Decision Support System for Managing Complex Construction Projects - Literature review Example

Student Name: Tutor: Title: Decision Support System for managing complex construction projects Course: Date Decision Support System for managing complex construction projects Literature Review Importance of Decision Support System Decision making is a crucial feature that happens at every phase of a project such as design, implementation, maintenance, and construction. Decision making is imperative in every phase. These decisions affect the decisions that will affect the tasks be carried out. To realize decision making process that is effective, project managers as well as other project managers must grasp other similar or related projects (Kerzner, 2013). Construction is risky, dynamic, challenging and the same time rewarding. Construction activities are variable and uncertain, and usually associated with risks that are present and complex within construction projects. Particularly large-scale construction projects are growing increasingly complex as well as variable in nature, and many uncertainty and risk factors are inherent. During the management of construction projects focus has to be directed to planning since it is an important stage of decision making process (Taylan et al, 2014). Decision support systems refers to systems under control of many or one decision makers that offer an organised set of tools to influence to portions of decision-making situation in order to improve the effectiveness of decision outcomes. How to execute construction project Successful execution of the construction projects is depended on arriving at the right decision in all stages of the project. Decision support systems (DSS) describe computer-based systems which help organizational and business in processing of complex decision-making environment. Many researchers have discussed the challenge of risk and uncertainty identification within construction projects (Taylan et al, 2014). The evaluation of hierarchical structure for project uncertainty and risk should offer evaluators the convenience for easy understanding and practical usage. The evaluation structure has to avoid very complex on risk factors as well as the hierarchy requires expressing the basic risk and uncertainty condition for the project (Kerzner, 2013). The key dimensions as well as factors for construction project risk have been assessed through comprehensive consultation and investigation. The decision making process begins at the tactical and strategic levels with selection of both type of infrastructure and the study. Decision maker encounter problems in selection of decision makers. Management of decision support system The basic decision support system comprises of data management, user interface and model-based management. The user interface is crucial in supporting direct communication between the system and decision makers (Kerzner, 2013). User interface that is friendly is essential with regard to achieving intensive engagement between people making decisions and the computer. The data management system comprises of a database which stores relevant data for the situation and it is managed normally by database software. The model management aid the system through analytical capacity by data formulation. According to Baloi and Price (2003), there are main approaches of decision support system that can be defined with regard to the input they are able to handle as well as type of decision processes that they can facilitate including data-driven, communication driven, model-driven, knowledge-driven, and document-driven. Decision support systems have been used in various research application areas that are diverse (Kerzner, 2013). Within the real-world practices, decision support system applications have to be implemented using overlapping approaches. Figure 1: Schematic view of Decision Support System Construction Tendering Normally tendering is a complex task having conflicting interest between construction managers, various sets of criteria, objectives, objectives as well as solution alternatives (Kerzner, 2013). Multiple decision makers who are situated in geographically dispersed areas can achieve interactive collaboration and communication using networking technologies through the application of communication-driven decision support system approach (Galliers & Leidner, 2014).  It is referred to as group decision support systems where decision models and data are shared by a group of decision makers. Within some specific problems, it can be the integration of model-driven and communication-driven approach. Data-driven decision support system permits accessing, manipulation, processing as well as analysing huge amounts of data that is structured by use of computerised techniques like for data warehousing as well as OLAP (Karumanasseri & AbouRizk, 2002). It shows manipulation and processing time-series of real-time operational data, historical data, and external data. Document-driven decision support system Document-driven decision support system is a new approach in decision support system. It assist decision makers in accessing, managing, retrieving and analysing unstructured documents using integrated databases as well as processing technologies. The major function of model-driven decision support system is founded on quantitative approach. It provides decision support with the analytical model employing tools of decision analysis, financial, algebraic, optimization, simulation, stochastic, statistics as well as logic modelling (Demirkan & Delen, 2013). The approach may be divided into three stages comprising of analysis, solution and formulation. The knowledge-driven decision support systems make use of knowledge inference engines in the understanding and solving problems domain and give alternative actions to the decision makers. The knowledge concerning a specific domain is usually stored within a reusable state with the assistance of different AI methods like object-based, rule-based, statistic-based, induction-based or logic-based (Galliers & Leidner, 2014).  The capacity of decision support system as computerised tools in helping decision-making process has attracted many studies for over the last half a century to improve decisions within the complex construction project. Most stages in the complex construction process involve crucial decisions that have to be made by engineers, contractors, clients, architects and consultants. Strategies for Improvement The surge of unstructured information compels the need of coming up with strategies that enhance and improve organizational and individual decision-making through associating automated tools within decision systems. This is a big research challenge in decision support system owing to standard tools constructed for structured data analysis. The traditional decision support system does not have the capacity of confronting the dynamic as well as ill-defined data (Galliers & Leidner, 2014). Majority of decision support system in construction are focused on processing of quantitative data whereby the systems only analyze factual values. Quantitative Systems Quantitative systems do not have the capacity of scrutinising the real problem from the text. Decision support systems manage as well as process input of semi-structured and unstructured problems for the purpose of supporting the decision-making process (Taylan et al, 2014). Owing to social and economic development and risk management consciousness, the significance of risk evaluation is taking prominence in project management. Most of the projects possess the features of large-scale as well as great position within the national economy. The essence for risk management DSS is essential and valuable. Risk factors within the projects are complex and have close association with one another (Dey, 2001). Decision support system for urban structure maintenance involves many stakeholders as well as limited resources. Inadequate finances are always a big challenge hence decision making problems during the tactical stage are a priority setting. Challenges There are various problems of maintenance decision process that can be identified and recognised within the decision support system. Consequently, a step by step approach for supporting priority setting as well as strategy selection has to be in place. Inspection of the infrastructure components entails a vision inspection as well as measurement methods occasioning element condition evaluation (Baloi & Price, 2003). Elements and inspections’ conditions re-evaluation are carried out again four 4 months to 1 year considering the size of the city. In many circumstances the construction industry usually has a poor reputation for handling risk and uncertainty sufficiently, occasioning poor performance characterised by failure in meeting budgets and deadlines, as well as attendant loss to owners and contractors. Effective uncertainty and risk management is a kind of incentive to contractors since their profit margin will be improved and secured. Nevertheless, construction uncertainty and risk can hardly completely be eliminated (Karumanasseri & AbouRizk, 2002). Owing to advances in computer technologies as well as information exchange capabilities, there is the need to develop a decision support system that can help contractors in arriving at critical risk management decisions during the phases of contracting as well as construction. Decision Support System vs Decision making Decision support system describes a subset of computer-based information system. Decision making is synonymous with management. Dey (2001) expostulates that construction decision support system refers to a computer-based interactive human-computer decision making system which supports decision makers as opposed to replacing them, make use of models and data and solves problems with varying levels of structure both structured and non-structured (Kerzner, 2013). DSS emphasizes on the effectiveness and not efficiency in the decision processes. Decision support systems are designed for the purpose of interactively supporting all stages of the user’s decision making process. When coming up with a DSS development framework it is important to realize that various activities have to be assigned to the user or the computer (Taylan et al, 2014). Consequently, a decision has to be made on which activities is the mandate of the decision maker and which one is to be assigned to the computational system. It is crucial for the contractors to recognize the risk factors that will be encountered and means of planning risk management strategies in the course of awarding large-scale construction projects. Bibliography Baloi, D. and Price, A.D., 2003. Modelling global risk factors affecting construction cost performance. International journal of project management, 21(4), pp.261-269. Demirkan, H. and Delen, D., 2013. Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud. Decision Support Systems, 55(1), pp.412-421. Dey, P.K., 2001. Decision support system for risk management: a case study. Management Decision, 39(8), pp.634-649. Galliers, R.D. and Leidner, D.E., 2014. Strategic information management: challenges and strategies in managing information systems, Routledge, London Karumanasseri, G. and AbouRizk, S., 2002. Decision support system for scheduling steel fabrication projects. Journal of construction engineering and management, 128(5), pp.392-399. Kerzner, H., 2013. Project management: a systems approach to planning, scheduling, and controlling. John Wiley & Sons, New York. Taylan, O., Bafail, A.O., Abdulaal, R.M. and Kabli, M.R., 2014. Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing, 17, pp.105-116. Read More

