Indiana University-Purdue University Indianapolis (IUPUI) / Affordable and reliable supply chain visibility is becoming increasingly important as the complexity of the network underlying supply chains is becoming orders of magnitudes higher compared to a decade ago. Moreover, this increase in complexity is starting to reflect on the cost of goods and their availability to the consumers. Optimizing the physical distribution phase in supply chain by providing increased visibility to trading partners can directly reduce product cost. Current supply chain information systems often lack the ability to cost-effectively relay ground truth in- formation in near real time to all stakeholders and most importantly to the supplier and the customer during the transport of the shipment. This thesis presents a solu- tion that addresses this gap through a distributed architecture. The solution enables small, medium and large businesses to interact in a dynamic and shipment-centric manner through a private blockchain sub-ledger that digitizes the transfer of custody for each shipment. Information in this private ledger is augmented by a public event ledger that reflects the movement of the shipment in real time. Third party monitors are engaged in the validation of the geolocation of the shipments by posting their physical proximity in the form of events to the public ledger.

Plantilla para elaborar Tesis de Maestría en Supply Chain Management para optar el grado académico de Maestro en Supply Chain Management en el Programa de Maestría en Supply Chain Management. Escuela de Postgrado. Universidad Peruana de Ciencias Aplicadas.

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<p> This thesis summarizes theoretical results of two projects on the investigation of a novel organometallic polymer, the polyferrocenylsilanes(PFS). The study is carried out in collaboration with the experimental groups of Prof. Manners and Prof. Winnik at the Department of Chemistry of University of Toronto. </p> <p> In the first project, a rotational isomeric state (RIS) model is applied to study the configurational statistics of an ideal polyferrocenyldimethylsilane (PFDMS) chain: Fc[Fe(C5H4)2SiMe2]nH (Fe= Fe(C5H5)(C5H4)). The necessary conformation energies are derived from the molecular mechanics study of oligomeric (n=1,2) models for PFDMS reported by O'Hare et al.(J. Am. Chem. Soc. 1996, 118, 7578). In particular, pseudoatom and pseudobonds are introduced to describe the RIS chain of PFDMS, consistent with the special molecular geometry of the repeating ferrocene and organosilane units. The mean square unperturbed dimensions ((r^2)0, (R^2g)0), the characteristic ratios Cn(C∞) and the temperature coefficients dln (r^2)0/dT of PFDMS are calculated. The results show that an ideal PFDMS chain has a relatively low value of Coo and fast convergence of Cn to C∞ with increasing n, indicating a high static flexibility of this type of transition metal-containing polymer. The previously unknown Kuhn length of PFDMS is obtained based on the calculated C∞ </p> <p> In the second project, by taking PFS-b-PDMS/alkane as a model system, general phase behaviours of the self-assembled micelles in dilute crystalline-coil copolymer solutions (solvents are selective for the coil blocks) are investigated. Three types of aggregates - lamellar, rodlike and tubular micelles are studied based on the existing experimental observations. The computation results reveal three types of phase diagrams, namely, lamella-tube-rod phase diagrams with or without a triple point and lamella-rod phase diagrams. It is shown that lamella-tube-rod morphological transitions can be induced by changing the coil/crystalline block ratio or the temperature. Possible improvement of the theory and the current challenges of studying PFS-b-PDMS self-assembly in alkane solvents for both theories and experiments are discussed. </p> / Thesis / Master of Science (MSc)

Thesis (M.B.A.)--Boston University / PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you. / 2999-01-01

Supply chains are complex systems. Researchers in the Social and Decision Analytics Laboratory (SDAL) at Virginia Tech worked with a major global supply chain company to simulate an end-to-end supply chain. The supply chain data includes raw materials, production lines, inventory, customer orders, and shipments. Including contributions of this author, Pires, Sabin, Higdon et al. (2017) developed simulations for the production, customer orders, and shipments. Customer orders are at the center of understanding behavior in a supply chain. This dissertation continues the supply chain simulation work by improving the order simulation. Orders come from a diverse set of customers with different habits. These habits can differ when it comes to which products they order, how often they order, how spaced out those orders times are, and how much of each of those products are ordered. This dissertation is unique in that it relies extensively on Dirichlet and multiscale methods to tackle supply-chain order simulation. Multiscale model methodology is furthered to include Dirichlet models which are used to simulate order times for each customer and the collective system on different scales. / Doctor of Philosophy / This dissertation continues the supply chain simulation work of researchers (Pires et al. (2017)) in the Social and Decision Analytics Laboratory (SDAL) at Virginia Tech by improving the order simulation. Orders come from a diverse set of customers with different habits. These habits can di er when it comes to which products they order, how often they order, how spaced out those orders times are, and how much of each of those products are ordered. This dissertation is unique from the previous work at SDAL which considered few of these factors in order simulation and introduces statistical methodologies to deal with the complex nature of simulating an entire supply chain's orders.

