The impact of coordination and information on transport procurement

Transport cost is second in importance after production cost in industry. It is the purpose of the present paper to study the impact of information sharing and contractual instruments between a supply chain and its transport suppliers. After reviewing the literature, we propose a model to measure the benefits in terms of transport cost and standard deviation of transport cost. We evaluate three scenarios over one period reiterated for a shipper carrier two-echelon model with a mix of long- term and short-term procurement strategies: perfect information, asymmetric information and private information at one level of the supply chain. We evaluate the transfer in rent between carrier and shipper according to the information known and give some insights on optimal contract parameters.Supply chain management, coordination, contracts, information sharing, game theory, mechanisms

An integrated decision support framework for remanufacturing in the automotive industry

In today\u27s global economy, firms are seeking any and every opportunity to differentiate from competitors by reducing supply chain costs and adding value to end customers. One increasingly popular option, under growing consumer awareness and increasing legislation, is to reintegrate returned products into the supply chain to achieve economic benefits as well as improve sustainability. An important class of such reverse goods flows has to do with remanufacturing (reman), which refers to activities that restore returned products ( cores ) or their major modules to operational condition for using in place of new product or distributing through other channels (e.g., spare parts). While opportunities abound, some key complications reported in the literature include: 1) difficulty in timing the launch of reman product (while accounting for uncertainties associated with product life-cycle demand and core supply), 2) difficulty with capacity planning for remanufacturing (while accounting for the fact that volumes can be low and that facilities/lines should target multiple product families for economies of scale), and 3) operational difficulties in maintaining efficiencies in production planning and control of remanufacturing activities. These difficulties are mostly attributable to limited visibility and higher levels of uncertainty in reverse logistics (in comparison with forward logistics). Despite advances in the remanufacturing literature in the last two decades (both in the academic literature and practitioner community), there is no integrated decision support framework that can guide companies to successful launch and execution of remanufacturing operations. This is particularly true for companies that engage in both original equipment (OE) service as well as the independent after-market (IAM) in the automotive industry. This research aims to address these limitations by developing a decision support framework and necessary models for effective remanufacturing in the automotive industry. At the strategic level, we propose a unified approach to explicitly model and address issues of capacities as well timing the launch of remanufacturing programs for new product. We derive the optimal remanufacturing policy and extensively studied the drivers of cost-effective remanufacturing program for aftermarket services. Our policies exploit the ability to leverage OE production to support both the OE service operations as well as demand from the IAM. To the best of our knowledge, this research is the first attempt of its kind in the remanufacturing literature, as prior research treated these interrelated decisions separately. Valuable managerial insights are obtained by minimizing the discounted cash outflows caused by appropriate investment and core return inventory building decisions. We show that, under certain conditions, it may be optimal to delay the launch of the remanufacturing program to build up an adequate initial core return inventory. This may help in perfectly substituting virgin parts with remanufactured parts after end of the OE production run. At operational level, efficient production planning and control of reman parts for the supplier heavily impinges on the ability to accurately forecast core returns from customers (e.g., dealers, distributors). There are several challenges to this, including, the volume and diversity of customers served by the supplier, differences among individual customer warehouses in returning cores, large reman product catalogs, changing customer behaviors (often improving core return delays), and data sparsity. In this research we report the evidence for the effectiveness of hazard rate regression models to estimate core return delays in the context of remanufacturing. We investigate a number of hazard rate modelling techniques (e.g., parametric, semi-parametric etc.) using real-world datasets from a leading Tier-1 automotive supplier. Results indicate the effectiveness of the proposed framework in terms of stability and face validity of the estimates and in predictive accuracy

