Introduction There are many types of decision analysis that can be used for decision making. Different decision making can be conducted to predict future outcome. It is used for many purposes in order to achieve certain objectives. These will give accurate prediction or to prove the target set up by organization either it is achieved or not. In our case study, we would like to review our case study based on the different situation and how the implementation of decision analysis took place. After reviewing the case study, we would like to conclude based on three different case studies.
The contributions related to decision analysis from the case studies will also be explained in last of our discussion . It will give society more understanding and appreciate the knowledge that will benefits society in the current and future. For our case study number one will explain on the Decision Support Framework (DSF) that uses system-based approaches and decision science that will enable society to make decision. This decision analysis will revealed on definition, steps required making decision, how web based is used and others related to it.
Meanwhile, case study number two will be focusing on the implementation decision analysis in the operations such as operation in the area of energy, followed by manufacturing and services, in medical, military and lastly general areas. This will brief more on suitable applications used in different areas in order to achieve the desire result and minimizing unpleasant risk. Lastly, the case study number three will explained on problem structuring for Multi-Criteria Decision Analysis Intervention.
It is about the methodology to support decision making which involved multiple objectives that have to be pursued. In this case study it will give steps involved, main problem faced by decision analysis, structuring tools that available for decision analyst in deploying effective MCDA interventions, and others related to the case study also will be explained. From the case study above it will show how the subject of decision science is used in different ways or advance ways that introduced after experiencing different situations. Review on Decision Analysis in the Operations
Decision analysis is a set of quantitative method for analyzing decisions that used for the expected utility as the criterion for identifying preferred alternative. It also the tools for the making decisions on uncertainty and multiple conflicting objectives, and these tools can be especially useful when there is limited directly relevant data so that expert playing their part in decision making. According Cornell and Kirkwood (1991) provide detailed discussion of situation where the decision analysis will be useful. The decision analysis will used in several of applications.
There are six main areas that will be focus on and solved using the decision analysis. The first area is energy, followed by manufacturing and services, in medical, military and lastly general areas. The list of applications, present the significant details about methodological and implementations issues, which are classified into the areas of strategy and objectives generation, problem structuring or formulation, probability assessment utility or value assessment, sensitivity analysis, communication or facilitation, group issues and implementation.
From Cornell and Kirkwood (1991), they said that there is some subjectivity in deciding whether particular application is suitable for the decision analysis. An application had to be generally explicitly analyze alternatives for a decision problem using judgmental probabilities or subjectively assessed utility functions. Ambiguous cases were resolved by including the article if, on balance, it took a decision analysis approach. There is also some subjectivity in deciding whether an article reports an application.
Many of the surveyed articles report case are decision analysis to provide a specific decision problem. Other articles report analysis performed to provide background for policy making. In few cases, there is no application but the material is direct interest applications. The first main area is decision analysis on application of energy. Energy applications are classifieds into bidding and pricing, environmental risk, product and project selection, strategy and technology choice. For biding and pricing, a conventional economic analysis had proven inconclusive due to the large uncertainties involved.
The tree-based approach accommodates the uncertainties, including substantial probabilistic dependence, and displays the judgmentally assessed probabilities explicitly. The results reinforced the position of those in management who wanted to retain the refinery until a substantial offer was made, and this school of thought prevailed. From Keefer (1995), he uses three-point discrete-distribution approximation in conjunction with probabilities trees to help find the worth from a potential buyer’s viewpoint of a refinery that a major oil company had shutdown.
For environmental risk, the efforts to use multiatribute value analysis and decision conferencing that being used by French (1996), is an aid in responding to nuclear accidents. He presented the multiattribute value hierarchy that was structured in a series of decision conferences held as part of the International Chernobyl Project. He described that development of system to aid in choosing short and medium-term countermeasures to build a decision support system for emergency response and situation when there is risk of an imminent accident.
For product and project selction, the use of project appraisal methodology (PAM) includes multiattribute scoring function which is to calculate expected benefits from research and developments projects at multilevel funcing. Benefit-to-cost ratios are calculated from expected benefits and the funding increments and these ratios are used to help allocate research and development budget. The use of value focused thinking to structure and quantifies basic values in the context of strategic planning.
