Supply Chain Disruption Costs Study in International Containerised Maritime Transportation

The global economy relies highly on international trade, and the international maritime transport system acts as the lifeblood carrying and transporting materials and goods globally, realizing the economy globalization in an effective and efficient way. However, globalization increases the interdependence and complexity of global supply chains and drives it to be more vulnerable to disruptions. Meanwhile, the international marine transport system is a complex and intertwined system exposed to high risks and decreased safety due to its very accessibility and operational flexibility. Thereby, global supply chains integrated with international maritime transportation systems are inherently vulnerable to various disruptions. Studies of supply chain disruptions particularly quantifying transport related disruption costs are becoming increasingly important. However, research on maritime transport related supply chain disruptions, in particular, quantifying its disruption costs is under-represented in the transport literature, due largely to the features of supply chain disruptions, but also because of the complexity of maritime related supply chains. Current research in transportation has tended to concentrate on shippers’ transport mode choice and port selection. In the context of a global market, however, the behaviour of maritime containerised shippers has to be viewed as a complex decision and an integral element of the supply chain management strategy. Those shippers’ transportation choice decisions should be emphasized and studied to reveal their behaviour changes between normal operations and disruption circumstance. This research adds to the paucity work on investigating the maritime transport related supply chain disruptions and quantifying its disruption costs based on shippers’ maritime transportation choice behaviour. It presents the results of a microanalysis of freight transport choice decisions in an international containerised maritime transport chain context. The Latent Class Model (LCM) is applied to identify the key service attributes and its preference heterogeneity in maritime transportation and to estimate the marginal values for the quality of maritime transport service with and without a disruption, simultaneously, quantifying the disruption costs through comparing each attribute’s marginal value difference between normal and disruption operations. The Seemingly Unrelated Regression model (SURE) is utilized to explore the sources influencing shippers’ preference heterogeneities. In doing so, we are able to gain an understanding as to where and how much should be invested in order to facilitate recovery in the case of a disruption based on the view of the maritime participants’ perspectives. The research results confirm freight rate, transit time, reliability, damage rate, and frequency as the key service attributes influencing shippers’ transport choice. They also reveal shippers’ VOT increase by more than four-times, VOR nearly double, and VOD increase about twenty percent if a disruption takes place, and identify shippers’ transport decisions vary with its product, shipment, company and supply chain characteristics no matter with or without a disruption. This research quantifies the costs of supply chain disruption in containerised maritime transport context for the first time, and its results provide useful industrial implications for maritime transport chain related parties

Supply Chain Disruption Costs Study in International Containerised Maritime Transportation

The global economy relies highly on international trade, and the international maritime transport system acts as the lifeblood carrying and transporting materials and goods globally, realizing the economy globalization in an effective and efficient way. However, globalization increases the interdependence and complexity of global supply chains and drives it to be more vulnerable to disruptions. Meanwhile, the international marine transport system is a complex and intertwined system exposed to high risks and decreased safety due to its very accessibility and operational flexibility. Thereby, global supply chains integrated with international maritime transportation systems are inherently vulnerable to various disruptions. Studies of supply chain disruptions particularly quantifying transport related disruption costs are becoming increasingly important. However, research on maritime transport related supply chain disruptions, in particular, quantifying its disruption costs is under-represented in the transport literature, due largely to the features of supply chain disruptions, but also because of the complexity of maritime related supply chains. Current research in transportation has tended to concentrate on shippers’ transport mode choice and port selection. In the context of a global market, however, the behaviour of maritime containerised shippers has to be viewed as a complex decision and an integral element of the supply chain management strategy. Those shippers’ transportation choice decisions should be emphasized and studied to reveal their behaviour changes between normal operations and disruption circumstance. This research adds to the paucity work on investigating the maritime transport related supply chain disruptions and quantifying its disruption costs based on shippers’ maritime transportation choice behaviour. It presents the results of a microanalysis of freight transport choice decisions in an international containerised maritime transport chain context. The Latent Class Model (LCM) is applied to identify the key service attributes and its preference heterogeneity in maritime transportation and to estimate the marginal values for the quality of maritime transport service with and without a disruption, simultaneously, quantifying the disruption costs through comparing each attribute’s marginal value difference between normal and disruption operations. The Seemingly Unrelated Regression model (SURE) is utilized to explore the sources influencing shippers’ preference heterogeneities. In doing so, we are able to gain an understanding as to where and how much should be invested in order to facilitate recovery in the case of a disruption based on the view of the maritime participants’ perspectives. The research results confirm freight rate, transit time, reliability, damage rate, and frequency as the key service attributes influencing shippers’ transport choice. They also reveal shippers’ VOT increase by more than four-times, VOR nearly double, and VOD increase about twenty percent if a disruption takes place, and identify shippers’ transport decisions vary with its product, shipment, company and supply chain characteristics no matter with or without a disruption. This research quantifies the costs of supply chain disruption in containerised maritime transport context for the first time, and its results provide useful industrial implications for maritime transport chain related parties

