Collaborative and adaptive supply chain planning

Dans le contexte industriel d'aujourd'hui, la compétitivité est fortement liée à la performance de la chaîne d'approvisionnement. En d'autres termes, il est essentiel que les unités d'affaires de la chaîne collaborent pour coordonner efficacement leurs activités de production, de façon a produire et livrer les produits à temps, à un coût raisonnable. Pour atteindre cet objectif, nous croyons qu'il est nécessaire que les entreprises adaptent leurs stratégies de planification, que nous appelons comportements, aux différentes situations auxquelles elles font face. En ayant une connaissance de l'impact de leurs comportements de planification sur la performance de la chaîne d'approvisionnement, les entreprises peuvent alors adapter leur comportement plutôt que d'utiliser toujours le même. Cette thèse de doctorat porte sur l'adaptation des comportements de planification des membres d'une même chaîne d'approvisionnement. Chaque membre pouvant choisir un comportement différent et toutes les combinaisons de ces comportements ayant potentiellement un impact sur la performance globale, il est difficile de connaître à l'avance l'ensemble des comportements à adopter pour améliorer cette performance. Il devient alors intéressant de simuler les différentes combinaisons de comportements dans différentes situations et d'évaluer les performances de chacun. Pour permettre l'utilisation de plusieurs comportements dans différentes situations, en utilisant la technologie à base d'agents, nous avons conçu un modèle d'agent à comportements multiples qui a la capacité d'adapter son comportement de planification selon la situation. Les agents planificateurs ont alors la possibilité de se coordonner de façon collaborative pour améliorer leur performance collective. En modélisant les unités d'affaires par des agents, nous avons simulé avec la plateforme de planification à base d'agents de FORAC des agents utilisant différents comportements de planification dits de réaction et de négociation. Cette plateforme, développée par le consortium de recherche FORAC de l'Université Laval, permet de simuler des décisions de planification et de planifier les opérations de la chaîne d'approvisionnement. Ces comportements de planification sont des métaheurisciques organisationnelles qui permettent aux agents de générer des plans de production différents. La simulation est basée sur un cas illustrant la chaîne d'approvisionnement de l'industrie du bois d'œuvre. Les résultats obtenus par l'utilisation de multiples comportements de réaction et de négociation montrent que les systèmes de planification avancée peuvent tirer avantage de disposer de plusieurs comportements de planification, en raIson du contexte dynamique des chaînes d'approvisionnement. La pertinence des résultats de cette thèse dépend de la prémisse que les entreprises qui adapteront leurs comportements de planification aux autres et à leur environnement auront un avantage concurrentiel important sur leurs adversaires

Methodology for Seamless Supply Chain Planning

Today, enterprises are typically in a constant process of acquiring and updating its information technologies, however typically without an overall view of the global inter and intra enterprise’s system integration. Researchers have been proposing methodologies and platforms to assist such integration of applications and data. However, implementing new technologies in organizations is a difficult task, since its quality needs for architectures development are more exigent and critical than ever, due to the systems complexity, dimension and to the interoperability requirements to interact with third party applications and infrastructures. This paper proposes a methodology for seamless Supply Chain Planning (SCP), by using a domain reference ontology, data model representation standards, software components evaluation and interoperability checking processes. The methodology VALTE is used to assure that enterprises use tools for SCP compliant to semantics, represented in a common reference ontology, created by the MENTOR methodology. These two horizontal methodologies are vertically supported by interoperability checking processes, which assure an interoperable supply chain planning system

Supply Chain Optimization Studies: A Literature Review and Classification

Supply chain planning is an integrated process in which a group of several organizations, such as suppliers, producers, distributors and retailers, work together. It comprises procurement, production, distribution and demand planning topics. These topics require taking strategical, tactical and operational decisions. This research aims to reveal which supply chain topics, which decision levels, and which optimization methods are mostly studied in supply chain planning. This paper presents a total of 77 reviewed works published between 1993 and 2016 about supply chain planning. The reviewed works are categorized according to following elements: decision levels, supply chain optimization topics, objectives, optimization models. 

Supply Chain Optimization Studies: A Literature Review and Classification

Supply chain planning is an integrated process in which a group of several organizations, such as suppliers, producers, distributors and retailers, work together. It comprises procurement, production, distribution and demand planning topics. These topics require taking strategical, tactical and operational decisions. This research aims to reveal which supply chain topics, which decision levels, and which optimization methods are mostly studied in supply chain planning. This paper presents a total of 77 reviewed works published between 1993 and 2016 about supply chain planning. The reviewed works are categorized according to following elements: decision levels, supply chain optimization topics, objectives, optimization models. 

