Good Example Of Report On Processing Module

Type of paper: Report

Topic: Organization, Evaluation, Management, Business, Diversity, Game, Decision, Model

Pages: 8

Words: 2200

Published: 2020/12/17


The most fundamental question in business operations is the reasons for operation. As such, there prevails diversity in the operational reasons for an organization with majority of the pundits not laying forth clear cut descriptions. Some pundits assert market leadership while others denote building plausible customer relationship and effective management of risk. Nonetheless, the most fundamental and overarching theme among the diverse pundits is the concept of interactions in the operational facets of an organization. Interactions denote the organizational relationships with customers, suppliers and diverse business partners. Furthermore, the incorporation of information technology and e-business plays a pivotal role towards enriching and broadening the interactions among organizations. Thus, the business interactions with the diverse stakeholders lay forth the notion of effective economic models towards an evaluation of the operational prospects of an organization. The incorporation of the qualitative comparative analysis, fuzzy logic concepts and the game theory are three plausible models towards evaluation of the operational prospects of an organization. Through the use of the three models, an organization such as BLS limited can evaluate the operational challenges and implement the right models towards handing the evident challenges. Thus, this paper will evaluate BLS limited and incorporate the right model as per the case questions.

Describe game theory, qualitative comparative analysis and fuzzy logic concepts

First and foremost, a business denotes a highly complex web of interactions with the diverse stakeholders. Thus, any decision that is undertaken by a business results into a ripple effect within their operational mandate. It is imperative to acknowledge that any decision made by an organization results into multiple effects on the entities that interact with the organization. The concept of ignoring the interactions results into unexpected or implausible effects within an organization. Thus, the game theory is highly effective in providing an in-depth study of the interactive decision making whereby the outcomes of each player or stakeholder is dependent on the actions of the others. As such, within the game theory, each decision maker is a player of the organization and in making a decision or making a choice pertaining to a strategy, taking into account the choice of the others is fundamental (Dodge, 2011, pg 92). The use of the game theory is towards the provision of plausible analysis of the evident interactions towards plausible decision making as a result of the interactions of the diverse stakeholders.
Secondly, the fuzzy models denote the operation on informational granules that are either fuzzy sets or fuzzy granules. The fuzzy granules denote the abstract realizations of the diverse concepts that are incorporated towards business modeling. Furthermore, as modeling in an organization is realized at a higher and abstract level, fuzzy models result into the rise to a more holistic architecture towards highlighting of the three fundamental functional models of:

Input interface

Output interface
Thus, the fundamental facets of the fuzzy model denote an analysis of the knowledge competency of the organization, aligning to the interface, processing and laying forth the most plausible decisions for the organization. Therefore, as per the diverse pundits, the use of the fuzzy concept or model is towards laying forth an effective blueprint for the organization towards analysis and strategy formulation for successful outcomes. Hence, an organization should incorporate the fundamental prospects of the fuzzy model towards numerical data generation and analysis of the results (Hartley, 2010, pg 28). Furthermore, after the development of the concepts, the model is implemented towards acceptable mappings and numbers that ensure successful outcomes in the business.
Thirdly, the qualitative comparative analysis was developed by Charles Ragin. As a tool towards statistical analysis, the QCA is incorporated towards analysis of data sets through the investment into listing and counting. The concept of listing and counting denotes an evaluation of the combinations of the variables to be evaluated within an organization then application of the rules pertaining to logical inferences of the data supports. In the case of categorical variables, QCA commences by listing and numerical counting of all the diverse cases and the diverse cases are examined through assignment of a unique value or alphabetical data (Hartley, 2010, pg 28). Most fundamentally, the QCA is used towards bridging the qualitative and quantitative analysis. The incorporation of the QCA provides an in-depth evaluation of the cross sectional cases and provision of the most plausible approach towards examination of a given case within an organization or operational mandate.
Explain the relationship in terms of similarities and differences between game theory, qualitative comparative analysis and fuzzy logic concepts in modelling decision making under uncertainty
The use of the QCA, Fuzzy models and the game theory is highly imperative towards an in-depth evaluation of the data and operational factors that impact on the operations. Thus, from the analysis of the three models, their similarities include:

They provide powerful tools towards analysis of the causal complexity

Through the use of the three models, it is highly possible to analyses the causal factors that are very complex. The causal factors denote diverse combinations of the causal condition that are capable of resulting into similar or diverse outcomes. Thus, through the inculcation of the three models, plausible comparison of the cross-facts that are grounded on the evident case research practices (Dodge, 2011, pg 92). The use of the three models denotes an analysis of the causes and effects within the operational spheres of an organization.

