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BID STRATEGY: DETERMINING THE MARKUP

Pat Pannell and John D. Murphy Jr.

Department of Industrial Sciences

Colorado State University

Fort Collins, Colorado

Introduction

Submitting bids, or bidding, for construction contracts is the life blood of a construction company. The first step towards being awarded a construction contract is to participate in a competitive bid. Most construction contracts are awarded to the lowest bidder. If a company does not bid, it will not be awarded contracts, and thus, has no chance of earning monetary return. The competitive bidding process in the United States is one of the most unforgiving paradigms in the world of business. It more closely approximates pure competition than any other business arrangement (Ioannou, 1993). Other countries use different methods of determining contract awards, but in the United States the low bid method is still the predominant choice (Ioannou, 1993). From a standpoint of responsibility and quality of work, the lowest bidder will not always be the best bidder. However, in this culture, the lowest bidder is usually awarded the contract.

The construction bidder must have the lowest bid to be awarded the contract. However, the bid amount must include a margin of markup to cover overhead and profit (Griffis, 1992). The bidder must procure enough work, at sufficient margins of markup, to cover general overhead costs (Clough, 1981). Once general overhead costs, for a fiscal year, are covered the bidder can begin to make a profit on any subsequent work in that year. The bidder must arrive at the best, or optimum, bid affording the greatest probability of being low and including the maximum margin of markup allowed by the market (Park, 1979). The strategy to arrive at the best bid is known as a bid strategy.

Problem Statement

The purpose of this paper is to define a bid strategy that can be easily instituted into a bidder's day to day operation, increasing the likelihood that the bidder will submit more low bids, while also having the maximum margin of mark up included in the bids.

Assumptions

A bid strategy is a dependent paradigm. Before a bid strategy can be used effectively, a bidder must have a consistent method of determining and compiling costs. A bid strategy is used to determine percentage margin of markup to be added to the bidder's cost. The percentage is determined by analyzing the relationship of competitor's past bids, to the bidder's cost for those contracts (Adrian, 1982). If the computation of cost is not consistent from one competition to the next, the strategy will have no validity.

Definition of Terms

The term bidder is used to denote the company which is adjusting their bid by using the bid strategy. The bidder, and the bidder's margin of markup is the subject of the bid strategy.

The competitor is any company, other than the bidder, involved in the competition for a particular contract. The competitor is the entity whose actions the bid strategy seeks to predict.

Margin of markup, is the percentage of the bidder's estimated cost of a contract that is added to the bid amount to contribute to overhead and profit. The bid amount is the total of the cost and the markup amount.

Overhead is the cost, incurred by a contractor to remain in business (Clough, 1981). This cost includes expense of the home office, executive salaries, and advertising, to name only a few of the total expenses.

Literature Review

Bidding strategy was first studied by Friedman (1956) who introduced a model to predict a competitor's bid for a current job, based on a probability derived from the relationship of a competitor's past bids to the bidder's cost on those past bids. Friedman (1956) assumed that all bids for a particular contract were independent of each other. In other words, no competitor was using a similar procedure to evaluate other competitor's bids. This has been the greatest criticism of the model (Park, 1979). However, the procedure was uncomplicated and easily implemented.

Gates (1967) and later Benjamin(1972) also perceived the independence issue as a problem with Friedman's model. They used highly complex mathematical techniques in an attempt to remedy the independence issue. Their results had mathematical merit, but effectively made an uncomplicated procedure too complex for effective implementation (Park 1979).

Griffin (1992) accepted the independence issue. His addition to the paradigm was to factor the competitor's volume of work on hand into an equation to determine the competitor's bid in relation to the bidder's cost. This was plausible from a mathematical standpoint, but created a cumbersome procedure because of the difficulty in collecting the data.

Because of the simplicity of the Friedman (1956) model, Park (1979) developed a procedure based on it. According to Park (1979), the first step in initiating a bid strategy is to assemble a data base of all competitor bids on all contracts for which the bidder has competed in the past three to six months. Fig. l illustrates the data base. The competitor bids are expressed as a percentage of the bidder's cost on the same contract. The individual percentages are listed in the extreme left column. The number of bids falling at a corresponding percentage range of the bidders costs are noted in the second column from the left. The number of bids is totaled at the bottom of the column. The second column from the right shows the number of bids equal or higher than the corresponding percentage range represented in the left column. The right column is the cumulative percentage of the bids. This data base is the beginning of the probability analysis for the unknown competitor.

Fig. 2 illustrates the probability graph for the unknown competitor. The values in the left column of Fig. 1 are charted along the "X" axis of the graph. The values in the right column of Fig. 1 are charted along the "Y" axis of the graph. The intersections of the "X" and "Y" values form the probability line. The probability of underbidding one unknown competitor is determined by referencing the bid as a percent of cost along the "X" axis to the probability line, then referencing that point on the probability line to the probability percentage on the "Y" axis. The markup percentages and the probability percentages are entered into Fig. 3.

A similar data base, with graph, is constructed for each frequent competitor. This procedure is illustrated by Fig. 4 and 5. The associated percentages are entered into Fig. 6.

Fig. 3 illustrates the procedure for determining the probabil­ity of underbidding a group of competitors at several markup percentages. The left column is the markup percent. The markup percent is the percentage above 100 percent derived from Fig. 2, which corresponds to the bidder's anticipated contribution to overhead and profit on this bid. The percentages in column 1 are the percentages read from the "Y" axis in Fig. 2 at the corresponding percentage above 100 percent. To compute the percentage probability of underbidding more than one competitor, the percentage probability of underbidding one competitor is multiplied by itself for each additional competitor. This procedure is illustrated by columns 1, 2, and 3.

