Decision-Making Models In Organizations

Types of Decisions

Decision making models refers to techniques and processes that are carried out while making a choice in an organization among different courses of action. The criticality of decision making cannot be emphasized enough as the effectiveness of results, solely depend on the course of action chosen in a decision- making exercise. In an organization, wide alternatives of decisions are made all the time. These decisions vary in importance and promptness. Some decisions may affect the lives of many people and others may change the course taken by an organization. For example, decisions made in a hospital are a matter of life and death. It is therefore imperative that the decision makers carefully analyze and evaluate alternative courses of action before a decision is made to achieve a certain goal.

According to (Chand, 2017), while the process of making decisions may be a logical one, it is often a difficult task and, in most cases, involve ethical issues. Therefore, before deciding, it is important to consider whether the decision is fair, does it corrupt the moral laws, does it break laws of an organization and most importantly, does it break the overall laws of a country. Despite the technicality and importance of  decisions, it is worth noting that not on all cases, a decision requires too much thought nor does all decisions result to major consequences. For example, waking up in the morning and heading to work does not require much thought as such routine decisions normally have an automated response. Such decisions are known as programmed decisions (Ireland, R. D., & Miller,, 2002). However, there those decisions that call for proper analysis, intensive information gathering and careful selection of alternatives. These are known as nonprogrammed decisions. For example, introduction of a new product by a company to the market or a drop in the company’s stocks in the market.

Under nonprogrammed decisions, there are three different decision-making models that cannot be ignored. Firstly, is the rational decision-making model (IRENEGOLL, RAKESH B.SAMBHARYA, 1998).  In this model, the first step is to ensure a decision criterion is formulated before sorting the alternatives. This step prevents forming a liking for an alternative and then formulating the criteria that favors that alternative. The general format of the rational model is generating all possible alternatives that covers a wide range of courses of action and hence arrive at a highly effective decision that do not sacrifice one possible alternative for another (Chand, 2017). 

Decision-Making Models

Secondly, is the bounded rationality/ administrative man model. This model has been based on the concept that does not consider individual rationality but rather takes an assumption that people normally settle for less while finding the best solution. This is because of the constraints of time, enough information and processing capabilities. This model focusses on the alternative that will provide a solution that satisfies the goal in the best way.

Thirdly, is the retrospective decision- making model. In this model, the decision is made intuitively and later the decision-makers try to justify their choice. This model was developed after an observation was made on graduating students when make their choice of a job. It was noted that the students made their choice early in the recruiting process but kept an open mind for possible alternatives in the future. Through this process, an individual is convinced that the decision made was rational and logical (Chand, 2017).

The case study chosen for this purpose, is that of the smart drone delivery; the decision-making models used, the value added to the business as a result of using that model and the advantages as well as disadvantages of applying that kind of decision-making model to the business. A drone is an unmanned aerial vehicle(UAV) that is used to transport packages and parcels from the company to its customers. With the emergence of new inventions and advancement of Internet of Things(IoT) together with artificial intelligence(AI), the development of smart drones has been skyrocketing in a bid to solve common problems of product delivery. The military has been using drones for a long time for surveillance, supplies delivery to troops and for warfare. However, the use of drones was not available for commercial use but has now been recently introduced with big companies like Amazon being on the forefront of utilizing this market niche (Newman, 2017).

The use of drones has been available to other industries such as security monitoring, surveys and safety in agriculture and construction and in taking aerial views of news and traffic. However, it is just but recently that the use of drones have ventured in product delivery. In many developed countries, the concept is still under development and they are testing different ways in which drone delivery can be actualized. The issue of safety is the main hindrance of drone implementation in the product delivery industry. The United States Federal Aviation Authority is working together with NASA to put in place a system that will regulator the use of drones in product delivery before allowing large fleets of drones on air (Golson, 2017).