Sustainable supply chain network design integrating logistics outsourcing decisions in the context of uncertainties

Les fournisseurs de services logistiques (3PLs) possèdent des potentialités pour activer les pratiques de développement durables entre les différents partenaires d’une chaîne logistique (Supply Chain SC). Il existe un niveau optimal d'intégration des 3PLs en tant que fournisseurs, pour s’attendre à des performances opérationnelles élevées au sein de toute la SC. Ce niveau se traduit par la distinction des activités logistiques à externaliser de celles à effectuer en interne. Une fois que les activités logistiques externalisés sont stratégiquement identifiées, et tactiquement dimensionnées, elles doivent être effectuées par des 3PLs appropriés afin d’endurer les performances économiques ; sociales ; et environnementales de la SC. La présente thèse développe une approche holistique pour concevoir une SC durable intégrant les 3PLs, dans un contexte incertain d’affaires et politique de carbone. Premièrement, une approche de modélisation stochastique en deux étapes est suggérée pour optimiser à la fois le niveau d'intégration des 3PLs, et le niveau d'investissement en technologies sobres au carbone, et ce dans le contexte d’une SC résiliente aux changements climatiques. Notre SC est structurée de façon à capturer trois principales préoccupations du Supply Chain Management d’une entreprise focale FC (e. g. le fabricant) : Sécurité d’approvisionnement, Segmentation de distribution, et Responsabilité élargie des producteurs. La première étape de l'approche de modélisation suggère un plan stochastique basé sur des scenarios plus probables, afin de capturer les incertitudes inhérentes à tout environnement d’affaires (e. g. la fluctuation de la demande des différents produits ; la qualité et la quantité de retour des produits déjà utilisés ; et l’évolution des différents coûts logistiques en fonction du temps). Puis, elle propose un modèle de programmation stochastique bi-objectif, multi-période, et multi-produit. Le modèle de programmation quadratique, et non linéaire consiste à minimiser simultanément le coût logistique total espéré, et les émissions de Gaz à effet de Serre de la SC fermée. L'exécution du modèle au moyen d'un algorithme basé sur la méthode Epsilon-contraint conduit à un ensemble de configurations Pareto optimales d’une SC dé- carbonisée, avant tout investissement en technologie sobre au carbone. Chacune de ces configurations sépare les activités logistiques à externaliser de celles à effectuer en interne. La deuxième étape de l'approche de modélisation permet aux décideurs de choisir la meilleure configuration de la SC parmi les configurations Pareto optimales identifiées. Le concept de Prix du Carbone Interne est utilisé pour établir un plan stochastique du prix de carbone, dans le cadre d'un régime de déclaration volontaire du carbone. Nous proposons un ensemble des technologies sobres au carbone, dans le domaine de transport des marchandises, disposées à concourir pour contrer les politiques incertaines de carbone. Un modèle stochastique combinatoire, et linéaire est développé pour minimiser le coût total espéré, sous contraintes de l’abattement du carbone; limitation du budget, et la priorité attribuée pour chaque Technologie Réductrice de carbone (Low Carbone Reduction LCR). L'injection de chaque solution Pareto dans le modèle, et la résolution du modèle conduisent à sélectionner la configuration de la SC, la plus résiliente aux changements climatiques. Cette configuration définit non seulement le plan d'investissement optimal en LCR, mais aussi le niveau optimal d’externalisation de la logistique dans la SC. Deuxièmement, une fois que les activités logistiques à externaliser sont stratégiquement définies et tactiquement dimensionnées, elles ont besoin d’être effectuées par des 3PL appropriées, afin de soutenir la FC à construire une SC durable et résiliente. Nous suggérons DEA-QFD / Fuzzy AHP- Conception robuste de Taguchi : Une approche intégrée & robuste, pour sélectionner les 3PL candidats les plus efficients. Les critères durables et les risques liés à l’environnement d’affaires, sont identifiés, classés et ordonnés. Le Déploiement de la Fonction Qualité (QFD) est renforcé par le Processus Hiérarchique Analytique (AHP), et par la logique floue pour déterminer avec consistance l'importance relative de chaque facteur de décision, et ce, conformément aux besoins logistiques réels, et stratégies d'affaires de la FC. L’Analyse d’Enveloppement des Données (DEA) Data Envelopment Analysis conduit à limiter la liste des candidats, uniquement à ceux d’efficiences comparables, et donc excluant tout candidat moins efficient. La technique de conception robuste Taguchi permet de réaliser un plan d'expérience qui détermine un candidat idéal nommé 'optimum de