The theoretical models result into set theoretical methods transformation into social inquiry

The three models are based on the analysis of the set relations and not correlations. As aforesaid, the three models focus towards an evaluation of the interactional relationship, causes and effects on the multiple stakeholders. Henceforth, due to the social theory verbal mandate and verbal formulations are extensively set theoretic in nature, the three models focus on provision of a closer link to the organization through harmonization of qualitative and quantitative factors (Dodge, 2011, pg 92).

The three models are a bridge between the qualitative and quantitative analysis

Majority of the aspects evaluated through the three models denote familiarity with the cases which in return demands an in-depth knowledge. Furthermore, the three models are capable of pinpointing the deciding cross care patterns which are the usual domain in the quantitative evaluation. Organizations that use the three models result into cross case patterns analysis in respect to the differences in cases and the heterogeneity in regards to the diversity in causally relevant conditions. Thus, the investment into the three models provides a fundamental environment towards linking the qualitative and quantitative models towards successful evaluation.
On the other hand, from the evaluation of the three models, there prevail disparities in regards to the fundamental theoretical framework that denote the inculcation of the models. As such, the game theory, as per the diverse pundits mainly emphasizes on the interactions among the diverse players or participants. The interactions among the participants result into clarity in the description of the impacts evident by the organizational strategy implementation. On the other hand, the QCA is more of a statistical model that focuses on aligning the qualitative causal factors to the quantitative statistical tools. The harmonization of the qualitative and quantitative tools result into effective analysis of the two theoretical and numerical aspects results into plausible case analysis. On the other hand, the fuzzy model is more of a theoretical overview of the organizational approach towards analysis and decision making of the diverse factors that impact on the organizational operations. As such, the theoretical framework lays forth clear cut processes in regards to management of the diverse factors that impede on the operational facets of the business.
Discuss at least four uncertain factors (variables) that you would consider as an ORA whose company is operating in an environment that has been described above and how these factors could be effectively modelled with game theory and fuzzy logic concepts

Infrastructure, routing problems, poor road networks and costs of operation

An evaluation of the case study reveals that there are diverse outcomes that emanate from the operational changes and environment in which the organization operates. Thus, from the evaluation of the operational outcomes emanating from the poor road network, infrastructural and routing problems, it is evident that there is a plausible approach towards analysis of the most applicable model for evaluation. In majority of the organization, games or actions that the diverse players exude have a direct impact on the other players. As such, from the evaluation of the case study, it is evident that the country’s non-investment into the infrastructure impacts on the revenues and costs of operation of the organization. Thus, one plausible approach in game theory that can be used to study the operational problems at BSL is the decision analysis. The decision analysis denotes an evaluation of the probabilities pertaining to the case study. As an assumption, in the instance that the routing problems are resolved, infrastructure developed and road networks improved, profitability is bound to prevail. On the other hand, in the instance that the uncertain environment prevails, costs of operations and slack in revenue is bound to prevail. Moreover, from the evaluation of the Fuzzy model, it denotes an evaluation of the diverse laid out approaches such as tubular and decision trees towards an evaluation of the factors of causal and effects that impact on the organization. Hence, the most plausible approach towards analysis of the BSL organization is the fuzzy model decision tree. The fuzzy model entails an evaluation of the transversal factors that depend on the values of the attributes. Only a given path traversed and a sole terminal node should be reached in the organization. From the analysis of the case study, the terminal nodes should denote either an increase or decrease in revenue as a result of the uncertainties of the economic environment. Thus, the four factors that can be modeled through the game theory include:


