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The purpose of this paper is to deal with the application of the stochastic inventory model to the three‐tier supply chain and verify the values obtained by mathematical model in physical simulation.

The paper investigates three‐stage serial supply chain with stochastic demand and fixed replenishment lead‐time. Inventory holding costs are charged at each stage, and each stage may incur a consumer backorder penalty cost charged by primary supplier to secondary supplier. The customer‐demand follows Poisson distribution. The base stock model is implemented for inventory control at both suppliers. Physical simulation is then designed in such a way that it satisfies all the assumptions for mathematical model. Simulation is run to verify the values obtained from mathematical model.

Computer simulation is designed to include all the assumptions made by mathematical model. Hence, mathematical base stock model and computer simulation model are comparable. Demand follows Poisson distribution in both cases. The backorder cost and inventory holding cost are calculated in each phase of simulation and summarized. The paper infers that the total inventory cost is optimum in phase II, in which reorder point is same as that calculated by mathematical model. In phase I, total inventory cost is more than that of phase II because of backorders. In phase III, excess inventory increased the total cost. Thus, the values obtained from mathematical model produce optimal inventory cost. Base stock model is effective when the demand is not deterministic and service factor assumed in mathematical model is 0.9, which is quite acceptable. Base stock model assumes replenishment order quantity as 1 and the total inventory cost decreases with replenishment lead time. Base stock model is beneficial for supply chains having short replenishment lead time. Computer simulation results indicate that discrete event simulations can be used to model stochastic systems like organizational supply chains and to validate the results from mathematical models.

The paper offers a review of simulation work aiming to support improvement of agility in the supply chain.

Nowadays, in many organizations, products are not delivered instantly. So, the customers should wait to receive their needed products, which will form a queueing-inventory model. Waiting a long time in the queue to receive products may cause dissatisfaction and churn of loyal customers, which can be a significant loss for organizations. Although many studies have been done on queueing-inventory models, more practical models in this area are needed, such as considering customer prioritization. Moreover, in many models, minimizing the total cost for the organization has been overlooked.

This paper will compare several machine learning (ML) algorithms to prioritize customers. Moreover, benefiting from the best ML algorithm, customers will be categorized into different classes based on their value and importance. Finally, a mathematical model will be developed to determine the allocation policy of on-hand products to each group of customers through multi-channel service retailing to minimize the organization’s total costs and increase the loyal customers' satisfaction level.

To investigate the application of the proposed method, a real-life case study on vaccine distribution at Imam Khomeini Hospital in Tehran has been addressed to ensure model validation. The proposed model’s accuracy was assessed as excellent based on the results generated by the ML algorithms, problem modeling and case study.

Prioritizing customers based on their value with the help of ML algorithms and optimizing the waiting queues to reduce customers' waiting time based on a mathematical model could lead to an increase in satisfaction levels among loyal customers and prevent their churn. This study’s uniqueness lies in its focus on determining the policy in which customers receive products based on their value in the queue, which is a relatively rare topic of research in queueing management systems. Additionally, the results obtained from the study provide strong validation for the model’s functionality.

The purpose of this paper is to analyze the effects of supply chain coordination on inventory management while the retailer inventory cycle consists of a shortage period and the backorder rate linearly decreases as a function of shortage duration. It is intended to consider how on‐hand inventory and shortage durations are altered when the decisions are centralized.

Mathematical modelling of inventory costs for the retailer and the vendor is used to formulate objective functions. The vendor sets his inventory period as an integer multiple (n) of the retailer inventory cycle in which the integer multiple is a decision variable. Solution spaces of models are analyzed to determine two other decision variables including, on‐hand inventory duration and shortage length.

The integrated model consists of a unique pseudo convex area when (n) is fixed and as a result, there is a unique minimum point. Based on numerical examples and sensitivity analysis, in most situations coordinated inventory management reduces total costs of the supply chain and cost reduction rate increase at larger production rates.

This paper is a combination between production‐inventory models in two‐stage supply chains and partial backordering, which has appeared in single inventory models. To the best of the authors' knowledge, no mathematical model has yet been proposed. Moreover, the benefits of synchronization are analyzed through numerical examples.

The 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.

In 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.

There 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.

Only 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.

Stress 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.

The 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.

This project attempts to present a space component inventory classification system for space inventory replenishment and management. The authors propose to adopt a classification system that can incorporate all the different variables in a multi-criteria configuration. Fuzzy logic is applied as an effective way for formulating classification problems in space inventory replenishment.

A fuzzy-based approach with ABC classification is proposed to incorporate all the different variables in a multi-criteria configuration. Fuzzy logic is applied as an effective way for formulating classification problems in space inventory replenishment of the soil preparation system (SOPSYS) which is used in grinding and sifting Phobos rocks to sub-millimeter size in the Phobos-Grunt space mission. An information system was developed using the existing platform and was used to support the key aspects in performing inventory classification and purchasing optimization.

The proposed classification system was found to be able to classify the inventory and optimize the purchasing decision efficiency. Based on the information provided from the system, implementation plans for the SOPSYS project and related space projects can be proposed.

The paper addresses one of the main difficulties in handling qualitative or quantitative classification criteria. The model can be implemented using mathematical calculation tools and integrated into the existing inventory management system. The proposed model has important implications in optimizing the purchasing decisions to shorten the research and development of other space instruments in space missions.

