Cubeware – Business Intelligence Software
The designed DSS model for NPV calculation is shown below.
Figure 1: NPV Visual DSS model
(Source: Created by author)
Initially, five columns are taken into consideration as proceeding year (Year 0) 2017 and consecutive four years.
As mentioned in the case study, primarily aspects are mentioned as “Market at time of introduction as 420,000 units per year”, whereas; the market share is most likely to be 10%. The market growth is provided as 15% per year. Therefore, in row “volume produced in the market” for year 1, states 420000 and in each year it increases by 15%. Now, considering 10% market share, “volume produced” row is prepared that includes 10% of each year’s volume produced in market. Selling price and production cost per unit is mentioned as $55 and $31 respectively. Revenue gained is estimated by multiplying volume produced and selling price per unit and production cost is estimated by multiplying volume produced and production cost per unit. Annual overhead cost and initial investment is considered as $210,000 and $1,850,000 respectively. Now, the profit gained is estimated with taking “Revenue gained – Production cost – Annual overhead cost”. The present value is estimated taking formula including Profit gained and discount rate. Now, NPV (0) is used for estimating the difference between present value and initial investment.
Based on the developed DSS model, the Net Present Value (NPV) is $1,259,404. Therefore, the claim that NPV being above $2 million is incorrect. Based on the designed model, the report is shown as following:
Figure 2: NPV Estimation report
(Source: Created by author)
Figure 3: Risk Analysis
(Source: Created by author)
As per the production planning, market share distribution is followed with triangular probabilistic distribution among 5%, 10%, and 15%. Initial investment is uniformly distributed between $1,500,000 and $2,500,000; whereas, unit cost is normally distributed between mean of $30 and standard deviation of $5. Annual overhead cost is distributed in triangular probabilistic distribution among $150,000, $210,000, and $315,000.
In this consideration, 100 iterations are taken and Monte Carlo simulation (Risk Analysis) is accounted for specific aspects as market share, initial investment, production cost per unit, annual overhead cost, and NPV.
Figure 4: Cumulative Probabilities Report
(Source: Created by author)
As from the cumulative probabilities report, probability ranges are considered for NPV estimation. The above report clearly shows that there is less than 30% chance of gaining NPV of $1,047,292 (that is greater than $1 million). Therefore, chance of gaining more than $1 million net present value is more than 20% or greater.
IBM Cognos – Business Intelligence Software
Figure 5: Cumulative Probabilities chart
(Source: Created by author)
However, the decision criteria state that “The Company is unwilling to proceed if there is a 20% or greater chance that the net present value will be less than $1,000,000 (1 million)”. Therefore, as per cumulative probabilities report and graph, company should accept the proposed production of the product.
Figure 6: Model for uncertain scenario
(Source: Created by author)
Some aspects are considered and further analysis is continued in the model. As per uncertainties of CEO, the selling price per unit is uniformly distributed among between $75 and $45. Moreover, the production cost per unit is normally distributed under mean of $30, standard deviation of $10.
In this consideration, 100 iterations are taken and Monte Carlo simulation (Risk Analysis) is accounted for specific aspects as market share, initial investment, production cost per unit, selling price per unit, annual overhead cost, and NPV.
Figure 7: Cumulative Probabilities Report
(Source: Created by author)
As per the uncertainties in selling price and unit cost, the CEO considered uniform distribution and normal distribution respectively. Now, as the cumulative probabilities report is prepared, the report clearly shows that there is 80% chance of gaining more than $1 million net present value.
Figure 8: Cumulative Probabilities chart
(Source: Created by author)
However, now the decision criteria state that “The Company is willing to proceed if there is at least 80% chance that the net present value will be greater than $1,000,000 (1 million)”. Therefore, as per cumulative probabilities report and graph, company should accept the proposed production of the product.
