,
environmental collaboration and sustainability performance’, in Procedia CIRP, pp. 695–699.
doi: 10.1016/j.procir.2014.07.035.
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.
Mani, Z. and Chouk, I. (2017) ‘Drivers of consumers’ resistance to smart products’, Journal of
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strategy orientation’, Applied Economics, 48(47), pp. 4502–4509. doi:
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Competition’, Harvard Business Review, (November), pp. 64–89. doi:
10.1017/CBO9781107415324.004.

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Porter, M. E. and Heppelmann, J. E. (2015a) ‘How Smart, Are Transforming Connected
Products Companies’, Harvard Business Review, 93(10), pp. 1–30. doi:
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