MGT602 Business Decision Analytics: Scenario Analysis Report

This report analyzes two scenarios related to business decision-making. Scenario A examines an Australian organization's approach to identifying the creative capabilities of its Research, Development, and Design (RD&D) staff through email communication analysis and meeting room facilitation, including the use of clustering techniques to select key members. Scenario B focuses on a service-based company's use of Leximancer concept mapping to assess communication strategies between management and field staff, highlighting the importance of communication, customer focus, and support availability. The report offers recommendations to improve decision-making processes, emphasizing the significance of communication, organizational change management, and the effective use of decision-making tools and techniques, such as hierarchical clustering and concept analysis.

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Running Head: MGT602
Business Decision Analytics
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Research Analysis
Executive Summary
In today’s business context most of the working environments are changing continuously
by implementing innovative or creative techniques to more and more number of people to work
together. It is significant for every company to overcome globalization that will help in
developing a virtual team. Hence, most of the organizations have overcome the demand of
globalization by forming virtual teams. The virtual team makes wise use of technologies that will
prove to be beneficial for the organization to execute its process systematically. Thus, skilled
workers prefer to work in the traditional organizational workplace environment. Therefore, the
paper provides with the discussion of the two scenarios that is scenario A and scenario B where
each of the scenarios discusses the significance of the decision-making process within an
organization. Also, it provides with recommendations to improve the process of decision-making
based on different techniques and tools.

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Research Analysis
Table of Contents
Introduction.................................................................................................................................................3
Discussion of Scenario A............................................................................................................................3
Discussion of Scenario B.............................................................................................................................6
Recommendations.......................................................................................................................................8
Conclusion...................................................................................................................................................8
References.................................................................................................................................................10
Appendices................................................................................................................................................12

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Research Analysis
Introduction
The paper demonstrates the evaluation of the usefulness of the decision-making tools that
reflects upon the decision-making style. It will provide with the comparison as well as contrast
that critically evaluates the decision-making process and style. It will evaluate and examine the
decision-making system and techniques that will engage group decisions and enhance
sustainable results. It will critically examine emerging technologies and tools for the effective
decision-making process. The virtual team works together to facilitate innovative or creative
ideas to bring success to the organization. Thus, the paper will focus on organizational change
management skills and decision-making that will improve group functioning.
Discussion of Scenario A
There were 800 people in the mid-sized Australian organization where 80 people were
widely involved in the Research, Development, and Design (RD&D) work. The primary
objective of the organization is to recognize the creative as well as innovative capabilities of
their RD&D staff members. To distinguish their business from their competitors, the firm makes
wise use of the capabilities of their staff (Duan & Xiong, 2015). To research the creative or
innovative capabilities of RD&D staff members the company has developed a project that is
carried out by the employees. The organization will get the significant information of the staff
members that could be accessed by using email-based communication among the staff members
of RD&D. The company has mapped the rates and directions along with email connectivity of
the employees. The organization has developed a map of the unstructured information of RD&D
that is provided in the emails of the employees. The map provides a node that recognizes detail
information of the employees along with their workstation in which they are logged in (Fan, Lau
& Zhao, 2015). The number of lines into or out of the nodes indicates the intensity of traffic and
the lines that connect different nodes and directions indicates the rates of connectivity. To
prepare tacit unstructured information and to discuss the information provided in an explicit
email, the project seeks an interpretation of the decision made. The RD&D staff members will be
provided with different meeting rooms with a facilitator, visual aids and a group support system.
The project will make wise use of communication technology system that will support
the meetings of the staff members, and in turn, it will help the staff members to get data implicit
or explicit information. To develop this process the resources that are being provided in each

