Construction supply chain economic policy implementation for sectoral change : moving beyond the rhetoric

Construction sector policy makers have the opportunity to create improvements and develop economic, social and environmental sustainability through supply chain economics. The idea of the supply chain concept to improve firm behaviour and industry performance is not new. However there has been limited application and little or no measurement to monitor successful implementation. Often purchasing policies have been developed with sound strategic procurement principles but even these have had limited penetration in to the processes and practices of infrastructure agencies. The research reported in this paper documents an action research study currently being undertaken in the Australian construction sector which aims to explore supply chain economic policy implementation for sectoral change by two government agencies. The theory which informs this study is the emerging area of construction supply chain economics. There are five stages to the project including; demand analysis, chain analysis, government agency organizational audit, supplier strategy and strategic alignment. The overall objective is towards the development of a Supplier Group Strategy Map for two public sector agencies. Two construction subsectors are examined in detail; construction and demolition waste and precast concrete. Both of these subsectors are critical to the economic and environmental sustainability performance of the construction sector and the community as a whole in the particular jurisdictions. The local and state government agencies who are at the core of the case studies rely individually on the performance of these sectors. The study is set within the context of a sound state purchasing policy that has however, had limited application by the two agencies. Partial results of the study are presented and early findings indicate that the standard risk versus expenditure procurement model does not capture the complexities of project, owner and government risk considerations. A new model is proposed in this paper, which incorporates the added dimension of time. The research results have numerous stakeholders; they will hold particular value for those interested in regional construction sector economics, government agencies who develop and implement policy and who have a large construction purchasing imprint and the players involved in the two subsectors. Even though this is a study in Australia it has widespread applicability as previous research indicates that procurement reform is of international significance and policy implementation is problematic.

Extending knowledge management across the supply chains in the construction industry : knowledge sharing in construction supply chains

Supply chain management and knowledge management have emerged as two distinct business philosophies in the last decade. Both are making rapid inroads into the construction industry. The premise of this paper is that knowledge management would make it possible for all the trading partners in a supply chain to reap benefits. Current research in knowledge management in the construction industry is generally targeting those big organisations that are main contractors. This has restricted the scope of knowledge management, and limits the benefits to a few, rather than the whole industry. If the construction industry as a whole is to prosper and improve its productivity, strategies for knowledge management strategy at the industry level must be established. This paper argues the case for extending the scope of knowledge management across the full extent of the supply chain, and attempts to identify the benefits that may arise out of sharing knowledge across the supply chain.

Data mining on prices of highway project main material based on WEB

The management of main material prices of provincial highway project quota has problems of lag and blindness. Framework of provincial highway project quota data MIS and main material price data warehouse were established based on WEB firstly. Then concrete processes of provincial highway project main material prices were brought forward based on BP neural network algorithmic. After that standard BP algorithmic, additional momentum modify BP network algorithmic, self-adaptive study speed improved BP network algorithmic were compared in predicting highway project main prices. The result indicated that it is feasible to predict highway main material prices using BP NN, and using self-adaptive study speed improved BP network algorithmic is the relatively best one.

Supply chain sustainability: a relationship management approach

Purpose: In this research we examined, by means of case studies, the mechanisms by which relationships can be managed and by which communication and cooperation can be enhanced in sustainable supply chains. The research was predicated on the contention that the development of a sustainable supply chain depends, in part, on the transfer of knowledge and capabilities from the larger players in the supply chain. Design/Methodology/Approach: The research adopted a triangulated approach in which quantitative data were collected by questionnaire, interviews were conducted to explore and enrich the quantitative data and case studies were undertaken in order to illustrate and validate the findings. Handy‟s (1985) view of organisational culture, Allen & Meyer‟s (1990) concepts of organisational commitment and Van de Ven & Ferry‟s (1980) measures of organisational structuring have been combined into a model to test and explain how collaborative mechanisms can affect supply chain sustainability. Findings: It has been shown that the degree of match and mismatch between organisational culture and structure has an impact on staff‟s commitment level. A sustainable supply chain depends on convergence – that is the match between organisational structuring, organisation culture and organisation commitment. Research Limitations/implications: The study is a proof of concept and three case studies have been used to illustrate the nature of the model developed. Further testing and refinement of the model in practice should be the next step in this research. Practical implications: The concept of relationship management needs to filter down to all levels in the supply chain if participants are to retain commitment and buy-in to the relationship. A sustainable supply chain requires proactive relationship management and the development of an appropriate organisational culture, and trust. By legitimising individuals‟ expectations of the type of culture which is appropriate to their company and empowering employees to address mismatches that may occur a situation can be created whereby the collaborating organisations develop their competences symbiotically and so facilitate a sustainable supply chain. Originality/value: The culture/commitment/structure model developed from three separate strands of management thought has proved to be a powerful tool for analysing collaboration in supply chains and explaining how and why some supply chains are sustainable, and others are not.

