scholarly journals Inventory management in reverse logistics with imperfect production, learning, lost sales, subassemblies, and price/quality considerations

2021 ◽  
Author(s):  
Ahmed MA El Saadany
Keyword(s):  
Inventory Management ◽  
Reverse Logistics ◽  
Time Interval ◽  
Learning Effects ◽  
Research Gaps ◽  
Holding Costs ◽  
Recovery Programs ◽  
Established Line ◽  
Inventory Holding ◽  
First Time

Reverse Logistics is the flow and management of products, packaging, components, and information from the point of consumption (i.e., the market) to the point of origin (i.e., manufacturers and suppliers). It is a collection of practices similar to those of supply chain management, but in the opposite direction, from downstream to upstream. Reverse logistics is a valuable solution to the hazards jeopardizing the environment, and it involves activities such as reuse, repair, remanufacture, refurbish, reclaim and recycle.Reverse logistics became an established line of research, covering several areas, including inventory control; though, several research gaps still exist, such as: ignoring switching costs between production and remanufacturing processes and learning effects, the assumption that production and remanufacturing processes are of perfect quality, remanufactured products are assumed to be as-good-as new, the assumption that returned products are treated as whole products while ignoring disassembly, collection rate of used items is independent of price and quality, and the assumption that pure remanufacturing and production policies are optimal. These research gaps are addressed in mathematical models to bring reverse logistics optimization closer to reality. Deterministic and stochastic components are considered here with numerical examples and results discussed. The key conclusions are as follows:The inclusion of the first time interval where no remanufacturing/repair exists, results in preventing the overestimation of inventory holding costs in the repairable stock. Assuming production and remanufacturing processes to be perfect, or ignoring learning effects in these processes, might not capture the benefits that product recovery programs are supposed to bring. Although works in the literature assumed pure remanufacturing is mathematically attainable but not feasible, this study shows that the pure remanufacturing case is not valid mathematically, which proves it to be infeasible. It is favourable to compensate customers to settle for remanufactured products instead of new ones. Considering disassembly of returns in the modelling of reverse logistics is proven beneficial. Finally, mixed production and remanufacturing policies are optimal rather than pure ones; and the inclusion of price and quality to determine return and collection rates is crucial.

scholarly journals Inventory management in reverse logistics with imperfect production, learning, lost sales, subassemblies, and price/quality considerations

2021 ◽  
Author(s):  
Ahmed MA El Saadany
Keyword(s):  
Inventory Management ◽  
Reverse Logistics ◽  
Time Interval ◽  
Learning Effects ◽  
Research Gaps ◽  
Holding Costs ◽  
Recovery Programs ◽  
Established Line ◽  
Inventory Holding ◽  
First Time

Reverse Logistics is the flow and management of products, packaging, components, and information from the point of consumption (i.e., the market) to the point of origin (i.e., manufacturers and suppliers). It is a collection of practices similar to those of supply chain management, but in the opposite direction, from downstream to upstream. Reverse logistics is a valuable solution to the hazards jeopardizing the environment, and it involves activities such as reuse, repair, remanufacture, refurbish, reclaim and recycle.Reverse logistics became an established line of research, covering several areas, including inventory control; though, several research gaps still exist, such as: ignoring switching costs between production and remanufacturing processes and learning effects, the assumption that production and remanufacturing processes are of perfect quality, remanufactured products are assumed to be as-good-as new, the assumption that returned products are treated as whole products while ignoring disassembly, collection rate of used items is independent of price and quality, and the assumption that pure remanufacturing and production policies are optimal. These research gaps are addressed in mathematical models to bring reverse logistics optimization closer to reality. Deterministic and stochastic components are considered here with numerical examples and results discussed. The key conclusions are as follows:The inclusion of the first time interval where no remanufacturing/repair exists, results in preventing the overestimation of inventory holding costs in the repairable stock. Assuming production and remanufacturing processes to be perfect, or ignoring learning effects in these processes, might not capture the benefits that product recovery programs are supposed to bring. Although works in the literature assumed pure remanufacturing is mathematically attainable but not feasible, this study shows that the pure remanufacturing case is not valid mathematically, which proves it to be infeasible. It is favourable to compensate customers to settle for remanufactured products instead of new ones. Considering disassembly of returns in the modelling of reverse logistics is proven beneficial. Finally, mixed production and remanufacturing policies are optimal rather than pure ones; and the inclusion of price and quality to determine return and collection rates is crucial.

scholarly journals Commodity Futures: An Important Role In Inventory Management

2011 ◽  
Vol 6 (1) ◽  
pp. 82
Author(s):  
John H. Reed
Keyword(s):  
Inventory Management ◽  
Commodity Futures ◽  
Raw Material ◽  
Production Environment ◽  
Holding Costs ◽  
Inventory Costs ◽  
Eoq Model ◽  
Commodity Futures Markets ◽  
Control Decision ◽  
Inventory Holding

In a production environment costs of raw material inventories can vary dramatically thereby affecting inventory holding costs. Thus savings achieved through the implementation of the results of an EOQ model, which assumes stable inventory costs, may not be applicable where these costs vary. This paper addresses this problem by demonstrating how to integrate the commodity futures markets into an inventory control decision in order to help stabilize the price of inputs.

