Bidding Portfolio Process Map Framework – Decision Making Analysis

Abstract:

There are difficulties to collect actual bidding cost from the different contractors records. Many contractors consider it part of the overhead and not important to calculate. Part of this difficulties is the unidentified or different bidding process methods and where the process starts and where it end?

Therefore, identification of the bidding process, will help to identify its actual cost. We will discuss here only the contractors bidding process. Although, there are similarities for consultants and suppliers process with less cost expected.

Researches found that, construction bid preparation costs estimated to be approximately 0.25% of the bidder’s annual turnover or alternatively 1% of the projected contract sum.  For the top 400 USA contractors alone, with an estimated turnover of over USD 338 billion in 2008 and USD 290 billion in 2009, this amounts to bid preparation costs of USD 846 million and USD 727 million respectively. An alternative view is that bidding for a USD 100 million contract costs a staggering USD 1 million for bid preparation. This is clearly a substantial investment for a contractor when there is no certainty of winning the bid.  Furthermore, selecting the best bid values and client/markets is similarly a persistent and complex decision for contractors.

Research to date has different views on identifying the factors involved on the d2b and organising them into different categories and groups. Therefore, contractors consider these models to be “black boxes” or unduly complicated and their use has been consequently limited.  So far, there are no known Decision to Bid (d2b) models being used in the construction industry.  Instead, it is likely that bidders use certain systematic methods to make their decisions. However, with the confidentiality around such a sensitive business process, little is known about this.

The research aims to improve the bid winning probability and the decision-making process by reducing bidding costs for the contractors in order to provide value for money for the client and the end user.  This is done; first, reconstructing the complicated bid process into a simple process for use in different situations. Second, to investigate the potential use of strategic management concepts and business process reengineering methods as an aid to the d2b.

Key words: biding, decision making, decision to bid, process, pre contract, bid process, factors

 

1. introduction

Research to date has been unable to establish methods or models used by contractors in making their decision to bid (d2b). However, three important findings have been made which help identify such a model. Firstly, the factors influencing the decision are known. Secondly, it is known that no contractor is officially using any model known by researchers. Thirdly, many contractors need a model to help their d2b.  This paper reviews these three findings and outlines some approaches for future modelling work.

2. Literature Review

As Lowe and Skitmore (2006) summarise, the “bidding decision is complex and dynamic. It involves two crucial decisions: first, whether or not to bid (tender) for a project, and second, the determination of the bid price. There are well in excess of 1000 papers Seydel (2003) for the mark up decision stage however, there has been comparatively little in the way of objective research into the decision to bid stage”.

Subsequently, Bageis and Fortune (2009), identified 87 factors affecting the construction d2b– finding that the extent to which bidders use these factors depends on the size and type of their organisations (Ahmad, 1990; Drew & Skitmore, 2001; Egemen & Mohamed, 2007).

2.1 Categorisation of Factors

Researchers have divided these factors into different categories and groups which have resulted in the development of different d2b and mark up models.  For example, one categorisation is into subjective or objective decisions (Fellows & Langford, 1980; Ahmad & Minkarah, 1987; Skitmore, 1989), while others include monetary or non-monetary (Skitmore, 1989); profitability, risk exposure and continuity (Seydel & Olson, 1990); opportunities, resources, project relationship and project procedures, project characteristics, risks and competitive advantage (Lowe & Praver, 2004 positive and negative (Wanous, Boussabaine, & Lewis, 2003; El-Mashaleh, 2010); firm-related factors, ‘project-related factors’ and market conditions/expectations and strategic considerations (Egemen & Mohamed, 2007); market conditions (Runeson & Skitmore, 1999; Egemen & Mohamed, 2007; Oo, Drew, & Lo, 2008); project elements (Lowe & Parvar, 2004); and client and the type and size of the construction work (Drew & Skitmore, 1990; Drew & Skitmore, 1997; Drew, Lo, & Skitmore, 2001).

Other work has decomposed the factors and organized them into seven functional categories with sub factors (Lowe & Parvar, 2004).  Lin and Chen (2004) divided the factors into two levels; level one with seven categories and level two with two or three sub factors.  Similarly, El-Mashaleh (2010) followed Wanous et al. (2003) and divided the factors into negative and positive ones. One result of this was to discover that the ranking of factors varies according to different contractors and situations and that future research would need to take this into account.

