Type of paper: Report

Topic: Game, Decision, Company, Theory, Game Theory, Outcome, Logic, Decision Making

Pages: 8

Words: 2200

Published: 2020/12/12

Management Decision Making

Management Decision Making


Decision-making is a fact of life. The process can get complex in the business world when it has to be undertaken in uncertainty. This paper seeks to discuss three approaches, namely, the game theory, the qualitative comparative analysis and the fuss logic concepts that businesses can utilize in decision-making in this kind of condition. In addition, it will utilize the approaches to developing a model that can be utilized by BMG204 Logistics Solutions (BLS) limited to improve efficiency, reduce operating costs and increase its revenues in its transport business.

In business, politics and other different spheres of life, the participants often make decisions. These decisions most often impact on the individual players and others in the industry (Chiung-Wen, Yusin & Chun-Hsiung 2010, p. g 45). So it is important that players and others involved understand how decision made by one player affects other players in any particular field. Hence, the application of game theory, which is a specialized study of decision- making in places where high number of players make choices with possible effects of the interest of other players in the field.It is the study of cooperation and conflict. The game theory concepts mostly apply where the actions of these players are interdependent. That is where an individual’s action has the potential to affect those of others (Dweiri & Kablan 2006, p. g 67).
The game theory's strength lies in the methodology it offers for both the restructuring and the analyzing of challenges for strategic use (Li et al. 2011, p. g 97). It is a mathematical tool that requires those making decisions to enumerate clearly the players in the field and the strategic options available to them. The decision-maker then examines their tastes, preferences, and reactions. The game is the object study in the game theory, which represents an interactive situation. The game usually involves at least two players. A game with one problem is referred to as a decision problem. There are different forms of games (Li et al. 2011, p. g 98). Firstly, there is a coalition game, which specifies the benefits a player can gain through cooperation. A cooperative game theory looks onto such games in regard to the power held by each player and the division of proceeds. Non-cooperative game theory is concerned with a deep and comprehensive analysis of strategic choices. It is based on the rationale that the details, ordering and timing of the choices of the players are critical in establishing the outcome of the game (Oderanti & DeWilde 2010, p. g 45). It is essential to note that cooperation does arise in on cooperative games when the agents in the field realize that such an action serves their best interests. There are important assumptions in the game theory. The key assumption is that players are rational beings. According to Anderson et al. (2011) a rational player is one who takes an action which is likely to give the best outcome given that he knows the action the other player will take.

Qualitative comparative analysis

Qualitative Comparative Analysis (QCA) is a technique used to analyze how different conditions contribute causally to a desired outcome (Lee et al. 2008, p. g 66). QCA begins with recording of how each condition affects the outcome of interest, which is then minimized by identifying simple set of conditions that are possible causes of each and every observed outcome. The results are then expressed in either standard language or as Boolean algebra (Lee et al. 2008, p. g 67). For instance, a combination of V condition and W condition or a combination of X condition and Y condition will result in Z outcome. Such is illustrated more clearly in Boolean algebra as V*W + X*Y=Z.
QCA is a theory that bridges quantitative and qualitative analysis. Most often QCA cases require familiarity with the case at hand, which on its part require thorough and comprehensive knowledge. In addition, QCA can separate various complex aspects of causation. Firstly, it can configure causal conditions not only single causes. In many situations, a particular outcome is as a result of a combination of more than one cause. Secondly, it can distinguish a scenario where there exist different ways in which a desired outcome can occur. Thirdly, it can configure asymmetric causes. In certain scenarios, failure might occur not necessary because of the non-existence of factors of success but for others factors referred to as asymmetric causes. QCA helps to determine such an occurrence and, as a result, helps in drawing the right conclusions from a given situation. Fourthly, QCS can distinguish causal conditions that are important or adequate. There are situations where two conditions exist in a situation of failure or success but only one of them affects the outcome. In other instances, conditions exert unequal influence on the outcome of interest. QCA helps in making such a determination (Oderanti, Oluseyi & Wilde2010, p. g 67).
In the identification of relevant cases and possible causal conditions, four steps are used. The first is pointing out the outcome of interest and cases that seem to lead to this outcome as well as finding out in depth about the positive cases. The second is the identification of negative cases. That is those cases that appears to be potential causation but failed to display it. The third step is the identification of major causal conditions based on the outcome and relevant theories and knowledge. In this step, it is of essential to think in terms of different causal combinations that might generate the outcome of interest. The last step is streamlining the causal conditions as practically as possible. For instance, it entails a combination of two conditions when they appear as a substitute for each other.