By eliciting strategic objectives for the attributes utility fuctin it will illustrate value tradeoffs at the strategic level. For technology choice the utilization a variety of analytical methods including Monte Carlo Simulation, linear programming and statistical analysis in developing both process and life-cycle cost models and use multiattribute decision analysis to integrate the model results and serve as the basis for extensive sensitivity analyses. The second areas that are using the decision analysis is the manufacturing and services.
Those applications are classified into finance, product planning, R&D project selection and strategy. In the finance application, the tools of decision trees, judgmental probabilities and simulation to analyze operational risk in probabilistic fashion. The use of decision analysis as a framework for making cost-effective risk management decisions. Mostly bank utilized this method in implementing an ongoing corporate-wide contingency planning for all operation’s services.
For product and planning part, the use of simple decision tree and accompanying sensitivity analysis to help convince the management of a large logistic operation to waive mandatory annual exhaustive inventory counts in warehouses where sample counts showed high accuracies to maintain the accurate inventory records. For R&D project selection, initially the analysis helped in focusing the attention on critical aspects of the project and subsequent analyses were used to monitor project’s progress.
For strategy planning the process of integrates concepts and tools from strategy with those from decision analysis and creates structured interactions between management decision board and strategy development team. Such as Bodily and Allen ( 1999 ), Strategic Decisions’s Group’s six step dialogue process for creating , analyzing, choosing and implementing business strategies. In the medical area the use of Bayes rule helped in focusing attention on appropriate values for relevant probabilities and for the cost of possible errors.
The sensitivity analysis showed the proposed testing was inappropriate over the plausible range values of parameters. For military part, in the application on the development of an air mission planning model for use in combat simulation model. The crux of the planning model is a mixed integer programming formulation (MIP) that allocates the optimum number and type of aircraft and munitions against each target, but its data vary with the uncertain state of the weather. The plan with the highest expected value is obtained by solving a simple decision tree that utilizes solutions to the MIP for each possible state of the weather.
In public policy, the optimization model with an exponential value function with zero-one is to assist the U. S. National Cancer Institute with a proposal funding decision. The decision is modeled as a portfolio selection problem where an uncertain budget is to be allocated to various states in the U. S. to fund smoking reduction initiatives. These formalized methods were used to reduce the impact of political pressures on the decision makers. For general areas, the use of weighted-additive value model to rank-order various merger options as well as remaining as separate rganizations. This application mainly demonstrates the process of applying decision analytic techniques to strategy formulation and implementation, rather than the technical details of arriving at an optimal solution using such techniques. For example, the merged between The Institute of Management Sciences (TIMS) and the Operations Research Society of America (ORSA) to form the Institute for Operations Research and the Management Sciences (INFORMS). To conclude, decision analysis played the most important role in many fields.
All those decisions made are using various types of tools in decision analysis making. By using all the tools the results are mostly very accurate. Decision analysis help the world for having balance and optimize recourses management. Problem Structuring for Multi-Criteria Decision Analysis Interventions Multi-Criteria Decision Analysis (MCDA), a methodology for supporting decision making when multiple objectives have to be pursued, has been extensively used to support wide variety of complex decision problems. In this article, it discussed problem structuring for MCDA interventions.
There is a limited literature how to structure MCDA models and also discussing two keys problems structuring tasks concerning the earliest of an MCDA interventions and to propose a general framework for conducting MCDS interventions. There are two main problems faced by decision analysis when conducting MCDA interventions; defining the problems and scoping participation. In defining the problem, the significance of problem formulation in organizational decision making it is surprising that the literature on MCDA has devoted relatively minor attention to the processes of articulating and defining a multi-criteria problem.
The assumption made by many practitioners that structuring a multi-criteria decision problem is more ‘art than science’ and that can be learned from experience. From this it suggest that experienced analysts are able to recognise familiar patterns or structures of problems, and use them as templates to build their decision models. However, other experience suggests that the use of decision analytic structures are well suited to problem situations that are clearly defined, but less so when they are ill-structured or ‘messy’.
