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Modelling Contractor's Bidding Decision

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Modelling Contractor's Bidding Decision Engineering Management in Production and Services Volume 9 • Issue 1 • 2017 received: 1 September 2016 accepted: 20 January 2017 Modelling contractor’s pages: 64-73 bidding decision Sławomir Biruk, Piotr Jaśkowski, Agata Czarnigowska A B S T R A C T The authors aim to provide a set of tools to facilitate the main stages of the competitive bidding process for construction contractors. These involve 1) deciding whether to bid, 2) calculating the total price, and 3) breaking down the total price into the items of the bill of quantities or the schedule of payments to optimise contractor cash flows. To definefactors that affect the decision to bid, the authors rely upon literature on the subject and put forward that multi-criteria methods are applied to calculate a single measure of contract attractiveness (utility value). An attractive contract implies that the contractor is likely to offer a lower price to increase chances of winning the competition. The total bid price is thus to be interpolated between the lowest acceptable and the highest justifiable price based on the contract attractiveness. With Corresponding author: the total bid price established, the next step is to split it between the items of the schedule of payments. A linear programming model is proposed for this purpose. Sławomir Biruk The application of the models is illustrated with a numerical example. The model produces an economically justified bid price together with its breakdown, Lublin University of Technology, Faculty maintaining the logical proportion between unit prices of particular items of the of Civil Engineering and Architecture, Department of Construction Methods schedule of payment. Contrary to most methods presented in the literature, the and Management, Poland method does not focus on the trade-off between probability of winning and the price e-mail: [email protected] but is solely devoted to defining the most reasonable price under project-specific circumstances. The approach proposed in the paper promotes a systematic approach to real-life Piotr Jaśkowski bidding problems. It integrates practices observed in operation of construction Lublin University of Technology, Faculty enterprises and uses directly available input. It may facilitate establishing the of Civil Engineering and Architecture, contractor’s in-house procedures and managerial decision support systems for the Dpt. of Construction Methods and Management, Poland pricing process. e-mail: [email protected] K E Y W O R D S decision support; decision to bid; pricing strategy, contractor cash flows, linear Agata Czarnigowska programming Lublin University of Technology, Faculty of Civil Engineering and Architecture, Dpt. of Construction Methods and DOI: 10.1515/emj-2017-0007 Management, Poland e-mail: [email protected] Introduction contractor’s experience and business intuition are not Tendering stays one of the most popular means enough to ensure that the tender procedures entered of selecting contractors to carry out construction by the contractor offer a good trade-off between costs works. Any invitation to tender is an opportunity, and (bid preparation costs including the opportunity cost thus the contractor needs to decide if to explore it of using the scarce time to prepare this bid and not (use time and resources to prepare a bid) or decline it the other) and benefits (winning a contract that is in search for better options. Several factors need to be profitable, would maintain the contractor cash flows, considered in the process of decision-making. The or allow them to win a better market position). 64 Engineering Management in Production and Services Volume 9 • Issue 1 • 2017 With an invitation to tender accepted, a decision ture varied considerably, although the most significant on the bid price needs to be taken. Apart from items factors in the decision to bid usually include the cli- defined in the tender documents, whose cost can be ent’s reliability, the need for work, the expected simply calculated, many components of the price are number of competitors (chances to win the job), and estimated based on a less tangible input; these are experience with such projects. This may be attribut- risks and profit. The bid price is expected to be high able, on the one hand, to economic conditions vary- enough to guarantee that the contractor recovers all ing strongly according to the location and the date of costs and earns a decent profit and, at the same time, the survey. On the other hand, the profile and number low enough to beat the competition. Low bidding of interviewees, as well as ranking methods, strongly increases chances of winning the contract but reduces affected the results. In the above-presented studies, chances of making a profit. Therefore, the contractor’s the most popular methods of pointing to key criteria decisions whether to bid and later what price to offer, were based on average scores calculated according to are complex and call for decision support tools. The