 

However, modelling the d2b is still very much in the theoretical stage; contractors consider the models that have currently been developed to be “black boxes” or unduly complicated – resulting in their limited use to date (Lowe & Skitmore, 2006).  This suggests that yet more preliminary work is needed, with the continued analysis of relevant influencing factors into a more meaningful and intuitive classification system.  One approach to this is to categorise the factors under the bid process elements of market conditions, client, contractor, bid and project.  As explained below, this is connected with previous research involving the use of a limited set of only two or three elements.

2.2 Time Dimension Effect

As mentioned above, the importance of the factors influencing the d2b varies according to the situations involved.  A major issue for this in construction contracting is the time period involved.  Economic circumstances change over time.  Invitations to bid occur almost randomly, being affected by the contractor’s reputation and amount of work coming onto the market.  This in turn, together with bid levels, affects contracts won and lost and ultimately the contractor’s turnover, profit and workload.  This then affects future invitations to bid and bid levels etc ad infinitum (Skitmore, 1989).

Despite the fact that all researchers acknowledge the long timeline of the bid process – from identifying the opportunity to bid to the final decision – the time dimension is included only indirectly in most analyses. Lin and Chen (2004), for example, include it in the level of decision making.  Skitmore and Runeson (2006) analyse individual bidding behaviour over time for different bids, Oo et al. (2008) consider it in terms of a scenario of recessing and booming markets, and Lowe and Skitmore (2006), consider the factors that vary over time, from project to project and from organisation to organisation.   To date, however, the time dimension has been not considered in the factors identifying, categorising and model building for the d2b.

2.3 Number of Factors

Surveys by Ahmed and Minkarah (1988), Abdelrazig (1995), Wanous, Boussabaine, and Lewis (1998), Odusote and Fellows (1992) and Shash (1993) within the construction industry have identified and ranked 31, 37, 38, 42 and 55 factors respectively that are perceived to influence the d2b decision. Most recently, this has been consolidated into a list of 87 factors (Bageis & Fortune, 2009).  From a model building perspective, however, this number of factors is problematic as an increased number of factors (variables) is known to have an adverse effect on the accuracy of the estimated weights in the model (Oo et at., 2008).  This suggests that some means is needed to minimise the number of factors involved in order to reduce the margin of error.

To do this involves either discarding factors that are least relevant of combining factors in some way into a new, reduced set of factors.  Either way, in view of the quite extensive work done in this field to date, to do this will require some careful justification.

One solution to this problem is to treat each factor as comprising an elemental dimension and a time dimension.  This lends itself to a reduction in elements but allowing a larger number of factors by combining the two dimensions involved, as discussed later.

2.4 Contractors’ Need For a Model

Bageis and Fortune (2009) found that 50% of classified contractors in the Saudi Arabian market think they need a d2b aid, with a further 40% of classified contractors saying they do not think they need such an aid and 7 being unsure. The perceived need for a bid decision aid increases to approximately 60% for unclassified contractors.  This poses important questions concerning the need for a model (complicated or not) and the need to develop a clear bid process map where all elements are identified and all related effects are considered.

3. Methodology

The research pursues the theme of categorising influencing factors into groups. Shash and Abdul-Hadi (1992) designed the (closed box) mark up model contains categories similar to the those suggested in this paper. This categorises their 37 factors into; the project characteristics, project documents, company characteristics, bidding situation, and economic situation. For the 87 factor list, a different categorise is needed to design an open source model specifically for the d2b. This may be linked to Shash and Abdul-Hadi’s (1992) model and models developed through Saaty’s (1982 Analytic Hierarchy Process (AHP).  This involves the following 11 points:

1. Including the client as an element.
2. Merging the project documents and project characteristics within the bidding situation.
3. Understanding the project element as the lessons learnt from finishing a project and its effect on new d2b making.
4. Leaving the factor weight to be defined by each contractor in the first instance. Later, a revised weight can be calculated for a new contract.
5. Grouping the factors under five bid process elements as shown in Figure  1 (Appendix 1) as they are permanent and physical.
6. Enabling every element to be dealt with separately, by dividing the factors horizontally by the bid process and vertically by the time dimension.
7. Taking the time dimension and available data into consideration.
8. Providing the facility to make a gradual phase decision before receiving the bid documents.
9. Adopting the AHP decision making tool in gradual d2b making, which simplifies the process by the need for complicated equations.
10. Restricting consideration to the d2b stage not the mark up stage. It will still be possible, however, to link the both d2b and mark-up models for future research.
11. Developing two open models that may be used and developed by contractors.

