Changing procedures, installing and using new information technology and elevating managerial practices to the point where decision models are in everyday use has been known to be stressful to organizations that pursue such improvements. In many cases, the inability of the organization to embrace such changes dooms the improvement initiatives. A simple example of the how improvements can be threatened by resistance to change can be found in the use of pre- fabrication shops by some contractors to assemble components in a controlled environment. Advantages of pre-assembly include increased production time and reduced labor costs compared to performing the assembly process in the field where poor weather conditions and space limitations may cause work delays. The increase in productivity and savings in labor costs out weigh additional costs encountered due to pre-fabrication and re-handling. Some site personnel, particularly job foremen, may not favor pre-fabrication due to fear of loss of control on material and installation. Upper management for some companies has developed incentive programs to introduce site staff to the benefits of pre-fabrication and to facilitate a change of culture and acceptance of the process.
Better materials management practices and decision-making models could increase efficiency in operations and reduce overall costs. Increasing pressures on project costs and completion times are motivating the need to make supply-chain decisions in a coordinated fashion and in consideration of minimizing total supply-chain cost without causing shortages. The next chapter will describe the use of decision-modeling techniques to design a framework for an integrated system of decision support for material procurement for the electrical contractor.
5.4 References
Bernold, L. E., Treseler, J. F. (1991). “Vendor Analysis for Best Buy in Construction,” Journal of Construction Engineering and Management, ACSE, 117(4), 645-658
Damodara, K. (1999). “Materials Management: The Key to Successful Project Management,” Journal of Management in Engineering, ASCE, 15(1), 30-34
Perdomo, J.L., Thabet, W. (2002). “Material Management Practices for the Electrical Contractor,” Proceedings of the International Workshop on Information Technology in Civil Engineering, ASCE, Washington, D.C
Stukhart, G. (1995). Construction Materials Management, Marcel Dekker Inc. New York
Thabet, W. (2001). A Framework for an Integrated Materials Management System, Research Proposal Submitted to the Electrical Contracting Foundation, Inc.
Thabet, W., Badinelli, R. (2003). A Design of a Decision Support System for Material Supply Chain, Research Proposal Submitted to the Electrical Contracting Foundation, Inc.
Thabet, W., Perdomo, J. (2003). A Framework for an Integrated Material Management System, Research Report Submitted To the Electrical Contracting Foundation, Inc.
Thabet, W., Perdomo, J. (2002). “Material Management Challenges for the Electrical Contractor,” Electrical Contractor Magazine, 67(10), 110-112
CHAPTER SIX: DECISION MODELING
APPROACH
Decision analysis can be defined as a methodical approach to decision making that allows managers to handle problems where different alternatives and/or a certain degree of uncertainty are involved. Decision analysis overlaps operations research and statistics that has the purpose to model and analyzing decisions made by decision makers. The objective purpose of decision analysis is to assist decision makers in making better decisions. Options are essential for decision analysis, because if only one option is available, there is no choice to make, thus no decision (Cooke and Slack, 1984). Clemen (1996) argues that the steps in decision analysis are the following: identification of the decision and objectives, identification of alternatives, modeling the problem structure, choosing the better alternative, sensitivity analysis, if further analysis is not needed, then implement the chosen idea. He states that the decision analysis process is iterative and what-if scenarios should be considered. Decision making is the process of making a selective judgement when presented with different alternatives consisting of several decision variables, and often defining a course of action. Decision making studies the identification and selection of alternatives based on the values and preferences of the decision maker. When a decision is made, it is implied that there are different alternative choices that are considered, and the decision maker wants to choose the one that best fits with his goals and objectives (Harris, 1998). Oglesby et al (1989) and Heller (1998) state that decision making involves three different steps: identification of the decision to be made, seeking out feasible alternatives, and choosing the most suitable alternative.
Models are representations, with assumptions, of our interpretation of reality and not reality itself. This representation should include the relevant aspects of the process being modeled. Models therefore illustrate simplifications of more complex real situations and/or processes. Decision modeling attempts to develop a model of the decision process used to make important decisions. A decision model is an analytical tool, usually in the form of a computer application, that assists a decision maker in estimating the outcomes of different alternatives and quantifying
the tradeoffs inherent in choosing one alternative over another. This modeling approach presumes that a number of different factors are considered when comparing various alternatives. In addition, in such type of analysis, some of the factors could have more impact than others. The decision maker weighs the effects of each parameter on the different alternatives. Based on the judging of the importance of the effect of the parameters, the decision maker chooses the “best” alternative (The Futures Group, 1994).
Decision models are ever-present in the materials management processes of industries other than construction and have proven their worth in improving productivity and profitability. Fundamentally, a decision model describes quantitatively the cause-effect relationship between two sets of causative factors and the set of evaluative measures that the decision maker uses in order to judge the desirability of each alternative. The causative factors are divided into two sets. The controllable factors are those that constitute the alternatives or decision variables. The un- controllable factors are called parameters and must be measured, estimated or forecasted. The evaluative measures are called performance measures because they quantify the “performance” of each decision alternative.