Discuss About The Uncertain Multi Attribute Decision Making?

Project Objectives

Question:

Discuss About The Uncertain Multi Attribute Decision Making?

A decision making situation can be define as a critical situation where an organization demand for managerial decision making process to expand their organizational performance, to avoid damages or select and evaluate alternatives. In most of the cases decision is an opinion, judgment or position get hold of after consideration. Data Center Enhancements Inc. is an advanced IT consulting company specialized in large-scale infrastructure design and strategic planning services is currently working on a project to enhance their services and implement their business to upper level. As they are trying to increase their business their storage facility also needs to update. The company want to implement their storage facilities to reduce their capital expenditures, increase system agility and enhance systems management. There is mainly two way the storage service can be implement as a new in house facilities or cloud. Now the decision making team is to consider all option and make a suitable decision that suits the company. It involves judgment, thinking and measured action to allocate unalterable distribution of capitals with the determination of reaching a preferred objective. It also has its essence in economic theory include ascription of meaning, information seeking and subsequent execution action. Eventually, the purpose of decision making is to lessen uncertainties among what actually occurs and what was estimated to occur. Cost, time and scope are the main fundamentals of any project. This three constrictions is certain to affect the task’s outcome. Over decision making it is not essential to plot for risks however reality check can ensure the correct path to be taken.

It is essential for this project to get a successful start for focusing on real issue and aware of peculiarities of stakeholders (Ford and Richardson 2013). According to one experienced project manager how the team will work totally depend on the first team meeting tone if it is disorganized or bogged down could lead to self-subsequent implementation action. Allotment a good kick off meeting is an effective way to embark at the beginning of a project subsequent application action (Xu 2015). There are mainly three objectives managers try to accomplish in first meeting.

1. First, the chances will be explored of implementing both in housing and cloud deployment ion.
2. Second, the cost and other analysis will be considered to check the company benefits.
3. The third and most important objectives is to choose the correct platform and proceed.

A cost investment project evolution needs initial variables like product values and initial capital expenditure and ongoing operating expenditures including construction prediction over the financial lifespan of the project and suitable period of equipment or services (Yu 2013). Some of the variables can be estimated by analyzing the current situation and have a little uncertainty. Other variables may have higher uncertainty and lack of relative confidence. Many methods with changeable degrees of complexity are recycled in organization for handling improbability in assets investment judgments.

Factors Affecting the Project Outcome

Sensitive analysis- sensitivity analysis can be referred as technical indication of probability in a project. This analysis begin with base case situation using input variables and analyzing the change of specific percentage below and above expected value (Busenitz and Barney 2017). Example, for in housing facilities company needs to pay the IT employee every month and in other hand the also have to pay the cloud service provider. For each variables the investment will vary in different manner like there will be 1000 dollar/ per month needs for in housing facilities while in cloud the 800 dollar will be more than enough.

Scenario Analysis – It is the technique to reflect investment profitability and the range of likely variable values. In scenario analysis circumstance such as less recovery, lower production, higher production cost are compared and calculated (Endsley 2017). There are mainly two types of scenario as bad scenario and good scenario. For In housing facilities company needs more space but in cloud there is no space needed as the service provider will take the responsibility of managing and monitoring.

Computer simulation – From above probability analysis of each variables are combined and in a computer simulation. The outcome as effectiveness of the profitability quantity is has been determined for each combination (Orlovsky 2013). Hundreds of randomly generated combinations for each variable are normally analyzed.

The type of information available for a project is responsible for evaluating the decision for decision maker such types can be 1. Certainty 2. Risk 3. Uncertainty. When complete information of the situation is available then the decision situation is referred to as decision under certainty. If the decision alternatives under consideration is expected in several probable outcomes and the probability of each outcomes can be assessed such decision situation is under risk. Example of certainty, as the company is growing the data is growing gradually too and to store this vast amount of data  in housing facilities they need to store data manually and also need to update the hardware and software from time to time. In case of cloud this issue is avoidable because the service provider is fully responsible for monitoring and manage those data. Uncertainty data loss occur at any time in inn-house facilities it is nearly impossible to collect this data this could be define as risk. In other hand the managers has no idea of the probabilities for each possible outcome the decision is considered as uncertainty. In the past the risk and uncertainty considered similar as distinguishable entities.

Storing data is not a difficult, the dare is to store data reliably and securely so can be revealed and analyzed dominant insight for improved decision making and stay up to date (Gupta, Seetharaman & Raj, 2013). Groups need to choose suitable platform that can store data such as product, customer data and supplier data. Decisions will be made after analyzing followed facts:

• Cost efficiency- in-housing facilities has some drawbacks as the implementing storage system is extremely expensive as the software and hardware needs to supplant time to time for updated sorting systems and increasing amount of data.
• Lack of Understanding – for appropriate analyze and store dynamic data vast amount of understanding is needed. While in cloud there is no need of understanding as service provider will take care of most things.
• Vulnerabilities- one of the main aspects of good storage system is security. Cloud can lack the security issue while in-housing

Conclusion

Decision making is very critical section of business as some decisions are easy to make and can be done rapidly without much time and struggle in decision making process. Other in critical situation needs substantial consideration of the circumstances surrounding the decision. As we discussed in the report cloud is preferable than in-housing because that need lake of monitoring and management also cost efficient. The company can save lot of their budget investment as they do not need to invest on purchasing, managing and update software and hardware. This spontaneously reduce the cost of contracting employees who would perform these responsibilities in an in-house site.

References

Busenitz, L.W. and Barney, J.B., 2017. Differences between entrepreneurs and managers in large organizations: Biases and heuristics in strategic decision-making. Journal of business venturing, 12(1), pp.9-30.

Criminisi, A. and Shotton, J. eds., 2013. Decision forests for computer vision and medical image analysis. Springer Science & Business Media.

Endsley, M.R., 2017. The role of situation awareness in naturalistic decision making. Naturalistic decision making, 269, p.284.

Ford, R.C. and Richardson, W.D., 2013. Ethical decision making: A review of the empirical literature. In Citation classics from the Journal of Business Ethics (pp. 19-44). Springer, Dordrecht.

Hui, L. and Nicolis, A., 2014. Equivalence principle for scalar forces. Physical review letters, 105(23), p.231101.

Mota, D.F. and Shaw, D.J., 2017. Evading equivalence principle violations, cosmological, and other experimental constraints in scalar field theories with a strong coupling to matter. Physical Review D, 75(6), p.063501.

Orlovsky, S.A., 2013. Decision-making with a fuzzy preference relation. In Readings in Fuzzy Sets for Intelligent Systems (pp. 717-723).

Power, D.J., Sharda, R. and Burstein, F., 2015. Decision support systems. John Wiley & Sons, Ltd.

Velasquez, M. and Hester, P.T., 2013. An analysis of multi-criteria decision making methods. International Journal of Operations Research, 10(2), pp.56-66.

Wakker, P.P., 2013. Additive representations of preferences: A new foundation of decision analysis (Vol. 4). Springer Science & Business Media.

Xu, Z., 2015. Uncertain multi-attribute decision making: Methods and applications. Springer.

Yu, P.L., 2013. Multiple-criteria decision making: concepts, techniques, and extensions (Vol. 30). Springer Science & Business Media.

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