Supply Chain Coordination under Trade Credit and Quantity Discount with Sales Effort Effects

The purpose of this paper is to investigate the role of trade credit and quantity discount in supply chain coordination when the sales effort effect on market demand is considered. In this paper, we consider a two-echelon supply chain consisting of a single retailer ordering a single product from a single manufacturer. Market demand is stochastic and is influenced by retailer sales effort. We formulate an analytical model based on a single trade credit and find that the single trade credit cannot achieve the perfect coordination of the supply chain. Then, we develop a hybrid quantitative analytical model for supply chain coordination by coherently integrating incentives of trade credit and quantity discount with sales effort effects. The results demonstrate that, providing that the discount rate satisfies certain conditions, the proposed hybrid model combining trade credit and quantity discount will be able to effectively coordinate the supply chain by motivating retailers to exert their sales effort and increase product order quantity. Furthermore, the hybrid quantitative analytical model can provide great flexibility in coordinating the supply chain to achieve an optimal situation through the adjustment of relevant parameters to resolve conflict of interests from different supply chain members. Numerical examples are provided to demonstrate the effectiveness of the hybrid model

Responsible Inventory Models for Operation and Logistics Management

The industrialization and the subsequent economic development occurred in the last century have led industrialized societies to pursue increasingly higher economic and financial goals, laying temporarily aside the safeguard of the environment and the defense of human health. However, over the last decade, modern societies have begun to reconsider the importance of social and environmental issues nearby the economic and financial goals. In the real industrial environment as well as in today research activities, new concepts have been introduced, such as sustainable development (SD), green supply chain and ergonomics of the workplace. The notion of “triple bottom line” (3BL) accounting has become increasingly important in industrial management over the last few years (Norman and MacDonald, 2004). The main idea behind the 3BL paradigm is that companies’ ultimate success should not be measured only by the traditional financial results, but also by their ethical and environmental performances. Social and environmental responsibility is essential because a healthy society cannot be achieved and maintained if the population is in poor health. The increasing interest in sustainable development spurs companies and researchers to treat operations management and logistics decisions as a whole by integrating economic, environmental, and social goals (Bouchery et al., 2012). Because of the wideness of the field under consideration, this Ph.D. thesis focuses on a restricted selection of topics, that is Inventory Management and in particular the Lot Sizing problem. The lot sizing problem is undoubtedly one of the most traditional operations management interests, so much so that the first research about lot sizing has been faced more than one century ago (Harris, 1913). The main objectives of this thesis are listed below: 1) The study and the detailed analysis of the existing literature concerning Inventory Management and Lot Sizing, supporting the management of production and logistics activities. In particular, this thesis aims to highlight the different factors and decision-making approaches behind the existing models in the literature. Moreover, it develops a conceptual framework identifying the associated sub-problems, the decision variables and the sources of sustainable achievement in the logistics decisions. The last part of the literature analysis outlines the requirements for future researches. 2) The development of new computational models supporting the Inventory Management and Sustainable Lot Sizing. As a result, an integrated methodological procedure has been developed by making a complete mathematical modeling of the Sustainable Lot Sizing problem. Such a method has been properly validated with data derived from real cases. 3) Understanding and applying the multi-objective optimization techniques, in order to analyze the economic, environmental and social impacts derived from choices concerning the supply, transport and management of incoming materials to a production system. 4) The analysis of the feasibility and convenience of governmental systems of incentives to promote the reduction of emissions owing to the procurement and storage of purchasing materials. A new method based on the multi-objective theory is presented by applying the models developed and by conducting a sensitivity analysis. This method is able to quantify the effectiveness of carbon reduction incentives on varying the input parameters of the problem. 5) Extending the method developed in the first part of the research for the “Single-buyer” case in a "multi-buyer" optics, by introducing the possibility of Horizontal Cooperation. A kind of cooperation among companies in different stages of the purchasing and transportation of raw materials and components on a global scale is the Haulage Sharing approach which is here taken into consideration in depth. This research was supported by a fruitful collaboration with Prof. Robert W. Grubbström (University of Linkoping, Sweden) and its aim has been from the beginning to make a breakthrough both in the theoretical basis concerning sustainable Lot Sizing, and in the subsequent practical application in today industrial contexts

Sustainable Inventory Management Model for High-Volume Material with Limited Storage Space under Stochastic Demand and Supply