Taguchi' ; un Benchmark pour comparer les 3PLs candidats. Par suite, le 3PL le plus efficient est celui le plus proche de cet optimum. Nous conduisons actuellement une étude de cas d’une entreprise qui fabrique et commercialise les fours à micro-ondes pour valider la modélisation stochastique en deux étapes. Certains aspects concernant l’application de l’approche sont reportés. Enfin, un exemple de sélection d’un 3PL durable pour s’occuper de la logistique inverse est fourni, pour démontrer l'applicabilité de l'approche intégrée & robuste, et montrer sa puissance par rapport aux approches populaires de sélection.The Third-Party Logistics service providers (3PLs) have the potentialities to activate sustainable practices between different partners of a Supply Chain (SC). There exists an optimal level of integrating 3PLs as suppliers of a Focal Company within the SC, to expect for high operational performances. This level leads to distinguish all the logistics activities to outsource from those to perform in-house. Once the outsourced logistics activities are strategically identified, and tactically dimensioned, they need to be performed by appropriate 3PLs to sustain economic, social and environmental performances of the SC. The present thesis develops a holistic approach to design a sustainable supply chain integrating 3PLs, in the context of business and carbon policy uncertainties. First, a two-stage stochastic modelling approach is suggested to optimize both the level of 3PL integration, and of Low Carbon Reduction LCR investment within a climate change resilient SC. Our SC is structured to capture three main SC management issues of the Focal Company FC (e.g. The manufacturer) : Security of Supplies; Distribution Segmentation; and Extended Producer Responsibility. The first-stage of the modelling approach suggests a stochastic plan based scenarios capturing business uncertainties, and proposes a two-objective, multi-period, and multi-product programming model, for minimizing simultaneously, the expected logistics total cost, and the Green House Gas GHG emissions of the whole SC. The run of the model by means of a suggested Epsilon-constraint algorithm leads to a set of Pareto optimal decarbonized SC configurations, before any LCR investment. Each one of these configurations distinguishes the logistics activities to be outsourced, from those to be performed in-house. The second-stage of the modelling approach helps the decision makers to select the best Pareto optimal SC configuration. The concept of internal carbon price is used to establish a stochastic plan of carbon price in the context of a voluntary carbon disclosure regime, and we propose a set of LCR technologies in the freight transportation domain ready to compete for counteracting the uncertain carbon policies. A combinatory model is developed to minimize the total expected cost, under the constraints of; carbon abatement, budget limitation, and LCR investment priorities. The injection of each Pareto optimal solution in the model, and the resolution lead to select the most efficient climate resilient SC configuration, which defines not only the optimal plan of LCR investment, but the optimal level of logistics outsourcing within the SC as well. Secondly, once the outsourced logistics are strategically defined they need to be performed by appropriate 3PLs for supporting the FC to build a Sustainable SC. We suggest the DEA-QFD/Fuzzy AHP-Taguchi Robust Design: a robust integrated selection approach to select the most efficient 3PL candidates. Sustainable criteria, and risks related to business environment are identified, categorized, and ordered. Quality Function Deployment (QFD) is reinforced by Analytic Hierarchic Process (AHP), and Fuzzy logic, to consistently determine the relative importance of each decision factor according to the real logistics needs, and business strategies of the FC. Data Envelopment Analysis leads to shorten the list of candidates to only those of comparative efficiencies. The Taguchi Robust Design technique allows to perform a plan of experiment, for determining an ideal candidate named ‘optimum of Taguchi’. This benchmark is used to compare the remainder 3Pls candidates, and the most efficient 3PL is the closest one to this optimum.We are currently conducting a case study of a company that manufactures and markets microwave ovens for validating the two-stage stochastic approach, and certain aspects of its implementation are provided. Finally, an example of selecting a sustainable 3PL, to handle reverse logistics is given for demonstrating the applicability of the integrated & robust approach, and showing its power compared to popular selection approaches. Keywords:Third Party Logistics; Green Supply Chain design; Stochastic Multi-Objective Optimization; Carbon Pricing; Taguchi Robust Design