The data management system comprises of a database which stores relevant data for the situation and it is managed normally by database software. The model management aid the system through analytical capacity by data formulation. According to Baloi and Price (2003), there are main approaches of decision support system that can be defined with regard to the input they are able to handle as well as type of decision processes that they can facilitate including data-driven, communication driven, model-driven, knowledge-driven, and document-driven.

Decision support systems have been used in various research application areas that are diverse (Kerzner, 2013). Within the real-world practices, decision support system applications have to be implemented using overlapping approaches. Figure 1: Schematic view of Decision Support System Construction Tendering Normally tendering is a complex task having conflicting interest between construction managers, various sets of criteria, objectives, objectives as well as solution alternatives (Kerzner, 2013).

Multiple decision makers who are situated in geographically dispersed areas can achieve interactive collaboration and communication using networking technologies through the application of communication-driven decision support system approach (Galliers & Leidner, 2014).  It is referred to as group decision support systems where decision models and data are shared by a group of decision makers. Within some specific problems, it can be the integration of model-driven and communication-driven approach.

Data-driven decision support system permits accessing, manipulation, processing as well as analysing huge amounts of data that is structured by use of computerised techniques like for data warehousing as well as OLAP (Karumanasseri & AbouRizk, 2002). It shows manipulation and processing time-series of real-time operational data, historical data, and external data. Document-driven decision support system Document-driven decision support system is a new approach in decision support system.

It assist decision makers in accessing, managing, retrieving and analysing unstructured documents using integrated databases as well as processing technologies. The major function of model-driven decision support system is founded on quantitative approach. It provides decision support with the analytical model employing tools of decision analysis, financial, algebraic, optimization, simulation, stochastic, statistics as well as logic modelling (Demirkan & Delen, 2013). The approach may be divided into three stages comprising of analysis, solution and formulation.

The knowledge-driven decision support systems make use of knowledge inference engines in the understanding and solving problems domain and give alternative actions to the decision makers. The knowledge concerning a specific domain is usually stored within a reusable state with the assistance of different AI methods like object-based, rule-based, statistic-based, induction-based or logic-based (Galliers & Leidner, 2014).  The capacity of decision support system as computerised tools in helping decision-making process has attracted many studies for over the last half a century to improve decisions within the complex construction project.

Most stages in the complex construction process involve crucial decisions that have to be made by engineers, contractors, clients, architects and consultants. Strategies for Improvement The surge of unstructured information compels the need of coming up with strategies that enhance and improve organizational and individual decision-making through associating automated tools within decision systems. This is a big research challenge in decision support system owing to standard tools constructed for structured data analysis.

The traditional decision support system does not have the capacity of confronting the dynamic as well as ill-defined data (Galliers & Leidner, 2014). Majority of decision support system in construction are focused on processing of quantitative data whereby the systems only analyze factual values.

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