Business Application Research Center is an organization uses different business intelligence tools that support business initiatives. BARC institute in Australia uses Cubeware and IBM Cognos as business intelligence software. Cubeware can easily build solutions that meet unique information and several user needs to support without programming. Cubeware helps to provide management dashboards and platform in different language for preparing and customizing reports (Kisielnicki & Misiak, 2016). For instance, Cubeware ETL and connectivity tools can be used for databases and accessing to wider ranges of data sources. It can provide rational writing to staging areas and it has SAP certified connectivity. Cubeware supports multidimensional databases with Microsoft analysis services, open source Palo, and SAP business information warehousing (Teegalapally et al., 2016). Cubeware frontend can support planning, dashboard, reporting, and analysis facilities. Cubeware is user-friendly and it supports Windows and web support.
Advantages of Incorporating BICCs
IBM Cognos is another tool that BARC uses; this business intelligence software solution helps to provide performance management and better decision-making. Cognos operates the business decision-making with measuring and monitoring its performance along with stating it with scoreboards for trackin primary metrics of the business. Reporting and analysis with IBM Cognos can view data, gain context access, trend understanding, and spotting anomalies (Laursen & Thorlund, 2016). Major planning, budgeting, forecasting facilities are provided from IBM Cognos software that are utilized in many departments and authorities. IBM Cognos software offers broadcasting capabilities such as reporting, preparing scorecards, dashboards, planning; so that it can operate as open and enterprise-class platform over several expertises (Bolos et al., 2016). Cognos provides the entrepreneurs to make decisions faster and better that can optimize business performance with shear reliabilities.
BARC institute has incorporated several Business Intelligence Competency Centers (BICCs) for some improvements and enhancements. For these improvements, BICC outperformed the companies without BICC placed in facilities (Larson & Chang, 2016). The advantages and improvements are identified as following:
Competitive advantage: Companies with BICC has advantage of persuasive organizational structure with several expertises. BICC implements suitable resource planning with activity distribution conducts suitable outcomes from the organizations.
Productivity: BICCs improves productivity and increases rate of production. BICC is capable of completing tasks and activities without including changes in business process (Zimanyi & Abello, 2016). Even in change management phases, BICC system is able to incorporate them quickly and appropriately.
Alignment: Alignment of business along with IT solutions can be incorporated to certain organizations that easily implement BICC and business intelligence software solutions.
Roles and responsibilities: Primary purpose of BICC is to plan and coordinate the business intelligence initiatives. Therefore, the companies that had implemented BICC are likely to include structured data management that companies without BICC (Mate et al., 2016). Companies with BICC can easily overview several business intelligence initiatives and activities that are yet in practice.
Pervasive usage: BICC has influence over deploying business intelligence software in other enterprises (Yeoh & Popovic, 2016). Organizations with BICC in operation, organizations are more likely to utilize business intelligence software than other companies without BICC.
In BARC institute, BICC and business intelligence tools provide major value to operations and incorporate suitable activities that are influential to projects. BICC has valuable responsibilities such as managing business intelligence projects; projects that requires implementing business intelligence initiatives (Laursen & Thorlund, 2016). BICC follows and develops BI standards and guidelines for managing architecture and applications. Business consulting is appropriate with using BICC centers according to technical infrastructure. As per user requirements and internal company administration, BICC sets priorities to activities so that it can achieve data access rights and manage user requirements.
Business intelligence (BI) success factors entirely contribute to organization’s advantage and it incorporates primary aspects of handling business with smart initiatives. The success factors are included as following:
Satisfaction with BI Initiatives: With BICC incorporation in organization, almost 50% of organizations are well-developed and they are supporting internal BI initiatives. Moreover, BICC can prioritize the initiatives in terms of activity accomplishment (Kisielnicki & Misiak, 2016). Henceforth, BARC achieved success with BI projects.
Quality of Information: BICC serves quality in information for coordination of BI projects with companies. Almost 70% of organizations with BICC achieved success of supporting quality in information along with providing proper value to services.
Coordination with BI Project Initiatives: Almost 63% organizations with BICC centers coordinate with BI project initiatives for achieving successful implementation with proper developments. Close corporation with several departments can implement requirements with quick implementation (Laursen & Thorlund, 2016). BICCs can neutralize the BI systems with optimizing their performance in decision-making.