The Role of Collaborative Governance in Blockchain-Enabled Supply Chains: A Proposed Framework

The blockchain age is dawning. Firms large and small are teaming up with partners and solution providers to deploy blockchain, especially in supply chains, often called the “sleeping giant” use case. But blockchain is still new, and despite early successes in simulated environments, how companies need to collaborate in a blockchain world is unclear. To help close the blockchain collaboration research gap, this design science study explores the technological and ecosystem business decisions required to deploy an interoperable blockchain solution. The research partially builds a supply chain artifact, and the challenges experienced by the design team prompts further investigation with twenty blockchain experts. With the discovery that effective and collaborative governance is a key mechanism to remove obstacles in blockchain deployment, the study concludes with a collaborative governance model. Inspired by public policy makers, the framework includes technological rules to assist practitioners as they collaborate in a blockchain world

The Opportunities of Rail in Freight Transport from China to Finland : Case Approach

The purpose of this thesis is to evaluate the viability of railway transport in the transport of goods from China to Finland. In the past decades, China has experienced immense economic growth and in 2014, China surpassed the USA as the world’s number one economy. The demand for transport from China has naturally exploded. Heavy investments in infrastructure have been made, yet congestion is still a problem. The One Belt, One Road initiative launched by the Chinese Government in 2013 aims to improve road and rail connections from China to its trading partners. As a result of investments made in Eurasian railway connections, the previously modest share of railway transport has begun to grow rapidly. Railway freight now provides a midway alternative to expensive air freight and sluggish ocean freight in terms of both cost and speed. This thesis explores the costs and delivery times of railway freight and other transport modes when moving freight from China to Finland. This thesis was conducted as an assignment and it takes a single case approach. This thesis utilizes a single case approach by examining the costs and delivery times of transport modes in the context of a case company. Both qualitative and quantitative approaches are utilized in order to gain a holistic understanding of the individual case. The main sources of data used are requests for quotation, participant observation, archival records and discussions. The main finding of this thesis is that railway freight is a viable alternative to ocean and air freight. Railway freight takes roughly half the time of ocean transport and the costs are not considerably larger than those of ocean freight. Air freight is indisputably the fastest and most reliable transport mode, but also evidently the most expensive. The second important finding is that with all transport modes, the freight costs per unit go down significantly when order quantity is increased. When only considering transport costs, it is advisable to order largest possible quantities. The third finding is that when transporting very small quantities, parcel services are always the best option

