Decision making can be defined as a course of choosing the finest amongst the diverse alternatives. It implies that it is the proceeds of choice making. There are assortments of alternatives in the departments as well as the organization in general. Prior to decision making, it is vital to appraise all available alternatives whose disadvantages and advantages are known. It assists the organization to make the most suitable decision and also the most crucial role of management (Bouyssou, Dubois, Prade, & Pirlot, 2013). Without other functions of management like organizing, staffing, controlling, directing, planning cannot be executed decision making is a vital managerial task.
Good utilization of company resources – Decision-making assists in resource utilization that eventually leads to the achievement of company objectives. The resources include materials, markets, money, men, methods, and machines (Bouyssou, Dubois, Prade, & Pirlot, 2013). The manager must make the appropriate decision for every available resource, which leads to improved use of those resources.
Business growth – Correct and swift decision making leads to improved utilization of resources. This will assist the organization to tackle new challenges and issues. Also, it assists the organization to achieve its goals. This will eventually result in swift organizational growth.
Facing challenges and problems – Better decision making assists the organization to handle new challenges and issues. Better and swift decisions assist to resolve issues and to admit new confrontations.
Achieving objectives – Sane decisions assist the organization to attain all its goals swiftly. It is based on the fact that sane decisions are made following the evaluation and analysis of all available alternatives.
Increases efficiency – Sane decisions assist an organization to increase its efficiency. Efficiency can be defined as the relation between cost and returns. Given that the cost is low and profits are high, there is efficiency and also vice versa. Sane decisions lead to improved profits and low expenses.
Facilitate creativity – Sane decision making foster creativity. This can be attributed to the fact that it assists to generate a new process, new products, and new ideas among others. This leads to innovation that offers a competitive advantage to the firm.
Motivates staff members – Sane decision making leads to employee motivation. Staffs are motivated to execute sane decisions. Organizations realize high returns when they implement rational decisions. Thus, it can offer non-financial and financial advantages to the staff.
Assists in policy-making and planning in an organization – Any plan or policy is developed via sane decision making. Plans and policies cannot be made without proper decision making. Planning entails making suitable decisions from an assortment of alternatives.
Appraisal of managerial performance – Managerial performance can be appraised through decisions. This implies that a manager is considered efficient and able if the decision is superior and vice versa.
Discharge of managerial function – Managerial tasks like controlling, organizing, planning, staffing, and directing cannot be performed without proper decision making.
For the enforceability, reliability, and rationality of decisions, company managers ought to pursue a chronological set of steps. A decision is considered rational if proper techniques are selected to attain the projected objectives (Triantaphyllou, 2000). The following are acknowledged phases involved in the decision-making process:
Decision analysis is a quantitative, visual and systematic method to evaluate and tackle crucial preferences confronted by organizations. Decision making uses an assortment of equipment to appraise all applicable data to help in the decision-making process and integrates elements of management techniques, psychology, economics, and training. It is usually applied to evaluate decisions that are made in the circumstance of diverse variables and which have various probable objectives or outcomes. It can be applied by groups or individuals seeking to make a decision connected to capital investments, strategic organizational decisions and risk management. A graphical representation of probable remedies or alternatives, as well as uncertainties and challenges, can be developed on an influence diagram or decision tree. Also, more complex computer models have been applied to assist in the process of decision analysis.
Managers can use the following three key variants of decision analysis, which include multi-attribute utility analysis, probabilistic forecasting, and decision tree analysis.
Decision tree analysis
This is the oldest and commonly used type of decision analysis. Elements of an effective decision tree analysis entail sensitivity analysis to direct refinements, subsidiary models to guarantee completeness and application of basic displays. Additionally, the integration and direction that the analysis provides to the participation of professional and the engagement of executive managers (Parnell & Tani, 2013). The top management should be familiar with decision analysis and its typical applications via the literature and thus should apply them on real decision to appraise their value. The decision makers usually spend some time generating a decision analysis of the idea. Also, they should keep in touch with the company executives.
AIL Company was given the chance of acquiring the defense market patent to a novel flight-safety scheme patent. The inventor proposed that he had strong technical superiority and solid patent status, though the product market was shaky, especially due to pending legislative proceedings. Also, the investor hinted that he would give the chance to another organization if AIL were not interested, thus he left to make a decision within a month, an epoch that is evidently insufficient to solve any existing uncertainties known to the organization.
AIL had not applied official decision analysis previously. However, the executive board understood the decision analysis model and its basic applications via literature and was eager to apply these approaches to a real decision to appraise its value. The decision regarding patent seemed to be a good contender for such a trial. A group of AIL staff and outside experts spent about a fortnight generating an analysis regarding the patent idea. All through they maintained communication with the AIL executives.
The examiners applied basic decision tree approaches. Exhibit 1 reveals the direct choice, to buy a six-month alternative on the patent right or reject the offer, and the major improbability that impacted the verdict. The fascination of every result or decision tree path is indicated by its current value earnings. The range is a loss of $700000 to a profit of $10.5 million. The projected value at every node in the decision tree is premeditated by picking a probability-weighted average of its constituents. Calculating the figures provides the projected figure of $100425. This means that AIL project to gain $100425 by purchasing the six-month patent rights option (Ulvila & Brown, 1982).
