The purpose of this research is to learn about quality measurement practices in a wood products supply chain. According to the Supply Chain Management paradigm, companies no longer compete as individual entities, but as part of complex networks of suppliers and customers, linked together by flows of materials and information. Evidence suggests that a high degree of integration between supply chain members is essential to achieve superior market and financial performance. This study investigates the potential benefits from adopting supply chain quality management practices, focusing specifically on quality measurement. A case-study was conducted to accomplish the objectives of the research. An exemplary wood products supply chain was studied in great detail. The current state was compared with best practices, as reported in the literature. Supply chain quality metrics were used to assess current performance and a simulation model was developed to estimate the impact of changes in significant factors affecting quality, such as production volume, on the supply chain's quality performance. Quality measurement practices in the supply chain of study are described in detail in this dissertation. A high degree of internal integration was observed in the focal company, attributed in great part to the leadership of management, which formulates comprehensive quality planning, specifying quality measurement practices and goals. These practices provide the company with a competitive advantage, and have undoubtedly contributed to its relatively strong market share and financial performance. Significant improvements in defect rate and on-time performance at all levels in the supply chain have been achieved in great part thanks to current initiatives. There is room for improvement, however, regarding external integration; the supply chain of study could benefit from more information sharing with its external suppliers and increasing its supplier development efforts. There is also a lack of true measures of supply chain quality performance that could facilitate tracing variances back to their origin upstream the supply chain. Supply chain metrics must reflect the contribution of each supply chain member to the overall performance, and span the entire supply chain. This is the first study that looks in depth at quality measurement practices from a supply chain perspective. It is also one of very few studies of supply chain management applied to the wood products industry. Examples are presented of how a supply chain performance measurement system can be developed. Results from this research show that it is important to adopt a supply chain perspective when designing a performance measurement system, not least to avoid sub-optimization. Poor quality at any point in the supply chain eventually translates into higher prices for the final customer, is detrimental to customer dissatisfaction, and hurts profitability; with the end result of declining competitiveness of the entire system. / Ph. D.

Yes / Thermally responsive highly branched poly(N-isopropyl acrylamide)s (HB-PNIPAM) were prepared and end-functionalised to give polymers with acid or trihistidine end groups. These polymers exhibit a broad coil-to-globule transition across a wide temperature range which can be measured using covalently attached fluorescent tags. The acid chain ends provided a material with a distinct change in solution behaviour at pH close to the pKa of the carboxylate group. At pH 11 this polymer did not show a cloud point up to 50 °C but fluorescence measurements on the labelled polymers showed that a coil to glubule transition did take place. The globular state, above the LCST, appeared to be more swollen if the end group carried charge then when it was uncharged. A polymer with trihistidine and free carboxylate chain ends, which contained multiple charges at various pH, did show LCSTs at all pH and the polymer globule was shown to be swollen at each pH.

Yes / Purpose: The aim of this research is to evaluate if the Complex Adaptive Systems theory can be used to explain resilience strategies within the pharmaceutical supply chain Research Approach: An in depth review of literature surrounding resilience in the pharmaceutical supply chain. In order to pursue this study agenda, data was collected from Scopus, the largest peer review journal as well as EBSCOhost. The PRISMA guideline was adopted in the systematic review process where 34 peer reviewed papers in the field of CAS, supply chain and supply chain resilience were identified with respect to methodologies employed, location of the study and approaches. Findings and Originality: The systematic review of literature shows that there are inherent similarities between the concept of resilience and the CAS theory. The CAS theory explains that PSC’s are dynamic, have emergent behaviours complex, adaptive, interconnected as well as possess schemas that regulate their operations. Hence if resilience strategies are to be employed to mitigate disruptive events they need to be harnessed in a manner to fit this particular supply chain. This work is innovative as it provides a new insight into the contemporary discourse on resilience strategy creation and deployment, examining the use of this theory in the PSC, and thus provides original contribution. Research Impact: This study contributes to the existing literature base, by providing theoretical underpinnings in the area of resilience and the pharmaceutical supply chain. This furthers the CAS agenda, SCR agenda and also presents an innovative output which warrants more detailed analysis and feasibility testing. Practical Impact: Complexity principles are multi-scaled and multi-domain and as such the suggestions put forward in this theoretical framework can be adopted in various supply chain networks as well as disruptive events. It provides new insights with regards to structures for managers seeking to design and improve resilience supply chains, a key element of which is the adoption of a holistic analysis by SC managers when developing resilience strategies. This is critical if disruptions are to be identified and mitigated before their impact is felt.

