Analysis Of Cloud-Pty Limited Software Launch Decision And The Role Of Smart, Connected Products In Business Analytics

Feasibility Analysis of Cloud-Pty Limited’s New Software Launch Decision

The company, which has been chosen in this case, is “Cloud-Pty Limited”. It is actually a cloud-based software development company that is based in Brisbane, Australia. The organisation is deciding to start newer and better responsive cloud-based software applications in the required market. Not very long ago, the dynamic and competitive advantage has formulated some very wrong decisions of investment. At present the senior management requires a crucial analysis of all sorts of innovative products in the market. The aim in this case is to give valuable insights to the senior management regarding the feasibility of the new product. Visual DSS software and Monte-Carlo Simulation procedures are utilized for proper understanding regarding the software launches.

 

Based on the decision support models which have been developed utilizing the visual DSS, the value of NPV has been obtained is $5440551. It can therefore be said that the summarized NPV is higher than $2 million. Thus it can be said that in case the manager launches the software in the market at present, it would prove to be a correct decision. The software launch decision would have been correct.

 

In case of risk analysis, it is required to analyse the effect of variation in the share of the market, the cost of producing, overheads and also the primary investment regarding the NPV. In this connection it can be said that

 

• It is most probable that the market share will range between 5% to 15% and the most probable value would be 10%
• The unit cost follows a normal distribution. Its mean is $30 and its standard deviation is $12
• The interval between which the overhead cost can vary can be in the interval between of $15000 and $35000. The most suitable value in this case can be $215000 each year.
• The initial investment requirement follows a uniform distribution with the range $1000000 to $2000000

 

The decision, which is to be taken in this case, is regarding the launch of the software when it needs to be 20% or more probability is in the case that the net value at present would be $1000000. It needs to be understood whether the launch decision is correct or not.

The calculated cumulative NPV in case of a 20% chance is $3030930 which is seen to be greater than the value $1000000. In cases it is considered that the chance for the cumulative NPV is 10%, the value of the cumulative NPV is found to be $2421586. Therefore, it can be said that not only a 20% but in fact a 10% value of the chance of risk also shows the net present value which is considered to be greater than $1 million.

It needs to be interpreted if the software can be launched easily in the market for the NPV $1 million with risk which is less than 10%.

As the CEO of the organisation got the analysed outcomes he was concerned regarding the assumptions which had been mentioned in the NPV model. The CEO basically focuses on certain uncertainties of the particular model. The CEO needs to focus on certain problems in the model.

• The distribution of the selling price is between $65 and $45.
• The unit cost follows a normal distribution, which as a mean of $25 and standard deviation is $5.
• CEO requires to move forward for the launch of the software in case there was an 80% chance that the NPV would be greater than the value of $2500000.
• Based on the analysis of the Visual DSS model, the cumulative NPV with more than 80% probability is not less than $6,138,115. It can be seen that the value is much lesser than $2500000. Thus it can be said that the software possesses the credibility to be worthy of a market launch.

Incorporating Uncertainties and Risk Analysis

Based on the market value as well as the assumptions and uncertainties, the product is capable of clearing the criteria and the check points which are necessary for interpretation. The understanding and analysis of the questions has led to the interpretation that the CEO needs to accept the proposed production of a specific product. The main reason for this is because the product meets all the criteria which impacts a particular decision.

Demonstration 1

 

Figure 1: Complete Car sales

The analysis of the car sales data is represented in the above figure. The analysis helps us find whether the information is there about car models’ sales. The model clearly shows that the image shows that highest labour cost is for the United Kingdom after which comes the United States of America. It is clearly seen that the highest sum of the price of sales is for United Kingdom. The highest sum of the sales price for the United Kingdom is considered to be 15725000.

 

Figure 2: Car Sales DB9

The image above is derived from the filtering of the data in the Power BI. The data needs to be shown for the model DB9. The value of the total spare parts the company sells is 44K. The sum of the total sales price for the United States is 3609410.

The usage of the Power BI has led to the information that has been analysed on the car sales their models as well as spare parts. The data contains detailed information on the sales parts irrespective of counties and years. After section of a model, the information connected to the DB9 was derived. Using of the Power BI the information was segregated. There is also the possibility of assumption validation.

Demonstration 2

Figure 3: Information on Funding Recipients

The figure presents information on funding in Queensland. The top right hand corner gives information on the funding got by the department.

 

Figure 4: Research Funding’s on Research Fellowships

The research fellowship funding is available for purpose of agriculture, forest and fisheries along with art and recreational sectors. A total amount of 9300000 has been committed for fellowships. The second figure analyses the information of Research funding in Queensland. The lower left hand corner shows all the departments, though the department has a funding for research. The filtering of the data in this case was not operationally accurate.