Exhibit 1. AIL’s Decision Tree
The techniques of the examination (assigning figures, calculating outcomes, specifying the tree) are clear-cut. The efficacy of the appraisal, however, relies on how the evaluation procedure is controlled than on the techniques (Parnell & Tani, 2013). Five elements that are usually absent in abortive trials to implement decision analysis marked this application as a success.
The focal point of the evaluation was basic tree indicated in exhibit 1. The most prevalent blunder that an amateur at decision analysis engages is to incorporate everything the decision entails in the decision tree. It is a definite approach to get wrong outcomes, and can only be identified by the examiners. Thus, such a decision tree is less likely to impact the manager’s resolution.
The clue is to create a basic tree that incorporates the significance of the issue by including its vital aspects. In this event, the chief aspect impacting revenues were the likelihood of exercising the decision, pursuing the second contract and receiving the preliminary contract.
Use of Subsidiary approaches
The examiners at AIL created models to improve projections of the most susceptible inputs. Using a basic tree does not make the analysis incomplete or coarse; subsidiary schemes can guarantee any desired extent of sophistication and detail. In this circumstance, the examiners utilized three subsidiary models. They used one to compute present values, the other to establish yearly earnings and the other to evaluate the possibility of getting a contract.
The final model revealed crucial aspects like the terms and timing of a probable legislative directive for the system, the potency of probable competitive systems, and the probability of a near crash or crash of a plane during the subsequent several months. The examiners used a third model to evaluate likelihood distributions of income from the contracts that revealed improbabilities in the figure of units, the profit margin and the price per unit. Every subsidiary representation was solid and could be demonstrated on one chart, which the examiners utilized to answer the executive manager’s questions regarding how the statistics in the chief representation were obtained. At AIL diverse individuals were involved with every element of the representation, such that every professional could concentrate on the field of their expertise. Those with great expertise regarding the opportunity of receiving the second contract, for instance, tackled the element of the issue but did not focus on other elements. The joint engagement of all professionals created a united image for executive management.
Exhibit II Risk outline for AIL’s patent buying decision distribution of incremental discounted incomes in millions of dollars.
Other firm’s experiences
A range of firms has applied decision tree analysis when making various decisions. For instance:
Question 3: Application of Simple Multi-Attribute Rating Technique (SMART) to the Outlined Decision Issue
The SMART approach is based on a liner additive form. This implies that the entire value of a specific alternative is computed as the summation of the performance score (value) of every criterion (attribute) multiplied with the weight of that attribute (Liesiö, 2014).
In SMART, a ranking of alternatives is assigned directly, in the normal degrees of the criteria. To maintain the rating of the alternatives and weighting of the criteria as split as possible, the diverse levels of criteria should be converted into a general internal scale. In SMART, this is calculated mathematically by the decision-maker through value function method (Liesiö, 2014). The basic and commonly utilized type of value function approach is the additive model, which can be applied via a linear scale especially for simple incidents.
Stage 1: Identification of decision-maker (s)
AIL executive managers and directors (the company vice presidents, president for business operations and development) were to make a decision as to whether to obtain the defense market rights to a novel flight-safety patent.
Stage 2: Identification of the issue (s); utility depends on the purpose and context of the decision
The alternative action plans are selected based on whether to carry on with a second contract, obtain an initial contract or likelihood of exercising the decision.
Stage 3: Identification of alternatives
This phase would categorize the results of probable actions, an information collecting procedure. In order to comprehend the decision issue facing AIL, it is vital to sub-divide them in phases to manage every probable aspect, the directors engaged in the refinement of the aspects. Through the use of interactive digital programs, the examiners evaluated how sensitive the outcomes were to transformations in the inputs to the analysis tree.
Stage 4: Identification of criteria
It is crucial to restrict the dimensions of value. This can be attained by combining criteria or by restating, or by omitting insignificant criteria. A criterion will not be considered if it has a very low weight. The decision analysis in AIL was stretched in a manner that could be more receptive to how the decision maker required to understand. The professionals at AIL created formulas to refine estimates of the most receptive inputs.
Stage 5: Assigning values to every criterion
Ranking in decision making is easier compared to creating weights. It will be straightforward if there is one decision maker, but for a group, it will require a thorough examination of relative significance. The decisions were assigned value based on NPV. AIL had to analyze the risk profiles of all the alternatives they experienced. They were either to make a loss or make profits based on their decisions.
Stage 6: Determining the weight of every criterion
The most crucial dimension can be assigned a weight of 100. Since the process needs an increasing figure of comparisons, there is a necessity to restrict the figure of objectives. AIL calculated the probability of acquiring a contract. The model evaluated crucial aspects like the terms and timing of a probable legislative mandate for the system, probability of having a crash or near-crash, the potency of probable competitive models.