Case Study: Smart Drone Delivery

However, in Rwanda, Africa, the idea has already been put in place and has now been operational since the year 2016. A California- based start-up known as Zipline has been delivering blood to hospitals in Rwanda through the use of drone delivery. The business has been successful so far and Zipline company is now planning to expand to other countries like Tanzania to deliver not only blood but also other medical supplies (Magdalena, 2018). The decision-making model taken by the Zipline company in this industry is with no doubt the bounded rationality model. As the decision-making process in this model involves the achievement of a goal, the Zipline company made a choice of delivering medical supplies in an undeveloped country and chose to forego all other possible alternatives that could have employed this idea. The consequences of undertaking this business venture were not anticipated and it seems it was more like a “Hail Mary” kind of decision. Despite having carried out a feasibility study, a successful outcome of making this decision was unknown (Foth, 2017).

With regard to Zipline decision-making process, it is empirical to say that, a sequential approach was used, in that a possible solution was examined one at a time by deciding on using its drone infrastructure. This is evidence in the idea of delivering medical products only instead of identifying all possible alternatives and deciding when a best solution has been arrived at. This is one of the concepts in a bounded rationality model. Additionally, the model provides a heuristic concept in that assumptions are made that focus on finding alternatives in areas that have a high success rate. In this case, the Zipline company chose an undeveloped country like Rwanda that has minimal use of drones and capitalized on that particular disadvantage. Another assumption is that Rwanda does not have regulatory laws that guide the drone industry, so the idea was fairly new to the target environment (Mcveigh, 2018). 

According to (Foth, 2017), the Zipline company raised $25 million in funding and each product delivery would cost the partner government $15-$25. That money could have been invested in building and upgrading roads in Rwanda, which could have not only eased the transportation of medical supplies but also could have been beneficial to the overall economy of the country. However, the Zipline chose the most satisficing method, a concept under the bounded rationality model, where the course of action taken only meets the minimal requirements set rather than the one that produces the best solution.

Value Added to the Business

The decision model refers to a way of organizing and managing business logic that aid in making business decisions. Business logic is a way that offers descriptive guide on how to arrive at a conclusion through careful evaluation of facts. Therefore, it is right to say that business logic involves the intellectual part in the decision -making process. The decision model allows decomposition of business logic and then groups the rules of the business into categories that create decision making models that are simple to manage and understand. In this case, we will focus on Rational decision making model and Retrospective decision- making model with regard to the drone delivery industry.

As a newly developed concept, drone delivery is increasingly gaining popularity with its wide range of application and possibilities. Besides being efficient and fast, drone delivery is expected to cut the transport cost of ordered products from the company to the consumer. However, before the concept gets to its full-blown implementation, there are many decisions that should be made. Incorporating a decision-making technique is paramount to having the right systems in place and achieving the targeted goals. In this case scenario there are two decision-making models that can be employed, the rational/classical model and the retrospective models.

To start with, the rational decision- making model uses an analysis, step-by-step process and facts to provide a solution. It is a practical analytical process that is used to evaluate data to produce a decision based on facts (IRENEGOLL,RAKESH B.SAMBHARYA, 1998). This model uses the following six steps to effectively come up with a decision.