Fuzzy logic concepts

Fuzzy logic is used to transforms expertise into IF-Then rules (Braathen & Sendstad 2004, p. g 78). It is a form of logic that deals with approximation rather than exact argument. Traditionally, there are only true and false values that characterize variables; in this case variables have a varying true value that ranges from 0 to 1. Additionally, this form of logic handles the so-called partial truth. In such case, the value of truth varies from the total truth to total falsehood. There is also the use of linguistic variables which are controlled by the application of specific functions. The fuzzy logic has many applications in the business world where there is often uncertainty in the outcome. In today's businesses, operations and resources most often demand to be scheduled simultaneously. Especially in the production and transportation industry decisions of IF-Then are often unavoidable. In decision-making processes, situation analysis may lead to complex formulations that can call for compound prediction. In such cases, fuzzy logic comes to play as agents establish what would possibly happen after occurrences of a particular event (Ding & Liang 2005, p. g 12).


The game theory, qualitative comparative analysis, and the fuzzy logic concepts have a number of similarities as well as differences in modeling decision making under uncertainty. Firstly, all the three techniques attempts to identify possible moves of different players during uncertainty. Secondly, all the three concepts start from the assumption that players are rational beings, and, as a result, will probably take such actions that best serves their interest. The game theory and QCA involve a thorough examination of a given situation. They also differ in the sense that while the former seeks to find out the move that different players are likely to take, the latter attempts to analyze the causal conditions that could have led to a given outcome. Additionally, while the game theory attempts to show the likely moves of opponents, QCA goes a step further and detail the strength of different causal conditions. That is; the latter does not settle in merely describing the outcome but in analyzing the strength of different players in the field. Both QCA And the game theory demand thorough theoretical study in order to understand the possible moves of different agents. However, fuzzy logic is based on the approximation in accordance with the IF-Then concept. That is, in the case of the latter, the decision charts with certain derived-conclusions that vary depending on the situation (Timothy & Ross 2011 p. g 87). The decision maker identifies certain scenarios that automatically lead to a specified outcome. In the game theory, the decision maker makes decision on the possible moves of his opponent based on a deep understanding of their reaction and preferences, while in the Fuzzy logic such a decision is only based on some identified if-them conclusions (Timothy & Ross 2011, p. g 12).
The game theory, QCA, and fuzzy logic are more different than they are similar; however, their difference is their strength. In collaboration, one approach filling the void of the other, they can be used to make the most appropriate decision in moments of uncertainty.


BMG204 Logistics Solutions (BLS) Limited is a company that specializes in road transport business in Nigeria. Currently, the company has a large fleet of trucks that regularly travel throughout the country. Despite being one of the most established companies in this industry, the company’s operation has been facing numerous challenges because of various uncertainties. As the Operation Research Analyst (ORA) of the company, I have identified four variables that have led to increased cost of operations and declining revenues over the last few years.
The first variable is inadequate transport infrastructure. Very many regions of this vast country are not connected by either roads or railways. This poses a serious problem as it demands the use of small vehicles for easier penetration. These vehicles record high operation costs due to the limited number of goods they carry. Secondly, the country is characterized by excessively poor road networks, which seem to have worsened with the current regime. Poor road networks increase the cost of operations for two reasons. To start with, they increase the amount of time that goods take to reach their destinations, which means that fleet will be less utilized. Additionally, they demand constant repair and maintenance of the fleet that is also a very expensive undertaking.
Thirdly, corruption is proving to be such a nightmare to the company's operation. Traffic police and other officials come up with flimsy reasons so that they can get an opportunity to receive bribes. Having in mind that this is a nation where being clean is more expensive than being corrupt, the company’s official have resorted to such vices, which is also proving to be very costly to the company. The last variable is theft of transit goods. It has come to the company attention that the drivers and turn boys collaborate with highway robbers to rob goods. Despite the fact that insurance companies assume the losses, the process is often the length. In addition, it has served to paint a bad picture to the company in the eyes of the public. These four uncertainties are the reasons as to why the company has been experiencing dwindling fortunes over the last few years.

