Strategic decision making entails taking a specific action plan by use of various tools and premeditated approach (Wiersema, 2015). A decision tree is one such tool/model. A decision tree is a graphical model representing decisions with likely consequences that include results, utilities, and liabilities. It is a way of representing a model that comes with conditional accounts Nowak (2017). The decision tree also known as a predictive model is a way in which algorithms containing conditional controls are displayed. According to Schmid (2013), it is one way of representing decisions with their possible outcomes, consequences, utility, and costs. The following is a decision tree representing the chances on whether to use a high quality supplier or low quality supplier.
The decision tree above contains three main features. These include the supplier decision, the available suppliers, and the chance of lateness. The supplier decision, in this case, represents the available options from which the decisions will be drawn. The tree is comprised of branches and nodes with the first node having the firm choosing between the supplier with high quality or the supplier with the low quality. Each of the branches represents an option to choose a given decision. The chance event is at the beginning of each decision is represented as 1, 2, 3, 4, and 5 nodes. Each course of action will represent a given outcome of that specific chance event. Decision points represented by a, b and c are an alternative course of actions that will be available to choose from and that is within my control.
Choosing quality means that I will contend with the probability of lateness on delivery. However, at chance event 1, I will not be in a position to control the time delivery meaning that I will face a situation whereby the goods are delivered late. In the event of going by this decision, I will only have the option to urge the supply to be prompt in the delivery to minimize the probability of inconveniencing the customers. Choosing on decision point 2 will result into time delivery but low-quality suppliers. At decision point 2, I have two chance events of contending with inadequacy and adequacy. At chance event 4, I will face the consequences of adequate suppliers. However, at chance event 5, I will face the consequence of inadequate suppliers, a situation that may inconvenience my customers. The best option that will, therefore, minimize and reduce inconvenience to the clients is at decision point 1 with which I will contend with the consequences of either a one-month lateness of timely delivery. It would, therefore, make sense to go by decision point 1 and work towards ensuring timely delivery without compromising the quality of the product, a situation that may be detrimental to the reputation of the business.Conditions, on the other hand, are the various states of nature that exists which may not be controlled (Nowak 2017). Chances of lateness are the possible consequences or combinations of certain decisions. From the decision box, there are discrete paths that originate which represent the various alternatives available to choose from. The outcome arrived at is represented by following the available path down to the available alternatives. According to Schmid (2013), a decision tree may either be made simple or complex depending on the variables created. To improve on stability, a decision tree needs to have manageable variations that can be lowered and worked with comfortably. According to Liang, Feng, and Yang (2016), to balance the data set, it is important to fit them preceding fitting the decision tree.
My decision to pick on the supplier was based on the paths represented in the decision tree. I decided to take on the supplier based on the high probability of prompt delivery. According to Yang and others, it is important to combine all the available information so as to have a crystal clear understanding of the situation at hand before arriving at a decision (2016). Assessing the problem critically is part of solving the puzzle. All the available problems that human beings face are either related to artificial intelligence, reports derived from human beings, and the physical world around us. Creating an informed understanding of these three elements provides a platform for solving the problem under consideration. Boosting and bagging are important methods that can be utilized to maintain the trees variance. From the decision tree above, any decision undertaken comes with its consequences. However, the management has the opportunity to review all available options by going through the alternatives to come up with the best decision. For example, from the decision tree above, the management needs to choose the best option or alternative that will ensure prompt delivery.
A project can be successful or not depending on the type of the decisions that were made at the initial stages (Nowak, 2017). If wrong and hasty decisions are made then there is a great chance of the project failing and not attaining the set target. Approaching the task at hand from an informed point of view will definitely reduce any chances of things going wrong. This will help in reducing any kind of risks that may be faced by the project managers. In this video, Foltz states that when making decisions, we have to ask ourselves whether there are certain factors that exist which may affect the decision that we make and the probability that these factors exist or not (2012). He terms this as the decision analysis process, which states that before arriving at any decision, one has to take into account all the various factors that are in existence for the outcome to be fitting. Decision theory also employs the use of mathematical techniques that leads one into deciding from a pool of available choices with the hope of seeing better results in the near future (Management science tutorial, 2013). If one is certain of the events that are bound to take place from now heading into the future, then the decision to be made is a sure one. But if the certainty of the events is not guaranteed then the decision to be made should be critically analyzed to avoid any possibility of a negative outcome.