Empirical research has shown that the definition of problems, particularly those of the ill structured type, is not given, but continually negotiated among members of the organisation before and during an intervention. This process of negotiation can be conceptualised as follows. First, managers are constantly striving to make sense of their internal and external environments in order to manage and control their organisations. This sense-making process is aided with the help of a unique mental framework that is developed through experience, and which includes systems of beliefs and values.
A ‘problem’ emerges when the use of such mental framework, to make sense of a particular situation, leaves the manager uneasy or dissatisfied because she/he does not know how to deal with that situation. Because different managers will experience different problems by applying their own unique mental frameworks to what might be thought of as the same situation, the analyst will not be able to think and talk about the ‘problem’ without ascribing an owner or owners to it.
Second, the problem which will eventually be presented to the analyst is the result of a process of discussion within the organisation, most typically within a team of managers. Thus we might expect that when the analyst starts an MCDA intervention with a given problem presented by the client, the reality is that other versions of the same problem are likely to exist. Because there not so much to model what will become the actual multi-criteria decision problem to be solved it become a challenge for the analyst at this stage is then, but to identify and model the different perceptions of the problem held by different managers.
Several problem structuring tools are available to support this task. These include, for example, cognitive mapping; soft systems methodology dialog mapping; strategic choice approach and group model building. This is important because, when managers define a problem, it will be defined in their own language and based on their own interpretations of the problem, their own experience or expertise, their own value systems, and so forth.
A problem defined in this way will thus include factors that may not be typically regarded as legitimate variables in a standard MCDA modelling project, but that are nevertheless important if the analyst wishes to understand the needs and concerns of any particular client or client group. The challenge for the analyst is, thus, being able to formally map aspects of the problem in the terms of the concepts used by the client. For if there is a doubt in the client’s mind about whether the correct concepts have been taken into account, she/he is unlikely to believe in the solution to the problem, let alone act upon it.
To recognize the problem definition in organizations involves negotiation between managers with multiple word-views about the problem has some practical implication for the analyst. First, if the MCDA intervention is intended to have some effect on the organisation, the analyst may need to discuss with the client a redefinition of the problem before trying to help and second when working with a client group whose members have different views or interpretations of the problem, the analyst must choose whose interpretation to pay attention to.
The choice made must free from favouritism of one another interpretations. Once the problem has been defined with the client or client group, the analyst should be in a good position to identify a particular decisional element of the problem upon which a relevant a multi-criteria evaluation model can be built. A quite useful tool at this stage is Keeney’s concept of decision framing, which connects the strategic objectives of the organisation with the fundamental objectives for the particular decision and the alternatives to be considered.
However, before proceeding, the analyst must scope the required levels of participation needed for the subsequent stages of the intervention. In scoping participation a careful analysis of 4000 decisions has been conducted in a variety of organizations and found that almost half of them ‘failed’ in terms of implementation or the achieved result. Discovering that the overriding reason for these failures was due, in large part, to the failure of decision makers to attend to the interests and information held by the key stakeholders of the organisation.
Within the context of an MCDA intervention, attention to stakeholders is needed to assess and enhance political feasibility of decision implementation. Attention to stakeholders is also important to satisfy those involved and the intervention has followed rational, fair and legitimate procedures. There are several tools for stakeholder analysis available in the literature. The most widely used techniques include the power-interest grid, star diagram, and stakeholder influence map; and stakeholder-issue interrelation diagram and problem-frame stakeholder maps.
Whichever stakeholder identification techniques are used, the actual process of choosing which stakeholders to involve in the intervention is often the result of several iterations along the following generic stages. These processes should be designed by the analyst to gain needed information, build political acceptance and address some important questions about legitimacy, representation and credibility. However, the analysts should encourage the client to include stakeholders only when there are good and prudent reasons to do so.