Operational challenges during a pandemic: an investigation in the electronics industry

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sanjoy Kumar Paul ◽  
Priyabrata Chowdhury ◽  
Md. Tarek Chowdhury ◽  
Ripon Kumar Chakrabortty ◽  
Md. Abdul Moktadir
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Inventory Management ◽  
Demand Management ◽  
Electronics Industry ◽  
Content Type ◽  
Holding Costs ◽  
Inventory Holding ◽  
Practical Implications

PurposeThe recent coronavirus disease 2019 (COVID-19) pandemic poses numerous challenges to supply chains. This pandemic is quite unique when compared to previous epidemic disruptions and has had a severe impact on supply chains. As a result, the operational challenges (OCs) caused by COVID-19 are still unknown among practitioners and academics. It is critical to comprehensively document current OCs so that firms can plan and implement strategies to overcome them. Consequently, this study systematically identifies and ranks COVID-19-related OCs.Design/methodology/approachThis study uses an integrated methodology combining expert interviews and the best-worst method (BWM) to analyze the results. The data have been collected from the electronics industry of Bangladesh, an emerging economy. This study also conducts a sensitivity analysis to check the robustness of the results.FindingsThe results reveal 23 COVID-19-related OCs under five categories: sourcing, production and inventory management, demand management and distribution, return management and after-sales service, and supply chain-wide challenges. The quantitative investigation reveals that overstock in finished goods inventory, low end-customer demands, order cancellations from dealers and retailers, high inventory holding costs and lack of transportation are the top five OCs.Practical implicationsThe findings will help practitioners to understand the OCs and allow them to prepare for future major disruptions and formulate long-term strategies for operations during and after the COVID-19 pandemic.Originality/valueThis study contributes to the literature on supply chain complexity and challenges by considering a major pandemic outbreak. Moreover, the study also contributes to the knowledge on emerging economies, which have been largely neglected in the current literature.

scholarly journals Period-Life of a Branching Process with Migration and Continuous Time

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 868
Author(s):  
Khrystyna Prysyazhnyk ◽  
Iryna Bazylevych ◽  
Ludmila Mitkova ◽  
Iryna Ivanochko
Keyword(s):  
Random Process ◽  
Generating Function ◽  
Continuous Time ◽  
Branching Process ◽  
Probability Generating Function ◽  
Time Interval ◽  
The Moment ◽  
First Time ◽  
Critical Branching Process ◽  
Number Of Particles

The homogeneous branching process with migration and continuous time is considered. We investigated the distribution of the period-life τ, i.e., the length of the time interval between the moment when the process is initiated by a positive number of particles and the moment when there are no individuals in the population for the first time. The probability generating function of the random process, which describes the behavior of the process within the period-life, was obtained. The boundary theorem for the period-life of the subcritical or critical branching process with migration was found.

State-of-the-art literature review on inventory models for perishable products

2018 ◽  
Vol 15 (3) ◽  
pp. 306-346 ◽  
Author(s):  
Vaibhav Chaudhary ◽  
Rakhee Kulshrestha ◽  
Srikanta Routroy
Keyword(s):  
Inventory Management ◽  
Systemic Review ◽  
Current Status ◽  
Future Research ◽  
Inventory Models ◽  
Research Gaps ◽  
Content Type ◽  
Perishable Items ◽  
Perishable Inventory ◽  
The Future

PurposeThe purpose of this paper is to review and analyze the perishable inventory models along various dimensions such as its evolution, scope, demand, shelf life, replenishment policy, modeling techniques and research gaps.Design/methodology/approachIn total, 418 relevant and scholarly articles of various researchers and practitioners during 1990-2016 were reviewed. They were critically analyzed along author profile, nature of perishability, research contributions of different countries, publication along time, research methodologies adopted, etc. to draw fruitful conclusions. The future research for perishable inventory modeling was also discussed and suggested.FindingsThere are plethora of perishable inventory studies with divergent objectives and scope. Besides demand and perishable rate in perishable inventory models, other factors such as price discount, allow shortage or not, inflation, time value of money and so on were found to be combined to make it more realistic. The modeling of inventory systems with two or more perishable items is limited. The multi-echelon inventory with centralized decision and information sharing is acquiring lot of importance because of supply chain integration in the competitive market.Research limitations/implicationsOnly peer-reviewed journals and conference papers were analyzed, whereas the manuals, reports, white papers and blood-related articles were excluded. Clustering of literature revealed that future studies should focus on stochastic modeling.Practical implicationsStress had been laid to identify future research gaps that will help in developing realistic models. The present work will form a guideline to choose the appropriate methodology(s) and mathematical technique(s) in different situations with perishable inventory.Originality/valueThe current review analyzed 419 research papers available in the literature on perishable inventory modeling to summarize its current status and identify its potential future directions. Also the future research gaps were uncovered. This systemic review is strongly felt to fill the gap in the perishable inventory literature and help in formulating effective strategies to design of an effective and efficient inventory management system for perishable items.