Yes / The effective management of pharmaceuticals in the National Health Service (NHS) is critical to patient welfare thus any risks attached to this must be identified and controlled. At a very basic level, risks in the pharmaceutical supply chain are associated with product discontinuity, product shortages, poor performance, patient safety/dispensing errors, and technological errors (causing stock shortages in pharmacies) to name but a few, all of which incur risk through disruption to the system. Current indications suggest that the pharmaceutical industry and NHS practitioners alike have their concerns as to the use of generic supply chain strategies in association with what is perceived to be a specialist product (pharmaceuticals). The aim of the study undertaken was to gain a more realistic understanding of the nature and prevalence of risk in the Pharmaceutical Supply Chain (PSC) to be used as a basis for a more rigorous research project incorporating in-vestigation in the UK, Europe and USA. Data was collected via a workshop forum held in November 2005. The outputs of the workshop indicated that there were thirty-five prevalent risks. The risks were rated using risk assessment catego-ries such as impact, occurrence and controllability. The findings indicated that the risks identified are similar to those prevalent in industrial supply chains, regardless of the idiosyncrasies of pharmaceuticals. However, the group consen-sus was that caution must be applied in how such risks are addressed, as there are aspects of the product that highlight its uniqueness e.g. criticality.

The business landscape faces various challenges that have profound implications for their supply chains. These challenges include supply shortages, sustainability efforts, demographic shifts, rising transportation costs, and the disruptive impact of global crises. Media discussions are abuzz with strategies to tackle these challenges. These strategies involve shifting towards localized production, deliberately diversifying supplier networks, and reinforcing inventories for critical products. However, this dissertation explores a potential beyond these conventional approaches: the data-driven process intelligence. Already today, leading companies are developing multi-faceted strategies that are part of data-driven process intelligence. This dissertation embarks on a comprehensive exploration of integrating emerging data-driven technologies with traditional concepts to unlock supply chain efficiencies. We present four pivotal areas, each elaborated upon in distinct chapters. Advanced technologies, including information systems, digital twins, sensor networks, and machine learning, are each harmonized with more traditional concepts such as service innovation, business value, information value, and combinatorial optimization. The first main chapter, Service Innovation Through Collaborative Information Systems, focuses on developing a collaborative urban delivery system that seamlessly integrates participants regardless of their digital maturity. This chapter underscores the potential of collaborative information systems in bridging digital divides within supply chains, particularly for local retailers striving to attract e-commerce customers. The second main chapter, Deriving Business Value From Digital Twins, explores the application of digital twin technology in modeling and optimizing construction site logistics. It illuminates how digital twins can elevate decision support systems and analytics dashboards, offering invaluable insights into logistics operations. The findings underscore the transformative power of digital twins in automating and enhancing visibility. The third main chapter, Optimizing Sensor Networks Through Information Value Analysis, addresses the pivotal issue of prioritizing investments in information technology, specifically sensor networks. An information value analysis is presented to assess the significance of accurate and timely information in augmenting supply chain efficiency. This analysis equips organizations with the ability to make informed decisions about their investments. The fourth main chapter, Combinatorial Optimization Enriched Machine Learning, introduces an innovative approach that harmonizes combinatorial optimization with machine learning. This approach can be applied to solve intricate and uncertain logistics challenges, e.g., the dynamic and stochastic inventory routing problem. The chapter showcases the synergy between machine learning and combinatorial optimization, emphasizing the potential of advanced algorithms and learning methods in optimizing logistics operations under dynamic conditions. In conclusion, this dissertation offers a comprehensive perspective on how data-driven