Decision trees assist managers in studying and understanding the possible outcomes before making any choices regarding important matters (Sullivan, 2017). A decision tree helps managers to have all possible scenarios that include outcomes, and follow-ups. Any uncertainty in many cases helps top-level managers to think of a different scenario or position. According to Nowak (2017), with the use of the tree, one is able to see a visual representation of the probability of available outcomes. With access to such information, the manager will be in a prime position to arrive at informed decisions. With the application of the tree, one is able to view what effects the decision made will have an overall perspective. The same case explains the reasons why decision tree is important during risk analysis as it helps the top management to weigh the available options in the event of a threat or if plans do not work as planned. For instance, the DecisionTools Suite comprises of different programs that are important and can be used in simulations for analysis. For example, the StartTools is an important tool for statistical analysis or any form of anticipation. The decision tree can thus be fashioned to suit any situation provided that there is a good understanding of the matter to be addressed. It can be used by engineers, programmers and other technicians employed in various fields (Stevenson 2008). They can also be of importance when it comes to assessing the available alternatives before making any form of investment in the business sector. For example, while considering introducing a new product in the market, the decision tree can help in analyzing any potential threat such as a competitor or failure of the product to make it successfully in the market.
The use of decision trees carries with it various advantages. One of these is that they are easy to prepare and the data required is little. Another importance is that the parameters being used in the preparation do not affect the overall performance of the tree, meaning it cannot be altered by external influences. Decision trees are, additionally, easy to interpret and do not require special skills for one to decode the information contained in the diagram. This means that anyone can use it since it does not require any set of special skills.
In conclusion, every human being is faced with a choice that they need to make at some point in their lives. From the decision tree above, I will ensure that I opt for the best solution that will minimize any loss of time and maximize on prompt delivery by paying a close attention to all options available. At the same time, it will be critical to put in mind all available alternatives for future reference. The expected outcome is aimed at helping the organization and utilizing the resources of the business while at the same time helping the facility to utilize the decision tree strategy. The decision taken will not only help the facility to maximize on its service delivery but will also help prevent any loss of time. The choices made today have an impact on what is to come in the future. It is, therefore, vital to analyze all the probable outcomes before arriving at any form of decision.
Chron, D. S. (2017, October 13). Retrieved from https://smallbusiness.chron.com/advatages-decision-trees-75226.html
Foltz, B. (2012, Dec 30) Operations management 101: Introduction to decision analysis [video file]. Retrieved from https://www.youtube.com/watch?v=Hy48AFKEepoLiang, Y., Feng, X., & Yang, F. (2016, Mar). Combining sources of evidence with reliability and importance for decision making. Central European Journal of Operations Research, 24(1), 87-106.
Management Science Tutorial. (2013, Jul 24). Decision theory basics [video file]. Retrieved from https://www.youtube.com/watch?v=r7HEKtockRsNowak, M. (2017). Defining project approach using decision tree and quasi-hierarchical multiple criteria method. Procedia Engineering, 172, 791-199.
Risk Precis. (2016, Feb 15). Using decision trees for risk analysis [video file]. Retrieved from https://www.youtube.com/watch?v=f5199Q9hwYSchmid, H. (2013, November). Probabilistic part-ospeech tagging using decision trees. In New methods in language processing (p. 154).
SIMAFORE, B. D. (2011, July 12). 4 key advantages of using decision trees for predictive analytics. Retrieved from http://www.simafore.com/blog/bid/62333/4-key-advantages-of-using-decision-trees-for-predictive-analytics
Stevenson, W. (2018). Operations management (13th ed.) New York, NY: McGraw-Hill Irwin. ISBN-13;9781259667473
Wiersema, M. (2015). Executive Decision-Making: Linking Dynamic Managerial Capabilities to the Resource Portfolio and Strategic Outcomes.
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