Evaluation of sales and operations planning in a process industry

Cette thèse porte sur la planification des ventes et des opérations (S±&OP) dans une chaîne d'approvisionnements axée sur la demande. L'objectif de la S±&OP, dans un tel contexte, est de tirer profit de l'alignement de la demande des clients avec la capacité de la chaîne d'approvisionnement par la coordination de la planification des ventes, de la production, de la distribution et de l'approvisionnement. Un tel processus de planification exige une collaboration multifonctionnelle profonde ainsi que l'intégration de la planification. Le but étant d'anticiper l'impact des décisions de vente sur les performances de la chaîne logistique , alors que l'influence de la dynamique des marchés est prise en compte pour les décisions concernant la production, la distribution et l'approvisionnement. La recherche a été menée dans un environnement logistique manufacturier multi-site et multi-produit, avec un approvisionnement et des ventes régis par des contrats ou le marché. Cette thèse examine deux approches de S±&OP et fournit un support à la décision pour l'implantation de ces méthodes dans une chaîne logistique multi-site de fabrication sur commande. Dans cette thèse, une planification traditionnelle des ventes et de la production basée sur la S±feOP et une planification S±fcOP plus avancée de la chaîne logistique sont tout d'abord caractérisées. Dans le système de chaîne logistique manufacturière multi-site, nous définissons la S±&OP traditionnelle comme un système dans lequel la planification des ventes et de la production est effectuée conjointement et centralement, tandis que la planification de la distribution et de l'approvisionnement est effectuée séparément et localement à chaque emplacement. D'autre part, la S±fcOP avancée de la chaîne logistique consiste en la planification des ventes, de la production, de la distribution et de l'approvisionnement d'une chaîne d'approvisionnement effectuée conjointement et centralement. Basés sur cette classification, des modèles de programmation en nombres entiers et des modèles de simulation sur un horizon roulant sont développés, représentant, respectivement, les approches de S±&OP traditionnelle et avancée, et également, une planification découplée traditionnelle, dans laquelle la planification des ventes est effectuée centralement et la planification de la production, la distribution et l'approvisionnement est effectuée séparément et localement par les unités d'affaires. La validation des modèles et l'évaluation pré-implantation sont effectuées à l'aide d'un cas industriel réel utilisant les données d'une compagnie de panneaux de lamelles orientées. Les résultats obtenus démontrent que les deux méthodes de S±feOP (traditionnelle et avancée) offrent une performance significativement supérieure à celle de la planification découplée, avec des bénéfices prévus supérieurs de 3,5% et 4,5%, respectivement. Les résultats sont très sensibles aux conditions de marché. Lorsque les prix du marché descendent ou que la demande augmente, de plus grands bénéfices peuvent être réalisés. Dans le cadre de cette recherche, les décisions de vente impliquent des ventes régies par des contrats et le marché. Les décisions de contrat non optimales affectent non seulement les revenus, mais également la performance manufacturière et logistique et les décisions de contrats d'approvisionnement en matière première. Le grand défi est de concevoir et d'offrir les bonnes politiques de contrat aux bons clients de sorte que la satisfaction des clients soit garantie et que l'attribution de la capacité de la compagnie soit optimisée. Également, il faut choisir les bons contrats des bons fournisseurs, de sorte que les approvisionnements en matière première soient garantis et que les objectifs financiers de la compagnie soient atteints. Dans cette thèse, un modèle coordonné d'aide à la décision pour les contrats e développé afin de fournir une aide à l'intégration de la conception de contrats, de l'attribution de capacité et des décisions de contrats d'approvisionnement pour une chaîne logistique multi-site à trois niveaux. En utilisant la programmation stochastique à deux étapes avec recours, les incertitudes liées à l'environnement et au système sont anticipées et des décisions robustes peuvent être obtenues. Les résultats informatiques montrent que l'approche de modélisation proposée fournit des solutions de contrats plus réalistes et plus robustes, avec une performance prévue supérieure d'environ 12% aux solutions fournies par un modèle déterministe

Replenishment support decision model for a try­-before-you-­buy retail fashion e­commerce

A try before you buy business model is a type of sales strategy in which customers are allowed to test a product before making a purchase. As the try before you buy online business model is a topic on which there is limited public scholarly research, the purpose of this research is to provide an initial approach to the subject by presenting a tool to support the replenishment strategy of Curve Catch, a fashion e­commerce retailer. A simulation engine characterized by two main components has been built: replenishment and a demand generator. Model development is built on artificially generated data based on real data of Curve Catch. Based on the literature inherent to inventory management and through the use of simulation ­optimization, the model provides managerial guidance on how to manage r,Q policy in a system where most goods shipped to customers are returned. The tool highlights the need to optimize the use of reorder point and economic order quantity to achieve better business performance. This is because conventional formulas, if not adjusted to the specific setting, perform sub optimally. The model also provides insights into the levels of lost sales due to out stocking and the quality of service provided to customers. Studying the relationships among these three KPIs provides insight into what trade­offs are relevant in planning a continuous review replenishment strategy.Um modelo de negócio de "experimentar antes de comprar" é um tipo de estratégia de vendas onde os clientes têm a possibilidade de testar um produto antes de fazer a sua compra. Uma vez que este modelo de negócio online é um tópico sobre o qual não existe nenhuma pesquisa académica pública, o objetivo desta estudo é fornecer uma abordagem inicial ao assunto, fornecendo uma ferramenta para apoiar a estratégia de reposição da Curve Catch, um e ­ commerce de moda. Foi construído um motor de simulação caracterizado por dois componentes principais: reposição e gerador de procura. O desenvolvimento do modelo baseia ­se em dados gerados artificialmente com base em dados reais da Curve Catch. Com base na literatura inerente à gestão de stocks e através do uso de simulação­otimização, o modelo fornece orientação empresarial sobre como gerir a política r, Q em um sistema onde a maioria dos produtos enviados para os clientes é devolvido. A ferramenta destaca a necessidade de otimizar o uso do ponto de reordenação (reorder point) e da quantidade de encomenda económica (economic order quantity), para alcançar um melhor desempenho do negócio. Isso ocorre dado que as fórmulas convencionais, se não forem ajustadas para o ambiente específico, serão desempenhadas de maneira subaproveitada. Para além disso, o modelo fornece insights sobre os níveis de vendas perdidas devido ao esgotamento e a qualidade do aten dimento ao cliente. O estudo das relações entre essas três métricas­chave fornece insights sobre quais trade­offs são relevantes no planeamento de uma estratégia de reposição de revisão contínua.