Road networks
Routing problems
Revenue yield
Using the uncertain four factors identified in c above, prepare an integrated decision model (Fuzzy game model) to demonstrate how game theory and fuzzy logic concepts could be used to develop logistics and vehicle routing models that could be adopted by BMG 204 logistic solutions limited described above
As the first node, it entails the evident negative uncertainties within the organisation that can result into either plausible or implausible revenue. Through effective strategy implementation by the organisation, effective performance is bound to prevail in the organisation hence profitability.
Improved business improvement through improvements in road networks, infrastructure and routing. The improvement in the diverse aspects result into plausible revenue at BSL
Use your own knowledge of games, road transport, decision models as well as distribution and networks models to develop your own strategic fuzzy game models that illustrate road transport operations
An analysis of the road transport network, it is evident that it is a dynamic game that exudes complete information. As such, the transport operations exude an environment in which all the players have up-to-date information pertaining to the payoffs and operational setbacks. Thus, there are two stages of evaluation for the organization. The first stage is the shift in operations which entails operating with low costs and the second stage is conformity to the business environment that entails operating at high costs. The two stages of operation determine the operational success of the organization. In the second stake of operations, the action set has two incumbent effects either to be aggressive or accommodate the operational uncertainties (Ron, 2013, pg 13). On the other hand, the first stage entails shift in operations shih results into an empty stage whereby the individual can do nothing that impacts on the transport network. Thus the operational model in the transport operations can either be shift in operations or conform to the operational uncertainties through either aggressive performance or accommodation of the dynamisms of the business environment.
Using your economic and business knowledge to develop sample fuzzy rules and game strategies that you would consider for the knowledge base of such fuzzy game decision model
On the other hand, the economic approach using the fuzzy model denotes an evaluation of the decision tree pertaining to the operational approaches that an organization can incorporate. As such, from the analysis of the organization the organization has two approaches towards successful or failure in their operations (Ron, 2013, pg 17). The successful operations denote use of the referential neurons that denote the investment into dominance of inclusion in the performance dynamics. Inclusion entails consultative performance strategy that invests in partnerships with different businesses in the transport operations to ensure effectiveness in performance. On the other hand, dominance should denote buying out of the competitors in the market. Through buying out of the competitors, the business can maintain monopoly in transport operations and sustain profitability through individualized pricing (Ron, 2013, pg 17). On the other hand, the aggregate node in the analysis of the decision tree entails a more alignment to the business environment uncertainties towards effective decision making.

Discuss possible limitations of your model

Network planning or the decision tree approach, in reference to (Kolli, 2013, pg29) is an iterative aspect which is incorporated with a mandate of making predictions on the time taken for the completion of a given project. (Kolli, 2013, pg 29) asserts that the network planning makes extensive use of topological design within its calculation. The technique is vastly incorporated in business, project management, production within a manner which is nonlinear and evaluation of the strategies towards effective performance in the organization. From the evaluation of BSL and the effective models or approaches towards laid forth, the diverse limitations include.
Does not lay forth the most plausible strategy towards analysis of the uncertainties in the business environment. The models provide four main options that the organisations can undertake as per the fuzzy and game theory.
The models are theoretical frameworks that do not provide in-depth evaluation of the business environment and the diverse operational strategies that result into the uncertainties in the business environment. Through the non-analysis of the business environment, the strategic options may be rendered ineffective towards successful outcomes in the business.


Business operations depend highly on the environment and the emanating factors that may impact on successful operations. As such, from the evaluation of the game, fuzzy and QCA models, it is evident that they play a pivotal role in strategy formulation. Through organizational implementation, proper strategies are bound to be formulated and implemented for successful outcomes.

Reference List:

Dodge E. 2011. 5 steps to a 5 AP microeconomics/macroeconomics, 2012-2013 edition. New York: McGraw Hill Professional.
Hartley, K. 2010. Problems of economic policy (Routledge revivals). New Jersey: Taylor & Francis.
Johnson, B., & Hart, A. 2013. Managing operations. New Jersey: CRC Press.
Kamauff, J. 2009. Manager's guide to operations management. New York: McGraw Hill Professional.
Kaynak, H. 2013. Total quality management and just-in-time purchasing: Their effects on performance of firms operating in the U.S. London: Routledge.
Kolli, S. 2013. Production & operations management essentials. New York: Research & Education Assoc.
Murthy, R. 2005. Production and operations management. New Jersey: New Age International.
Ron, C. 2013. Mathematical statistics for economics and business. London: Springer.
Singh, D. 2012. Banking regulation of UK and US financial markets. New York: Ashgate publishing.
Yomba, S. 2009. Micro economics to macroeconomics: The concept of market exchange rate. New York: AuthorHouse.

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