Stage 7: Calculation of a weighted average of the values assigned for every alternative
This phase enables normalization of the comparative significance into weights summing to 1. The examiners applied an additional model to evaluate likelihood distributions of incomes from the contracts that showed risks in the profit margins, the cost per unit and figure of units.
Stage 8: Making an interim decision
Every subsidiary formula was solid and could be represented on one chart that examiners utilized to respond to the executive manager’s query regarding how the statistics in the chief model were obtained. The alternative of waiting and looking for sublicense had 94% chances of resulting to a loss, 6% prospect of leading to a distribution with a projection of around $830000 and could lead to an NPV of around $50000. This representation the decision makers to settle for the less uncertain plan although it provided a slightly lower NPV.
Stage 9: Performing sensitivity analysis
Sensitivity analysis is utilized to evaluate how solid the selection of an alternative is to transformation in the statistics applied in the statistics. The most significant aspect was the close responsibility of the executive management with the analysis group throughout the evaluation. The contact guaranteed that:
By using an interactive digital program, the examiners established how sensitive the outcomes were to transformations in the contributions of the tree. The analysis was also stretched in a manner that could be most relevant to the decision maker’s projections. The sensitivity analysis had to evaluate whether the contract met the guaranteed criteria. The company would make its decision based on the value of benefits.
Exhibit 2 reveals the risk sketches of the choices AIL faced. Buying the patent could provide the projected NPV of earnings of almost $100000, a 60% opportunity of losing almost $ 700000, a 29% opportunity of losing almost $125000, and an 11 percent opportunity of having profited from a distribution with an expectation of almost $5.25 million (Ulvila & Brown, 1982). Looking for a sublicense, waiting and the alternative would have a 94% likelihood of leading to a loss and 6% probability of resulting to a distribution with a projection of around $830000 and would lead to an NPV of almost $50000. This representation endorsed a unanimous verdict by the decision-making team to opt for the less uncertain plan although it provided a slightly lower NPV (Ulvila & Brown, 1982).
Question 4: Strengths and Limitations of SMART
The configuration of the SMART approach is comparable to that of customary CBA owing to the fact that the total “value” is computed as a weighted summation of the impact scores. Also, in the CBA the unit prices operate as weights and the “impacts scores” are the enumerated CBA impacts (Zopounidis & Pardalos, 2010). This close rapport to the recognized CBA approach is fascinating and makes the technique simple to understand for the decision maker (s).
In a screening stage where a number of disappointingly performing alternatives are discarded leaving split alternatives to be regarded in more facet, the SMART approach is not constantly the appropriate choice. According to Hobbs and Meier (2012), SMART has an inclination of oversimplifying the issue if utilized as a screening approach as the top few alternatives are habitually very familiar (Hobbs & Meier, 2012). Instead, diverse weight scales ought to be utilized and alternatives that conduct well under every distinct weight profile should be selected for further examination (Smith & Von Winterfeldt, 2004). Additionally, it will assist to spot the most solid alternatives. The SMART approach has rather increased demands on the intensity of detail in the input information. Appraisal of value functions is vital for every low-level characteristic, and weights ought to be allocated as a trade-off.
SMART analysis entails utilization of direct rating approach of selecting raw weights. This is because it is cognitively basic and thus it is presumed to give more accurate and consistent decisions from the decision-maker (s). There is normalization of raw weights and the normalization procedure gives diverse theoretical distributions for the ranks. This implies that the named distributions are the ROD weights.
In conclusion, owing to the above examination, it should be noted that it is vital for every organization to perform decision analysis of making for great part of their verdicts, since issues that are regarded as minor can be evaluated at an advanced angle with certain decision-making equipment, though it might be restricted by resources like cognitive biases and time. Thus, organizations are advised to develop a branch that will successfully tackle all decision analysis aspects that are likely to impact the organization both directly and indirectly.
Bouyssou, D., Dubois, D., Prade, H., & Pirlot, M. (2013). Decision Making Process: Concepts and Methods. Hoboken, NJ: John Wiley & Sons.
Hobbs, B. F., & Meier, P. (2012). Energy Decisions and the Environment: A Guide to the Use of Multicriteria Methods. Berlin, Germany: Springer Science & Business Media.
Liesiö, J. (2014). Measurable Multiattribute Value Functions for Portfolio Decision Analysis. Decision Analysis, 11(1), 1-20.
Parnell, G. S. & Tani, S. N. (2013). Handbook of decision analysis. Hoboken, N.J: John Wiley & Sons.
Smith, J. E., & Von Winterfeldt, D. (2004). Anniversary article: decision analysis in management science. Management science, 50(5), 561-574.
Triantaphyllou, E. (2000). Multi-criteria Decision Making Methods: A Comparative Study. Boston, MA: Springer US.
Ulvila, J. W., & Brown, R. V. (1982). Decision analysis comes of age. Harvard Business Review, 60(5), 130-141.
Zopounidis, C., & Pardalos, P. M. (2010). Handbook of multicriteria analysis. Berlin: Springer-Verlag.
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