Sustainable Supply Chain Management

The book is a collection of studies dedicated to different perspectives of three dimensions or pillars of the sustainability of supply chain and supply chain management - economic, environmental, and social - and other aspects related to performance evaluation, optimization, and modelling of and for sustainable supply chain management, and thus presents another valuable contribution to sustainable development and sustainable way of life

Methodology and Tools to Make Predictions from Sporadic Delivery Data

RÉSUMÉ: Au cours de la révolution industrielle, les entreprises manufacturières ont vu naître la notion d’intégration verticale; elles ont acquis des matières premières qu'elles ont transformé en produits finis et livrés à leurs clients. Bien que l'intégration verticale ait été très efficace, à une certaine époque, en raison du contrôle centralisé de la qualité et de la production, elle a également conduit à la création de grandes organisations peu flexibles, qui évoluent difficilement et lentement, et souvent moins capables de tirer parti des technologies émergentes. Les technologies émergentes, les progrès en télécommunications et en transport ont permis aux entreprises de différentes régions d’améliorer leur collaboration, de produire plus efficacement et, ont finalement mené aux réseaux de production et à l'émergence de la gestion de la chaîne d'approvisionnement. La gestion d'une chaîne d'approvisionnement nécessite une compréhension précise des exigences à tous les niveaux de la chaîne. Cependant, cette compréhension des besoins des partenaires de la chaîne d'approvisionnement dépend fortement du partage d'information entre eux. Le partage d'informations entre ces partenaires n'est pas toujours possible et le fournisseur est alors obligé de rechercher d'autres sources d'informations. Les fournisseurs peuvent par exemple disposer des données historiques provenant de leurs registres de livraison. On peut alors s'attendre à ce que ces données fournissent une bonne indication des besoins des clients. Dans la pratique, les registres de livraison sont mal adaptés pour prédire les exigences futures de la demande en raison de la relation non linéaire entre la consommation et les opérations de livraison. Notre recherche a révélé plusieurs défis lors de la tentative d'interprétation de l'information recueillie à partir des données de livraison. Les données de livraison reflètent plus que les comportements de consommation des clients. Les décisions logistiques, telles que le calendrier, la fréquence de livraison, le volume et le nombre de camions, entre autres, sont reflétés dans les données de livraisons, malgré que ces décisions ne soient pas motivées par le client. Une méthode pour extraire les informations de comportement de consommation à partir des données de livraison a donc été nécessaire. Un deuxième point est de savoir comment gérer des prédictions pour une large population de clients. La globalisation de tous les besoins de production présente une vue d'ensemble de l'organisation, mais peu de connaissances sont révélées sur les comportements de consommation individuels. Enfin, même lorsque les prédictions sont faites à un niveau global, il est besoin d’une méthode pour appliquer ces prédictions au niveau individuel de chaque client. Dans cette recherche, nous proposons une méthode pour calculer des prévisions au niveau individuel de chaque client à partir d'un grand ensemble de données globales. La littérature est unanime quant au fait que le partage d'informations collaboratif au sein d'une chaîne d'approvisionnement est bénéfique, mais les auteurs reconnaissent également que d'autres données doivent parfois être substituées, et que ces données peuvent être corrompues ou faussées par des effets de globalisation et d’amplification. Il y a une lacune dans la littérature quant à la façon d’interpréter les données et de les rendre utiles pour l'analyse. Nous répondons à cette lacune en proposant une méthode de substitution des données de livraison aux données de consommation. Nous trouvons également une lacune dans les écrits concernant la segmentation du marché qui utilise généralement des variables descriptives pour distinguer le niveau de similitude entre les clients. Les auteurs ne traitent pas de la façon d'établir des segments lorsque les variables descriptives ne sont pas