Role of Smart, Connected Products in Business Analytics

How Smart, Connected Products Are Transforming Competition

Introduction

As discussed by Mohelska and Sokolova (2016), in the recent times the role of information technology has been depicted with revolutionizing the products. “Smart, connected products” are seen to offer several opportunities in various categories of products. Some of the main understanding needs to be considered with the “higher product utilization, greater reliability, new functionality and capabilities” to transcend across the traditional product boundaries. In general, these technologies are comprised with of three core elements- “physical components, smart components and connectivity components”. The physical components are depicted with mechanical and electrical parts. The smart components are further referred with the components such as “control software, engine control unit, sensors and microprocessors”. The connectivity aspect is seen to include the protocols with the wired and wireless connections including ports and antennae. The changing aspect of the product is depicted with the disrupting value chains which compels the organizations to retool and innovate in their internal strategies. In addition to this, smart, connected products allows for the application for the implementation of the new set of strategies to capture a significant aspect of sensitive data. The different types of the other benefits of the smart connected products has been depicted with the redefining the relations with the traditional business partners and defining the role of the companies in expanding the industry boundaries. The significant discourse of the study has aimed at illustrating the way smart, connected products contribute to the business analytics and transform the companies for BI (Business Intelligence) (Porter and Heppelmann 2014).

The implementation of the business analytics may be segregated in four phases of the product cloud. The very first phase is related to the application of the smart products in terms of the software applications for running on the remote servers which is managed with the “monitoring, controlling and optimizing the product functions”. The second next important rule is considered with defining the rules between the business logic and capabilities of the big data analytics. This is seen with populating the algorithms in terms of the product thereby revealing the insights on the new products (Fahimnia, Sarkis and Davarzani 2015). The third important phase is considered with the application development and execution environment which allows for the rapid creating applications for smart, connected business applications with the use of run-time tools data access and visualization. The fourth phase is considered with the “big data” database which has enabled normalization of the real time data and historic product.  It needs to be understood that the combination of the four elements are seen to be based on the connection between the cloud and product. The various aspects of the product hardware are embedded in form of “sensors, processors, connectivity ports which supplements the electrical and traditional mechanical components”.  Moreover, the smart connected products assist in transforming the competition by the implementation the tools responsible for “managing user authentication, system access and secure the product connectivity”. In addition to this, application of smart, connected products is regarded as the information taken from different types of the external sources such as “weather, commodity, traffic, energy prices, social media and geo-mapping”. In addition to this, smart connected products integrate the data with the core enterprise systems such as “PLM, CRM and ERP” (Staff 2014).

Data Validation using Microsoft Power BI

The application of the concept of “smart, connected products” has an important role in the manufacturing sectors. In various types of the other cases the heavy machinery made by “Schindler’s”, the “PORT” technology reduces the elevator waiting times by more than 50%. This is seen to be done by the prediction of the demand patterns in the elevator and calculating the fastest time to the destination and assigning of appropriate elevators to move the passengers more swiftly. In terms of the energy sectors ABB’s smart technologies has confirmed huge amount of real time data in terms of the distributing, generating and transforming of changes in temperatures for secondary substations. In addition to this, the application of the “smart, connected products” facilitates the opportunities to the companies for building new technology infrastructure which will comprise of several layers of implementation known as “technology stack”. These factors have further allowed the organisations to allow the companies to modify the “software applications”, “implement network communications” and have provisions for product (Lefeuvre et al. 2016).

The “smart, connected products” has enabled companies in forming the new relationships with the customers in requiring the marketing practices and new set of skills. The companies accumulating the analysis of the products are able to gain new insights in allowing “better positioning, create value for customers and enable better positioning of the offerings by making use of effective communication” (Chin, Tat and Sulaiman 2015).

It has been further discerned that “smart connected products” has substantially increased the range of the potential capability of the products and features. In different cases, the low marginal cost for adding more sensors and new software application which has large fixed cost associated to the infrastructural development and product cloud. Organizations such as Tesla needing repairs are able to autonomously call for the corrective software downloads and notify the customers with an invitation for valet pick up service and delivering the vehicle to the tesla facility (Kim et al. 2014).

The cloud system is often seen to create a competitive advantage by enabling the companies to optimize and control the design of all the part of the systems which is seen to be relative to one another. The company allows for maintaining the control over technology and provide direction for the development of the product cloud. The “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” (Harwood et al. 2014).

Conclusion

The main learnings of the study have evaluated three core elements of the system which is considered with “physical components, smart components and connectivity components”. The concept of the business analytics is divided into four phases. The first phase related to product cloud refers to product applications, the next phase is seen with the rules/analytics engine and the third phase is considered with application platform and final stage is considered with the product data database.

References

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.

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.