Analysis of Inventory Management in Reverse Supply Chain Using Stochastic Dynamic Programming Model

2008 ◽  
Author(s):  
Badr O. Johar ◽  
Surendra M. Gupta
Keyword(s):  
Dynamic Programming ◽  
Inventory Management ◽  
Reverse Logistics ◽  
Stochastic Dynamic Programming ◽  
Public Awareness ◽  
Financial Burden ◽  
Probabilistic Approach ◽  
Complex Nature ◽  
Stochastic Dynamic

Reverse logistics is a critical topic that has captured the attention of government, private entities and researchers in recent years. This increase in the concern was driven by current set of government regulations, increase of public awareness, and the attractive economic opportunities. Also, environmentalists have always demanded Original Equipment Manufacturers (OEMs) to be more involved and be responsible of their products at the end of its life cycle. However, the uncertainty in quality of items returned, and its quantity discourage OEMs from participating in such programs. Because of the unique problems associated and the complex nature of the reverse logistics activities, numerous studies have been carried out in this field. One of those crucial areas is inventory management of End-of-Life (EOL) products. The take back program could possibly bring financial burden to OEM if it is not managed well. Thus, an efficient yet cost effective system should be implemented to appropriately manage the overwhelming number of returns. Previously, we have analyzed the problem based on the assumption that the number of core products returned and disassembled parts and subassemblies are known in advance. In this paper, we introduce a probabilistic approach where different quality levels of for every component disassembled are considered and different probabilities of these qualities given the quality of the returned product. The model utilizes a multi-period stochastic dynamic programming in a disassembly line context to solve the problem, and generate the best option that will maximize the system total profit. A numerical example is given to illustrate the approach. Finally, directions for future research are suggested.

Coordinating lot sizing and integrated production and distribution scheduling with batch delivery and holding cost

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maedeh Bank ◽  
Mohammad Mahdavi Mazdeh ◽  
Mahdi Heydari ◽  
Ebrahim Teimoury
Keyword(s):  
Supply Chain ◽  
Lot Sizing ◽  
Mixed Integer ◽  
Time Interval ◽  
Content Type ◽  
Integrated Production ◽  
Holding Costs ◽  
Integrated Production And Distribution ◽  
Distribution Scheduling ◽  
Production And Distribution

PurposeThe aim of this paper is to present a method for finding the optimum balance between sequence-dependent setup costs, holding costs, delivery costs and delay penalties in an integrated production–distribution system with lot sizing decisions.Design/methodology/approachTwo mixed integer linear programming models and an optimality property are proposed for the problem. Since the problem is NP-hard, a genetic algorithm reinforced with a heuristic is developed for solving the model in large-scale settings. The algorithm parameters are tuned using the Taguchi method.FindingsThe results obtained on randomly generated instances reveal a performance advantage for the proposed algorithm; it is shown that lot sizing can reduce the average cost of the supply chain up to 11.8%. Furthermore, the effects of different parameters and factors of the proposed model on supply chain costs are examined through a sensitivity analysis.Originality/valueAlthough integrated production and distribution scheduling in make-to-order industries has received a great deal of attention from researchers, most researchers in this area have treated each order as a job processed in an uninterrupted time interval, and no temporary holding costs are assumed. Even among the few studies where temporary holding costs are taken into consideration, none has examined the effect of splitting an order at the production stage (lot sizing) and the possibility of reducing costs through splitting. The present study is the first to take holding costs into consideration while incorporating lot sizing decisions in the operational production and distribution problem.

scholarly journals NUTRITION OF THE HOST AND NATURAL RESISTANCE TO INFECTION

1949 ◽  
Vol 89 (5) ◽  
pp. 529-539 ◽  
Author(s):  
Howard A. Schneider
Keyword(s):  
Inbred Mouse ◽  
Natural Resistance ◽  
Single Infection ◽  
Mouse Strains ◽  
Time Interval ◽  
Special Cases ◽  
Resistance To Infection ◽  
Relationship Of ◽  
First Time ◽  
The Relationship

The double strain inoculation (DSI) method of testing for natural resistance to infection has been examined in the instance of mouse salmonellosis. The DSI method has been found capable of detecting differences in natural resistance due to genetic as well as nutritional causes. A difference in response to Salmonella infection was found for the first time between the two "susceptible" inbred mouse strains, BSVR and BSVS. Whereas BSVS mice for the most part survived an intraperitoneal injection of 103 "avirulent" S. typhimurium, BSVR mice all succumbed. The relationship of the DSI test to the usual single infection test has been discussed and it is suggested that such single infection tests are special cases of the DSI test, since they involve a heterogeneous bacterial population which can be considered as a mixture of cultures of differing virulence and in which, by a single injection, the usual time interval between the two injections of the DSI method has been reduced to 0.

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