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Research Analysis
meeting room will provide instructions to the group process (Hashem et al., 2015). It will also
ensure that each staff member present in the meeting room must have cooperation. Also, it will
help the groups to develop explicit information.
The following table presents the nodes and the clusters of six staff members of RD&D
department who has been chosen to participate in the meeting.
SR/No. Clusters Nodes identified per cluster
1 40 4
2 32 7
3 38 3
4 04 4
5 19 4
6 75 4
The table below presents the key members of clusters those who are chosen to attend the
meetings. It shows the staff members of RD&D based on the meeting rooms and the cluster.
SR/
No.
Rooms Clusters Key Members Selected to
Attend Meetings
1 A 1 Corporate Research and
Development Staff
2 B 2 Research and Development staff
3 C 3 Technician
4 D 4 Senior Staff, Research and
Development
5 E 5 Senior Research and Development
staff
6 F 6 Production team leader

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Research Analysis
To combine some clusters of different components, the clusters help in connecting a
massive number of objects. Every cluster those who are similar to each other are chosen by
grouping a set of numbers in the same group (Hartmann, Zaki, Feldmann & Neely, 2016). It is
entirely based on segmenting customer portfolio that is again based on various demographics,
transaction behavior, and behavioral attributes. Hierarchical techniques are determined to be the
standard technique that is being used for the clustering of the nodes. In this method among the
set of clusters, the endpoint is different while the numbers present in every cluster appears to be
the same. By recognizing the capabilities of the staff members in the RD&D department the
clusters and nodes were chosen that differs from the business of their competitors (Lee,
Ardakani, Yang & Bagheri, 2015). The emails of the staff members provide with the
unstructured information based on their capabilities. To identify the capabilities, several meeting
rooms have been arranged for the meeting. Communication technology system will be used by
the project to organize the meeting. This will eventually help in selecting the particular key
members those who will attend the meeting.
In the particular analysis dangling nodes has not been used because dangling nodes is
used to remove or delete a node from the cluster (Bello-Orgaz, Jung & Camacho, 2016).
Whereas, in the analysis, no particular clusters has been eliminated because eliminating a cluster
would make it difficult to select the key members. Instead of dangling nodes, unconnected nodes
have been used to choose the key members. The map shows two unconnected nodes that
indicates the members those who are not selected for the meeting. As the members are not
chosen to attend the meeting hence there capabilities will not be identified (Hazen, Skipper,
Boone & Hill, 2018). In this particular scenario making use of unconnected nodes makes it easier
to select the staff members of RD&D department who will participate in the meeting.
The primary purpose of selecting the clusters is thoroughly based on connecting the
objects of a similar nature. While doing the business analysis the two key members those who
are chosen to attend the meeting are Corporate Research and Development Staff and Research
and Development staff who are nearer to each other. To some extent, the job of both the
departmental staff is the same (Marjani et al., 2017). Thus they are selected for the meeting.

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Research Analysis
Discussion of Scenario B
The Leximancer Concept map provides with the significant relationship among the
concepts such as lines and placement and importance of the concepts. Management, open,
customer, communication, focused and results are the six themes that are presented within the
map. Each theme is presented inside a balloon. All the balloons except focused are interlinked
with each other that demonstrates the act that all have some relationship with each other
(Sivarajah, Kamal, Irani & Weerakkody, 2017). It is observed from the map that all the themes
are connected with the concept of communication. It clearly states that to execute the business
successfully there must be proper communication between the management and field staff. Thus,
every organization communication plays a significant role in executing the business effectively
to proceed with the work smoothly.
In the particular scenario, the Leximancer concept analysis is entirely based on wide
service-based Australian company that possesses 200 service staff that reports to 40 managers all
over ten service centers. The service level support provided by the employees is recognized by
the company. To implement this process, the organization have developed a project that detects
the vital support provided by the management and the roles and responsibilities of the staff
members (Huang, Wang, Zhang & Zhang, 2017). Thus, a telephonic and face-to-face interview
of the field staff and the management have been organized, and the results of the interviews have
been recorded. It has been analyzed from the map that the relationship amongst the staff
members and managers must be strong to deliver efficient work to achieve the target. The
Leximancer Concept analysis presents the fact that the concept focused in not connected with
any of the other concepts. This means that the management of the organization is not focused on
their work to achieve the organizational goals. This will greatly affect the organization to get
better results as expected (Güçdemir & Selim, 2015).
The Leximancer Concept analysis presents the fact that the field staff members of the
organization communicate with each other with the help of sending emails, interacting through
the training process, creating an open atmosphere and using a simple language. All these factors
greatly help in making appropriate decisions related to the business that will help in getting
better results. The communication is entirely related to the execution and the procedure of the
business to achieve the goal. It is noticed that communication amongst the managers and

