Regarding your concern about whether historical bidding data can benefit future bids for our bus contract, this is to introduce and recommend a more effective bidding model which could help predict the winning bid and maximize financial returns of the company during bidding process. Firstly, how does the model work?
Based on the analysis of the past industry bid data, I discover from the Regression Model (See Exhibit 1) that a strong correlation exists between the winning bid price and several objective factors including Orion cost per bus, the number of units to be purchased, and the bus model, 35’ or 40’, while it has little to do with the condition whether it is diesel-powered or high-floored.
Although it’s still hard for us to determine whether to increase the volume of sales or to increase margin at the same volume and produce an absolute price to win the bid due to complex situations, the model does help us gain access to the winning bid price prediction and provide us with an optimal price by taking profit, expected value and winning probability into consideration.
Specifically, the highest expected value is the best bidding choice to maximize financial returns. Secondly, how can we apply it in the bid for Louisville, KY bus supply contract?
With the aid of the above model, a suggested range of winning bid price is generated which is between $248,001 and $278,189, implying the best choice lies within the interval. Further, Exhibit 2 shows an opposite direction between profit and winning probability, that is, the more profit, the less probability to win, and vice versa. As a result, the key point is how to achieve a best joint between them. Thanks to the model, we can easily arrive at the bid price $259,000 for the contract with the highest expected value $15,034, which is the optimal theoretical bid price with maximum returns in this case.
In addition, other subjective factors such as estimates of competitors’ bids should also be considered before making the final decision. The above analysis reveals a significant impact that historical bidding data exerts on the success of our future bid. Accordingly, it’s strongly advisable to collect more information and build database so that more factors could be considered and analyzed for the future bidding. I’m always at your service if you’re interested in in-depth discussion.