No / This paper presents a stochastic automaton approach to stock ordering for retailers of time-limited goods, in the modern supply chain network. The rationale applied is that by ordering in small quantities frequently, overstocking will be reduced, capital liquidity improved and wastage limited. A consequence for the complete supply chain is that such an approach could substantially minimise the reactive bullwhip effect, leading to more efficient utilisation, production and agility throughout the chain. Such agility and flexibility can only be achieved by full integration of stock inventory monitoring technologies (such as RFID) with enterprise integration systems (such as ERP) connected to suppliers, mediated by the internet. We undertake a comparative simulation study of stock ordering using a stochastic automaton and a naive traditional approach. This shows that stochastic ordering, prompted by a stochastic automaton, exhibits characteristic properties that are a prerequisite for reducing the bullwhip effect, thus enabling agile inventory management.

Game theory and mechanism design have emerged as an important tool to model, analyze,and solve decentralized design problems involving multiple agents that interact strategically in a rational and intelligent way. Some examples of these design problems include: auctions and markets in electronic commerce; network economics; dynamic pricing; routing protocols in wireless networks; resource allocation in computational grids; algorithms for selfish agents;etc. The motivation for this doctoral work springs from the high level of current interest indesigning innovative mechanisms for solving emerging game theoretic problems in the area of electronic commerce. In this thesis, we focus on three such problems and advance the current art in mechanism design while developing new, innovative mechanisms to solve the problems. The first problem we explore is the highly strategic problem of forming a high value E-business supply chain by choosing the best mix of supply chain partners. In our research, we formulate the supply chain formation problem as a mechanism design problem in a generic wayand show that the well known VCG (Vickrey-Clarke-Groves) mechanisms provide an apt frame-work for solving this problem. We provide a compelling example of a three stage automotive distribution network to illustrate the power and e±cacy of the proposed methodology. The second problem we model and solve is that of designing a revenue maximizing sponsored search auction. This is a problem that is faced by every Internet search engine, such as Google,MSN, and Yahoo!, whenever it receives a search query. In our research, we take a comprehensivelook at existing auction mechanisms for this problem. Our work leads to an innovative new auction mechanism, which we call OPT (optimal mechanism), that exhibits a superior level ofperformance. The proposed mechanism extends, in a non-trivial way, the well known Myersonoptimal auction to the specific setting of sponsored search auctions. The proposed mechanism maximizes the revenue to the search engine and also satisfies two crucial properties, Bayesian incentive compatibility and interim individual rationality. The third contribution is in respect of Stackelberg problems, which are game theoretic problems that involve hierarchical and sequential decision making. By focusing attention on an important subclass of these problems, namely the Single-Leader-Rest-Followers (SLRF) problems, we extend all relevant aspects of classical mechanism design theory to the case of SLRF problems. We derive many important results in respect of procurement auctions with reserve prices using the developed theory. The research carried out as part of this doctoral work, we believe, advances the current art in mechanism design while developing innovative mechanisms to solve those problems.

Antimicrobial resistance (AMR) is the process where the bacteria develop resistance towards the treating effect of an antibiotic drug. AMR poses an alarming threat to human health causing around 700,000 deaths per year around the globe. If appropriate measures to combat the resistance are not taken, the number of deaths globally could increase to around 10 million by the year 2050. There are various factors driving the growth of AMR of which antibiotic shortages are common. A clear insight into the pharmaceutical supply chain is necessary to understand the reasons causing antibiotic unavailability. Ensuring access to medicines is one of the major objectives of pharmaceutical supply chains. Pharmaceutical firms compete in a volatile market to increase their profits. Antibiotics render slim profit margins to pharmaceutical firms; declining profits and increasing costs of production have led to firms outsourcing their operations to suppliers in different geographical locations. This in turn forms complex supply chain structures with various actors of a single drug chain being dispersed across the globe. The complexity in these supply chains lead to antibiotic supply interruptions. National drug shortages drive the risk of AMR, and these shortages are caused when pharmaceutical supply chains are weak or fragile. Therefore, the pharmaceutical supply chains need to be thoroughly analysed. This thesis aims to explore the different possible upstream supply chain structures that could exist in pharmaceutical supply chains. The study also highlights the factors that motivate the firms to choose different supply chain structures. This research is based on the existing literature on pharmaceutical supply chains. Qualitative semi-structured interviews, reports and existing research articles guided the authors in building a typology of upstream pharmaceutical supply chains based on: how different processes are handled by the MAH, the geographical location of operations in the chain, and the sourcing strategy of the Market Authorisation Holder (MAH) who owns the license for the drug. The findings of this study outline how a pharmaceutical firm could possibly structure the upstream supply chain based on its strategies. This study is limited to conceptualizing only the actors involved in the direct supply chain of the focal firm (MAH), further research including actors in the extended supply chain needs to be performed to get deeper insights into pharmaceutical supply chains.