disponibles. Nous comblons cet écart en proposant une méthode qui établit des segments de marché en fonction du comportement passé démontré. La littérature sur la segmentation de marché se concentre sur le découpage d'une population en segments pour faciliter l'analyse comme la prévision. Il y a peu de conseils sur la façon de désagréger des données et d'appliquer les analyses précédentes aux clients individuels. Nous avons proposé une méthode pour cela. Enfin, pour tenter de combler le besoin d'une méthode de validation des résultats de la segmentation du marché, nous proposons une solution qui établit les segments en fonction du comportement démontré et qui vérifie ensuite si les attributs descriptifs peuvent aboutir à des résultats de segmentation similaires. Un jeu de données réel est utilisé dans cette recherche pour tester les méthodes proposées. L'ensemble de données comprend les données de livraison d'un fournisseur pour l’ensemble de ses clients pendant plus de cinq ans; plus d'un million d'événements de livraison sont inclus. Les données ont été triées pour éliminer les valeurs aberrantes, laissant 75% des données brutes et 3000 clients uniques pour l'étude de cas. Les composants de notre recherche sont présentés en quatre parties qui fonctionnent ensembles pour résoudre le problème général. Chaque composant a cependant des applications potentielles dans d'autres domaines et pourrait être utilisé pour résoudre d'autres types de problèmes. Dans la première partie, les données sont préparées pour l'analyse. Les premières tentatives pour résoudre le problème de la recherche supposaient que l'ensemble de données brutes pourrait simplement être divisées en tranches mensuelles et ensuite utilisées pour élaborer une prévision. Les résultats étaient extrêmement diffus à tel point qu’aucune information n'a été révélée. Nous avons proposé une méthode pour résoudre ce problème. La deuxième partie aborde le problème du nombre trop important de clients pour permettre une analyse prévisionnelle individuelle. Nous avons proposé une méthode pour segmenter les clients en fonction de leurs comportements démontrés. La troisième partie de notre recherche est une méthode permettant de générer des prévisions par segment, puis d'appliquer ces prévisions à des clients individuels. Dans la dernière partie de la recherche, nous tentons de valider et d'améliorer la méthode en intégrant des variables externes telles que le climat, l'emplacement et les caractéristiques propres au domaine industriel concerné. Nous pensions que les comportements étaient influencés par ces facteurs. Les résultats montrent qu'il existe en réalité très peu de corrélation entre les comportements réels des clients et ces attributs. Ceci est surprenant sachant que la segmentation des clients basée sur des attributs descriptifs est une pratique commerciale courante. Les contributions de cette recherche sont importantes dans trois catégories : méthodologique, scientifique et pratique. La stratégie méthodologique utilisée ici démontre que les nouveaux problèmes n’impliquent pas nécessairement le besoin de nouveaux outils. Nous commençons avec un problème d'entreprise et recherchons des outils établis pour le résoudre. Bien que les outils ne soient pas nouveaux ou uniques, leur combinaison et leur application l'est. Sur le plan scientifique, nous proposons un cadre d'étapes interconnectées pouvant être appliquées séquentiellement pour résoudre un problème métier complexe. Un ensemble de données volumineuses, globales et stochastiques est trié, interprété et transformé en une solution offrant des informations prévisionnelles. Les différentes étapes proposées peuvent également être utilisées individuellement et appliquées dans d'autres domaines pour aider à résoudre d'autres types de problèmes. L'étude de cas qui a inspiré cette recherche est un vrai problème fourni par notre partenaire industriel. Les méthodes proposées dans cette recherche permettent de trier les données, de supprimer les informations corrompues ou faussées et d'afficher des résultats exploitables. Une fois que les modèles de comportement sous-jacents peuvent être vus, la situation de l'entreprise peut être mieux cernée, et les connaissances nouvellement disponibles peuvent aider à prendre des décisions d'affaires. La dernière partie de la recherche est importante dans sa rupture d'un paradigme. Beaucoup