Reducing Risk by Leveraging Information for a Competitive Advantage in Bidding

INTRODUCTION Competitive bidding is an integral part of many businesses generating substantial revenues. On products that are standard or similar, the primary purchase criterion is price. Within this highly competitive environment, it is common practice in many industries to require all interested firms to submit a sealed bid for a given contract to supply goods or services. This includes many manufacturers and service organizations that are selling to other manufacturers of original equipment, as well as wholesalers, retailers, federal, state, and municipal governments, and others. The contract is awarded to the firm with the lowest bid. The process of developing a bid requires the manager to rely on a high level of quantitative and intuitive skills in determining the price (Rothkopf, 2007). The bidding system is part of a marketing decision support system (MDSS) used by either a sales or mark3eting group. MDSS was described for a division of General Electric by Lillis and McIvor (1985). Their MDSS bidding system provided an analysis of the data but no model to assess the probability of winning. In most situations, the manager needs a model to assist in responding to all contracts. Therefore, the components of a bidding support system should include a bidding model as depicted in Figure 1. [FIGURE 1 OMITTED] This case study describes the development of a system for pricing contracts in the pharmaceutical industry. The decision process involves the collection and assembly of the information to be used in a model to assist the manager in evaluating contract bids. The bidding model focuses on that aspect of competitive bidding wherein a manager routinely bids for the right to supply his company's products to another over a specific period of time, usually one year. The contracts involve similar, non patented products that may be identical or interchangeable, permitting the purchaser to buy from anyone of several competing sources. A large customer base creates a high volume of activity, with several bids every day for a product. With a large number of contracts generated for each product, the importance of an individual bid diminishes. Thus, no single contract jeopardizes the survival of the company or has a significant impact on the company's profitability. Substantial research efforts were devoted to different aspects of the bidding problem. Comprehensive bibliographies on competitive bidding were compiled by Stark and Rothkopf (1979) and Rothkopf and Harstad (1994). Engelbrecht-Wiggans (1980) reviewed the state of the art in his survey on auctions and bidding models. A survey by Laffont (1997) focused on the application of game theory to auction data, and Rothkopf and Park (2001) defined the issues to be resolved in designing an auction. Quantitative approaches to competitive bidding can trace their roots to the research of Friedman (1956), who published the earliest biographical listing. The models proposed by Friedman evaluated an individual contract using expected value. Construction projects (Chen, 2007; Dyer & Kagel, 1996; Elmaghraby, 1990; Skitmore, 2002), federal government contracts (Samuelson, 1986), and oil lease procurement (Keefer, Smith, & Back, 1991) research assesses an individual contract where the bidder carefully evaluates the value of the contract as it relates to the survival of the company or in terms of a substantial impact on the company's profitability (Pfeifer & Schmidt, 1990). Here, the bidder has a long period of time (weeks or months) in which to determine a single bid. More complex single auctions such as withdrawable bid situations (Harstad & Rothkopf, 1995; Rothkopf, 1991) have also been modeled. In contrast to the "one-shot" situation, this paper deals with a sequence of bids, each prepared in less than an hour and each of which has a small impact on profit. A sequence of bids to supply products or services presents a more complex situation. …