They should not be included when their involvement is not needed, impractical, or inappropriate. Firstly the analyst and client initiate the process by doing a preliminary stakeholder analysis using any of the analysis techniques cited above. Secondly after reviewing the results of this analysis, a larger group of stakeholders can be assembled if judged appropriate. The assembled group should be asked to brainstorm the list of stakeholders who might need to be involved in the intervention. Lastly, both analyst and client finalise the various groups who will have some role to play in the intervention.
These will typically include the sponsors and champions, a coordinating group, a core decision analysis team, and various advisory or support groups. We are going to the next stage in the intervention process which is structuring MCDA Evaluation Models. There are three main tasks in structuring MCDA evaluation models: the representation of objectives in a value tree, the definition of attributes to measure the achievement of objectives and the identification of decision alternatives. In structuring value trees is always to represent the objectives that decision makers want to achieve.
In many multi-criteria models, but particularly so in multi attribute utility/value models, these objectives are organised as a value tree. A value tree decomposes the overall objective of an evaluation into operational objectives, which can be more easily employed to assess the performances of decision alternatives. Two approaches have classically been suggested for structuring a value tree: top-down and bottom-up. Behavioural research has also discovered that these two approaches (top-down and bottom-up) may generate value trees with different shapes, as values are ‘constructed’ instead of merely extracted from decision-makers’ minds.
Therefore the choice of approach is clearly an important modelling decision that the analyst has to make. Other possible tools for structuring a value tree involve the use of probes and grouping of ideas, such as Belton and Stewart’s CAUSE probes and Parnell’s affinity diagrams. Another set of tools for such purpose involves qualitative models that represent causality/influence between variables. For defining attribute, search objective placed at the bottom level of the value tree, an associate attribute should be specified.
This attribute is a performance index employed to measure the impact of adopting each decision alternative on the organisational objective which is being pursued. There are two dimensions for classifying attributes: in terms of its alignment with the objective which is being pursued and the way it is measured. The descriptions of these two dimensions are the way the objective is measured – Direct or Indirect; a direct attribute measures directly the degree of attaining the objective and a proxy attribute measures indirectly the concern expressed by the objective, by assessing the degree of achievement of its associated objective.
And the type of attribute – Natural or Constructed; a Natural attributes measure directly the concern expressed by the objective, are of general use and have a common interpretation and constructed attributes measure directly, using indicators created specifically by the analyst, the concern expressed by the objective. Independently of its type, each attribute should follow five properties to be employed in a MCDA evaluation model; unambiguous, comprehensive, direct, operational and understandable.
Consequences and value trade-offs using the attribute can be clearly understood by the decision making group and communicated to other stakeholders. Quantitative attributes tend to be less ambiguous than qualitative ones. A key point about comprehensiveness is that the upper and lower limits of the attribute are well-specified otherwise it would distort value trade-offs. Finally, it is critical that attributes are understandable, particularly if the analysis involves a group of decision makers and the modelling is conducted in a facilitated mode. Lastly in structuring MCDA evaluation models we focused on identifying decision alternatives.
The other major task in an MCDA model structuring is the definition of which decision alternatives will be assessed by the evaluation model. Traditionally, MCDA has taken an alternative-focused thinking perspective, where the set of options was assumed as given and stable. However, the identification and creation of new alternatives is certainly one of the most important aspects of any MCDA intervention. No matter how careful and sophisticated the evaluation model is; if the decision alternatives under consideration are weak, it will lead to a poor choice.
An important aspect in structuring an MCDA model is that the decision alternatives should have the same. If the analyst is careless about this aspect, it may be difficult to create a coherent value tree. There are several tools that may be employed in the creation/definition of decision alternatives, such as brainstorming techniques, cognitive mapping, and dialog maps, among others. Particularly useful tools are the ones where decision alternatives are created from considering the decision-makers’ objectives or stakeholders’ values.
This process can be repeated for each of the fundamental objectives present in the value tree. Once the list of objectives is exhausted, the same procedure can be done for two objectives at once. Another way of creating a new option is by combining existing alternatives, trying to maintain the best features of each alternative. Recently we have used a value-focused brainstorming usinga cognitive map – which allowed eliciting, organising and displaying a large set of ideas from a client group – these ideas were then grouped as decision alternatives.