d'entreprises utilisent dans la prémisse de leur planification d'entreprise, que les attributs descriptifs sont essentiels pour prédire les comportements des clients. Nos résultats montrent que ces types d'attributs ne sont pas nécessairement très clairement corrélés avec le comportement de consommation, notamment quand il y a du biais important lié au caractéristiques intrinsèques du fonctionnement de l’entreprise. La recherche présentée ici forme un cadre pour acquérir des connaissances à partir d'un ensemble de données brutes qui sont inutilisables en l’état. L'étude de cas fournit une méthode pour mettre en oeuvre le cadre proposé et un ensemble viable de résultats est produit.----------ABSTRACT: Managing a supply chain requires an accurate understanding of the requirements at all levels of the chain; understanding requirements of the supply chain partners is therefore highly dependent on information sharing between partners. Information sharing, however, is not always possible and the supplier is forced to look for other sources of information. Suppliers usually have historical data from its delivery records which can be expected to provide a good indication of the customers’ requirements. In practice, delivery records do not perform well for predicting future demand requirements due to the non-linear relationship between delivery transactions and consumption. Delivery records reflect more than just the customers’ consumption behaviors. Logistics decisions, such as timing, frequency, and volume of deliveries are also reflected in the delivery records. A method to extract the consumption behavior information from the noisy data is necessary. A second challenge is how to manage predictions for a large population of customers. Aggregating all production requirements together presents a high-level view of the organization, but little knowledge is revealed regarding consumption behavior. Lastly, once predictions are made at an aggregated level, a method to apply the predictions at the customer level is lacking. In this research, we propose a method for developing customer level forecasts from a large, noisy dataset. Our research has revealed several gaps in the literature which we propose to address. The literature is unanimous in opinion that collaborative information sharing within a supply chain is beneficial, but substitute data must sometimes be used; that data may be corrupted or noisy due to aggregation and bullwhip effects. We address a gap in the literature as to how to address the noise in the data and make it useful for analysis. We also find a gap in the literature regarding market segmentation which generally utilizes descriptive variables to distinguish the level of similarity between customers. The literature does not address how to establish segments when descriptive variables are not available. We address this gap with our proposed method that establishes market segments based on demonstrated past behavior. The literature on market segmentation all focusses on combining a population into segments to facilitate analysis such as forecasting. There is little guidance on how to de-segment and apply those subsequent analyses to the individual customers. We proposed a method for that. Finally, in attempt to address the gap of a method to validate market segmentation results, we propose a method that establishes segments based on demonstrated behavior and then test whether descriptive attributes can achieve similar segmentation results. A real dataset is used in this research to test the proposed methods. The dataset consists of a supplier’s delivery records for all its customers for over five years; more than one million delivery events are included. The data was cleaned to remove outliers leaving 75% of the raw data and 3000 unique customers for the case study. The components of our proposition are presented in four parts that work together for solving one specific problem. Each component has potential applications in other domains and might be utilized in solving other types of problems. Despite their individual uniqueness, the four parts are also sequentially dependent on their preceding part. The research presented here forms a framework for gaining knowledge from an otherwise unusable dataset. The case study provides a platform for validating the proposed framework and a viable set of results is produced

