The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three ‘pillars’ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a ‘boomerang’ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems

On two-echelon inventory systems with Poisson demand and lost sales

We derive approximations for the service levels of two-echelon inventory systems with lost sales and Poisson demand. Our method is simple and accurate for a very broad range of problem instances, including cases with both high and low service levels. In contrast, existing methods only perform well for limited problem settings, or under restrictive assumptions.\u

Warehousing and Inventory Management in Dual Channel and Global Supply Chains

More firms are adopting the dual-channel supply chain business model where firms offer their products to customers using dual-channel sales (to offer the item to customers online and offline). The development periods of innovative products have been shortened, especially for high-tech companies, which leads to products with short life cycles. This means that companies need to put their new products on the market as soon as possible. The dual-channel supply chain is a perfect tool to increase the customer’s awareness of new products and to keep customers’ loyalty; firms can offer new products online to the customer faster compared to the traditional retail sales channel. The emergence of dual-channel firms was mainly driven by the expansion in internet use and the advances in information and manufacturing technologies. No existing research has examined inventory strategies, warehouse structure, operations, and capacity in a dual-channel context. Additionally, firms are in need to integrate their global suppliers base; where the lower parts costs compensate for the much higher procurement and cross-border costs; in their supply chain operations. The most common method used to integrate the global supplier base is the use of cross-dock, also known as Third Party Logistic (3PL). This study is motivated by real-world problem, no existing research has considered the optimization of cross-dock operations in terms of dock assignment, storage locations, inventory strategies, and lead time uncertainty in the context of a cross-docking system. In this dissertation, we first study the dual-channel warehouse in the dual-channel supply chain. One of the challenges in running the dual-channel warehouse is how to organize the warehouse and manage inventory to fulfill both online and offline (retailer) orders, where the orders from different channels have different features. A model for a dual-channel warehouse in a dual-channel supply chain is proposed, and a solution approach is developed in the case of deterministic and stochastic lead times. Ending up with numerical examples to highlight the model’s validity and its usefulness as a decision support tool. Second, we extend the first problem to include the global supplier and the cross-border time. The impact of global suppliers and the effect of the cross-border time on the dual-channel warehouse are studied. A cross-border dual-channel warehouse model in a dual-channel supply chain context is proposed. In addition to demand and lead time uncertainty, the cross-border time is included as stochastic parameter. Numerical results and managerial insights are also presented for this problem. Third, motivated by a real-world cross-dock problem, we perform a study at one of the big 3 automotive companies in the USA. The company faces the challenges of optimizing their operations and managing the items in the 3PL when introducing new products. Thus, we investigate a dock assignment problem that considers the dock capacity and storage space and a cross-dock layout. We propose an integrated model to combine the cross-dock assignment problem with cross-dock layout problem so that cross-dock operations can be coordinated effectively. In addition to lead time uncertainty, the cross-border time is included as stochastic parameter. Real case study and numerical results and managerial insights are also presented for this problem highlighting the cross-border effect. Solution methodologies, managerial insights, numerical analysis as well as conclusions and potential future study topics are also provided in this dissertation

Dual-Channel Warehouse and Inventory Management with Stochastic Demand

This study examines the inventory policy for the emerging dual-channel warehouse, which has a unique structure where the warehouse is divided into two areas: one for fulfilling online orders and the other for storing products and fulfilling offline orders. A multi-item inventory model was developed considering the warehouse capacity constraint, demand, and lead time uncertainty. Solution methods are provided for both uniform and normal distributions. Adopting the proposed inventory policy for a dual warehouse is cost effective and adds flexibility to the warehouse and supply chain. The study also offers managerial insights on some critical issues faced by companies operating in a dual-channel context

Nash Game Model for Optimizing Market Strategies, Configuration of Platform Products in a Vendor Managed Inventory (VMI) Supply Chain for a Product Family