individually adopted crisp or fuzzy scales, with a more paper puts forward models that facilitate rational or less rigorous approach to checking the consistency decision-making in terms of bid/no-bid, unit rate of opinions and the reliability of findings. pricing, and defining the overall price of a job. The With the criteria at hand, numerous methods focus of the models is on maximising the net present were proposed to compare invitations to bid to find value of the project cash flows (excess of sums to be those potentially most promising. Some authors received from the client over cost). Application of the aimed at creating models based on records on quali- models is illustrated with a numerical example. ties of previously selected invitations to tender allow- ing the user to assess a particular invitation as worth or not worth considering. These models were either parametric, such as logistic regression (Lowe & Par- 1. Literature review var, 2004; Hwang & Kim, 2016), or non-parametric, for instance, based on artificial neural networks 1.1. Decision to bid (Wanous et al., 2003). Complex knowledge-based expert systems can also be found in the literature A contractor’s bidding department can produce (Egemen & Mohamed, 2008). only a limited number of bids at the same time. Other authors applied multi-criteria analyses to A decision to assign resources to the analysis of one provide a ranking within a set of options. The meth- client’s invitation to bid implies that other invitations ods range from the simplest additive scoring models are rejected. A quick selection of most promising (Stevens, 2012), to allowing for the imprecise and invitations poses a practical problem, and, therefore, subjective character of input by using fuzzy logic (Lin literature is rich in analyses of selection criteria and & Chen, 2004; Tan et al., 2010). With the wide selec- methods. tion of multi-criteria methods constantly developed The first question, which is relatively simple to (Saaty, 2000; Triantaphyllou, 2000; Köksalan et al., answer, is what criteria affect the contractor’s decision 2011), these examples present but a small fraction of to tender. Estimating and tendering handbooks pro- research on the subject. vide guidelines on selection criteria based on the experience of their authors (among others, Brook, 1.2. Defining the bid price when 2011, p. 96; Cartlidge, 2013, p. 3; Stevens, 2012, p. 93). cheapest bid wins Enquiries on factors affecting the bid/no bid decision were conducted in many countries: in Great Britain According to Mochtar and Arditi (2000), con- (Shash, 1993), Egypt (Hassanein, 1996), the United struction pricing strategies can be divided into two States (Ahmad & Minkarah, 1998), Syria (Wanous et groups: cost-based pricing and market-based pricing. al., 2000), Singapore (Chua & Li, 2000), Saudi Arabia Most models presented in the literature assume that (Bageis & Fortune, 2009), Poland (Leśniak & Ple- the definition of the price is a two-stage process that bankiewicz, 2015), Australia (Shokri-Ghasabeh comprises “first the calculation by the estimator of & Chileshe, 2016), and Nigeria (Oyeyipo et al., 2016). the true commercial cost to the contractor/subcon- As the authors drew from each other, the lists of initial tractor, followed by the adjudication or settlement criteria were rather consistent regardless of the coun- process…” (Cartlidge, 2013, p. 221). The latter consist try of origin. Criteria rankings presented in the litera- of adding allowances for cost-affecting risks and 65 Engineering Management in Production and Services Volume 9 • Issue 1 • 2017 uncertainties, company overheads and profit to chances for getting an attractive job (big, prestigious, obtain the bid figure (Mochtar & Arditi, 2000). This or just badly needed in the times of recession). They traditional approach to costing is suitable in tradi- may also provide justification for inflated mark-up tional procurement routes, where the contractor has that reduces the probability of winning the contract little influence on materials and construction meth- to compensate for additional related risks, e.g. with ods, i.e. the factors that determine most construction undefined scope in fixed price contracts, or just to use costs. the opportunity. Thus, current objectives may not be Another group of models uses the concept of convergent with the long-term aims of maximising market-based pricing (Best, 1997) or target costing the expected value of profit, and statistical methods (Cooper & Slagmulder, 1997), where the price is may be not enough to depict such complex relation- prompted by the demand side of the market and ships. Therefore, many authors refer to artificial intel- objectively corresponds to the client’s perceived value ligence techniques that capture relationships between of the job. If this can be established, the contractor contract properties and mark-up value, consider (or, in fact, the whole value chain that cooperates to more criteria than just profit maximisation, and allow satisfy the client) strives to “engineer” the cost of for the uncertainty of input using, e.g., fuzzy logic.
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