1. Definition of the problem. – in this case, the drone industry contains a problem of not having an established system that regulate and control the drone delivery industry. Additionally, there is the problem of safety, whereby the drones can be interfered with while in transit and then used to perform a malicious attack on the public. Moreover, there is a problem of how accurate the drones will deliver the packages to the intended customer?
2. Identification of the decision criteria. – it is concerned with identifying variables that will give more insight to the problem and help solve it. The variables will give more relevant information that will be used to determine a course action. The criteria are mostly based on values and beliefs. Therefore, in our case, decision will be based on the belief that the drones will not interfere or disrupt the customer privacy. The delivery will be done in the knowledge of only the customer and the delivery company. Also, the drones will be able to last long enough in the air until the package is delivered to its destination.
3. Allocation of weights to the criteria. This step involves giving priority to the criterion that is most demanding in the decision-making process. In our case, ensuring customer safety and privacy carries the biggest weight. The other criteria are just for drone efficiency and its effectiveness and therefore share the same weight.
4. Development of alternatives. This step involves coming up with possible solutions to the identified problems. Intense analysis is done to generate all possible alternatives that can be used to solve the criteria identified. In our case, possible alternatives would be such as, getting a system in place that will identify and regulate all drones flying in an area. Any unknown drone shall be shut down. This will help control the issue of safety. Additionally, the drones should contain high-tech security protocols that will prevent any malicious hi-jerking. Moreover, a drone infrastructure should be built that will create the drone pathways and control drone traffic in the air. Lastly, there should be drones of different power that will facilitate package delivery over long distances.
5. Evaluation of the alternatives.   In this step, all the possible solutions are evaluated to narrow them down and rank them on the basis of their effectiveness to solve the problem. In our case, regarding the safety of the public from flying drones, the first alternative would be to uniquely identify and control all drones flying within a certain area. The second alternative would be only to allow flying of drones with high-tech inbuilt security protocols. The third alternative would be to create a drone infrastructure system that the drones are supposed to follow and adhere to.  
6. Choosing the best alternative. The choice that provides the best possible outcome is chosen. The process of choosing this alternative involves a multiple of alternatives and criteria ranking. The combination with the highest multiple provides the best solution and a decision is made.
7. Implement the decision. The decision ceases to be a theory and it is put in practice. In our case, implementing the security and privacy systems.
8. Evaluation of the Decision. The decision is gauged on the outcomes of the implementation. If the decision produces the anticipated results, then the use of rational decision- making model served its purpose. If not, then the decision makers go back to the drawing board and make a different choice from the list of possible alternatives.

(SM Turpin, MA Marais, 2004)

The rational decision making model makes a number of assumptions. The model assumes that the decision makers understand the decision to be made, all the variables and possible alternatives are known, no bias is involved in the choice of alternatives and that the goal is to make the best decision. While this model may be beneficial as a guide in solving through problems, this model is time consuming and cannot be used to make decisions under pressure. Moreover, having all the information that compares the pros and cons of each alternative can lead to a concept known as analysis paralysis, in that too much time is spent on acquiring information and making analysis and no decisions are made (Gigerenzer, 2015).

The second decision making model that can be used in the drone delivery system is the retrospective decision-making model. In this model, decisions are made and then the decision-makers try to rationalize and justify their decision after. This model is mostly used to make decisions that require quick attention and there is no time to gather enough information for analysis or the information available is not sufficient to guarantee an informed decision. The decisions are solely made from intuition or observations from past experiences.

Advantages and Disadvantages

This kind of model fits for our case because the drone delivery industry is a new concept that does not have prior detailed information that could be used for analysis. However, that does mean the decision makers should ignore the factors affecting the decision process. The scientific and operational factors that can be analyzed and quantified should be well understood and considered. For the drone delivery system, proper research and testing should be carried out before the implementation is released to the public. Later on the choice in regard to the problems encountered in the drone delivery practice should be assessed and the necessary adjustments should be made. The managerial qualities, such as personal values, emotional interference, judgmental capabilities and individual psychological assessment are the key factors in producing the optimal decision using this process. 

The advantages of using this model include making a decision while keeping an open mind for possible solutions that may come along the way, timely decisions where few factors are considered, making of decisions whose consequences are not fatal and making decisions that are based on personal intuition, not facts. While for instance, in our case, the safety of the public and the privacy of the customer cannot be left to intuition, this model allows a new course of action to be taken and later, the justification for making that decision is made (Chand, 2017).

The drawback to this model is that it causes indecisiveness, in that the decision maker may come under pressure over the fear of the outcome which may lead to decisions taking longer to be made than they should have taken. Additionally, this model leads to a failure of not identifying the root problem. This may be because with no proper understanding of the problem, the decisions tend to cure the symptoms and not the root of the problem. Moreover, since the decisions are made on the basis of personal qualities, one opinion of the decision makers may influence the decision made without verification of the reliability of the information given (Vu, 2014).

Conclusion

The drone delivery system is critical because it deals with the public. Therefore, it is imperative that before arriving at a decision, the decision makers are fully aware of all the factors affecting the industry, extensive research is done to gather all the information required to make a sound decision, all possible alternatives are evaluated and the best and only the best alternative is chosen to make a decision. With regard to that, the rational decision-making model is the best for use ina drone delivery industry.

Assumptions

To test the effectiveness and the reliability of the rational decision-making process as used in the drone delivery system, analytical methods such as regression analysis, business trees and forecasting are used. 