This paper discusses how a manufacturer and its retailers interact with each other to optimize their product marketing strategies, platform product configuration and inventory policies in a VMI (Vendor Managed Inventory) supply chain. The manufacturer procures raw materials from multiple suppliers to produce a family of products sold to multiple retailers. Multiple types of products are substitutable each other to end customers. The manufacturer makes its decision on raw materials’ procurement, platform product configuration, product replenishment policies to retailers with VMI, price discount rate, and advertising investment to maximize its profit. Retailers in turn consider the optimal local advertising and retail price to maximize their profits. This problem is modeled as a dual simultaneous non-cooperative game (as a Nash game) model with two sub-games. One is between the retailers serving in competing retail markets and the other is between the manufacturer and the retailers. This paper combines analytical, iterative and GA (genetic algorithm) methods to develop a game solution algorithm to find the Nash equilibrium. A numerical example is conducted to test the proposed model and algorithm, and gain managerial implications.supply chain management;nash game model;vendor managed inventory

Evolution of Supply Chain Collaboration: Implications for the Role of Knowledge

Increasingly, research across many disciplines has recognized the shortcomings of the traditional “integration prescription” for inter-organizational knowledge management. This research conducts several simulation experiments to study the effects of different rates of product change, different demand environments, and different economies of scale on the level of integration between firms at different levels in the supply chain. The underlying paradigm shifts from a static, steady state view to a dynamic, complex adaptive systems and knowledge-based view of supply chain networks. Several research propositions are presented that use the role of knowledge in the supply chain to provide predictive power for how supply chain collaborations or integration should evolve. Suggestions and implications are suggested for managerial and research purposes

Multi-objective Dual-Sale Channel Supply Chain Network Design Based on NSGA-II

[[abstract]]In this study, we propose a two-echelon multi-objective dual-sale channel supply chain network (DCSCN) model. The goal is to determine (i) the set of installed DCs, (ii) the set of customers the DC should work with, how much inventory each DC should order and (iv) the distribution routes for physical retailers or online e-tailers (all starting and ending at the same DC). Our model overcomes the drawback by simultaneously tackling location and routing decisions. In addition to the typical costs associated with facility location and the inventory-related costs, we explicitly consider the pivotal routing costs between the DCs and their assigned customers. Therefore, a multiple objectives location-routing model involves two conflicting objectives is initially proposed so as to permit a comprehensive trade-off evaluation. To solve this multiple objectives programming problem, this study integrates genetic algorithms, clustering analysis, Non-dominated Sorting Genetic Algorithm II (NSGA-II). NSGA-II searches for the Pareto set. Several experiments are simulated to demonstrate the possibility and efficacy of the proposed approach.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙

Timely-Throughput Optimal Scheduling with Prediction

Motivated by the increasing importance of providing delay-guaranteed services in general computing and communication systems, and the recent wide adoption of learning and prediction in network control, in this work, we consider a general stochastic single-server multi-user system and investigate the fundamental benefit of predictive scheduling in improving timely-throughput, being the rate of packets that are delivered to destinations before their deadlines. By adopting an error rate-based prediction model, we first derive a Markov decision process (MDP) solution to optimize the timely-throughput objective subject to an average resource consumption constraint. Based on a packet-level decomposition of the MDP, we explicitly characterize the optimal scheduling policy and rigorously quantify the timely-throughput improvement due to predictive-service, which scales as $\Theta(p\left[C_{1}\frac{(a-a_{\max}q)}{p-q}\rho^{\tau}+C_{2}(1-\frac{1}{p})\right](1-\rho^{D}))$, where $a, a_{\max}, \rho\in(0, 1), C_1>0, C_2\ge0$ are constants, $p$ is the true-positive rate in prediction, $q$ is the false-negative rate, $\tau$ is the packet deadline and $D$ is the prediction window size. We also conduct extensive simulations to validate our theoretical findings. Our results provide novel insights into how prediction and system parameters impact performance and provide useful guidelines for designing predictive low-latency control algorithms.Comment: 14 pages, 7 figure

Modeling Overstock

Two main problems have been emerging in supply chain management: the increasing pressure to reduce working capital and the growing variety of products. Most of the popular indicators have been developed based on a controlled environment. A new indicator is now proposed, based on the uncertainty of the demand, the flexibility of the supply chains, the evolution of the products lifecycle and the fulfillment of a required service level. The model to support the indicator will be developed within the real options approach.overstock, stock management, real options