Mathematical Help for Manufacturing Companies

December 15, 2011
School of Engineering, Coimbatore

Can mathematics help a manufacturing company?

“Why not?” asks Dr. J. Ravichandran, Associate Professor, Department of Mathematics, School of Engineering, Coimbatore, who recently had his paper published in the Journal of Business and Industrial Marketing of Emerald Group, UK.

Dr. Satheesh Babu

The paper titled A Chi-Square-Based Heuristic Statistical Procedure for Approximating Bid Price in a Competitive Marketplace: A Case Study outlines a clear procedure for determining an optimal bid price.

“When I was in industry, I lost many tenders to the competition because of their pricing tactics,” shares Dr. Ravichandran.

“Being a statistician, I decided to do something to come up with the best pricing method for forthcoming tenders,” he says.

Dr. Satheesh BabuThat something was a rigorous application of regression analysis. “A bidder can determine the optimal bid price by comparing data of past bids of both self and competitors for various tenders. As long as such data is available, the method is easy to apply,” he shares.

The new and innovative method can benefit not only manufacturing companies but also all others who regularly obtain business through bidding against tenders.

In a brief editorial and executive summary, the journal described the importance of Dr. Ravichandran’s work.

“Bidding for a tender is a common but critical activity in an organization’s business since a successful bid can yield bulk orders. A marketing manager who responds to a tender always tries to win. However, winning a bid is not easy, due to the complexity involved in determining the optimum bid price, particularly for sealed bids.”

“When you have been told your bid for a business was unsuccessful, you may indeed never know why. You may never know how much your competitors came in below your bid price. However, once sealed bids are opened and the best bid is awarded, then, in order to justify the decision, it is customary for tenderers to reveal the prices quoted by all the bidders. The availability of this data on past bid prices is valuable. It can be used to determine the optimal price for future bids.”

“In his paper, Dr. J. Ravichndran makes the following points. In practice, for any company, sticking on to the bid price that can yield a huge profit margin is always welcome but one may lose the tender because of competitors’ lower prices. Losing such a tender may halt the business of a prosperous company. Hence, to be fully confident of winning the tender, the bidder may even go to the extent of proposing a bid price that may result in a breakeven or just marginal-loss situation. This situation may help the company to keep its business going in the competitive marketplace.”

Dr. Satheesh Babu

“No advice here on how to win the lottery or pick a winner at the races, but plenty of food for thought on how to reduce the odds and come through as a winner in that huge lottery which faces businesses, large and small throughout the world – the tendering process.”

Dr. Ravichandran was recently also invited by the Amrita School of Business to share these insights in an Executive Development Program organized for industry participants.


Purpose – The purpose of this paper is to propose a chi-square-based heuristic statistical procedure that considers the most recent bid prices of self and competitors in proposing an optimum winning bid price. The use of simple regression method to predict the adjusted optimum bid price is also considered.
Design/Methodology/Approach – In order to achieve this objective, the proposed approach uses past bid prices of the case company and its competitors. A heuristic chi-square statistical procedure is then developed to obtain the expected bid prices. The absolute difference between the prices is obtained and the same is adjusted to get the optimal bid price for the forthcoming bid.
Findings – It is demonstrated that the proposed heuristic chi-square-based approach is superior to that of the regression method, which is otherwise in practice in the case company, since the latter still gets support from the former. Further, the paper throws light on how a familiar statistical method could be conveniently but effectively applied as a tactic of marketing management.
Research Limitations – The method is developed based on the experience of the case company in bid participation. Therefore, the study on the performance of the application of the proposed method from the perspective of different types of companies can be taken up as a future work. Also, if appropriate, the time and other factors may be considered as other independent factors along with the tender quantity in predicting the tender price.
Practical Implications – The proposed heuristic chi-square procedure is simple to implement as the required data on bid prices are usually available once the bids are open. The case study reveals how the method can be successfully implemented to win bids in a competitive market place. Also, the procedure is more generic so that it can be adapted as it is or with slight modification as desired by companies of any nature participating in bids.
Originality/Value – The proposed approach would have a high value among manufacturing firms and marketing managers who participate in tenders with an aim to increase sales turnover which in turn will increase profit. The paper is unique of its kind and will help researchers to think of either
extending or proposing such new approaches as they need.

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