Inventory management in the manufacture of space instruments is one of the major problems due to the complexity of the manufacturing process and the large variety of items. The classification system can optimize purchasing decision-making in the inventory management process. It is also designed to be flexible and can be implemented for the manufacture of other space mission instruments.

This paper aims to give a comprehensive review, summary, and discussion on inventory models that have appeared in the literature. During these past ten decades, no seminal paper reviewing the field of inventory lot sizing has even been published. This limitation has been identified in the literature by several researchers over the years, with the sheer volume of the number of published inventory lot sizing models acting as a factor which has limited a research endeavor to review the literature on inventory lot sizing models.

This article reviews research on inventory lot size models and provides a review of previously published literature review papers on inventory models. Based on this initial review, the literature extending current research practices on inventory modeling in supply chains and in sustainable practices is presented. Directions for expanding research in these two areas are examined in light of concerns expressed in the historical use of inventory models and in light of a new inventory research paradigm.

In our paper, we have adopted a novel strategy to overcome this limitation by focusing our review on a review of inventory lot sizing review papers.

By adopting the methodology of reviewing published inventory review papers, we can contribute a comprehensive review of the inventory lot sizing literature that serves to provide in one paper a consolidation of inventory research that can serve as a single source to keep researchers up to date with the research developments in inventory lot sizing models. We also identify gaps in the field which could stimulate new research agendas in the areas of supply chain management and sustainable inventory practices.

This paper is concerned with the development of an intelligent inventory management system which aims at bridging the substantial gap between the theory and the practice of inventory management. The proposed system attempts to achieve this by providing automatic demand and lead time pattern identification and model selection facilities. The process of demand pattern identification together with the statistical tests used is discussed. The models incorporated cover deterministic demand models including: constant, quasi‐constant, trended and seasonal demand as well as stochastic demand models. This paper includes an empirical evaluation of the system on real data from the manufacturing and airline industries which shows that this system can lead to significant savings in inventory cost.

In recent decades there has been much interest and activity in the application of mathematical ideas for controlling inventory. However most of this has been related to the control of stock products whose demand is smooth and continuous. When demand is lumpy these methods are inefficient in their attempts to minimise thé operating cost. A simple regression model is developed for computing optimal (s, S) policies for items with lumpy demand patterns. Continuous review of inventory level is assumed and the lead time demand is approximated by the stuttering Poisson distribution. A grid of 864 known optimal policies has been used to provide the data for the calibration of the regression models. Numerical models are used to illustrate this approach. Extensive computational results show that this model provides excellent performance in estimating the optimal values of the control parameters s and S for wide ranges of demand and cost parameters.

Postponement (also known as delayed product differentiation) has been shown to be an effective supply chain strategy from an inventory‐reduction, service‐level improvement standpoint. However, there has been relatively little empirical research on identifying the drivers and/or obstacles to a successful implementation of a postponement strategy. This study aims to identify the importance of several managerial issues surrounding the implementation of such a strategy.

Exploratory depth interviews with company managers were conducted to identify and examine issues that may affect a successful implementation. Borrowing from the concept of triangulation, the findings are integrated with results reported elsewhere in the literature to understand the interrelated aspects of postponement implementation issues. This approach provides insights that would not be had if the results of each study were viewed independently of one another.

The identification of managerial issues implies that there is more to implementing a partial postponement strategy than the repositioning of inventories prescribed by a mathematical inventory model. Several issues that may influence a postponement implementation are identified and discussed including product integrity, operations scheduling, and organizational readiness.

The depth interview process is an empirical methodology subject to the limitations of judgmental conclusions, interpreter bias and small sample size. The results of this exploratory study provide a more in‐depth understanding of drivers and obstacles to implementing this supply chain strategy. The findings suggest the need for further investigation of the issues surrounding the decision to adopt postponement.

The findings have broad implications for manufacturing managers regarding the impact of adopting a postponement strategy on several aspects of the operations function.

Although principally exploratory in nature, the investigation demonstrates the need for researchers to identify and better understand the managerial issues surrounding an attempt to implement a specific partial postponement strategy.

The purpose of this paper is to present a literature review of the fresh produce supply chain management (FSCM). FSCM includes the processes from the production to consumption of fresh produce (fruits, flowers and vegetables).

Literature review is done by systematically collecting the existing literature over a period of 20 years (1989‐2009) and classifying it on the basis of structural attributes such as problem context, methodology and the product under consideration. The literature is also categorized according to the geographic region and year of publication.

There is an increase in interest towards FSCM still there is an absence of a journal with the prime attention towards FSCM. The key finding of this review is that the main interest is towards consumer satisfaction and revenue maximization with post‐harvest waste reduction being a secondary objective. It is revealed from the review that most of the literature is fragmented and is in silos. Lack of demand forecasting, demand and supply mismatch, lesser integrated approach etc are the major causes of concerns.

The authors have taken only the fresh produce (fruits, flowers and vegetables), authors may also look at other perishable items such as milk, meat, etc.

Result shows a product‐problem‐methodology mapping which may serve as a framework for the managers addressing issues in FSCM.

Most of the prior literature reviews are focused on a specific issue such as production planning or inventory management and ignore the broader perspective. There exists a need of having a detailed literature review covering all the operational issues in FSCM. This review fills this gap in the FSCM literature.