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Research Analysis
customers are not of a high level where the communication is usually based on the needs of the
customers. To present their demands and provide relevant feedback based on the requirements
the customers are not connected directly with the managers (Bansal, Sharma & Goel, 2017).
Also, it is observed that to provide the customers with appropriate resources support availability
plays a significant role. Support availability helps the customers to facilitate with the
technologies as per their needs. It provides profitability to the organization if it fulfills the needs
of the customers. Hence, the map presents the fact that there is a high level of support provided
to the customers.
On the other hand, it can be seen that there is proper communication amongst the field
staff members and the organizational management which is determined to be open. The
management team and the field staff members discuss the key issues openly to make an
appropriate decision related to the project. It is acknowledged that the decision-making process
will be greatly affected if proper communication between the managers and the field staff
members does not take place. Hence, a high level of communication results into proper execution
of the business activities. The map presents the factor that communication strategy is greatly
linked with the customers that will be beneficial for the company to develop its business
effectively. Therefore, communication is regarded as a channel for transferring knowledge and
exchanging information between different staff members (Choi, Chan, & Yue, 2017). The role of
the managers is determined to be vital as it comprises of some significant factors such as leading,
planning, organizing and controlling. Based on the analysis the managers are constantly focusing
on satisfying the requirements of the customers. Thus, to satisfy the needs of the customers the
field staff members and the managers must have a mutual understanding related to the incentive
payment system. It is because the managers are not at all ready to offer incentive payment
system due to a shortage of funding capacity to the customers.
Support availability has proved to be beneficial for the organization as per the results. It
is essential for an organization to have a better support system to execute the business accurately.
According to the results, better support has been provided to the customers as well as the staff
members that will enhance profitability. It is also acknowledged that there must be a change in
the customer service management to execute greater openness. This particular change helps in

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Research Analysis
making an appropriate decision to get outstanding results. Therefore, to make better decisions
based on the needs of the customers’ customer-service communication is significant.
Recommendations
The management team of the organization is highly recommended to execute its work
effectively to attain the organizational goals. It is important to select the clusters by analyzing the
fact that which nodes are nearer and connected. The organization could exceed with its process
of choosing the key members who will participate in the meeting by utilizing the method of soft
clustering. The use of the soft clustering method is to bring all the data together into a single
node for all the customers. It is helpful in detecting the appropriate members those who are
chosen to participate in the meeting. However, it is regarded as a relevant method to execute the
clustering process.
It is also recommended to the management team that it must consist of a proper
connection with every concept that is present in the Leximancer concept map. All the themes
should be connected directly with each other have a better way of doing work. It is also
significant that there should be a strong connection between the themes such as customers, field
staff, communication, management, and results. If all these concepts are not linked with each
other systematically than it will affect the results. The managers and field staff are recommended
to concentrate on providing high-quality work to the organization that will help in achieving the
target. The management team will be unable to satisfy the demand of the customers if they are
not focused on their work that will put a negative impact on the result of the company (Wei,
2016). In an organization, there must be high-level communication amongst the customers and
the managers to understand the demand of the customers to satisfy their needs. Thus, it is
significant for the firm to follow communication strategy systematically. Proper support
availability will be provided to the staff members and customers which will be profitability to
organizational members. Within the organization, this will result in better management of the
projects. It is also recommended to the organization to implement customer-service
communication that fulfills the requirements of the customers. Thus, it is essential for the
management to be interlinked with the concepts or themes that are presented in the Leximancer
Concept map.