An effective approach to verbose queries using a limited dependencies language model

Intuitively, any `bag of words' approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies have been mixed or inconclusive. To improve the situation, this paper shows how the natural language properties of the target documents can be used to transform and enrich the term dependencies to more useful statistics. This is done in three steps. The term co-occurrence statistics of queries and documents are each represented by a Markov chain. The paper proves that such a chain is ergodic, and therefore its asymptotic behavior is unique, stationary, and independent of the initial state. Next, the stationary distribution is taken to model queries and documents, rather than their initial distri- butions. Finally, ranking is achieved following the customary language modeling paradigm. The main contribution of this paper is to argue why the asymptotic behavior of the document model is a better representation then just the document's initial distribution. A secondary contribution is to investigate the practical application of this representation in case the queries become increasingly verbose. In the experiments (based on Lemur's search engine substrate) the default query model was replaced by the stable distribution of the query. Just modeling the query this way already resulted in significant improvements over a standard language model baseline. The results were on a par or better than more sophisticated algorithms that use fine-tuned parameters or extensive training. Moreover, the more verbose the query, the more effective the approach seems to become.

Up the junction? Exploiting knowledge-based development through supply chain and SME cluster interactions

Maximisation of Knowledge-Based Development (KBD) benefits requires effective dissemination and utilisation mechanisms to accompany the initial knowledge creation process. This work highlights the potential for interactions between Supply Chains (SCs) and Small and Medium sized Enterprise Clusters (SMECs), (including via ‘junction’ firms which are members of both networks), to facilitate such effective dissemination and utilisation of knowledge. In both these network types there are firms that readily utilise their relationships and ties for ongoing business success through innovation. The following chapter highlights the potential for such beneficial interactions between SCs and SMECs in key elements of KBD, particularly knowledge management, innovation and technology transfer. Because there has been little focus on the interactions between SCs and SMECs, particularly when firms simultaneously belong to both, this chapter examines the conduits through which information and knowledge can be transferred and utilised. It shows that each network type has its own distinct advantages in the types of information searched for and transferred amongst network member firms. Comparing and contrasting these advantages shows opportunities for both networks to leverage the knowledge sharing strengths of each other, through these ‘junctions’ to address their own weaknesses, allowing implications to be drawn concerning new ways of utilising relationships for mutual network gains.

Relational factors that explain supply chain relationships

Purpose: The purpose of this paper is to gain a better understanding of the types of relationships that exist along the supply chain and the capabilities that are needed to manage them effectively. ---------- Design/methodology/approach: This is exploratory research as there has been little empirical research into this area. Quantitative data were gathered by using a self-administered questionnaire, using the Australian road freight industry as the context. There were 132 usable responses. Inferential and descriptive analysis, including factor analysis, confirmatory factor and regression analysis was used to examine the predictive power of relational factors in inter-firm relationships. ---------- Findings: Three factors were identified as having significant influence on relationships: sharing, power and interdependency. “Sharing” is the willingness of the organisation to share resources with other members of the supply chain. “Power” relates to exercising control based on experience, knowledge and position in the supply chain. “Interdependency” is the relative levels of dependency along the supply chain. ---------- Research limitations/implications: The research only looks at the Australian road freight industry; a wider sample including other industries would help to strengthen the generalisability of the findings. ---------- Practical implications: When these factors are correlated to the types of relationship, arm's length, cooperation, collaboration and alliances, managerial implications can be identified. The more road freight businesses place importance on power, the less they will cooperate. The greater the importance of sharing and interdependency, the greater is the likelihood of arm's length relationships. ---------- Originality/value: This paper makes a contribution by describing empirical work conducted in an under-researched but important area – supply chain relationships in the Australian road freight industry.

Service identification through value chain analysis and prioritization

In a resource constrained business world, strategic choices must be made on process improvement and service delivery. There are calls for more agile forms of enterprises and much effort is being directed at moving organizations from a complex landscape of disparate application systems to that of an integrated and flexible enterprise accessing complex systems landscapes through service oriented architecture (SOA). This paper describes the deconstruction of an enterprise into business services using value chain analysis as each element in the value chain can be rendered as a business service in the SOA. These business services are explicitly linked to the attainment of specific organizational strategies and their contribution to the attainment of strategy is assessed and recorded. This contribution is then used to provide a rank order of business service to strategy. This information facilitates executive decision making on which business service to develop into the SOA. The paper describes an application of this Critical Service Identification Methodology (CSIM) to a case study.