Make It Work Inc. operates with dynamic technology for digital ecosystem and it operates under severe competition. The organization falls under category of digital ecosystem with spanning digital environment. In Make It Work Inc., the switching cost is lower for customers and company does not respond to calls rapidly. Obstacles in market space are lower as IT service providing organization can easily follow business model. However, Make It Work Inc., tends to face several competitors those are easily entering in markets with lucrative advantages. Several competitive organizations those are performing IT-service activities has one-man shop to corporate giants such as Best Buy. Therefore, business intelligence project development is entirely justified from Make It Work Inc.’s viewpoint of staying in competitive market.
Make It Work Inc. has emerged into digital business ecosystem that has lots of changes consideration along with dynamic process. Customer preference changes are almost frequent for Make It Work Inc. and their competitors’ moves are unpredictable. They need business intelligence (BI) project development with requirements of supporting variable customer demands and preferences with several competitive advantages. The organization entrepreneurs prefer their working activity to be compared with plumbers. Make It Work Inc. requires to provide instantaneous responses to all customers. Make It Work Inc. needs to develop information system for playing primary role in business. Web-based customer management for Make It Work Inc. helps in supporting customer services with appropriate information technology incorporation. Often in Make It Work Inc. services, the processes related to databases can follow reliable performance indicators. The organization tries to reach satisfactory level of customers so that their services can be preferred to customers every time.
Make It Work Inc. has several reasons for developing business intelligence projects with supporting smart and BI initiatives. The reasons are identified as following:
- Pondering future growth of market and attracting several customers with instant services and their query management. Moreover, Make It Work Inc. follows BI standards and guidelines to provide proper solutions as well.
- Make It Work Inc. tries to achieve expansion strategies to work with business models and several expertises in practice. Furthermore, the organization requires developing BI projects so that business model framework incorporation is possible in every aspect.
- Providing customer-centric services and helps can attract several customers in business. Make It Work Inc. followed this mentality to gain customer trust and utmost effect in business.
From business intelligence projects, Make It Work Inc. received several advantages and outcomes such as operating with digital dashboards. Make It Work Inc. incorporated dashboard for monitoring customer feedbacks and major queries in possible terms of operations. Their dashboards are prepared with showing in-depth details of customer reposes against time. Detailed logbook shows date, time, customer name, work order, address, performance index, and status. Dashboard clearly provides view of customer satisfaction over work order and status of queues. Moreover, BI project outcomes show detailed customer and service sales data chart showing appropriate client order completion.
References
Bolos, M. I., Sabau-Popa, D. C., Scarlat, E., Bradea, I. A., & Delcea, C. (2016). A Business Intelligence Instrument for Detection and Mitigation of Risks Related to Projects Financed from Structural Funds. ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, 50(2), 165-178.
Kisielnicki, J. A., & Misiak, A. M. (2016). Effectiveness of Agile Implementation Methods in Business Intelligence Projects from an End-user Perspective. Informing Science: the International Journal of an Emerging Transdiscipline, 19.
Larson, D., & Chang, V. (2016). A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 36(5), 700-710.
Laursen, G. H., & Thorlund, J. (2016). Business analytics for managers: Taking business intelligence beyond reporting. John Wiley & Sons.
Mate, A., Trujillo, J., García, F., Serrano, M., & Piattini, M. (2016). Empowering global software development with business intelligence. Information and Software Technology, 76, 81-91.
Teegalapally, V., Dhote, K., Krishna, V. S., & Rao, S. (2016). Survey on Data Profiling and Data Quality Assessment for Business Intelligence.
Yeoh, W., & Popovic, A. (2016). Extending the understanding of critical success factors for implementing business intelligence systems. Journal of the Association for Information Science and Technology, 67(1), 134-147.
Zimanyi, E., & Abello, A. (Eds.). (2016). Business Intelligence: 5th European Summer School, eBISS 2015, Barcelona, Spain, July 5-10, 2015, Tutorial Lectures (Vol. 253). Springer.
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