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1 – 10 of over 29000Marek Pawlak and Elżbieta Małyszek
The paper's purpose is to provide a method of reducing inventory costs in multi‐product and multi‐nodes supply chains (SC).
Abstract
Purpose
The paper's purpose is to provide a method of reducing inventory costs in multi‐product and multi‐nodes supply chains (SC).
Design/methodology/approach
The proposed approach is based on applying the classical inventory control models and simulation. This is a two‐stage approach in which inventory cost reduction in the SC occurs as a result of the appropriate selection of inventory control policies by its members.
Findings
Cost reduction in the whole SC may occur as a result of an appropriate choice of inventory control policies in every node. SC members can learn which inventory control policies should be applied. Results of the learning process are closely connected with a kind of collaboration between chain nodes. The best results of the whole system can happen with a simultaneous deterioration of operation of its component elements. Therefore, losses which occur in some chain members as a result of collaboration should be compensated.
Research limitations/implications
This approach does not offer an optimal solution. The authors do not know how far they are from an optimal solution. The approach should be tested for more complicated SC. The authors have not studied the strategy in which SC cells negotiate the level of their inventories.
Practical implications
SC cells should apply a local cooperation strategy. When the cost of the whole SC decreases, the cost in particular nodes may significantly increase. The start of collaboration in the SC can cause a deterioration of results in some companies; therefore this loss should be compensated.
Originality/value
The paper presents the development of a framework in which an application of different policies of inventory control and application of different collaboration strategies in the SC are studied. The framework enables the use of classical inventory control models in a coordinated manner, and SC members decide which policies should be applied only on the basis of earlier collected experiences.
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Joong Y. Son and Ryan K. Orchard
The purpose of this paper is to examine supply‐side disruptions in a supply chain, and to analyse the effectiveness of two inventory‐based policies for mitigating the impact of…
Abstract
Purpose
The purpose of this paper is to examine supply‐side disruptions in a supply chain, and to analyse the effectiveness of two inventory‐based policies for mitigating the impact of supply disruptions: maintaining strategic inventory reserves (the R‐policy), and using larger orders (the Q‐policy).
Design/methodology/approach
The paper assess the effectiveness of two inventory‐based mitigating policies implemented at a reseller when end customer demand is stable but supply can be disrupted. An analytical model is provided, and numerical experiments are conducted to evaluate the effectiveness of the policies for mitigating the impact of disruption under different disruption scenarios.
Findings
Results indicate that the R‐policy performs consistently better than the Q‐policy in terms of product availability measures, as tested under a wide range of frequency and duration of supply disruptions.
Practical implications
Supply chain trends of lean operations and global sourcing have exposed business organizations to a greater risk and have further raised the need to protect businesses against random supply disruptions.
Originality/value
The paper intends to contribute to the narrowing of the gap in the research of supply‐side disruptions. Further, the topic of inventory reserves has been discussed to date in only a very general sense; the paper proposes conditions for practical implementation and provides unique insights into the effectiveness of the use of strategic inventory reserves as a supply disruption mitigation policy.
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Linh Nguyen Khanh Duong, Lincoln C. Wood and William Yu Chung Wang
This research proposes a decision framework for using non-financial measures to define a replenishment policy for perishable health products. These products are perishable and…
Abstract
Purpose
This research proposes a decision framework for using non-financial measures to define a replenishment policy for perishable health products. These products are perishable and substitutable by nature and create complexities for managing inventory. Instead of a financial measure, numerous measures should be considered and balanced to meet business objectives and enhance inventory management.
Design/methodology/approach
This research applies a multi-methodological approach and develops a framework that integrates discrete event simulation (DES), analytic hierarchy process (AHP) and data envelopment analysis (DEA) techniques to define the most favourable replenishment policy using non-financial measures.
Findings
The integration framework performs well as illustrated in the numerical example; outcomes from the framework are comparable to those generated using a traditional, financial measures-based, approach. This research demonstrates that it is feasible to adopt non-financial performance measures to define a replenishment policy and evaluate performance.
Originality/value
The framework, thus, prioritises non-financial measures and addresses issues of lacking information sharing and employee involvement to enhance hospitals' performance while minimising costs. The non-financial measures improve cross-functional communication while supporting simpler transformations from high-level strategies to daily operational targets.
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Mohit Srivastava, Peeyush Mehta and Sanjeev Swami
The purpose of this paper is to determine the inventory replenishment policies when demand rate is a function of the inventory space allocated to the products on retail shelves…
Abstract
Purpose
The purpose of this paper is to determine the inventory replenishment policies when demand rate is a function of the inventory space allocated to the products on retail shelves. Existing results on inventory policies with inventory-level-dependent demand (ILDD) assume deterministic functional forms of the demand rate. In this paper, the authors model the inventory decisions when demand is a function of shelf-space allocation and random uncertainty. The authors provide managerial insights of this paper's results.
Design/methodology/approach
The demand rate is assumed to be a function of shelf-space allocation based on two settings in the literature. First, the authors model the demand rate as a function of initial shelf-space allocation. In the next setting, the authors assume that the demand rate is a function of instantaneous inventory level on shelves. In both the settings, the authors also model random demand uncertainty in addition to the shelf-space dependency of demand rate. The objective is to maximize the expected profit and determine the inventory parameters.
Findings
In addition to the demand uncertainty, the authors consider linear, power and exponential functional forms of demand rate. Inventory policy that maximizes expected profit is determined when demand rate is a function of initial allocation and displayed inventory level. The results are implementable for practitioners for optimizing the shelf-space allocation and related inventory policy.