Regression analysis involves a statistical approach that is used to find the relationship between two or more variables. Usually, there are dependent and independent variables. The change of a dependent variable is measured in relation to the independent variable (newgenapps, 2017). In decision making, the statistical analysis of these variables provide the criteria through which possible alternatives are developed. understanding relationships among these variables is key to making decisions about operations, marketing and finance. In our drone delivery system scenario regression is used for predictive analysis and operation efficiency to test two variables, the number of drones delivering packages and the demand for products. Predictive analysis provides information on future expectations of opportunities and risks. Additionally, operation efficiency strives to optimize the daily processes of a business ( Webster, 2013). To determine the mechanical structure and the capabilities of the drones being used, sample tests were carried out that compared the weight of the package being delivered by the drone with the time taken to deliver the package. The following data was obtained.

From the data in the table above,

Y represents the weight of the package in kg,

X represents the delivery time in minutes.

Figure 3.1

Using the regression line equation ∑(   we get r as

r=0.989329. This indicates a positive correlation between the two variables in that with every increase of the weight of the package, there is an increase in the delivery time of the package.

The coefficient of determination 0.978771 in which when put as a percentage becomes 97.8%, shows that there is a very strong relationship between the two variables. There is 97% of data that can be interpreted using regression line formula. This information can be used to plan for the future design of the drone as the need to transport heavier packages arise.

Additionally, the information analyzed by the regression line equation will help improve operation efficiency through proper packaging of products so as to ensure that the packages arrive in time. The rational decision making model provides the data required to carry out analysis using the regression line computation.

A decision tree consists of an outline of all possible outcomes to a related problem. Possible courses of actions are weighed against one on the basis of costs, benefits and probabilities. The information derived can be used to aid in decision making process. In our case, to find the optimal security for the drone, different alternatives were weighed against one another (Rodriguez, 2016).

The Decision Model

Let A be drone identification

Let A1 be Unable to be interfered with

Let A2 be able to be interfered with

Let B be drone traffic system

Let B1 be unable to be interfered with

Let B2 be able to be interfered with

Let C be Security Protocols

Let C1 be able to be interfered with

Let c2 be unable to be interfered with

90% not expensive.

60% A1

A A2 10% expensive

20% B1 95% not expensive

B B2 5% expensive

C 20%

C1 40% expensive

C2 60% not expensive

The rational decision model put together all alternatives that can be analyzed using a decision tree to provide empirical statements for the decision makers. The path with the highest percentage provides the best solution to the problem (F. Magee, 2016).

It is important that an organization is prepared for the future changes in the industry they operate. This is to ensure that they are aware of the changes and necessary adjustments are done to the organization. Forecasting involves the process of predicting future changes and trends in an industry. The forecasting process takes data patterns and evaluates it to produce meaningful information that will guide in decision-making. Additionally, forecasting analyzes a future threat and keep the organization aware so that significant changes are made to prevent and/avoid the threat from occurring. Using the rational decision-making method, forecasting can be used to identify an overlooked piece of information that can be used to develop possible alternatives for decision making (CLIVE W.J. GRANGER and MARK J. MACHINA, 2006).

In our case, since drone delivery is a fairly new industry, forecasting is used to determine the next step of action. As the drone delivery system is developed, using forecasting the progress of a company’s drone delivery department can be monitored and controlled against identified future expectations. For instance, drone delivery for commercial uses in many cities has not been authorized. Using forecasting, companies can determine the feasibility if the drone delivery project even before the FAA issues regulatory rules. This can help the company proceed with caution so as not to  go into an overproduction while the demand is nil (Schliebs, 2016). 

Conclusion

In a nutshell, The Rational decision-making model offers an in-depth decision -making guide that is applicable in most industries. Additionally, the model offers steps that ensure all possible choices to a problem are considered hence the margin of error is very low. The rational decision-making model offers detailed steps and information, so that even a low-experienced manager can afford to make informed and thorough decisions. By use of regression and decision trees techniques, a thorough analysis can be done that enables decision makers to use the analysis to make a sound decision that contributes to the development of the organization.

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