An Analysis of Decision Support System Models in Business

This report provides an analysis of decision support systems, focusing on analytical and simulation models within a business context. It explores system dynamics, model building processes, and the application of simple analytical models to simplify processes and scenarios. The report also delves into simulation models, examining their role in creating digital prototypes and predicting real-world performance. Specific issues related to Customer Relationship Management (CRM) software, such as data recording and management, are discussed, highlighting how these models can be used to identify problems and improve business outcomes. The report emphasizes the importance of revising analytical models and using simulation to improve business functions and financial performance. The conclusion summarizes the effectiveness of both simulation and analytical models in addressing business challenges and achieving desired outcomes, specifically in the context of CRM software.

Decision Support

Table of Contents
INTRODUCTION...........................................................................................................................1
TASK 1............................................................................................................................................1
TASK 2............................................................................................................................................1
TASK 3............................................................................................................................................2
TASK 4............................................................................................................................................2
TASK 5............................................................................................................................................3
TASK 6............................................................................................................................................3
TASK 7............................................................................................................................................3
TASK 8............................................................................................................................................4
TASK 9............................................................................................................................................4
TASK 10 .........................................................................................................................................4
REFERENCES................................................................................................................................5

INTRODUCTION
In the present report it analyses the generic description regarding the use of real system
within firm. However, using effective decision support system such as customer relationship
management software (CRM) that helps businesses to adopt construction of analytical and
simulation models. Thus, it identifies a specific issue faced by both these methods so that it could
be overcome effectively and efficiently (Scott and et. al., 2015). Analytical models faced
problems related to solving difficult problems and does not able to solve the problems at highest
possible place. While, simulation model faces issues related to test and plan the issues in regard
to overcome the limitations.
TASK 1
System dynamics is considered as the process of understanding the nonlinear behaviour
of complex systems and identify the stocks, flows, internal feedback and delay in time. Thus,
such type of system dynamics modelling helps in identifying the issues faced by business in
relation to overcome the problems faced by CRM. Here, business faces issues regarding
recording of data related to customers and employees so that results could be avoided. System
dynamics are considered as effective approach that results in applying the model in regard to
identify the feedback of responses and thus results in overcoming the issue so that problems
could be selected. Identifying the problems related to system so that data could not be managed
or recorded properly (Van Valkenhoef and et. al., 2013). It is significant for firm to bring
positive results so that identified issues could be overcome. Therefore, it is significant for system
dynamics to bring enhancement within the product or services so that issues could be avoided.
System dynamics modelling helps in identifying the feedback and delays in the time so that
identifies problems could be overcome.
TASK 2
Through adopting the model building process it helps in analysing the problems that
needs to be solved. Here, adopting system dynamics model results in assessing the problems and
making crucial decision variables so that it helps in avoiding the factors that could impact upon
firm. Also, management of firm need to formulate appropriate objectives in relation to assess the
issue faced within firm such as managing CRM software and thus record the data of customers
and information about them so that constraint set of records could be made (Guo and et. al.,
1

2015). Hence, it is significant for business to develop effective set of responses in relation to
improve the business performance. It can be identified the decision variables so that support
needs to be analyse so that appropriate resources could be used in terms of improving financial
performance.
TASK 3
The Simple Analytical Model focuses upon simplification of the processes and scenarios.
Unlike the stimulation models, it provides an enhanced insights in connection to various
parameters of sensitivity. It can be characterized as one of the early stage witnessed for
encouraging innovation within an organization. It can be considered as a valuable tool which is
also known to facilitate the decisions making process, supported by requisite evidence. The
process involved in this model is identification of problem, pursuant to which definitive
objections are formulated (Accorsi, Manzini and Maranesi, 2014). Certain pre-decisions are
undertaken and general alternatives are evolved. After conducting an effective analysis of the
available choices, an option is selected to implement the same. Lastly, the option so selected is
finally evaluated to assess the correctness and suitability of the process of implementation.
TASK 4
The primary rationale behind application of this model is to reveal specific causes which
may require the organization to delay the decision or not make the decision, considering the
future uncertainties. The basis of this model is to simplify the scenario and make a division of
various stages of solving the point of conflict or dispute. It is stated that this model enables the
organization to effectively simplify a situation, break it down into pieces and make an attempt to
produce a resolution (Bourouis and et. al., 2014). In pursuance to the same, the model is requires
identification of the issue at the preliminary stage, which is followed by consideration of the
alternatives which are available for perusal. After ascertaining the surrounding circumstances as
well as the available options, the model requires the organization to make a selection of a choice
and implement the same for resolving the situation.
2

