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.

The use of supply chain management to reduce delays : Malaysian public sector construction projects

Construction delays are a critical problem for Malaysian public sector projects. These delays have been blamed mainly on inefficient traditional construction practices that continue to dominate the current industry. This paper reports the progress to date of a Ph.D. research project aimed at developing a framework to utilize Supply Chain Management (SCM) tools to improve the time performance of Malaysian Government projects. The potential of SCM has been identified for public sector governance and its use in Malaysia is now being considered within the strategy of the Malaysian Construction Industry Master Plan (2006-2015). Encouraged by success in the UK, there is a cautious optimism concerning the successful application of SCM in Malaysia. This paper considers delay as a problem in Malaysian public sector projects, establishes the need to embrace SCM and then elucidates the need and strategies for the development of a delay reduction framework. A literature review, survey mechanism and structured interview schedule will be undertaken to achieve the research objectives. The final research outcome will be a framework that addresses root delay contributors (“pathogens”) and applies SCM tools for their mitigation.

Drivers of supply chain collaboration among Finnish manufacturers

This paper focuses on data exchange relationships and ways to improve collaboration in the supply chain. Initially, the paper examines the information needs and alternatives in supply chain management. In the second part, the paper identifies different sets of factors that are likely to influence information sharing with suppliers, from the manufacturers’ point of view. Results from a Finnish Manufacturing industry survey show that manufacturers provided substantial information on demand data, production schedules, and inventories to their suppliers. Respondents perceived delivery performance measured by the timeliness, accuracy, and defect rate of deliveries as the primary incentives for supplier collaboration. On the other hand, supplier image and the market in which the supplier operates were found to be less relevant in determining the intensity of collaboration.

Supply chain sustainability : a relationship management approach moderated by culture and commitment

This research explores the nature of relationship management on construction projects in Australia and examines the effects of culture, by means of Schwarz’s value survey, on relationships under different contract strategies. The research was based on the view that the development of a sustainable supply chain depends on the transfer of knowledge and capabilities from the larger players in the supply chain through collaboration brought about by relationship management. 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. The aim was to investigate how values and attitudes enhance or reduce the incorporation of the supply chain into the project. From the research it was found that the degree of match and mismatch between values and contract strategy impacts commitment and the engagement and empowerment of the supply chain.

Maintenance strategy optimization using a continuous-state partially observable semi-Markov decision process

Due to the limitation of current condition monitoring technologies, the estimates of asset health states may contain some uncertainties. A maintenance strategy ignoring this uncertainty of asset health state can cause additional costs or downtime. The partially observable Markov decision process (POMDP) is a commonly used approach to derive optimal maintenance strategies when asset health inspections are imperfect. However, existing applications of the POMDP to maintenance decision-making largely adopt the discrete time and state assumptions. The discrete-time assumption requires the health state transitions and maintenance activities only happen at discrete epochs, which cannot model the failure time accurately and is not cost-effective. The discrete health state assumption, on the other hand, may not be elaborate enough to improve the effectiveness of maintenance. To address these limitations, this paper proposes a continuous state partially observable semi-Markov decision process (POSMDP). An algorithm that combines the Monte Carlo-based density projection method and the policy iteration is developed to solve the POSMDP. Different types of maintenance activities (i.e., inspections, replacement, and imperfect maintenance) are considered in this paper. The next maintenance action and the corresponding waiting durations are optimized jointly to minimize the long-run expected cost per unit time and availability. The result of simulation studies shows that the proposed maintenance optimization approach is more cost-effective than maintenance strategies derived by another two approximate methods, when regular inspection intervals are adopted. The simulation study also shows that the maintenance cost can be further reduced by developing maintenance strategies with state-dependent maintenance intervals using the POSMDP. In addition, during the simulation studies the proposed POSMDP shows the ability to adopt a cost-effective strategy structure when multiple types of maintenance activities are involved.

Maintenance chain integration using Petri-net enabled multiagent system modeling and implementation approach

Engineering asset management (EAM) is a broad discipline and the EAM functions and processes are characterized by its distributed nature. However, engineering asset nowadays mostly relies on self-maintained experiential rule bases and periodic maintenance, which is lacking a collaborative engineering approach. This research proposes a collaborative environment integrated by a service center with domain expertise such as diagnosis, prognosis, and asset operations. The collaborative maintenance chain combines asset operation sites, service center (i.e., maintenance operation coordinator), system provider, first tier collaborators, and maintenance part suppliers. Meanwhile, to realize the automation of communication and negotiation among organizations, multiagent system (MAS) technique is applied to enhance the entire service level. During the MAS design processes, this research combines Prometheus MAS modeling approach with Petri-net modeling methodology and unified modeling language to visualize and rationalize the design processes of MAS. The major contributions of this research include developing a Petri-net enabled Prometheus MAS modeling methodology and constructing a collaborative agent-based maintenance chain framework for integrated EAM.