4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 4th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges. Due to the covid pandemic, CARMA 2022 is planned as a virtual and face-to-face conference, simultaneouslyDoménech I De Soria, J.; Vicente Cuervo, MR. (2022). 4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2022.2022.1595

Abordagem preditiva e adaptativa de gestão operacional aplicada à cadeia de suprimentos do varejo Omni-channel

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Produção, Florianópolis, 2020.A evolução tecnológica e a digitalização possibilitam a comercialização de produtos através de múltiplos canais e plataformas de forma integrada, propiciando a gestão de varejo omnichannel. Esse processo contínuo de integração das tecnologias digitais/virtuais aos processos gerenciais físicos dos diversos canais influencia na interação das organizações com os clientes. O comportamento de consumo dos clientes é influenciado em decorrência do aumento da conveniência, tornando, contudo, a gestão operacional das cadeias de suprimentos do varejo mais complexa. Para a gestão da cadeia de suprimentos de varejo omni-channel a complexidade reside na incerteza, oscilações no volume de vendas e incompatibilidade entre oferta e demanda. Para lidar com essa complexidade é necessária a adoção de abordagens inovadoras relacionadas a tecnologias de informação e métodos de decisão inteligentes, destacados pela indústria 4.0. No entanto, ainda faltam pesquisas sobre a conexão entre os mundos digital e real, principalmente quando se trata de cadeias de suprimentos de varejo omni-channel, que se baseiam na integração de fluxos e atividades multicanais para melhor atender ao consumidor. Neste contexto, esta pesquisa tem como objetivo propor uma abordagem preditiva e adaptativa para a gestão operacional combinando aprendizado de máquina para minimizar a incerteza, e otimização baseada em simulação para lidar com a sincronização entre oferta e demanda, aplicada à cadeia de suprimentos do varejo omni-channel. Para isso foram identificados os métodos de aprendizado de máquina, de simulação e de otimização aplicados à cadeia de suprimentos e a indústria 4.0 com o intuito de apoiar a escolha do método de redes neurais e da otimização baseada em simulação por meio do algoritmo genético. O método de redes neurais e a otimização baseada em simulação foram analisados por meio de aplicação de um caso teste, visando identificar a aplicabilidade do método levantado na literatura, na gestão operacional da cadeia de suprimentos varejista omni-channel. Em seguida, a abordagem preditiva e adaptativa é aplicada a uma empresa varejista brasileira e como resultado um modelo de gerenciamento operacional de demanda e suprimentos é proposto para a cadeia de suprimentos varejista omnichannel. Os resultados da aplicação do modelo evidenciaram uma redução dos custos da cadeia de suprimentos, do tempo de entrega dos produtos e da quantidade de pedidos provenientes da incompatibilidade de oferta-demanda. Dessa forma, a tese possibilitou a redução das incertezas proveniente da previsão de demanda, redução da falta de produtos na cadeia, e consequentemente um melhor gerenciamento da distribuição da cadeia de suprimentos.Abstract: Technological evolution and digitalization enable the commercialization of products through multiple channels and platforms in an integrated way, providing omni-channel retail management. This ongoing process of integrating digital / virtual technologies into the physical management processes of the various channels influences the interaction of organizations with customers. Customer consumption behavior is influenced by the increase in convenience, however, making the operational management of retail supply chains more complex. For the management of the omni-channel retail supply chain the complexity lies in uncertainty, fluctuations in sales volume and incompatibility between supply and demand. To address this complexity, it is necessary to adopt innovative approaches related to information technologies and intelligent decision methods, highlighted by industry 4.0. However, there is still a lack of research on the connection between the digital and real worlds, especially when it comes to omni-channel retail supply chains, which are based on the integration of multi-channel flows and activities to better serve the consumer. In this context, this research aims to propose a predictive and adaptive approach to operational management combining machine learning to minimize uncertainty, and simulation-based optimization to deal with synchronization between supply and demand, applied to the omni-channel retail supply chain. For this, the machine learning, simulation and optimization methods applied to the supply chain and industry 4.0 were identified in order to support the choice of neural networks method and simulation-based optimization through the genetic algorithm. The neural networks method and the simulationbased optimization were analyzed by applying a test case, aiming to identify the applicability of the method raised in the literature, in the operational management of the omni-channel retail supply chain. The predictive and adaptive approach is then applied to a Brazilian retail company and as a result an operational demand and supply management model is proposed for the omnichannel retail supply chain. The results of the model application showed a reduction in the supply chain costs, in the products fulfillment time and in the quantity of orders resulting from the incompatibility of supply and demand. In this way, the thesis allowed reduce uncertainties arising from demand forecasting, reduce product shortages in the chain, and thereby better manage supply chain distribution

The tabu ant colony optimizer and its application in an energy market

A new ant colony optimizer, the \u27tabu ant colony optimizer\u27 (TabuACO) is introduced, tested, and applied to a contemporary problem. The TabuACO uses both attractive and repulsive pheromones to speed convergence to a solution. The dual pheromone TabuACO is benchmarked against several other solvers using the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and the Steiner tree problem. In tree-shaped puzzles, the dual pheromone TabuACO was able to demonstrate a significant improvement in performance over a conventional ACO. As the amount of connectedness in the network increased, the dual pheromone TabuACO offered less improvement in performance over the conventional ACO until it was applied to fully-interconnected mesh-shaped puzzles, where it offered no improvement. The TabuACO is then applied to implement a transactive energy market and tested with published circuit models from IEEE and EPRI. In the IEEE feeder model, the application was able to limit the sale of power through an overloaded transformer and compensate by bringing downstream power online to relieve it. In the EPRI feeder model, rapid voltage changes due to clouds passing over PV arrays caused the PV contribution to outstrip the ability of the substation to compensate. The TabuACO application was able to find a manageable limit to the photovoltaic energy that could be contributed on a cloudy day --Abstract, page iii

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

New Concepts for Efficient Consumer Response in Retail Influenced by Emerging Technologies and Innovations