As mentioned above, the bid process elements change over time, unlike those included in one equation as with most previous research.  Therefore each factor’s weight has to be evaluated and updated over time, based on the available data and contractor situation.  Also, every element will have a rate calculated from factor weight (as found by previous research or as decided by each contractor). These rates will be calculated to give a time phase rate for the elements until the final decision is reached.

Of course, this approach is still quite complicated. To understand it further, consider the bid process elements (Fig. 01) and the contractors bidding process map (Fig. 02) over time.

 

FIGURE 01:  Bid Process Categorisation 

Here, Fig. 01 shows the five elements of the bid process:

1. The market (technical or geographical);
2. The client;
3. The contractor;
4. The bid circumstances (documents, competitors, etc); and
5. The project.

Fig. 02 below shows the contractor bidding process – from identification of the bid opportunity to the final d2b. The contractor process map represents the gradual decision making process, involving a fewer number of factors at each step in the process.

FIGURE 02: Process Map for Pre Bidding Decision Making Portfolio 

The first decision is whether or not to include the bid in the opportunity list.  This involves the consideration of a limited number of factors. This is then followed up and the database is updated to calculate the element rates.  For the second decision, more factors are then taken into account in order to decide whether or not to obtain the bid documents. This therefore depends on the available calculated bid rate (according to the available data).  Finally, the third decision incorporates more factors after a scan reading of the documents. Then if the decision is to bid, the d2b stage finishes and the mark up stage starts.  Note, however, that a decision to submit a high bid price is still considered to be a decision not to bid (Skitmore, 1989).

Defining what factors to include in each stage is not easy and depends on the available data, which was the main challenge in previous research in which it is clear that contractors adopt and weight all factors differently. Here, it is suggested that the contractor weights the factors independently for the first time. Therefore, unlike previous research (which tried to identify a mathematical equation), a typical guideline method is presented for use by each contractor. Contractors can evaluate the factors and weights presented and determine their own bid factors and weights accordingly.  Alternatively, they can assume them in the first stage and the model will calculate the new weights based on the calculated elements rates in a later stage.  Thus, the approach enables the transformation into a mathematic model by the use of a gradual decision making tool.

An Excel sheet can help in the sample method using factor weight averages to find the element rates based on the assumed weights. Then, new weight can be calculated from the elements rates. In this way, the method helps learning the principals involved rather than acting as the main model.

Another tool that can deal with a number of factors each time is Saaty’s (1982) Analytic Hierarchy Process (AHP).  This has the advantage of being able to reflect the time dimension.  AHP can also be used to reflect the gradual decision to bid based on time and the data for the five permanent bid elements.  For future research, the sample model can be compared with the AHP model for an indication of the accuracy of each model.

3.1 Bid process reengineering

Research on the d2b to date depends on identifying its influencing d2b factors as an important stage in the development of a more objective d2b approach. However, the bid process is dynamic and proactive, requiring the bidder to continually test and evaluate alternative solutions to the problems and opportunities presented in each bid (Lowe & Skitmore, 2006). The above proposals accommodate this to some extent by identifying the basic knowledge needed for the bid process and its time dimension, with the time between the first decision and the final submission giving contractors a chance to improve the factors which they think put them in a disadvantageous position. On the other hand, the calculated element rates enhance strategic management decisions relating to the external environment (clients, market and competitors) and the contractors’ internal environment.

To extend this further, indicates the need for another approach to improve the decision making involved – and that is to analyse the contractor’s business management process. This includes such aspects as increasing market power (winning more bids) by bid cost minimization, opportunities maximization (decision to submit more bids), competitive aggressiveness, developing its tangible and intangible resources, specialization, and services differentiation based on strategic management, lessons learnt tactics, risky bids evaluation and analysis, or providing unique services to their client to put them in advantage position to competitors.  One approach to this is Business Process Reengineering (BPR) and which has some potential for future research for bidding improvement and with a potential for the complete robotic systemisation of bidding and 90% automation of the bidding decision and process within a few years.

4. Conclusion

Many studies have been made aimed at making the current decision to bid (d2b) process more objective, transparent and accountable in order to improve the previous intuitive and personal experienced-based decisions. D2b models are a necessary outcome of this research as well as being important to enhance the contractor’s decision making process and to reduce bid preparation costs.  Progress to date has been limited due to the large number of influencing factors that has been identified.  The models that have been developed have also had little use in practice due to their complexity or lack of transparency.

