Decision-making in operation management is one of the most important processes that enable effective operations management under various circumstances.
The purpose of having such a set of models and framework for decision-making should rather be clear as some decisions that are made are more complex as compared to others and such difficult and complex decisions must be made based on some kind of rationality and well-conceived mathematical reason. It is also termed as management science or industrial science; decision-making based on a set of preconditions and parameters is something very basic to this discipline (Albrecht & Dasigi, 2014).
Several real-life examples involve an extensive amount of mathematical modeling and considerations of all sorts to make the final decision about the outcome (Anderson, et al., 2015).
As per Barlett, there are several examples of situations where the decision-making tools of the OR are used for making decisions effectively and reasonably. for instance, a production and assembly line using multiple machines with numerous quantitative factors that affect the efficiency and ultimately affect the total output, OR might be used to arrive at the exact values about the usage of the various machines depending on their efficiencies and other factors such that the output is maximized and wastage of other resources is minimized (Barlett, 2016).
Due to the very nature of the OR, it can be said to belong to any particular set of study or conventional disciplines. It is treated as a discipline of its own that has a wide variety of applications across a variety of industries and managerial fronts. As per Ferrell & Fraedrich, it uses the various tools that are derived from a variety of branches such as mathematics, statistics, economics, psychology, engineering, etc., and combines subsets of tools and knowledge to build a decision-making framework for any given set of conditions as applicable in the real world problems faced in the various management and industrial applications (Ferrell & Fraedrich, 2015).
As mentioned earlier by Ford & Richardson, the discipline of Operation research itself can be treated as a truly interdisciplinary subject. It has become part of most disciplines due to its capability to enable the managers or decision makers to arrive at a well-thought-out and defensible as well as equally rational decision in any situation (Ford & Richardson, 2013).
To make any progress in the decision-making scenario in the operation research discipline one needs to understand the following aspects of any given system:
More precisely, OR consists of the following six steps that enable the outcome in the form of a decision being taken by the manager:
Step I: Observing the problem environment
Step II: Analyzing and defining the problem
Step III: Develop a model
Step IV: Selecting appropriate data input
Step V: Providing a solution and testing its reasonableness
Step VI: Implementing the solution
In the domain of decision theory, it is important to understand the difference between the normative and descriptive kinds of decision-making. Additionally, the approach that is adopted for decision-making under varied conditions of certainty, risk, and complete uncertainty also needs to be elaborated upon (Luo, et al., 2017).
Firstly, the differentiation between the normative and the descriptive kind of decision-making within the premises of the discipline of operation research is, in theory, extremely basic. A normative decision theory is a hypothesis about how choices ought to be made, whereas the descriptive or subjective decision theory is a hypothesis about how the decisions are made.
The “ought to” in the previous sentence can be translated in numerous ways. There is, in any case, for all intents and purposes entire understanding among those who study and research the decision theory that it alludes to the requirements of rational and reasonable decision-making. In other words, a normative decision theory is a hypothesis about how the various decisions in any domain ought to be made keeping in mind the end goal to be rational (Mohammadi, Soleymani & Mozafari, 2014).
Standards of rationality and the subsequent decision that follows in any given situation are in no way, shape or form the main – or even the most critical – standards that one may wish to apply in basic leadership and management-related decision-making concerning any given task. Be that as it may, it is a practice to respect standards other than rationality standards as outer to the decision theory that forms the basis of the operation research (Parisio, Rikos & Glielmo, 2016). Decision theory does not; enter the scene until the moral or political standards are as of now settled. It deals with those normative issues that stay even after the objectives have been settled.
The rest of the regulating issues comprise a substantial piece of inquiries about the proper course of action when there is instability and the absence of data that would have otherwise made the decision-making possible and effective. It likewise contains issues about how an individual can organize her choices after some time and of how a few people can organize their choices in social choice systems.
On the off chance that the general needs to win the war, the choice scholar tries to tell him how to accomplish this objective. The question of whether he ought to at all attempt to win the war is not normally viewed as a choice hypothetical issue. Likewise, decision theory gives techniques to a business official to boost benefits and for various departments such as those dealing with environmental aspects and need to be precise about the limit to any toxic exposure and other similar agents, however, the essential question is whether they ought to attempt to do these things is not treated in the decision theory (Pettigrew, 2014).
Even though the extent of the regularizing is exceptionally restricted in decision theory, the refinement between rationality normative and subjective interpretations of the various speculations put forward as a part of the decision theory within the management science domain is frequently obscured. It is most certainly not remarkable, to aggravate ambiguities and even disarrays amongst normative and descriptive interpretations of one and a similar hypothesis (Romiszowski, 2016).
Presumably, a large number of these ambiguities could have been stayed away from. It must be surrendered, be that as it may, that it is more troublesome in decision theory as a sub-part of the management science than in numerous different orders to draw a sharp line between normative and descriptive interpretations. This can be unmistakably observed from the thought of what constitutes a distortion of a decision theory.
Decision making is unquestionably the most vital errand of a manager and it is regularly an extremely troublesome one. The area of decision-making assessment and analysis models falls between any of the two extreme ends of the spectrum.
This relies on the level of learning we have about the result of our activities. One pole on this scale is deterministic whereas the other pole of the scale ensures pure uncertainty in terms of the likelihood of the problem under risk. The portion in between these two poles is treated as the area where there is a certain amount of risk associated with the outcome of the given situation. The fundamental thought here is that for any given issue, the level of certainty changes among the managers and the decision-makers depending on how much learning everyone has about a similar issue (Snyder & Diesing, 2015). This mirrors the suggestion of an alternate arrangement by every individual. The likelihood is an instrument used to quantify the probability of an event for a situation. At the point when the probability is utilized to express uncertainty, the deterministic side has a likelihood of one or zero, while the flip side has an equal likelihood of all the outcomes.
Within the premise of the decision theory of the various management science practices and principles decision-making that takes place under pure uncertainty happens when the manager or the decision-making authority has no idea about the outcome of the situation. The decision that is to be taken in such a situation is almost entirely based on the bend of mind that the manager has- which can be anything out of being an optimist, pessimist, or least regret.
As per Vohs, et al, the decision-making framework that is usually adopted by managers in the case of the least regret is a follow-up from the thought process that the decision needs to be taken such that the manager feels least regretful about the decision. It uses Savag’s Opportunity Loss concept and makes use of the regret matrix to arrive at a decision (Vohs, et al., 2014).
In most situations, decisions that are taken under the condition of complete uncertainty lead to an often unreasonable or indefensible decision as the outcome of the whole process.
The modeling under the decision-making under risk takes the help of the EU or expected utility hypothesis. Over time, adjustments and some reassessments have been made in this approach to make it more efficient in the decision-making process on the part of the manager.
Another approach that is used for the same under the risk conditions is by the use of the safety rules. These techniques are used heavily in case of the various decision-making that takes place in the case of various economic and financial systems (Zhang, Shah & Papageorgiou, 2013). EU model of decision-making under risk has been described as a descriptive kind of decision-making model. The EU technique can be approached in two ways that entail- a normative approach that is based on the game theory and the other involves the summary measures based on the general properties of the functions involved (Zsambok & Klein, 2014).
The different techniques and approaches that have been enumerated to be used for the decision-making process under different conditions and circumstances including those involving some risk, those without any certainty, and those with certainty, need to be applied to understanding the initial state of the system as well as the system functions.
While most of the approaches that have been discussed and elaborated upon are quite adequate among themselves for most decision-making in the different applications, an adequate amount of enhancements are made to the same now and then to enable them to be more efficient in a variety of other situations in the management science.
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