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 = 0.10′.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
The Net present value (NPV) that has been estimated based on the result of model is $5440551.00. From the NPV, it can be said that the claim regarding the NPV being above $2 million is correct. It is correct from the fact that the NPV has been calculated for the first period only that is for 2018 and NPV(0) has been used in Visual DSS to determine the NPV. The NPV that has been calculated is above $2 million as evident from the results.
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
It has been determined that the senior management should accept the proposed production of product as the decision criteria of the company is not violated. The NPV that has been calculated at 20% or greater is more than $1,000,000 (1 million).
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
From the produced cumulative probabilities report and graph, it can be said that the CEO will accept the proposed production of the product. It is due to the fact that the NPV being calculated at 80% and less is greater than the decided value of $1,850,000. The NPV at less than 90% probability is calculated to be $6619214.
USA has the most SalePrice (sum) of the DB9 according to the designed dashboard by selecting DB9 in slicer.
Power BI in validation of business assumptions
Power BI is used for validating business assumptions as it provides data visualization which helps to easily understand status of the business. Power BI is a powerful analytics tool that helps to analyse data in an effective manner. In this particular demonstration, Power BI is used to easily determine that USA has the most SalePrice (sum) of DB9. Hence, Power BI can be considered as an essential tool that is used for validating business assumptions.
The list is presented below to illustrate on the sectors that receive Research Fellowship funding:
Potential issues with data validation based on fields
Data validation based on fields may raise potential issues such as if there are blank in the data of certain fields then the validation may give error or wrong results. Another issue that persists with validation based on fields is that the output data type may not be the same as that of source and the user have to changed it manually otherwise it will lead to erroneous data.
This report depicts the role of different smart and connected products in the business intelligence and also their usability effectiveness to drive any business towards massive commercial success. Due to frequent evolution of technology as well as products in intelligence and connected devices in business applications the entire business field is getting improved every day. The relationship between the Business Intelligence (BI) and all connected products are also illustrated in this report. With the features utilized by Business Intelligence (BI) the business enterprises can keep on improving their operational and functional strategies.
The new business capabilities and huge amount of information those are generally offered by the smart connected products helps to redefine the core functional activities of the company. Both the cloud based operating system and software has become integral part of the new products. Porter and Heppelmann (2014), has stated that different new product developing principles are emerging based upon the manufacturing component and other frequently changing processes. Not only this but also it has been found that, in order to secure the business functions IT security is considered as an important part that has to be maintained. In order to gain competitive advantages, the functions must have the ability to unlock the full value data. Besides data unlock proper management, governance and data security analysis are the simultaneous functions considering.
If it is found that the all the individual sensor reading are valuable then, over the time through identification of readings for different products the enterprises can uncover various insights. The data gathered from individual sensors like temperature from the car engine, throttle position, consumption of fuel can reveal the way through which the performances are interrelated to the engineering specification of the cars (Joachimsthaler et al. 2015). The reasons for which the problems are occurring, rather the linking combination of the readings are helpful whenever the root causes are determined as difficult to reduce. The data those are generated from the sensor which can measure the rate of vibration and heat can also forecast the imminent failure days as well as weeks. The application field of big data analytics can combine mathematics, computer science and business analysis techniques as well.
In order to understand the highlighted patterns, the big data analytics has eventually employed new additional techniques. Considering all these aspects it is found that, data from the smart and connected products, internal and external unstructured data are huge challenge to the enterprises. According to Porter and Heppelmann (2014), these factors can be arranged in an array which is comprises of sensor reading, location, sales history, warranty details, temperature etc. Wide range of data formats management and with the traditional data aggregation approach in terms of database and spreadsheet tables are not at all beneficial. One of the emerging solutions is “Data Lake” that can store data stream in the native format. The previous data and the new data can be studied with the help of the analytics tools that has four different categories such as descriptive, diagnostic, predictive as well as prescriptive.
The industry boundaries can be broadened and the existing products can be transformed with the help of the smart and connected products. The products those are separate as well as distinct can become part of the optimized systems for relating the products and components of the system. The companies those have been industry leaders from past few decades helps to shift the company boundaries and also play active role (Porter and Heppelmann 2014). The emergence of the products and systems highlights two different types of strategies and choice regarding the scope of the company. The first choice is about whether the company should spread their products or not and the second one is in order to build up connection between the products and information whether the company should provide a platform or not. It is expected that with the help of one of these components all the functional and operational parts can be eventually controlled.