This dissertation provides empirical and theoretical support for the antecedents and consequences of cyber resilience via three essays on cyber resilience. Essay 1 comprises 2 studies using a multi-method empirical research effort to determine whether emphasizing suppliers' implementation and use of business continuity management (S-BCM) is actually beneficial to buyers. In Study 1, data from 150 managers was collected via a survey-based questionnaire to determine whether buyers' adoption of monitoring supplier operational performance (MS-OP) and monitoring S-BCM (MS-BCM) enhances S-BCM implementation and use. Evidence from Study 1 suggests that MS-BCM is more effective than MS-OP. Moreover, the results suggest that while buyer power positively augments the effectiveness of MS-BCM, it actually has a diminishing effect on the effectiveness of MS-OP. Study 2 uses the data of 114 managers from a vignette-based experiment to determine whether S-BCM leads to improved buyer operational and financial performance. Study 2 offers evidence that confirms the positive link between S-BCM and buyer operational and financial performance. The results also suggest that the use of reward power further enhances the association between S-BCM and buyer performance. Using two studies, Essay 2 examines how supply chain power and learning can be related to cyber resilience capability. Study 1 indicated that powerful buyers and supply chain learning from new knowledge contribute to visibility to build cyber resilience while dominant suppliers are reluctant to share information. The results of Study-2 show that supply chain and operations managers believe that companies and their suppliers would have better operational performance if they invest in the accuracy of visibility. Moreover, supply chains properly can avoid, maintain, and recover from cyber disruption when real-time information is available. Essay 3 focuses on the role of downstream complexity along with enterprise resource planning (ERP) in building cyber resilience in supply chains. The results reveal that ERP systems help supply chains to mitigate the negative effect of downstream complexity on the impact of information sharing in a secure system needed to build cyber resilience in times of data breaches and cyber-attacks. Although the use of information technology increases cyber risk, supply chain managers should take advantage of ERP systems to mitigate the negative effect of complexity in supply chain cyber resilience.

The purpose of this three-essay dissertation is to synthesize and extend the effects of decentralization in decision-making and supply chain complexity in the context of supply chain resilience (SCRES).First essay contributes to theory and practice by expanding resilience thinking into including supply chain orientation and organizational structure and their implications and also responds to prior research arguing for the importance of identifying organizational factors that improve supply chain resilience. Second essay contributes to the supply chain organizational structure and SCRES literature by not just providing empirical support for decentralization of decision making in times of disruptions but more precisely by showing the factors that either impede or facilitate decentralization at the organizational level. Understanding the interplay among these factors is critical to explaining the lack of success for decentralization in the context of SCRES. Third essay contributes to practice by reviewing some of the major complexity drivers present in the supply chains and providing strategies along with a four-step process that practitioners can use to manage complexity.

Probabilistic graphical models are currently one of the most commonly used architectures for modelling and reasoning with uncertainty. The most widely used subclass of these models is directed acyclic graphs, also known as Bayesian networks, which are used in a wide range of applications both in research and industry. Directed acyclic graphs do, however, have a major limitation, which is that only asymmetric relationships, namely cause and effect relationships, can be modelled between their variables. A class of probabilistic graphical models that tries to address this shortcoming is chain graphs, which include two types of edges in the models representing both symmetric and asymmetric relationships between the variables. This allows for a wider range of independence models to be modelled and depending on how the second edge is interpreted, we also have different so-called chain graph interpretations. Although chain graphs were first introduced in the late eighties, most research on probabilistic graphical models naturally started in the least complex subclasses, such as directed acyclic graphs and undirected graphs. The field of chain graphs has therefore been relatively dormant. However, due to the maturity of the research field of probabilistic graphical models and the rise of more data-driven approaches to system modelling, chain graphs have recently received renewed interest in research. In this thesis we provide an introduction to chain graphs where we incorporate the progress made in the field. More specifically, we study the three chain graph interpretations that exist in research in terms of their separation criteria, their possible parametrizations and the intuition behind their edges. In addition to this we also compare the expressivity of the interpretations in terms of representable independence models as well as propose new structure learning algorithms to learn chain graph models from data.

published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

published_or_final_version / Microbiology / Master / Master of Medical Sciences

Bayesian analysis is definitely different from the classic statistical methods. Although, both of them use subjective ideas, it is used in the selection of models in the classic statistical methods, rather than as an explicit part in Bayesian models, which allows the combination of subjective ideas with the data collected, update the prior information and improve inferences. Drastic growth of Bayesian applications indicates it becomes more and more popular, because the advent of computational methods (e.g., MCMC) renders sophisticated analysis. In Bayesian framework, the flexibility and generality allows it to cope with very complex problems. One big obstacle in earlier Bayesian analysis is how to sample from the usually complex posterior distribution. With modern techniques and fast-developed computation capacity, we now have tools to solve this problem. We discuss Acceptance-Rejection sampling, importance sampling and then the MCMC methods. Metropolis-Hasting algorithm, as a very versatile, efficient and powerful simulation technique to construct a Markov Chain, borrows the idea from the well-known acceptance-rejection sampling to generate candidates that are either accepted or rejected, but then retains the current values when rejection takes place (1). A special case of Metropolis-Hasting algorithm is Gibbs Sampler. When dealing with high dimensional problems, Gibbs Sampler doesn’t require a decent proposal distribution. It generates the Markov Chain through univariate conditional probability distribution, which greatly simplifies problems. We illustrate the use of those approaches with examples (with R codes) to provide a thorough review. Those basic methods have variants to deal with different situations. And they are building blocks for more advanced problems. This report is not a tutorial for statistics or the software R. The author assumes that readers are familiar with basic statistical concepts and common R statements. If needed, a detailed instruction of R programming can be found in the Comprehensive R Archive Network (CRAN): http://cran.R-project.org / text

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