Inventory management and control has become an important management function, which is vital in ensuring the efficiency and profitability of a company’s operations. Hence, several research studies attempted to develop models to be used to minimise the quantities of excess inventory, in order to reduce their associated costs without compromising both operational efficiency and customers’ needs. The Economic Order Quantity (EOQ) model is one of the most used of these models; however, this model has a number of limiting assumptions, which led to the development of a number of extensions for this model to increase its applicability to the modern-day business environment. Therefore, in this research study, a sustainable inventory management model is developed based on the EOQ concept to optimise the ordering and storage of large-volume inventory, which deteriorates over time, with limited storage space, such as steel, under stochastic demand, supply and backorders. Two control systems were developed and tested in this research study in order to select the most robust system: an open-loop system, based on direct control through which five different time series for each stochastic variable were generated, before an attempt to optimise the average profit was conducted; and a closed-loop system, which uses a neural network, depicting the different business and economic conditions associated with the steel manufacturing industry, to generate the optimal control parameters for each week across the entire planning horizon. A sensitivity analysis proved that the closed-loop neural network control system was more accurate in depicting real-life business conditions, and more robust in optimising the inventory management process for a large-volume, deteriorating item. Moreover, due to its advantages over other techniques, a meta-heuristic Particle Swarm Optimisation (PSO) algorithm was used to solve this model. This model is implemented throughout the research in the case of a steel manufacturing factory under different operational and extreme economic scenarios. As a result of the case study, the developed model proved its robustness and accuracy in managing the inventory of such a unique industry

Approximate dynamic programming application to inventory management

2010 Summer.Includes bibliographical references.This study has developed a new method and investigated the performance of current Approximate Dynamic Programming (ADP) approaches in the context of common inventory circumstances that have not been adequately studied in the literature. The new method uses a technique similar to eligibility trace [113] to improve performance of the residual gradient method [7]. The ADP approach uses approximation techniques, including learning and simulation schemes, to provide the flexible and adaptive control needed for practical inventory management. However, though ADP has received extensive attention in inventory management research lately, there are still many issues left uninvestigated. Some of the issues include (1) an application of ADP with a scalable, linear operating capable, and universal approximation function, i.e., Radial Basis Function (RBF); (2) performance of bootstrapping and convergence-guaranteed learning schemes, i.e., Eligibility Trace and Residual Gradient, respectively; (3) an effect of latent state variables, introduced by recently found GARCH(1,1), to a model-free property of learning-based ADPs; and (4) a performance comparison between two main ADP families, learning-based and simulation-based ADPs. The purpose of this study is to determine appropriate ADP components and corresponding settings for practical inventory problems by examining these issues. A series of simulation-based experiments are employed to study each of the ADP issues. Due to its simplicity in implementation and popularity as a benchmark in ADP research, the Look-Ahead method is used as a benchmark in this study. Conclusions are drawn mainly based on the significance test with aggregate costs as performance measurement. The performance of each ADP method was tested to be comparable to Look-Ahead for inventory problems with low variance demand and shown to have significantly better performance than performance of Look-Ahead, at 0.05 significance level, for an inventory problem with high variance demand. The analysis of experimental results shows that (1) RBF, with evenly distributed centers and half midpoint effect scales, is an effective approximate cost-to-go method; (2) Sarsa, a widely used algorithm based on one-step temporal difference learning. (TD0), is the most efficient learning scheme compared to its eligibility trace enhancement, Sarsa(λ),or to the Residual Gradient method; (3) the new method, Direct Credit Back, works significantly better than the benchmark Look-Ahead, but it does not show significant improvement over Residual Gradient in either zero or one-period leadtime problem; (4) a model-free property of learning-based ADPs is affirmed under the presence of GARCH(1,1) latent state variables; and (5) performance of a simulation-based ADP, i.e., Rollout and Hindsight Optimization, is superior to performance of a learning-based ADP. In addition, links between ADP setting, i.e., Sarsa(λ)'s Eligibility Trace factor and Rollout's number of simulations and horizon, and conservative behavior, Le., maintaining higher inventory level, have been found. Our conclusions show agreement with theoretical and early speculations on ADP applicability, RBF and TD0 effectiveness, learning-based ADP's model-free property, and that there is an advantage of simulation-based ADP. On the other hand, our findings contradict any significance of GARCH(1,1) awareness, identified by Zhang [130], at least when a learning-based ADP is used. The work presented here has profound implications for future studies of adaptive control for practical inventory management and may one day help solve the problem associated with stochastic supply chain management

Operational Research IO2017, Valença, Portugal, June 28-30

This proceedings book presents selected contributions from the XVIII Congress of APDIO (the Portuguese Association of Operational Research) held in Valença on June 28–30, 2017. Prepared by leading Portuguese and international researchers in the field of operations research, it covers a wide range of complex real-world applications of operations research methods using recent theoretical techniques, in order to narrow the gap between academic research and practical applications. Of particular interest are the applications of, nonlinear and mixed-integer programming, data envelopment analysis, clustering techniques, hybrid heuristics, supply chain management, and lot sizing and job scheduling problems. In most chapters, the problems, methods and methodologies described are complemented by supporting figures, tables and algorithms. The XVIII Congress of APDIO marked the 18th installment of the regular biannual meetings of APDIO – the Portuguese Association of Operational Research. The meetings bring together researchers, scholars and practitioners, as well as MSc and PhD students, working in the field of operations research to present and discuss their latest works. The main theme of the latest meeting was Operational Research Pro Bono. Given the breadth of topics covered, the book offers a valuable resource for all researchers, students and practitioners interested in the latest trends in this field.info:eu-repo/semantics/publishedVersio