Mathematics in the Supply Chain

[no abstract available

The bullwhip effect: Progress, trends and directions

This is the final version. Available on open access from Elsevier via the DOI in this recordThe bullwhip effect refers to the phenomenon where order variability increases as the orders move upstream in the supply chain. This paper provides a review of the bullwhip literature which adopts empirical, experimental and analytical methodologies. Early econometric evidence of bullwhip is highlighted. Findings from empirical and experimental research are compared with analytical and simulation results. Assumptions and approximations for modelling the bullwhip effect in terms of demand, forecast, delay, replenishment policy, and coordination strategy are considered. We identify recent research trends and future research directions concerned with supply chain structure, product type, price, competition and sustainability

How to Make the Most Productive Intervention in a Complex Economic System

Information about supply and demand propagates through supply chains in a queueing network with people and computers as batch information processors. As each batch processor delays propagation of information whilst pursuing optimal local decisions, the effect is delay and distortion of the information that is used to commit resources to actions in the supply chain. This thesis investigates the effect of delay and imperfect information as a source of error, to establish the case for change in research focus from optimal exploitation of physical constraints to optimal exploitation of information. In the context of real world supply chains, the thesis asks "How does one make the most productive intervention in a complex economic system?" and pursues a meta-intervention which perpetually minimises the discovered error-term. Evidence from literature indicates that agent-based modelling permits real-time peer-to-peer communication and distributed optimisation. Based on the literature the research project designs and develops an agent-based model which operates in real-time without batch-processes and can perform incremental multi-objective optimisation under realistic (chronologically progressive) conditions for decision making. The agent based model is then used to investigate two real-world supply chains, as case studies, which reveals a significant improvement of profitability and order-fulfilment. The thesis concludes that agent-based modelling is a very promising direction for "making the most productive intervention" as it reduces delay to a minimum. Finally it recommends that continuous improvement of decision making methods is a role better suited for humans, rather than operational decision making where computers cope much better with the high amount of detailed information

A quantitative model for disruption mitigation in a supply chain

© 2016 Elsevier B.V. In this paper, a three-stage supply chain network, with multiple manufacturing plants, distribution centers and retailers, is considered. For this supply chain system we develop three different approaches, (i) an ideal plan for an infinite planning horizon and an updated plan if there are any changes in the data, (ii) a predictive mitigation planning approach for managing predictive demand changes, which can be predicted in advance by using an appropriate tool, and (iii) a reactive mitigation plan, on a real-time basis, for managing sudden production disruptions, which cannot be predicted in advance. In predictive mitigation planning, we develop a fuzzy inference system (FIS) tool to predict the changes in future demand over the base forecast and the supply chain plan is revised accordingly well in advance. In reactive mitigation planning, we formulate a quantitative model for revising production and distribution plans, over a finite future planning period, while minimizing the total supply chain cost. We also consider a series of sudden disruptions, where a new disruption may or may not affect the recovery plans of earlier disruptions and which consequently require plans to be revised after the occurrence of each disruption on a real-time basis. An efficient heuristic, capable of dealing with sudden production disruptions on a real-time basis, is developed. We compare the heuristic results with those obtained from the LINGO optimization software for a good number of randomly generated test problems. Also, some numerical examples are presented to explain both the usefulness and advantages of the proposed approaches

Synthesis of Optimization and Simulation for Multi-Period Supply Chain Planning with Consideration of Risks

Solutions to deterministic optimizing models for supply chains can be very sensitive to the formulation of the objective function and the choice of planning horizon. We illustrate how multi-period optimizing models may be counterproductive if traditional accounting of revenue and costs is performed and planning occurs with too short a planning horizon. We propose a “value added” complement to traditional financial accounting that allows planning to occur with shorter horizons than previously thought necessary. This dissertation presents a simulation model with an embedded optimizer that can help organizations develop strategies that minimize expected costs or maximize expected contributions to profit while maintaining a designated level of service. Plans are developed with a deterministic optimizing model and each of the decisions for the first period in the planning horizon are implemented within the simulator. Random deviations in demands and in upstream and downstream shipping times are imposed and the state of the system is updated at the end of each simulated period of activity. This process continues iteratively for a chosen number of periods (90 days for this research). Multiple replications are performed using unique random number seeds for each replication. The simulation model generates detailed event logs for each period of simulated activity that are used to analyze supply-chain performance and supply-chain risk. Supply-chain performance is measured with eleven key performance indicators that reveal system behavior at the overall supply-chain level, as well as performance related to individual plants, warehouses, and products. There are three key findings from this research. First, a value-added complement in an optimization model’s objective function can allow planning to occur effectively with a significantly shorter horizon than required when traditional accounting of costs and revenues is employed. Second, solutions with the value-added complement are robust for situations where supply-chain disruptions cause unexpected depletions in inventories at production facilities and warehouses. Third, ceteris paribus, the hybrid multi-period planning approach generates solutions with higher service levels for products with greater revenue per average production-minute, shorter average upstream lead times, and lower coefficients of variation for daily demand