The retail industry is continuously confronted with new challenges and experiences a transformation from a supplier’s market to a buyer's market. It is, thus, essential for the retail industry to consequently focus on, anticipate and fulfil consumer’s demands. Technologies and innovative business solutions can help to support to establish a required customer experience and, thereby, gain a competitive advantage. A multitude of new services and products, channels as well as players can already be identified which drive the transformation. Therefore, retailers need to understand current trends and technologies and identify as well as implement relevant solutions for their transformation since otherwise, new players will dominate the market. Hence, this dissertation aims to review and analyse new technologies which are coupled with innovative business activities in order to provide customer-centric retailing. For this purpose, this dissertation consists of five articles and derives four major contributions which introduce different approaches to establishing consumer satisfaction. Firstly, a core technology for retail is artificial intelligence (AI) which can be meaningful applied along the entire value chain and improve retailers’ positions. Two focus areas have been identified in this context which are (i) the optimisation of the entire retail value chain with the help of AI with the aim to derive transparency and (ii) the improvement of consumer satisfaction and relationship. Secondly, focussing on the consumer-retailer relationship in the digital era, a concept with a data architecture is proposed based on a real use case. The outcome was that a specific customer orientation based on data can increase the brand value and sales volume. Thirdly, the work presents that new shopping concepts, named unmanned store concepts, gain continuous growth. Unmanned store concepts employ a variety of new technologies, are characterised by attributes of speed, ease, as well as comfort, and are deemed to be the new ideal of the expectations of modern buyers. Two different directions have been deeper analysed: (i) walk-in stores and (ii) automated vending machines. The critical success factors for the usage of unmanned store solutions are distance as well as high consumer affinity for innovations. In times of the COVID-19 pandemic, which has a huge impact on retail, a continuous innovation capability still needs to be established. Finally, this work introduces a tool for systematic innovation management considering the current circumstances. Taken as a whole, this dissertation with its five articles deals with significant research questions which have not been approached so far. Thereby, the literature is extended by the introduction of novel insights and the provision of a deeper understanding of how retailers can transform their business into a more consumer-oriented way

CORPORATE SOCIAL RESPONSIBILITY IN ROMANIA

The purpose of this paper is to identify the main opportunities and limitations of corporate social responsibility (CSR). The survey was defined with the aim to involve the highest possible number of relevant CSR topics and give the issue a more wholesome perspective. It provides a basis for further comprehension and deeper analyses of specific CSR areas. The conditions determining the success of CSR in Romania have been defined in the paper on the basis of the previously cumulative knowledge as well as the results of various researches. This paper provides knowledge which may be useful in the programs promoting CSR.Corporate social responsibility, Supportive policies, Romania

Supply Chain

Traditionally supply chain management has meant factories, assembly lines, warehouses, transportation vehicles, and time sheets. Modern supply chain management is a highly complex, multidimensional problem set with virtually endless number of variables for optimization. An Internet enabled supply chain may have just-in-time delivery, precise inventory visibility, and up-to-the-minute distribution-tracking capabilities. Technology advances have enabled supply chains to become strategic weapons that can help avoid disasters, lower costs, and make money. From internal enterprise processes to external business transactions with suppliers, transporters, channels and end-users marks the wide range of challenges researchers have to handle. The aim of this book is at revealing and illustrating this diversity in terms of scientific and theoretical fundamentals, prevailing concepts as well as current practical applications

An Empirical Investigation Of Information Technology Mediated Customer Services In China

Information technology mediated customer service is a reality of the 21st century. More and more companies have moved their customer services from in store and in person to online through computer or mobile devices. Using 208 respondents collected from two Chinese universities, this paper investigates customer preference over two service delivery model (either in store or online) on five type of purchasing (retail, eating-out, banking, travel and entertainment) and their perception difference in customer service quality between those two delivery model. Results show that a majority of Chinese students prefer in store and in person for eating out. For ordering tickets for travel and entertainment, they prefer computer/mobile device. For retail purchasing and banking, less than half of the students prefer in person services. In general, the results show that ordering through computer/mobile devices has become more popular in China and has received higher rating for most of customer service quality except security compared to ordering in store. In addition, it is found that there exist a gender difference in purchasing preference and perception in service delivery quality in China