Simulating the winning bid: A generalized approach for optimum markup estimation

This paper introduces a novel approach to estimate the optimum markup to suit various bid evaluation strategies in which the winning bid is the one that is “closest but below the average”, “closest to average” or “the lowest responsible/responsive bid”. The paper introduces the term “winning markup” according to the bid evaluation strategy used. By tracking how the winning markup varies under various competitors’ bidding behaviors, the confidence to win all bidders at any markup can be determined and then used to determine the optimum markup decision. Based on this approach, the paper introduces a comprehensive model for optimum markup estimation, which has three main modules: (1) a behavior tracking mechanism that documents and analyzes competitors’ bidding behaviors; (2) a behavior adaptation module that predicts the bidding behavior (of expected) competitors under a range of variable characteristics for the project under bidding; and (3) a winning markup simulator to determine an optimum bidding strategy. To automate the process, a spreadsheet prototype was developed. A numerical example was also used and its results are compared to those of the well-known Freidman model to validate the approach. The developed model can be considered a generalized model that can be used to establish an optimum bidding strategy under various bid evaluation strategies.

► The winning markup (WM) under various bid evaluation strategies is introduced. ► Probability to win the bid is determined by tracking the WM variability using competitors' past bids. ► Optimum markup that maximizes both the probability of winning and the profit value is determined. ► A model is presented to archive, adapt then simulate historical bids to estimate an optimum markup. ► Comparison to well-known Friedman's model showed validity and proved generalization.