process intelligence has the potential to support supply chain management. It provides practical insights into integrating emerging technologies seamlessly with established concepts. Moreover, it underscores the significance of change management, continuous improvement, and a holistic approach in achieving enduring supply chain efficiency enhancements. By embracing these findings, organizations can confidently and competently navigate the evolving supply chain challenges. / Die Unternehmenslandschaft steht vor verschiedenen Herausforderungen, die tiefgreifende Auswirkungen auf ihre Lieferketten haben. Zu diesen Herausforderungen gehören Lieferengpässe, Bemühungen um Nachhaltigkeit, demografische Veränderungen, steigende Transportkosten und die störenden Auswirkungen globaler Krisen. In den Medien wird viel über Strategien zur Bewältigung dieser Herausforderungen diskutiert. Zu diesen Strategien gehören die Verlagerung der Produktion auf lokale Standorte, die bewusste Diversifizierung der Lieferantennetzwerke und die Aufstockung der Lagerbestände für kritische Produkte. Diese Dissertation erforscht jedoch ein Potenzial jenseits dieser konventionellen Ansätze: die datengesteuerte Prozessintelligenz. Bereits heute entwickeln Spitzenunternehmen vielfältige Strategien, die Teil einer datengesteuerten Prozessintelligenz sind. In dieser Dissertation wird eine umfassende Untersuchung der Integration neuer datengesteuerter Technologien mit traditionellen Konzepten zur Steigerung der Lieferketteneffizienz vorgenommen. Wir stellen vier zentrale Bereiche vor, die jeweils in eigenen Kapiteln behandelt werden. Fortgeschrittene Technologien, darunter Informationssysteme, digitale Zwillinge, Sensornetzwerke und Maschinelles Lernen, werden jeweils mit traditionelleren Konzepten wie Dienstleistungsinnovation, Geschäftswert, Informationswert und Kombinatorische Optimierung in Einklang gebracht. Das erste Hauptkapitel, Service Innovation Through Collaborative Information Systems, konzentriert sich auf die Entwicklung eines kollaborativen städtischen Liefersystems, das die Teilnehmer unabhängig von ihrem digitalen Reifegrad nahtlos integriert. Dieses Kapitel unterstreicht das Potenzial von kollaborativen Informationssystemen bei der Überbrückung digitaler Barrieren innerhalb von Lieferketten, insbesondere für lokale Einzelhändler, die E-Commerce-Kunden anziehen wollen. Das zweite Hauptkapitel, Deriving Business Value From Digital Twins, erforscht die Anwendung der digitalen Zwillingstechnologie bei der Modellierung und Optimierung der Baustellenlogistik. Es wird beleuchtet, wie digitale Zwillinge Entscheidungsunterstützungssysteme und Analyse-Dashboards verbessern können, indem sie wertvolle Einblicke in den Logistikbetrieb bieten. Die Ergebnisse unterstreichen die transformative Fähigkeit von digitalen Zwillingen bei der Automatisierung und Verbesserung der Transparenz. Das dritte Hauptkapitel, Optimizing Sensor Networks Through Information Value Analysis, befasst sich mit der zentralen Frage der Priorisierung von Investitionen in die Informationstechnologie, insbesondere in Sensornetzwerke. Es wird eine Informationswertanalyse vorgestellt, um die Bedeutung genauer und rechtzeitiger Informationen für die Steigerung der Lieferketteneffizienz zu bewerten. Diese Analyse versetzt Unternehmen in die Lage, fundierte Entscheidungen über ihre Investitionen zu treffen. Im vierten Hauptkapitel, Combinatorial Optimization Enriched Machine Learning, wird ein innovativer Ansatz vorgestellt, der Kombinatorische Optimierung mit Maschinellem Lernen verbindet. Dieser Ansatz kann zur Lösung komplizierter und unsicherer logistischer Herausforderungen eingesetzt werden, z. B. beim dynamisches und stochastisches Bestandsverwaltungs- und Tourenplanungsproblem. Das Kapitel zeigt die Synergie zwischen Maschinellem Lernen und Kombinatorischer Optimierung und hebt das Potenzial fortschrittlicher Algorithmen und Lernmethoden bei der Optimierung logistischer Abläufe unter dynamischen Bedingungen hervor. Abschließend bietet diese Dissertation eine umfassende Perspektive, wie datengesteuerte Prozessintelligenz das Lieferkettenmanagement unterstützen kann. Sie bietet praktische Einblicke in die nahtlose Integration neuer Technologien mit etablierten Konzepten. Darüber hinaus unterstreicht sie die Bedeutung von Veränderungsmanagement, kontinuierlicher Verbesserung und einem ganzheitlichen Ansatz, um die Effizienz der Lieferkette nachhaltig zu steigern. Durch die Berücksichtigung dieser Erkenntnisse können Unternehmen, die sich entwickelnden Herausforderungen in der Lieferkette sicher und kompetent bewältigen.

Boston University. University Professors Program Senior theses. / PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you. / 2999-01-02