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

Investigating the impact of networking capability on firm innovation performance:using the resource-action-performance framework

The author's final peer reviewed version can be found by following the URI link. The Publisher's final version can be found by following the DOI link.Purpose The experience of successful firms has proven that one of the most important ways to promote co-learning and create successful networked innovations is the proper application of inter-organizational knowledge mechanisms. This study aims to use a resource-action-performance framework to open the black box on the relationship between networking capability and innovation performance. The research population embraces companies in the Iranian automotive industry. Design/methodology/approach Due to the latent nature of the variables studied, the required data are collected through a web-based cross-sectional survey. First, the content validity of the measurement tool is evaluated by experts. Then, a pre-test is conducted to assess the reliability of the measurement tool. All data are gathered by the Iranian Vehicle Manufacturers Association (IVMA) and Iranian Auto Parts Manufacturers Association (IAPMA) samples. The power analysis method and G*Power software are used to determine the sample size. Moreover, SmartPLS 3 and IBM SPSS 25 software are used for data analysis of the conceptual model and relating hypotheses. Findings The results of this study indicated that the relationships between networking capability, inter-organizational knowledge mechanisms and inter-organizational learning result in a self-reinforcing loop, with a marked impact on firm innovation performance. Originality/value Since there is little understanding of the interdependencies of networking capability, inter-organizational knowledge mechanisms, co-learning and their effect on firm innovation performance, most previous research studies have focused on only one or two of the above-mentioned variables. Thus, their cumulative effect has not examined yet. Looking at inter-organizational relationships from a network perspective and knowledge-based view (KBV), and to consider the simultaneous effect of knowledge mechanisms and learning as intermediary actions alongside, to consider the performance effect of the capability-building process, are the main advantages of this research

Supply Chain Risk Management Frameworks and Models: A Review

Supply chain risk management (SCRM) is a relatively new scientific discipline aiming to support management in its everyday struggle against the inherent uncertainty of supply chain operations propagated mostly by demand and supply fluctuations, in terms of yields, capacity, costs and lead times. This paper focuses on a literature review of available SCRM frameworks and models. Using an appropriate combination of keywords, three established academic databases and a hard inclusion criterion, a final sample of 16 (starting from 922) relevant and above all, empirically validated SCRM frameworks/models papers are retrieved and studied in full. Following a systematic literature review approach and supported by a content analysis tool, the authors produce some useful results on the current research status and identify some of its shortcomings, which have to be addressed by researchers in the future, i.e. the immaturity of research in the field, the absence of a holistic approach for SCRM and finally the lack of a systematic approach to successfully identify risk propagation across contemporary and complex supply chain networks

Resolving forward-reverse logistics multi-period model using evolutionary algorithms

© 2016 Elsevier Ltd In the changing competitive landscape and with growing environmental awareness, reverse logistics issues have become prominent in manufacturing organizations. As a result there is an increasing focus on green aspects of the supply chain to reduce environmental impacts and ensure environmental efficiency. This is largely driven by changes made in government rules and regulations with which organizations must comply in order to successfully operate in different regions of the world. Therefore, manufacturing organizations are striving hard to implement environmentally efficient supply chains while simultaneously maximizing their profit to compete in the market. To address the issue, this research studies a forward-reverse logistics model. This paper puts forward a model of a multi-period, multi-echelon, vehicle routing, forward-reverse logistics system. The network considered in the model assumes a fixed number of suppliers, facilities, distributors, customer zones, disassembly locations, re-distributors and second customer zones. The demand levels at customer zones are assumed to be deterministic. The objective of the paper is to maximize the total expected profit and also to obtain an efficient route for the vehicle corresponding to an optimal/near optimal solution. The proposed model is resolved using Artificial Immune System (AIS) and Particle Swarm Optimization (PSO) algorithms. The findings show that for the considered model, AIS works better than the PSO. This information is important for a manufacturing organization engaged in reverse logistics programs and in running units efficiently. This paper also contributes to the limited literature on reverse logistics that considers costs and profit as well as vehicle route management

Opportunities for the digital transformation of the banana sector supply chain based on software with artificial intelligence

Artificial intelligence offers great opportunities for the supply chain, being this a competitive advantage for today’s changing market. This article aims to identify the impacts and opportunities that artificial intelligence software can offer to facilitate the operation and improve the performance of the supply chain in the banana sector in Colombia. The work methodology consists of six steps in which a total of 72 investigations were obtained. The sources of information were four databases. As a main conclusion, the supply chain of the banana sector has everything necessary for intelligent software based solutions to be implemented in order to achieve adaptation, flexibility and sensitivity to the context and domain of execution

An interactive product development model in remanufacturing environment: a chaos-based artificial bee colony approach

This research presents an interactive product development model in re-manufacturing environment. The product development model defined a quantitative value model considering product design and development tasks and their value attributes responsible to describe functions of the product. At the last stage of the product development process, re-manufacturing feasibility of used components is incorporated. The consummate feature of this consideration lies in considering variability in cost, weight, and size of the constituted components depending on its types and physical states. Further, this research focuses on reverse logistics paradigm to drive environmental management and economic concerns of the manufacturing industry after the product launching and selling in the market. Moreover, the model is extended by integrating it with RFID technology. This RFID embedded model is aimed at analyzing the economical impact on the account of having advantage of a real time system with reduced inventory shrinkage, reduced processing time, reduced labor cost, process accuracy, and other directly measurable benefits. Consideration the computational complexity involved in product development process reverse logistics, this research proposes; Self-Guided Algorithms & Control (S-CAG) approach for the product development model, and Chaos-based Interactive Artificial Bee Colony (CI-ABC) approach for re-manufacturing model. Illustrative Examples has been presented to test the efficacy of the models. Numerical results from using the S-CAG and CI-ABC for optimal performance are presented and analyzed. The results clearly reveal the efficacy of proposed algorithms when applied to the underlying problems. --Abstract, page iv

Cultural adaptation in Chinese-Western supply chain partnerships: dyadic learning in an international context

Article"This article is (c) Emerald Group Publishing and permission has been granted for this version to appear here. Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited."Purpose: Inter-firm learning, or dyadic learning, has been studied extensively in recent years however very little attention has been devoted to extending the concept to an international context and no formal definition exists. We propose ‘cultural adaptation’ as a special form of international dyadic learning and link it to supply relationship performance. Design/methodology/approach: Case studies in four Chinese-Western buyer-supplier relationships, providing cross-case replication, employing qualitative and quantitative methods. Data are triangulated by questionnaires, semi-structured interviews, and documentation. Findings: Qualitative and quantitative evidence shows that cultural adaptation can lead to mutual benefits (relationship rents) and inbound spillover rents for both parties in a supply relationship. Research limitations/implications: Using four cases and a small sample of key informants completing the questionnaire limits generalisability of findings. Practical implications: 1. We develop the causal relationship between cultural adaptation and mutual benefits motivating managers to adapt culturally. 2. We emphasize that the current relationship performance measures should include guanxi quality in order to adapt to the Chinese context. Originality/value: Building on Extended Resource Based Theory, stating that strategic resources may lie beyond a firm’s boundary and that relational and inbound spillover rents may be obtained from the relationship, the research contributes to dyadic or inter-organisational learning literature by empirically building causal relationships between cultural adaptation (as a form of international dyadic learning) and associated mutual benefits (relational and inbound spillover rents), using multiple data sources and methods and tentatively redefining the dyadic learning concept