Feasibility Analysis Of A Cloud-Based Software Application Using Visual DSS And Monte-Carlo Simulation

NPV Model

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.

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

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.

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

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.

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).

Model Output

It could be interpreted that the software could be launched easily in the market for the NPV $1 million with less than 10% risk.

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

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.

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).

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).

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.

Monte Carlo Simulation Model

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.   

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.

 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.  

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).

Analysis of Car Sales Data

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 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).

Analysis of Research Fellowship Funding

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).

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.

References

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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.

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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.

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