Although there is a natural tendency by decision makers to discard decision alternatives or options that may appear to generate some negative outcomes, any attempt at option evaluation should be contained at this stage. The assessment of alternatives should be left for the evaluation phase of the process and not intermingled with their creation. Another aspect concerning the identification of decision alternatives is that there are instances where the alternatives are comprised by a large set of sub-options.
There are some methods that can be used to structure complex decision alternatives. The strategy generation table proposed by Howard is a simple way of creating decision strategies from the combination of options under several dimensions. Another tool is the Analysis of Interconnected Decision Areas (AIDA) technique that is part of the strategic choice approach. In this technique the links between several ‘decision areas’ are represented, each one with several options, with their compatibility explored, in order to generate a list of possible option portfolios.
For example, in an intervention with a major international hotel company, we used AIDA to initially shape a strategic decision concerning how to tackle ‘cost of sale’, and produced a list of candidate interconnected strategic options, grouped in three areas (distribution, timing launch and scope level). After all has been discussed we now created a framework in problem structuring for MCDA interventions. The techniques for multi-criteria evaluations are already well established in the literature. However, there has been much less investment in the development of techniques to support the structuring stages of MCDA interventions.
We have reviewed both the mainstream problem structuring and MCDA literatures, and identified a number of modelling tools which can be used to support problem structuring in MCDA interventions. Perhaps more importantly, our foregoing discussion should have made clear to the reader of the important role that problem structuring plays in MCDA interventions. As a conclusion this article concluded that the importance of problem structuring for successful MCDA interventions, most of them have relied on ad hoc practices for structuring the problem.
The main aim of this article was to provide a review on tools that can help this pre- MCDA phase of problem structuring. Furthermore, the article also reviewed the main task involved in building an MCDA model per se, while attempting to provide a more integrated view of the latter with the problem structuring literature. As the article presented, there are a number of problem structuring tools available to help decision analysts deploy effective MCDA interventions.
However, from our discussion in this chapter it should be clear that, when the client is a group of managers mastering the tools is not sufficient. The analyst will also need skills for facilitating the process of problem definition which reflects the power and interests of the members of that group. Frequently, however, key uncertainties are present and should also be represented. In this article it believes that problem structuring for MCDA is a rich field of research, not only about suitable tools, but also about facilitated modelling in this context.
It thus suggests some directions for further research; Development of problem structuring methods: while the field of problem structuring methods (PSMs) is already well-established in Management Science, more research could be conducted on tools that could be tailored specifically for MCDA interventions. And Integrated use of problem structuring methods: the use of standard PSMs with MCDA requires transitions from a problem structuring model to a multi-criteria decision analysis model, which may prove challenging. Consequently, a direction of research is the development of methods that could provide a seamless transition.
Also tools for supporting structuring MCDA tasks: the paper reviewed some tools that could be employed for structuring value trees, defining attributes and indentifying decision alternatives. The development of new tools is, however, still an interesting area of research – particularly if they could be more based on psychological aspects and group dynamics. To summarise, this article provided an overview of the phases and tasks involved in structuring an MCDA model within an intervention– from defining the problem and identifying key stakeholders to building the MCDA model itself.
Problem structuring is a fundamental and challenging task for any MCDA intervention; thus we hope this chapter may help decision analysts involved in such interventions and may serve as background for researchers interested in this field. Decision Support Framework Implementation of the Web-Based Environmental Decision Analysis Application DASEES: Decision Analysis for a Sustainable Environment, Economy, and Society Solutions to the widespread environmental problems often are not amenable to a straightforward application of science-based actions.
These problems encompass large-scale environmental policy questions where environmental concerns, economic constraints, and societal values conflict causing seemingly intractable political situations. Research into the development of environmental applications that use systems-based approaches and decision science will enable society to make decisions that address these complex problems. The Decision Support Framework (DSF) supports structuring complex problems that help decision-makers and stakeholders transparently evaluate scientific and technical analyses within an economic and societal values context.