TASK 5
It is essential for designers to help simple analytical models that results in analysing the
specific causes which results in performing the operations and therefore results in improving
financial performance. Therefore, it is significant for overcoming the problems that has been
identified by business in relation to mange the CRM software and thus identifies the required
impact that firm has upon business functions (Jelokhani-Niaraki and Malczewski, 2015). Further,
it is significant for organization to identify the issues and overcome the same so that decision
variables are required to be avoided and thus crucial decision need to be taken that results in
improving the performance of firm in market.
TASK 6
It is essential for businesses to undertake analytical model and solution in terms of
revising the problems identified and thus improve the solutions so that appropriate outcomes
could be attained. Therefore, it is significant for carrying out CRM software in regard to revise
the analytical model and solution so that it need to be revised and thus enhance the analytical
model and solution. Through revising such model it helps in improving the identified problem
and thus overcome the same so that data could be recorded in an effective way. It is the best
approach which is required to be evaluated and thus results in analysing the outcomes effectively
and efficiently (Maloney and et. al., 2015).
TASK 7
The identified analytical model is required to be revised in an effective way and
therefore, it is essential for business to overcome the issues or problems identified such as
recording of information and thus improve the same so that it could be implemented in an
appropriate manner. However, if the analytical model implemented could not be revised than it
might impact the business performance as it influences the performance of firm in market.
Revising analytical model results in improving and revising analytical model. Hence, it is
significant for firm to overcome the issues and therefore, it results in managing and recording of
data within firm (Anchala and et. al., 2015). Thus, revising analytical model results in improving
the failure chances and undertakes effective time so that proper construction of objectives could
be attained. All such objectives are required to be improved that results in focusing upon raising
the standards so that issues could be improved in an effective and efficient way.
3

TASK 8
Simulation model is considered as the process of creating and analysing the digital
prototype in regard to identify the performance of firm in real world. However, carrying out
simulation modelling helps in using the same by software designers and under what conditions in
which ways a part could fail and improve the performance of firm in market. Further, it is
significant for business to manage the performance and thus record the issues faced by firm and
therefore, overcome the issues (Horita and et. al., 2015). Here, the assessed problem within
simulation is recording of information about customers within CRM. Therefore, it is essential for
businesses to adopt appropriate measures and thus results in overcome the identified issues so
that simulation model is being used and thus improve the performance in market. Also, it is
crucial for simulation model and thus identify the negative effects that impacts the simulation
process and raise their standards in regard to overcome the issues and manage the data in CRM.
TASK 9
Simulation model could be reflected that creating and analysing the business process in
regard to implement physical model and thus predict that performance of firm could be affected
in the real world. Therefore, it is significant for enterprise to undertake appropriate approaches so
that simulation could be improved and thus improves the financial performance of firm. It also
results in improving the CRM software and overcome the issues faced by firm such as recording
or managing the data of customers and employees. However, such information could be
improved and thus simulation modelling results in identifying the conditions and thus it could
lead the business functions in an effective way (Bonczek, Holsapple and Whinston, 2014).
TASK 10
It can be concluded from the report that there are two models required such as simulation
and analytical that helps in improving the issues found within firm. Hence, it is significant for
operations of firm to be improved so that appropriate outcomes could be attained (Qi and et. al.,
2014). However, firm need to overcome the recoding and managing issue faced by business and
then improve its performance so that desired outcomes in the form of improving CRM software
could be attained.
4

REFERENCES
Books and Journals
Accorsi, R., Manzini, R. and Maranesi, F., 2014. A decision-support system for the design and
management of warehousing systems. Computers in Industry. 65(1). pp.175-186.
Anchala, R. and et. al., 2015. Evaluation of effectiveness and cost‐effectiveness of a clinical
decision support system in managing hypertension in resource constrained primary health
care settings: results from a cluster randomized trial. Journal of the American Heart
Association. 4(1). pp.e001213.
Bonczek, R.H., Holsapple, C.W. and Whinston, A.B., 2014. Foundations of decision support
systems. Academic Press.
Bourouis, A. and et. al., 2014. An intelligent mobile based decision support system for retinal
disease diagnosis. Decision Support Systems. 59. pp.341-350.
Guo, Z.X. and et. al., 2015. An RFID-based intelligent decision support system architecture for
production monitoring and scheduling in a distributed manufacturing
environment. International journal of production economics. 159. pp.16-28.
Horita, F.E. and et. al., 2015. Development of a spatial decision support system for flood risk
management in Brazil that combines volunteered geographic information with wireless
sensor networks. Computers & Geosciences. 80. pp.84-94.
Jelokhani-Niaraki, M. and Malczewski, J., 2015. A group multicriteria spatial decision support
system for parking site selection problem: A case study.Land Use Policy. 42. pp.492-508.
Maloney, K.O. and et. al., 2015. An integrated Riverine Environmental Flow Decision Support
System (REFDSS) to evaluate the ecological effects of alternative flow scenarios on river
ecosystems.Fundamental and Applied Limnology/Archiv für Hydrobiologie. 186(1-2).
pp.171-192.
Qi, L. and et. al., 2014. C 2 SLDS: a WSN-based perishable food shelf-life prediction and LSFO
strategy decision support system in cold chain logistics. Food Control. 38. pp.19-29.
Scott, J. and et. al., 2015. A decision support system for supplier selection and order allocation in
stochastic, multi-stakeholder and multi-criteria environments. International Journal of
Production Economics. 166. pp.226-237.
Van Valkenhoef, G. and et. al., 2013. ADDIS: a decision support system for evidence-based
medicine.Decision Support Systems. 55(2). pp.459-475.
5