To further the development of the research are, therefore, it is suggested here to re-categorise the many factors involved into just five elements and a time dimension. With the use of MS Excel software and the AHP method, this is should reduce the complications and ease the decision making processes involved.  Further benefits are thought to be that it will be easy to be understood by contractors and that it can be tried with different bids without significant time. In addition, it is expected to significantly reduce the time involved in future bidding and give clear direction when contractors face many bid requests at the same time from different markets or clients.

Bid process reengineering is also identified as another prospect for future research.  Further research areas include the creation of educational material to undertake, implement and improve the understanding and practice of the d2b and bidding process in general.

5. References

Abdelrazig, A.A. (1995). Computerized AHP model for solving Bid/no-Bid decision problem. MSc Thesis, King Fahd University of Petroleum and Minerals, Saudi Arabia.

Ahmad, I. (1990). Decision-support system for modelling bid/no-bid decision problem. Journal of Construction Engineering and Management, 116(4), 595-608.

Ahmad, I., Minkarah, I. A. (1987). Optimum mark-up for bidding: a preference-uncertainty trade off approach. Civil Engineering Systems, 4(4), 170-174.

Bageis, A. S., & Fortune, C. (2009). Factors affecting the bid/no bid decision in the Saudi Arabian construction contractors. Construction Management and Economics, 27(1), 53 – 71.

Drew, D., & Skitmore, R.M.  (1997). The effect of contract type and contract size on competitiveness in construction contract bidding.  Construction Management and Economics 15(5), 469-489.

Drew, D.S., Skitmore, M., & Lo, H.P. (2001). The effect of client and type and size of construction work on a contractor’s bidding strategy. Building and Environment, 36(3), 393-406.

Drew, D., & Skitmore, R.M. (1990). Analysing bidding performance: measuring the influence of contract size and type. In V. Ireland & D. Cheetham (Eds.), Proceedings The International Council for Building Research Studies and Documentation, CIB W-55/65 (pp. 129-139). Sydney, Australia: International Council for Building Research Studies and Documentation.

Egemen, M., & Mohamed, A.N. (2007). A framework for contractors to reach strategically correct bid/no bid and mark-up size decisions. Building and Environment, 42(3), 1373-1385.

El-Mashaleh, M.S. (2010). Decision to bid or not to bid: a data envelopment analysis approach. Canadian Journal of Civil Engineering, 37(1), 37-44.

Lin, C.-T., & Chen, Y.-T. (2004). Bid/no-bid decision-making – a fuzzy linguistic approach. International Journal of Project Management, 22(7), 585-593.

Lowe, D., & Skitmore, M. (2006). Bidding. In D. Lowe & R. Leiringer (Eds.), Commercial management of projects: defining the discipline. Oxford, UK: Blackwell Publishing Ltd.

Lowe, D.J., & Parvar, J. (2004). A logistic regression approach to modelling the contractor’s decision to bid. Construction Management and Economics, 22(6), 643-653.

Oo, B.-L., D. S. Drew, H.-P. Lo.(2008) A comparison of contractors’ decision to bid behaviour according to different market environments. International Journal of Project Management 26 (4):439-447.

Runeson, G., & Skitmore, M. (1999). Tendering theory revisited. Construction Management and Economics, 17(3), 285-296.

Saaty, T.L. (1982). Decision making for leaders: The Analytical Hierarchy Process for Decisions in a Complex World. Belmont, CA: Wadsworth, Inc.

Seydel, J., & Olson, D.L. (1990). Bids considering multiple criteria. ASCE Journal of Construction Engineering and Management, 116(4), 609–623.

Shash, A.A. (1993). Factors considered in tendering decisions by top UK contractors. Construction Management and Economics, 11(2), 111-118.

Shash, A.A., Abdul-Hadi, N.H. (1992). Factors affecting a contractor’s mark-up size decision in Saudi Arabia. Construction Management and Economics, 10(5), 415-429.

Skitmore, R.M. (1989). Contract bidding in construction: Strategic management and modelling. Harlow, UK: Longman Scientific & Technical.

Skitmore, M., & Runeson, G. (2006). Bidding models: testing the stationarity assumption. Construction Management and Economics, 24(8), 791-803.

Wanous, M., Boussabaine, A.H., & Lewis, J. (1998). Tendering factors considered by Syrian contractors. In W. Hughes (Ed.), Proceedings of the 14th Annual ARCOM Conference (Vol. 2, pp. 535-543), Oxford, UK: Association of Researchers in Construction Management.

Wanous, M., Boussabaine, A.H., & Lewis, J. (2003). A neural network bid/no bid model: the case for contractors in Syria. Construction Management and Economics, 21(7), 737-744.

Visits: 1635