From the inception of the Decision Support System (DSS) (developed by the implementation of DSF), the implementation of the DSF was seen to be a web-based application of the principles and ideas explained by the framework conceptual model. The reasons for this are technical and functional. Technically, there is a large resource pool of software and hardware options for making customizable applications and associated tools capable of accessing information on the Web.
Functionally, a web-based application promotes stakeholder involvement and participatory decision-making supporting the key aim of using community deliberation processes in the DSF. Implementation of the Decision Support Framework is facilitated through the web-based application DASEES (Decision Analysis for a Sustainable Environment, Economy, and Society). DASEES is a web application framework containing a collection of linked decision analysis tools to implement a comprehensive and coherent decision analysis framework for environmental decision making.
According to DSF conceptual model, DASEES basically is organized in five steps which is understand the context, define objectives, develop options, evaluate options and lastly take action or implementation. The set of guidance and the software tools contained in DASEES are designed to educate decision-makers in using the conceptual model and allow them to create their own decision-specific model. There are some requirements to be met such as site navigation which insist the site can be attained easily. Besides that, it needs accurate content presentation and responsive graphical tool user interface (UI).
Lastly, it also claims data management and data analysis software. REFERENCES Stockton, T1, B. Dyson2, W. Houghteling1, K. Black3, M. Buchholtz ten Brink4, T. Canfield5, A. Vega2 M. Small6, A. Rehr 2011. Decision Support Framework Implementation of DASEES: Decision Analysis for a Sustainable Environment, Economy, and Society. U. S. Environmental Protection Agency, Cincinnati, OH, EPA /600/R-12/008. 1. Neptune and Company, Inc. 1505 Suite B, Los Alamos, NM 87544 2. U. S. Environmental Protection Agency, National Risk Management Research Laboratory, 26 W.
Martin Luther King Drive, Cincinnati, OH 45268 3. Neptune and Company, Inc. , 8550 W. 14th Ave. Suite 100, Lakewood, CO 80215 4. U. S. Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, 27 Tarzwell Drive, Narragansett, RI 02882 5. U. S. Environmental Protection Agency, National Risk Management Research Laboratory, 919 Kerr Research Drive, P. O. Box 1198, Ada, OK 74820 6. Department of Engineering and Public Policy, Carnegie-Mellon University, Baker Hall 129 5000 Forbes Ave. , Pittsburgh, PA 12513 Contributions of Decision Analysis
From the case studies, there are a lot of things that briefly explained the situation needed to use decision analysis. Decision Analysis in Operations explains the ways suitable operations must be used in order to achieve expected value. Different type of areas in crucial given times can be counter by using the suitable operations given. This also explains how to build decision support system for emergency response to overcome the situation of accident during the period short and medium term countermeasures. It contributes to specific knowledge of decision science in operating ifferent type of areas. The term of expected value, bias ruled and decision tree that become the essence of decision analysis is the best way to solve the problems and making good decisions. Besides that, the Multi-Criteria Decision Analysis (MCDA) Interventions expand the scope of decision science when they are using the concept of psychology for decision making. It can be seen when the MCDA emphasized on making sense of internal and external environments in order to manage and control their organizations that included the involvement of unique mental framework.
It contributes to more understanding how human being will have different type of solving the problems by having different experiences. Decision Support Framework Implementation of the Web-Based Environmental Decision Analysis Application (DASEES), it relies on the Information Technology by using web based software in making decision. It gives the society a clear ways to solve complex problems held in the organizations by using system-based approaches. It contributes to the easier way for organization to decision-makers to evaluate scientific and technical analyses within economic and societal value context.
Thus, it can be seen that is how decision analyst use the knowledge of decision science in order to make decision. The knowledge of this can be expand, improve and move along within the advancement of technology. It is not about the good or poor type of decision analysis but it is about on how people use decision analysis in suitable ways in order to get accurate result. The most important thing is the flexibility of the knowledge of decision science that will benefit the society.