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Research Analysis
Conclusion
Presently most of the virtual teams in the businesses work mutually to facilitate
outstanding results for the firm. The mutual understanding of the virtual team puts a positive
impact on the organization to make the relevant decision. It is observed that making a relevant
decision based on the organizational activities will help the organization to get better results.
However, the paper provided with the discussion of two scenarios A and B that is based on
evaluating decision-making tool. It reflected upon the decision-making styles and contrast that
considers the appropriate levels of rationality. Therefore, the paper examined the decision-
making system and techniques.

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Research Analysis
References
Bansal, A., Sharma, M., & Goel, S. (2017). Improved k-mean clustering algorithm for prediction
analysis using classification technique in data mining. International Journal of Computer
Applications, 157(6), 0975-8887.
Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and
new challenges. Information Fusion, 28, 45-59.
Choi, T. M., Chan, H. K., & Yue, X. (2017). Recent development in big data analytics for
business operations and risk management. IEEE transactions on cybernetics, 47(1), 81-
92.
Dedić, N., & Stanier, C. (2016, November). Towards differentiating business intelligence, big
data, data analytics and knowledge discovery. In International Conference on Enterprise
Resource Planning Systems (pp. 114-122). Springer, Cham.
Duan, L., & Xiong, Y. (2015). Big data analytics and business analytics. Journal of Management
Analytics, 2(1), 1-21.
Fan, S., Lau, R. Y., & Zhao, J. L. (2015). Demystifying big data analytics for business
intelligence through the lens of marketing mix. Big Data Research, 2(1), 28-32.
Güçdemir, H., & Selim, H. (2015). Integrating multi-criteria decision making and clustering for
business customer segmentation. Industrial Management & Data Systems, 115(6), 1022-
1040.
Hartmann, P. M., Zaki, M., Feldmann, N., & Neely, A. (2016). Capturing value from big data–a
taxonomy of data-driven business models used by start-up firms. International Journal of
Operations & Production Management, 36(10), 1382-1406.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The
rise of “big data” on cloud computing: Review and open research issues. Information
systems, 47, 98-115.

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Research Analysis
Hazen, B. T., Skipper, J. B., Boone, C. A., & Hill, R. R. (2018). Back in business: Operations
research in support of big data analytics for operations and supply chain
management. Annals of Operations Research, 270(1-2), 201-211.
Huang, W., Wang, H., Zhang, Y., & Zhang, S. (2017). A novel cluster computing technique
based on signal clustering and analytic hierarchy model using hadoop. Cluster
Computing, 1-8.
Lee, J., Ardakani, H. D., Yang, S., & Bagheri, B. (2015). Industrial big data analytics and cyber-
physical systems for future maintenance & service innovation. Procedia CIRP, 38, 3-7.
Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I. A. T., Siddiqa, A., & Yaqoob, I.
(2017). Big IoT data analytics: architecture, opportunities, and open research
challenges. IEEE Access, 5, 5247-5261.
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data
challenges and analytical methods. Journal of Business Research, 70, 263-286.
Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in
logistics and supply chain management: Certain investigations for research and
applications. International Journal of Production Economics, 176, 98-110.
Wei, G. (2016). Picture fuzzy cross-entropy for multiple attribute decision making
problems. Journal of Business Economics and Management, 17(4), 491-502.

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Research Analysis
Appendices
Appendix 1: Scenario A
Figure: Assessment 3 Scenario A Data
(Source: Dedić & Stanier, 2016, November)

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Research Analysis
Appendix 2: Scenario B
Figure: Assessment 3 Scenario B Data
(Source: Wang, Gunasekaran, Ngai & Papadopoulos, 2016)