After the model development and model solving phase is over, the numerical analysis con- ducted. The proposed models can be applied in any project context with the selection of
suitable variable and parameter values. However, this research has prepared the numerical example section based on construction supply chains from the UK.
Now the question is why there is a need for these kinds of collaborative supply chain prac- tices in the construction sector? According to UKCG (2012), the construction sector value chain worth around 14% of the total UK’s GDP. According to this report, it had contributed around £1203 mn. surplus to the UK’s economy between the years 2009-11. The report also says that every £1 invested in the construction sector generates £2.84 mn economic activ- ity. Due to its huge importance, it has even featured in the top three sectors for government support just after railway and health.
Despite the importance of the construction sector in UK economy, the report published by ECLLP (2013), states that construction supply chain integration is still an area which needs careful attention and improvement. This conclusion was based on a survey conducted among the UK construction practitioners by the Business Innovation and Skills department UK. The main purpose of the survey was to identify how the UK construction companies are performing with respect to the suggested supply chain integration approaches in the Latham report published in 1994. However, the responses suggest the supply chain integration as one of the lesser highlighted areas of construction projects. In chapter 1, this research has explained certain barriers of supply chain coordination. In chapter 4, this research further elaborated the different nature of project supply chains with respect to product supply chains with a special reference to the construction supply chain.
The relevant information such as project value duration etc. was collected from the ex- isting case studies as secondary data sources from the Association of Project Management, UK (https://www.apm.org.uk/). However, there are certain variables/parameters whose val- ues are confidential in nature and are not shared as publicly available data. These values are assigned/assumed as relevant.
There are certain distribution specific parameters used in the models proposed in this research. Again, some realistic and relevant values were assumed while preparing the numer- ical examples.
The next three chapters highlighted how the identified objectives have been addressed in this research. Each of these chapters consists of the details of the backdrop of the objectives, the proposed models, and their numerical analysis results.
Chapter 4
Supply Chain Coordination using Project
Contracts in Take it or Leave it situations
The importance of supply chain coordination was highlighted in chapter 1, and the related development in the literature was presented in chapter 2. These previous chapters also high- lighted that there are various problems associated with the projects in absence of the proper supply chain coordination. Time and cost overruns are the two main problems which arise in the absence of proper supply chain coordination. A few real-life projects such as Wembley stadium renovation project and Denver airport baggage handling system installation project were cited as cases of projects with subsequent time and cost overruns (Moore 2009). In fact, the Denver airport project was quoted by the author to have a significant coordination problem. On the contrary, there are few examples from practice that can be referred with coordination among the members of the supply chain as the key to the project success such as Turner and Townsend set an example of proper coordination of supply chain activities to complete the construction project of the University of Exeter’s newly developed Business School. This helped the project management team of Turner and Townsend to complete the project within the budget of £14 million, and approximately three years estimated time.
The focus of this chapter is to address the first objective of this research. That is how project contracts can coordinate the supply chains for a more general set of distributions. The steps of solving the coordination problem were explained in chapter 3. As decided, earlier in chapter 1, the supply chain in the decentralized setting would be considered coordinated when total benefits conforms to the maximum benefits which can be derived from the centralized
setting as defined by Cachon (2003)
In a supply chain context, contracts can specify parameters such as order quantity, price, time and delivery (Kanda et al. 2008). Due to this specificity, supply chain contracts can help the total supply chain achieve coordination (Giannoccaro et al. 2004). Based on the principle proposed by Cachon (2003), several authors proposed coordination models for supply chain as discussed in the literature review in chapter 2. In all of these papers, the authors used certain contractual terms as incentives to motivate the members of the supply chain to take decisions that are aligned with the overall goals and objectives. These contractual terms can take the form of flexible payments such as trade credit (Chen & Wang 2012), side payments such as a two-part tariff (Corbett et al. 2004), revenue sharing (Giannoccaro et al. 2004), discounting contractual terms such as price discount (Bernstein & Federgruen 2005a, Chen 2011b), quantity discount (Li & Liu 2006), price plus subsidy rate (Xiao et al. 2005), dis- counting using reverse supply chain conditions such as buy back (Chen 2011b), and returns (Chen & Xiao 2011b). However, these models are limited to the demand being the source of the uncertainty and the order quantity/price being the decision variable to be optimized.
Very little is known about coordination in project supply chains. In a project supply chain (with a project manager and a contractor), the project manager can verify the project com- pletion time and the cost upon completion. However, it is difficult for the project manager to verify the resource consumption rate of the contractor when the members belong to different organizations. This could lead to a misalignment of contractor’s selected resource consump- tion rate and the optimal resource consumption rate. As a result, it could lead to time and cost overruns.
Tools and techniques used for effective project management mainly reside in the litera- ture of civil engineering as its origin is from there (Kwon et al. 2010). As mentioned earlier, some commonly used tools from project management such as CPM and PERT work under deterministic to low uncertainty, but not in more uncertain environments (Klastorin 2004). Moreover, only a limited amount of research evidence has been found in the supply chain coordination literature with cost and time as the sources of uncertainty such as project supply chain. However, the importance is not negligible in this case. In a survey study, Akintoye et al. (2000) identified supply chain coordination as one of the key requirements for the suc- cess of the construction sector in the UK, but project supply chains often fail to coordinate.
A few authors have proposed conceptual models for coordinating construction supply chains such as an inter-organizational learning model (Love et al. 2002), a framework to influence co-development of decisions (Crespin-Mazet & Ghauri 2007), and a system-wide informa- tion system (Hadaya & Pellerin 2010). However, none of these models are quantitative in nature. Recently, Bayiz & Corbett (2005), Kwon et al. (2010) and Lippman et al. (2013) proposed coordination models using project contracts for the project supply chains. How- ever, these models assumed specific functional forms for the project completion time and the completion cost: an exponential function for completion time (Bayiz & Corbett 2005, Kwon et al. 2010) and a normal distribution for completion cost (Lippman et al. 2013). However, in practice, project completion times are often modelled as the uniform distribution in simu- lation (Lee, Arditi & Son 2013), the beta distribution (Golenko-Ginzburg 1988), the gamma distribution (Roy & Roy 2013), and the Weibull distribution (Abdelkader 2004). It has been not investigated in detail if the existing models will work with these distributions or not. Therefore, there is a need to explore if coordination models can be proposed for project sup- ply chains. To address this issue, this research aims to fulfill the first objective as mentioned before.
Objective 1. To extend the coordination model proposed by Kwon et al. (2010) with a general set of continuous distributions for project completion time in a Stackelberg model with ultimatum games.
This research considered the basic model proposed by Kwon et al. (2010) and Bayiz and Corbett (2005) for the extension. The following are the main features of the extensions to the existing model
• Model proposed by Kwon et al. (2010) used exponentially discounted cash-flows. Bayiz & Corbett (2005) used a linearly decreasing cash-flow. In this research, both exponential and non-exponential discounted cash-flows (linear, quadratic and so on) are considered for modelling.
• Unlike the existing models, this research considers an additional cost (Co). This is
independent of the resource consumption rate
function for the completion time and the completion cost.
The existing models are extended for various forms of continuous distributions for project completion time. It is analysed with take it or leave it (TIOLI) contracts with the help of concepts from Stackelberg games and ultimatum games.