A similar procedure for each frequent competitor is illus­trated by Fig. 6. Data from Fig. 3 and 6 is entered into Fig. 7.

Fig. 7 illustrates the computation of the probable profit factor, which is no more than a rating scale for markup percentages against the competitors involved. The prob­able profit factor is computed by progressively multiplying the percent probability of underbidding the number of unknown competitors from Fig. 3 times the percent probability of underbid­ding each known competitor as repre­sented by Fig. 6. The result of the progressive multiplication is then mul­tiplied by 100 to yield the probable profit factor. The highest probable profit factor occurs at the optimum margin of markup. Adding the opti­mum margin of markup to the cost will result in a bid that will maximize possible profits in the long run by yielding the low bid at the highest probable percentage of markup.

There are certainly expenses involved in bidding and there may be more contracts available to bid at a particu­lar time than a bidder has resources to handle. A bidder can determine the potential competitors for a particular bid by determining which competitors have received plans from the owner's representative. This information, and the owner's estimated bid amount, will be published by one of the many construction information sources, such as the Dodge Reports published by McGraw Hill (Griffis, 1992). The largest probable return on each contract for bid may be determined by multiplying the optimum margin of mark-up for each job times the owner's estimated bid amount. Using this information, it can be readily determined which job would have the greatest probability of earning the largest return. The contract, or contracts, having the most potential for return would be the ones the bidder would pursue.

Park (1979) reported that the procedure was empirically tested. Bidders using the procedure achieved markups of 40 to 60 percent of what they would have achieved if they knew competitor's bids in advance. In other words, the total of the biddeesmarkupswas40 to 60 percent of what it could have possibly been if the competitor's bid amounts were known, and the bidder could have bid one dollar less than the lowest competitor on each competition. The literature does not give any specifics relating to how many bidders used the procedure.

Park's (1979) analysis did not incorporate any procedure for determining the future validity of past data. In other words, all predictions of a competitor's bid are in relation to the competitor's past performance and are not affected by market trends that will determine whether the competitor will bid higher or lower than in the past. Park (1979) used Friedman's (1956) logic which was based on historical data with no method of determining when or if the market was turning.

Park (Park, 1979) provided no allowance for the volatility of the construction market. The market tends to change monthly. Competitors evaluate their work on hand, and it's contribution to overhead and profit, each month when incremental project billings and accounting statements are prepared (Griffis, 1992). After reviewing accounting statements, the competitor will fluctuate bid strategy as it relates to volume of work on hand. Bid amounts will rise as volume of work on hand rises and fall as volume of work on hand falls (Griffis, 1992). For this reason, the bid strategy is evaluated on a monthly basis as has been shown previously.

Once the bidder has determined which contracts have the most markup potential, the exact percentage of markup to be added to the cost of the contract must be determined. In Park's (Park, 1979) procedure the optimum markup, which determines which contracts to pursue, is based completely on historical information with no provision for short run market trends. If the market is rising, the optimum percentage of markup thus determined will be lower than necessary. If the market is falling the reverse will be true. A projection should be made to allow for the market trend.

Results

The calculation of the optimum percentage of markup for the unknown competitor will more closely reflect overall market conditions than will the optimum percentage of markup for a small number of known competitors. To facilitate the projection of the market trend, the optimum markup percentages for the unknown competitor, for at least three months preceding the present month, must be examined. Sample calculations of these percentages are shown in Fig. 8, 9,10, and 11. The optimum margins of markup from Fig. 8, 9, 10, and 11 are entered into a table as illustrated in Fig. 12. Using the data from Fig. 12, a graph is plotted as illustrated in Fig. 13. By projecting the trend line in Fig. 13 into the month of May the optimum margin of markup will be determined for that month. This projection will reflect the trend in the market over the last three months. This exercise results in the optimum percentage of markup for the unknown competitor which will be 7% for the month of May, as illustrated by Fig. 13.

If the bidder is competing against one, or more, known competitors, or a small group made up of a both known and unknown competitors, the optimum percentage of markup should also be projected ahead into the present month for that competition. The projection of such an optimum margin of markup, would be best accomplished by applying the percentage increase in the optimum margin of markup for the unknown competitor to the computed optimum margin of markup for the competitor group in question. The percentage increase is shown in the right column of Fig. 12.

To effectively utilize a statistical bid strategy as illustrated it is imperative that the data for the unknown competitor be updated monthly and the optimum percentage of markup be calculated monthly. By accomplishing this the bidder is kept abreast of market conditions and has a basis for projecting markups to allow for market trends.

Conclusion

Formulation and utilization of a statistical bidding strategy offers the bidder a tool to decide which contracts to pursue with available facilities and the optimum percentage of markup to add to the computed costs of the contract. The strategy considers not only the historical data on competitors, but also makes a projection of the fluctuation of the market based on historical data.

References

Adrian, J.J. (1982). Construction estimating. Reston: Prentice-Hall.

Benjamin, N.B.H. (1972). Competitive bidding: the probability of winning. Journal of the Construction Division, ASCE.

Clough, RH. (1981). Construction contracting. New York: Wiley.

Friedman, L. (1956). A competitive bidding strategy. Operations Research, No. 4.

Gates, M. (1967). Bidding strategies and probabilities. Journal of the Construction Division, ASCE, Paper No. 5159.

Griffis, F.H. (1992). Bidding strategy, winning over key competitors. Journal of Construction Engineering and Management, 118(1), 151-165.

Ioannou, P. G. (1993). Average-bid method -competitive bidding strategy. Journal of Construction Engineering and Management, 119(1), 131-147.

Park, W. R (1979). Construction bidding for profit. New York: Wiley.