Selection process of auto-ID technology in warehouse management: A Delphi study

This thesis was submitted for the degree of Doctor of philosophy and awarded by Brunel UniversityIn a supply chain, a warehouse is a crucial component for linking all chain parties. Automatic identification and data capture (auto-ID) technology, e.g. RFID and barcodes are among the essential technologies in the 21st century knowledge-based economy. Selecting an auto-ID technology is a long term investment and it contributes to improving operational efficiency, achieving cost savings and creating opportunities for higher revenues. The interest in auto-ID research for warehouse management is rather stagnant and relatively small in comparison to other research domains such as transport, logistics and supply chain. However, although there are some previous studies that explored factors for the auto-ID selection decision in a warehouse environment, those factors (e.g., operational factors) have been examined separately and researchers have paid no attention to all key factors that may potentially affect this decision. In fact, yet there is no comprehensive framework in the literature that comprehensively investigates the critical factors influencing the auto-ID selection decision and how the factors should be combined to produce a successful auto-ID selection process in warehouse management. Therefore, the main aim of this research is to investigate empirically the auto-ID technology-selection process and to determine the key factors that influence decision makers when selecting auto-ID technology in the warehouse environment. This research is preceded by a comprehensive and systematic review of the relevant literature to identify the set of factors that may affect the technology selection decision. The Technology-Organisation-Environment (TOE) framework has been used as lens to categorise the identified factors (Tornatzky & Fleischer, 1990). Data were collected by conducting first a modified (mixed-method) two-round Delphi study with a worldwide panel of experts (107) including academics, industry practitioners and consultants in auto-ID technologies. The results of the Delphi study were then verified via follow-up interviews, both face-to-face and telephone, carried out with 19 experts across the world. This research in nature is positivist, exploratory/descriptive, deductive/inductive and quantitative/qualitative. The quantitative data were analysed using the statistical package for social sciences, SPSS V.18, while the qualitative data of the Delphi study and the interviews were analysed manually using quantitative content analysis approach and thematic content analysis approach respectively. The findings of this research are reported on the motivations/reasons of warehouses in seeking to use auto-ID technologies, the challenges in making an auto-ID decision, the recommendations to address the challenges, the key steps that should be followed in making auto-ID selection decision, the key factors and their relative importance that influence auto-ID selection decision in a warehouse. The results of the Delphi study show that the six major factors affecting the auto-ID selection decision in warehouse management are: organisational, operational, structural, resources, external environmental and technological factors (in decreasing order of importance). In addition, 54 key sub-factors have been identified from the list of each of the major factors and ranked in decreasing order of the importance mean scores. However, the importance of these factors depends on the objectives and strategic motivations of warehouse; size of warehouse; type of business; nature of business environment; sectors; market types; products and countries. Based on the Delphi study and the interviews findings, a comprehensive multi-stage framework for auto-ID technology selection process has been developed. This research indicates that the selection process is complex and needs support and closer collaboration from all participants involved in the process such as the IT team, top management, warehouse manager, functional managers, experts, stockholders and vendors. Moreover, warehouse managers should have this process for collaboration before adopting the technology in order to reduce the high risks involved and achieve successful implementation. This research makes several contributions for both academic and practitioners with auto-ID selection in a warehouse environment. Academically, it provides a holistic multi-stage framework that explains the critical issues within the decision making process of auto-ID technology in warehouse management. Moreover, it contributes to the body of auto-ID and warehouse management literature by synthesising the literature on key dimensions of auto-ID (RFID/barcode) selection decision in the warehouse field. This research also provides a theoretical basis upon which future research on auto-ID selection and implementation can be built. Practically, the findings provide valuable insights for warehouse managers and executives associated with auto-ID selection and advance their understanding of the issues involved in the technology selection process that need to be considered.Damascus University, Syria and The British Council, Mancheste

Sustainable supply chain management trends in world regions: A data-driven analysis

This study proposes a data-driven analysis that describes the overall situation and reveals the factors hindering improvement in the sustainable supply chain management field. The literature has presented a summary of the evolution of sustainable supply chain management across attributes. Prior studies have evaluated different parts of the supply chain as independent entities. An integrated systematic assessment is absent in the extant literature and makes it necessary to identify potential opportunities for research direction. A hybrid of data-driven analysis, the fuzzy Delphi method, the entropy weight method and fuzzy decision-making trial and evaluation laboratory is adopted to address uncertainty and complexity. This study contributes to locating the boundary of fundamental knowledge to advance future research and support practical execution. Valuable direction is provided by reviewing the existing literature to identify the critical indicators that need further examination. The results show that big data, closed-loop supply chains, industry 4.0, policy, remanufacturing, and supply chain network design are the most important indicators of future trends and disputes. The challenges and gaps among different geographical regions is offered that provides both a local viewpoint and a state-of-the-art advanced sustainable supply chain management assessment