Network design decisions in supply chain planning

Structuring global supply chain networks is a complex decision-making process. The typical inputs to such a process consist of a set of customer zones to serve, a set of products to be manufactured and distributed, demand projections for the different customer zones, and information about future conditions, costs (e.g. for production and transportation) and resources (e.g. capacities, available raw materials). Given the above inputs, companies have to decide where to locate new service facilities (e.g. plants, warehouses), how to allocate procurement and production activities to the variousmanufacturing facilities, and how to manage the transportation of products through the supply chain network in order to satisfy customer demands. We propose a mathematical modelling framework capturing many practical aspects of network design problems simultaneously. For problems of reasonable size we report on computational experience with standard mathematical programming software. The discussion is extended with other decisions required by many real-life applications in strategic supply chain planning. In particular, the multi-period nature of some decisions is addressed by a more comprehensivemodel, which is solved by a specially tailored heuristic approach. The numerical results suggest that the solution procedure can identify high quality solutions within reasonable computational time

Coffee Supply Chain Planning under Climate Change

Coffee is a valuable crop for many tropical countries and provides an export value estimated at US$30.1 billion in 2019 worldwide. Coffee trees are climate sensitive. Published studies show that climate change is projected to have a negative impact on suitable growing areas for coffee beans, so the coffee bean production is facing a rising risk. At the same time, the consumption of coffee is increasing in recent years, especially in Asian countries. Therefore, the sustainability of the coffee industry has become a concern shared by all participants along the coffee supply chains. Decision making in arabica coffee bean cultivation, which includes long-term shade management and short-term annual management practices (e.g., fertilization management and irrigation management), and logistics is focused in this study on a global scale. Two-stage stochastic programming is adopted to minimize the total cost, including cultivation cost, roasting cost, shortage cost, and logistics cost under different climate scenarios. A case study for a global coffee beverage company is presented with data collected from both practice and literature. The arabica coffee species distribution model by citation (Ovalle-Rivera et al., 2015) shows that the suitable harvesting area will decrease due to climate change in most of the coffee growing countries by 2050. This study tries to answer the questions of whether arabica coffee yield will meet the consumption demand in the future. To increase the arabica coffee yield, medium-level shade management is a possible long-term strategy while management practices may be changed based on weather. To have a substantiable coffee supply chain, the global coffee beverage companies have programs to help coffee farmers through technical assistance and financial support. In addition, local governments may also provide support to farmers, such as educational programs, working condition improvement, and medical service

Towards coordination in robust supply networks

Supply chains nowadays frequently face risks caused by increased environmental volatility and performance inefficiency In this paper an integrated supply chain planning approach is suggested that combines the three aspects of optimisation, risk mitigation and decentralisation. The goal of this paper is to outline the research directions for industrially relevant and applicable methods for integrating robust and coordinated supply chain planning. © 201

The impact of supply chain analytics on operational performance: a resource-based view

This study seeks to better understand the role of supply chain analytics (SCA) on supply chain planning satisfaction and operational performance. We define the architecture of SCA as the integration of three sets of resources, data management resources (DMR), IT-enabled planning resources and performance management resources (PMR), from the perspective of a resource-based view. Based on the data collected from 537 manufacturing plants, we test hypotheses exploring the relationships among these resources, supply chain planning satisfaction, and operational performance. Our analysis supports that DMR should be considered a key building block of manufacturers’ business analytics initiatives for supply chains. The value of data is transmitted to outcome values through increasing supply chain planning and performance capabilities. Additionally, the deployment of advanced IT-enabled planning resources occurs after acquisition of DMR. Manufacturers with sophisticated planning technologies are likely to take advantage of data-driven processes and quality control practices. DMR are found to be a stronger predictor of PMR than IT planning resources. All three sets of resources are related to supply chain planning satisfaction and operational performance. The paper concludes by reviewing research limitations and suggesting further SCA research issues

Effective forecasting for supply-chain planning: an empirical evaluation and strategies for improvement

Demand forecasting is a crucial aspect of the planning process in supply-chain companies. The most common approach to forecasting demand in these companies involves the use of a simple univariate statistical method to produce a forecast and the subsequent judgmental adjustment of this by the company's demand planners to take into account market intelligence relating to any exceptional circumstances expected over the planning horizon. Based on four company case studies, which included collecting more than 12,000 forecasts and outcomes, this paper examines: i) the extent to which the judgmental adjustments led to improvements in accuracy, ii) the extent to which the adjustments were biased and inefficient, iii) the circumstances where adjustments were detrimental or beneficial, and iv) methods that could lead to greater levels of accuracy. It was found that the judgmentally adjusted forecasts were both biased and inefficient. In particular, market intelligence that was expected to have a positive impact on demand was used far less effectively than intelligence suggesting a negative impact. The paper goes on to propose a set of improvements that could be applied to the forecasting processes in the companies and to the forecasting software that is used in these processes