Bounded parameter Markov Decision Processes with average reward criterion

Bounded parameter Markov Decision Processes (BMDPs) address the issue of dealing with uncertainty in the parameters of a Markov Decision Process (MDP). Unlike the case of an MDP, the notion of an optimal policy for a BMDP is not entirely straightforward. We consider two notions of optimality based on optimistic and pessimistic criteria. These have been analyzed for discounted BMDPs. Here we provide results for average reward BMDPs. We establish a fundamental relationship between the discounted and the average reward problems, prove the existence of Blackwell optimal policies and, for both notions of optimality, derive algorithms that converge to the optimal value function.

Exponentiated gradient algorithms for conditional random fields and max-margin Markov Networks

Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the log-linear or max-margin objective function; the dual in both the log-linear and max-margin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the max-margin case, O(1/ε) EG updates are required to reach a given accuracy ε in the dual; in contrast, for log-linear models only O(log(1/ε)) updates are required. For both the max-margin and log-linear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be efficiently applied to problems such as sequence learning or natural language parsing. We perform extensive evaluation of the algorithms, comparing them to L-BFGS and stochastic gradient descent for log-linear models, and to SVM-Struct for max-margin models. The algorithms are applied to a multi-class problem as well as to a more complex large-scale parsing task. In all these settings, the EG algorithms presented here outperform the other methods.

Studies of granulosa cell maturation in dominant and subordinate bovine follicles : novel extracellular matrix focimatrix is co-ordinately regulated with cholesterol side-chain cleavage CYP11A1

During growth of antral ovarian follicles granulosa cells first become associated with a novel type of extracellular matrix, focimatrix, and at larger sizes follicles become either subordinate or dominant. To examine this, bovine subordinate (9.0±s.e.m. 0.4 mm; n=16), partially dominant (12.0±0.6 mm; n=18) and fully dominant (15.0±0.4 mm; n=14) follicles were examined by real time RT-PCR analyses of granulosa cells and by immunohistochemistry of focimatrix. Changes in the expression of FSH receptor, LH receptor, cholesterol side-chain cleavage (CYP11A1), 3β-hydroxysteroid dehydrogenase, aromatase (CYP19A1) and inhibin-α and β-B were observed as expected for follicle sizes examined. After adjusting for size differences, only CYP11A1 was significantly different between the groups, and elevated in dominant follicles. Also after adjusting for differences in size there were no significant differences in expression of focimatrix components collagen type IV α-1 (COL4A1), laminin β-2, nidogen 1 (NID1), and perlecan (HSPG2) or the volume density of NID1 and -2 and HSPG2. The volume density of focimatrix components in laminin 111 was significantly elevated in dominant follicles. Adjusting for analysis of more than one follicle per animal and for multiple correlations, CYP11A1 mRNA levels were highly correlated with the focimatrix genes COL4A1, NID1 and -2 and HSPG2. Thus, focimatrix may potentially regulate CYP11A1 expression, and the regulation of both could be important in follicular dominance.

Chain scission resists for extreme ultraviolet lithography based on high performance polysulfone-containing polymers

A series of polymers with a comb architecture were prepared where the poly(olefin sulfone) backbone was designed to be highly sensitive to extreme ultraviolet (EUV) radiation, while the well-defined poly(methyl methacrylate) (PMMA) arms were incorporated with the aim of increasing structural stability. It is hypothesized that upon EUV radiation rapid degradation of the polysulfone backbone will occur leaving behind the well-defined PMMA arms. The synthesized polymers were characterised and have had their performance as chain-scission EUV photoresists evaluated. It was found that all materials possess high sensitivity towards degradation by EUV radiation (E0 in the range 4–6 mJ cm−2). Selective degradation of the poly(1-pentene sulfone) backbone relative to the PMMA arms was demonstrated by mass spectrometry headspace analysis during EUV irradiation and by grazing-angle ATR-FTIR. EUV interference patterning has shown that materials are capable of resolving 30 nm 1:1 line:space features. The incorporation of PMMA was found to increase the structural integrity of the patterned features. Thus, it has been shown that terpolymer materials possessing a highly sensitive poly(olefin sulfone) backbone and PMMA arms are able to provide a tuneable materials platform for chain scission EUV resists. These materials have the potential to benefit applications that require nanopattering, such as computer chip manufacture and nano-MEMS.

Achieving supply chain sustainability : the role of relationship management, culture and procurement form

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 developing 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. 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. Effective supply chain management enhances organisation performance and competitiveness through the management of operations across organisational boundaries. Relational contracting approaches facilitate the exchange of information and knowledge and build capacity in the supply chain, thus enhancing its sustainability. Relationship management also provides the conditions necessary for the development of collaborative and cooperative relationships However, often subcontractors and suppliers are not empowered to attend project meetings or to have direct communication with project based staff. With this being a common phenomenon in the construction industry, one might ask: what are the barriers to implementation of relationship management through the supply chain? In other words, the problem addressed in this research is the engagement of the supply chain through relationship management.