Integrated Business Model

In consideration of the above variables and accordance with the game theory and fuzzy logic concepts in regard to uncertainty, as the company ORA I have come up with an integrated business model that incorporates both approaches. It is an efficient transport and vehicle routing models which, the company ought to consider in its transport business in Nigeria for effective route management.
The game theory expects that rational being should make the move that is beneficial. In this sense, we expect that the government of Nigeria is headed by rational politicians who can occasionally make the best decision for their country. Based on this understanding, the company will take advantage of its privileged position in the country to make two demands. Firstly, it will demand that government officials should stop demanding for bribes from our fleet lest we transfer operations to another business friendly jurisdiction. Prior research on this country leaders points to the fact that they can fight corruption when low-ranking individuals are involved and when it is the only way that can keep foreign investors. Such a move will also incorporate the fuzzy logic by the use of if-then decisions. Secondly, the company should apply the co-operative game theory with its drivers and turn boys. It is my take that the main reason they conduct themselves in unbecoming behavior is because their temporary unemployment makes them feel that they are not part and parcel of the company. Applying both the game theory and the fuzz logic, the company should buy their loyalty in exchange for permanent employment. It is my take that the company should not take up the cost of poor and inadequate road transport. The clients and subsequently the end consumers should pay the cost otherwise the fleet should terminate operations to inaccessible destinations.


The model will serve to reduce some of the operation costs. However, it has its limitations and the most important being its inability to effectively handle the twin variables of poor road networks and inadequate transport infrastructure. In addition, it is not certain that threatening the government in order to end bribery among the company’s fleet will end the vice. However, the model is not so much about certainty but the most probable action or reaction based on the realities on the ground.


There are different approaches that can aid in making decisions in times of uncertainties including the game theory, the qualitative comparative analysis, and the fuss logic concepts. Despite their limitations in different situations, these approaches can be used in collaboration with each other to realize some desired outcomes. Using the game theory and the fuss logic concept the author has developed a model intended to improve operations in BMG204 Logistics Solutions (BLS) Limited, a transportation company, which operates in Nigeria. Regardless of the model's weakness, it will serve to lower the company's operation costs and increase revenues.

Reference List

Anderson, R., Sweeney, D.J., William, A. & Martin, K. 2011. An Introduction to Management Science: Quantitative Approaches to Decision Making, (13th Edition) South-Western Cengage Learning. ISBN-13: 978-0-538-47565-5, ISBN-10: 0-538-47565-x.
Braathen, S & Send stand. 2004. "A hybrid fuzzy's logic/constraint satisfaction problems approach automatic decision making in simulation game model," System, Man, and Cybernetic, Part B: Cybernetics, IEEE Transactions on , vol.34, no.4, pp.1786,1797, Aug. 2004: DOI: 10.1109/TSMCB.2004.828591
Chiung-Wen Hsu, Yusin Lee & Chun-Hsiung Liao. 2010. Competition between high-speeds and convention rail system: A game theoretical approach, Expert Systems with Applications, Volume 37, Issue 4, April 2010, Pages 3162-3170.
Dweiri, F. T & Kablan, M. M. 2006. Using fuzzy's decision-making for the evaluations of the project management internal efficiencies. Decision Support Systems 42 (2), 712-726. http://www.sciencedirect.com.
Ding & G. Liang. 2005 Using fuzzy's MCDM to select partner of the strategic alliance for liner shipping. Information Sciences, 173(1-3):197–225.
Li, Karray, Hipel & Kilgour M. 2011. Fuzzy approaches to the game of chicken's. IEEE Transaction on Fuzzy's System, 9(4):608–623.
Lee, C. Chan, Hui & D. Zheng. 2008. Cooperation in n-person evolutionary snowdrift game in scale-free Barabsi-Albert networks. Physica A: Statistical Mechanics and its Applications, 387(22):5602–5608.
Oderanti F. DeWilde P. 2010. Dynamics of Business Games with Management of Fuzzy Rules for Decision Making. Journal of Productions Economics 2010., 128(1), pp. 96-109. (available in Web of Knowledge)
Oderanti, Oluseyi & Wilde. 2010. "Automatic fuzzy decision-making system with learning for competing and connected businesses." Expert Systems with Applications 38.12 (2011): 14574-14584.
Timothy J. Ross. 2011. Fuzzy Logic with Engineering Applications. Oxford: Oxford University Press.

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