Hierarchical models for 2D presence/absence data having ambiguous zeroes: With a biogeographical case study on dingo behaviour

This dissertation is primarily an applied statistical modelling investigation, motivated by a case study comprising real data and real questions. Theoretical questions on modelling and computation of normalization constants arose from pursuit of these data analytic questions. The essence of the thesis can be described as follows. Consider binary data observed on a two-dimensional lattice. A common problem with such data is the ambiguity of zeroes recorded. These may represent zero response given some threshold (presence) or that the threshold has not been triggered (absence). Suppose that the researcher wishes to estimate the effects of covariates on the binary responses, whilst taking into account underlying spatial variation, which is itself of some interest. This situation arises in many contexts and the dingo, cypress and toad case studies described in the motivation chapter are examples of this. Two main approaches to modelling and inference are investigated in this thesis. The first is frequentist and based on generalized linear models, with spatial variation modelled by using a block structure or by smoothing the residuals spatially. The EM algorithm can be used to obtain point estimates, coupled with bootstrapping or asymptotic MLE estimates for standard errors. The second approach is Bayesian and based on a three- or four-tier hierarchical model, comprising a logistic regression with covariates for the data layer, a binary Markov Random field (MRF) for the underlying spatial process, and suitable priors for parameters in these main models. The three-parameter autologistic model is a particular MRF of interest. Markov chain Monte Carlo (MCMC) methods comprising hybrid Metropolis/Gibbs samplers is suitable for computation in this situation. Model performance can be gauged by MCMC diagnostics. Model choice can be assessed by incorporating another tier in the modelling hierarchy. This requires evaluation of a normalization constant, a notoriously difficult problem. Difficulty with estimating the normalization constant for the MRF can be overcome by using a path integral approach, although this is a highly computationally intensive method. Different methods of estimating ratios of normalization constants (N Cs) are investigated, including importance sampling Monte Carlo (ISMC), dependent Monte Carlo based on MCMC simulations (MCMC), and reverse logistic regression (RLR). I develop an idea present though not fully developed in the literature, and propose the Integrated mean canonical statistic (IMCS) method for estimating log NC ratios for binary MRFs. The IMCS method falls within the framework of the newly identified path sampling methods of Gelman & Meng (1998) and outperforms ISMC, MCMC and RLR. It also does not rely on simplifying assumptions, such as ignoring spatio-temporal dependence in the process. A thorough investigation is made of the application of IMCS to the three-parameter Autologistic model. This work introduces background computations required for the full implementation of the four-tier model in Chapter 7. Two different extensions of the three-tier model to a four-tier version are investigated. The first extension incorporates temporal dependence in the underlying spatio-temporal process. The second extensions allows the successes and failures in the data layer to depend on time. The MCMC computational method is extended to incorporate the extra layer. A major contribution of the thesis is the development of a fully Bayesian approach to inference for these hierarchical models for the first time. Note: The author of this thesis has agreed to make it open access but invites people downloading the thesis to send her an email via the 'Contact Author' function.

Reconfiguring the higher education value chain

Forces of demand and supply are changing the dynamics of the higher education market. Transformation of institutions of higher learning into competitive enterprise is underway. Higher education institutions are seemingly under intense pressure to create value and focus their efforts and scarce funds on activities that drive up value for their respective customers and other stakeholders. Porter’s generic ‘value chain’ model for creating value requires that the activities of an organization be segregated in to discrete components for value chain analysis to be performed. Recent trends in higher education make such segregation possible. Therefore, it is proposed that the academic process can be unbundled into discrete components which have well developed measures. A reconfigured value chain for higher education, with its own value drivers and critical internal linkages is also proposed in this paper.

How does small business impact on utilization of industry-led supply chain innovation capacity

This research uses confirmatory factor analysis and structural equation modelling to examine how organizational size - made up of four dimensions - control, resources, trust and complexity - impacts on utilization of industry-led supply chain innovation capacity in a traditional agribusiness industry, the Australian beef industry. It confirms small business rather than larger business accords greater importance to exploiting supply chain dynamic capabilities, particularly in relation to utilizing industry –led supply chain innovation capacity. For small business in Australian beef supply chains, being agile and able to adapt and align their business practices with supply chain partners is integral to ensuring these businesses remain relevant and competitive in this market. In theoretical terms this is supported by authors in the dynamic capabilities literature as they argue these types of capabilities enable organizations to innovate faster (or better), often leading to the creation of newer sources of competitive advantage.

Remaining useful life prediction using elliptical basis function network and Markov Chain

This paper presents a novel method for remaining useful life prediction using the Elliptical Basis Function (EBF) network and a Markov chain. The EBF structure is trained by a modified Expectation-Maximization (EM) algorithm in order to take into account the missing covariate set. No explicit extrapolation is needed for internal covariates while a Markov chain is constructed to represent the evolution of external covariates in the study. The estimated external and the unknown internal covariates constitute an incomplete covariate set which are then used and analyzed by the EBF network to provide survival information of the asset. It is shown in the case study that the method slightly underestimates the remaining useful life of an asset which is a desirable result for early maintenance decision and resource planning.