Tesis por compendio / [ES] Los sistemas tradicionales de planificación y control de la producción (PPC) se centran en producir lo que demanda el mercado, con la calidad, el calendario y los volúmenes previstos al mínimo coste, ajustándose al mismo tiempo a las disrupciones de la cadena de suministro. La exploración e implementación de nuevos avances tecnológicos en el marco de la industria 4.0 (I4.0), como sistemas ciberfísicos (CPS), fabricación en la nube (CMfg), fabricación aditiva (AM), big data, inteligencia artificial y la Internet de las cosas (IoT), podrían cambiar aspectos organizativos tales como las responsabilidades de PPC. En este contexto, no se identificaron estudios sobre un sistema para la toma de decisiones, arquitecturas y marcos conceptuales para los nuevos sistemas inteligentes de PPC e I4.0. En este contexto de nuevos cambios tecnológicos y organizativos a los que tienen que hacer frente las pequeñas y medianas empresas (PYMEs), surge el problema de diseñar herramientas de PPC que permitan la integración y colaboración de las operaciones de producción. Así, basándose en las nuevas tecnologías de producción digital y en las herramientas organizativas que darán soporte a las fábricas inteligentes conectadas del futuro, se identificó la falta de un sistema integrado de PPC e I4.0. Esta tesis doctoral es un compendio de artículos que abordan una amplia revisión bibliográfica sobre la PPC en un entorno de I4.0. También, se propone un marco conceptual y el diseño de modelos y algoritmos para la toma de decisiones y dar soporte a las funciones de PPC en un contexto digital I4.0 basado en las nuevas tecnologías de producción digital y herramientas organizativas que darán soporte a las fábricas inteligentes colaborativas y conectadas del futuro. Los modelos matemáticos y algoritmos propuestos se centran en resolver el problema del diseño y planificación de una cadena de suministro sostenible y resiliente en la que las decisiones estratégicas y tácticas se toman de forma integrada. Los modelos, algoritmos y método de resolución se han programado en Python. Los modelos han sido validados mediante un software que genera instancias de datos sintéticos y permite evaluar la complejidad computacional de los mismos. El desarrollo de este tipo de modelos y algoritmos supone una contribución al ámbito académico e investigador y, concretamente, en el área de PPC. / [CA] En l'actualitat, els sistemes tradicionals de planificació i control de la producció (PPC) se centren en produir el que demanda el mercat, amb la qualitat, el calendari i els volums previstos al mínim cost, ajustant-se al mateix temps a les pertorbacions. L'exploració i implementació de nous avanços tecnològics, com CPS, fabricació en el núvol (CMfg), fabricació additiva (AM), big data, intelligència artificial i el IoT, podrien canviar aspectes organitzatius, com les responsabilitats de PPC. En aquest context, no es van identificar estudis sobre un sistema per a la presa de decisions, arquitectures i marcs conceptuals per als nous sistemes intelligents de PPC i I4.0. En aquest context de nous canvis tecnològics i organitzatius als quals han de fer front les petites i mitjanes empreses (PIME), sorgeix el problema de dissenyar eines de PPC que permeten la integració i collaboració de les operacions de producció. Així, basant-se en les noves tecnologies de producció digital i en les eines organitzatives que donaran suport a les fàbriques intelligents connectades del futur, es va identificar la falta d'un sistema integrat de la PPC i I4.0. Aquesta investigació és un compendi d'articles que aborden una àmplia revisió bibliogràfica sobre la PPC en un entorn I4.0. També proposa un marc conceptual i el disseny de models i algorismes per a la presa de decisions i per a donar suport a les funcions de PPC en un context digital I4.0 basat en les noves tecnologies de producció digital i eines organitzatives que donaran suport a les fàbriques intelligents col·laboratives i connectades del futur. Els models matemàtics i algorismes proposats se centren en resoldre el problema del disseny d'una cadena de subministrament sostenible i resistent en la qual les decisions estratègiques i tàctiques es prenen de forma integrada. Els models, algorismes i mètode de resolució s'han programat en Python. Els models han sigut validats mitjançant un programari que genera instàncies de dades sintètiques i permet avaluar la complexitat computacional dels models. El desenvolupament d'aquesta mena de models i algorismes suposa una important contribució a l'àmbit acadèmic. / [EN] Currently, traditional production planning and control (PPC) systems focus on producing what the market demands with the expected quality, schedule and volumes at a minimum cost, while adjusting for disruption. The exploration and implementation of new technological advances, such as CPS, cloud manufacturing (CMfg), additive manufacturing (AM), big data, artificial intelligence and the Internet of Things (IoT), could change organisational aspects like PPC responsibilities. In this context, no studies on a system for decision making, architectures and conceptual frameworks for the new intelligent systems of PPC and industry 4.0 (I4.0) have been identified. In this context of new technological and organisational changes that small-and medium-sized enterprises (SMEs) have to face, the problem of designing PPC tools that enable the integration and collaboration of production operations arises. Thus, based on the new digital production technologies and organisational tools that will support the connected smart factories of the future, lack of an integrated PPC and I4.0 system was identified. The present doctoral thesis is a compendium of articles addressing a comprehensive literature review on PPC in an I4.0 environment. It also proposes a conceptual framework and the design of models and algorithms for decision making and to support PPC functions in a digital I4.0 context based on the new digital production technologies and organisational tools that will support the collaborative and connected smart factories of the future. The proposed mathematical models and algorithms focus on solving the problem of designing a sustainable and resilient supply chain where strategic and tactical decisions are made in an integrated way. The models, algorithms and resolution method have been programmed in Python. The models have been validated by means of software that generates synthetic data instances and allows the models' computational complexity to be evaluated. The development of this type of models and algorithms is a significant contribution to the academic field. / I would like to thank the following projects and universities for having financed the publications included in this doctoral thesis: • European Commission Horizon 2020 project entitled "Crop diversification and low-input farming cross Europe: From practitioners' engagement and ecosystems services to increased revenues and value chain organisation' (Diverfarming), grant agreement 728003. • Spanish Ministry of Science, Innovation and Universities project entitled 'Optimization of zero-defect production technologies enabling supply chains 4.0 (CADS4.0)' (RTI2018-101344-B-I00). • European Union H2020 program with grant agreement no. 958205 "Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)". • European Union H2020 Program with grant agreement nº 825631 "Zero- Defect Manufacturing Platform (ZDMP)". / Cañas Sánchez, HE. (2023). Models, Algorithms and Digital Technologies for the Automation and Collaboration of Connected Smart Factories in an Industry 4.0 Environment [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202312 / Compendio