In order to gain big data opportunities the enterprises may tempted to enter into the relevant products. However, sudden entry to new products includes high level risks and many other operational abilities as well. Thus, before entering to such product the company ought to identify a lucid position (Joachimsthaler et al. 2015). Expansion of product scope is beneficial and attractive. In order to optimize the systems it gives opportunities to improve performance along with co-designing capabilities. The company should stick to its knitting and deliver open connectivity if their optimization is not dependent on individual product design approach. These opportunities will provide advanced IT and technology driven environment to the company whenever IT played itself out. The companies whose products are central among the overall products will hold the best of the position for entering to the related products. The manufacturers who produce lesser number of critical machines are less capable to attract the consumers which are helpful to take the system in a broader environment.
Conclusion
From the overall discussion it can be concluded that, big data analytics and Business Intelligence (BI) play role to gain business success. Both in terms of commercial success and competitive advantages big data analytics and business intelligence are helpful. It helps to build successful and secured relationship between the product and the all connected devices. The business organizations can utilize the feature of these technologies to obtain the revolutionary changes in the field of technology and its operation. With the help of advanced technologies the business can drive its operation and other functions towards massive success. Apart from this, it is also found that with the help of technologies the companies can rapidly transform their application strategies. In order to implement such business strategies smart and connected products are also beneficial because it provides the characteristics offers by the business intelligence. Besides these, the other benefits that the BI offers include faster reporting, analyzing and planning ability. Besides improve data quality it also offers improved consumer’s satisfaction, better business decision making capability, that are also elaborated in this report.
Fan, S., Lau, R.Y. and Zhao, J.L., 2015. Demystifying big data analytics for business intelligence through the lens of marketing mix. Big Data Research, 2(1), pp.28-32.
Joachimsthaler, E., Chaudhuri, A., Kalthoff, M., Burgess-Webb, A. and Bharadwaj, A., 2015. How smart, connected products are transforming competition. Harvard business review, 93(1), p.4.
Larson, D. and Chang, V., 2016. A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 36(5), pp.700-710.
Laursen, G.H. and Thorlund, J., 2016. Business analytics for managers: Taking business intelligence beyond reporting. John Wiley & Sons.
Mitri, M. and Palocsay, S., 2015. Toward a model undergraduate curriculum for the emerging business intelligence and analytics discipline. Communications of the Association for Information Systems, 37(1), p.31.
Porter, M.E. and Heppelmann, J.E., 2014. How smart, connected products are transforming competition. Harvard Business Review, 92(11), pp.64-88.
Sallam, R.L., Tapadinhas, J., Parenteau, J., Yuen, D. and Hostmann, B., 2014. Magic quadrant for business intelligence and analytics platforms. Gartner RAS core research notes. Gartner, Stamford, CT.
Sharda, R., Delen, D., Turban, E., Aronson, J. and Liang, T.P., 2014. Businesss Intelligence and Analytics: Systems for Decision Support-(Required). London: Prentice Hall.
Essay Writing Service Features
Our Experience
No matter how complex your assignment is, we can find the right professional for your specific task. Contact Essay is an essay writing company that hires only the smartest minds to help you with your projects. Our expertise allows us to provide students with high-quality academic writing, editing & proofreading services.
Free Features
Free revision policy
$10Free bibliography & reference
$8Free title page
$8Free formatting
$8How Our Essay Writing Service Works
First, you will need to complete an order form. It's not difficult but, in case there is anything you find not to be clear, you may always call us so that we can guide you through it. On the order form, you will need to include some basic information concerning your order: subject, topic, number of pages, etc. We also encourage our clients to upload any relevant information or sources that will help.
Complete the order form
Once we have all the information and instructions that we need, we select the most suitable writer for your assignment. While everything seems to be clear, the writer, who has complete knowledge of the subject, may need clarification from you. It is at that point that you would receive a call or email from us.
Writer’s assignment
As soon as the writer has finished, it will be delivered both to the website and to your email address so that you will not miss it. If your deadline is close at hand, we will place a call to you to make sure that you receive the paper on time.
Completing the order and download