Originality/value
Most of the extant results on inventory policy with shelf-space-dependent demand do not model the demand uncertainty. The authors model a variety of functional forms of demand rate with ILDD in addition to the demand uncertainty. The results are a building block for more applications in inventory management for real-life applications.
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Suresh Garg, Prem Vrat and Arun Kanda
The demand variability in case of assembly line operations can be absorbed either by multi‐skilling of operators on the line, empowering them to handle a wider mix of…
Abstract
The demand variability in case of assembly line operations can be absorbed either by multi‐skilling of operators on the line, empowering them to handle a wider mix of work‐elements or by holding finished goods inventory. This paper examines trade‐offs between these two groups of policies by developing a simulation‐based model. Four policies are evaluated and their cost implications examined to enable decision makers to choose the best policy depending upon the situation specific parameters. A case study to illustrate the proposed model is presented and results are found to be insightful. A methodology to identify training needs in case of multi‐skilling is also developed.
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This paper proposes a new inventory model with inspection policy because in practice the received orders may contain non- conforming (NC) items. So, a buyer who receive an order…
Abstract
Purpose
This paper proposes a new inventory model with inspection policy because in practice the received orders may contain non- conforming (NC) items. So, a buyer who receive an order from a supplier should use an inspection policy.
Design/methodology/approach
The inspection policy is assumed to be zero-defect single sampling. Under this policy a lot is accepted only if no defect has been identified in the inspected sample. The fraction of NC is assumed to be a random variable following a Binomial distribution and the number of NC items detected by inspection assumed to be a random variable, which follows a hypergeometric distribution. Order quantity and sample size are the two decision variables. A solution procedure is presented for the proposed model. The proposed procedure presents the optimal solution.
Findings
Numerical examples presented to illustrate the procedure outlined for the proposed model and its applicability. The results of numerical examples and comparing them with traditional EOQ model reveal that by the proposed model, the buyer could reduce total cost that shows the efficiency and validity of the proposed model.
Originality/value
The novelty of this paper is the new proposed model that considers inspection policy in inventory management. The proposed model determines sample size as well as order quantity to consider both subject of inventory management and quality control, simultaneously.
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Vaibhav Chaudhary, Rakhee Kulshrestha and Srikanta Routroy
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Practical implications
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.
Originality/value
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.
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The yield of defective items and emissions of greenhouse gases in supply chains are areas of concern. Organizations try to reduce the yield defective items and emissions. In this…
Abstract
Purpose
The yield of defective items and emissions of greenhouse gases in supply chains are areas of concern. Organizations try to reduce the yield defective items and emissions. In this paper, a constrained optimization model is developed with consideration of the yield of defective items and strict carbon cap policy simultaneously and then optimized. Further, sensitivity analyses have been carried out to draw different managerial insights. Precisely, we have tried to address the following research questions: (1) how to optimize the cost for a two-echelon supply chain considering yield of defective items and strict carbon cap policy, (2) how the total expected cost and total expected emissions act with changing parameters.
Design/methodology/approach
The mathematical modeling approach has been adopted to develop a model and further optimized it with optimization software. Costs and emissions from different areas of a supply chain have been derived and then the total cost and total emissions have been formulated mathematically. One constrained mixed-integer nonlinear programming (MINLP) problem has been formulated and solved considering emissions-related, velocity and production related-constraints. Further, different sensitivity analyses have been derived to draw some managerial insights.
Findings
In this paper, many decision variables have been calculated with a set of basic values of other parameters. It has been found that both cost and emissions can be controlled by controlling different parameters. It has been also found that some parameters have very little or no influence either on cost or emissions. In most cases, originations may exhaust the given limit of carbon cap to optimize their costs.
Originality/value
In spite of my sincere efforts, no paper has been found that has considered the yield of defective items and strict carbon cap policy simultaneously. In this paper, it is assumed that both demand and defect rates are random in nature. The model, presented in this paper may give insights to develop different supply chain models with consideration of both defective items and strict carbon cap policy. Sensitivity analyses, drawn in this paper may give deep insights to managers and carbon regulatory bodies.
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Albert Munoz and Quan Spring Zhou
The paper explores and characterizes antifragility in simple inventory systems exposed to demand variability, providing the initial inroads to operationalizing antifragility in…
Abstract
Purpose
The paper explores and characterizes antifragility in simple inventory systems exposed to demand variability, providing the initial inroads to operationalizing antifragility in the context of inventory management. Antifragility refers to the feature of a system that can benefit from uncertainty, rather than suffer from it. The paper expands the concept of inventory beyond that of risk mitigation and towards one of enabling antifragility.
Design/methodology/approach
The study employs analytical and simulation modelling of an inventory system with two classes of demand. To separate the influence of factors, a simple inventory policy with a range of fixed order quantities is modelled, allowing for the identification of antifragile regions in an experimental space.
Findings
Outputs uncover a variety of performance outcomes, ranging from settings where additional inventory yields no benefit, to areas where additional inventory results in increasing normalized profit with increasing uncertainty, demonstrating antifragility. In between these regions, increases in normalized profit are bounded, and confined to specific regions.
Research limitations/implications
This research expands academic understanding of inventory as a vehicle to achieving antifragile outcomes. Although this paper does not solve for an optimal policy as typical inventory research does, it instead characterizes the antifragile outcomes within simple inventory systems. Further research should be carried out to investigate antifragility in settings of greater complexity and design ordering policies leveraging inventory towards a gain from uncertainty.
Practical implications
Typically, inventory is used to buffer against uncertainty, and, with a given amount of inventory, the performance is expected to degrade with increasing variability. In this paper, the authors demonstrate that certain levels of additional inventory can result in antifragility and increase profitability as uncertainty increases, extending beyond traditional conceptualizations of inventory and uncertainty.
Originality/value
Empirical research into designing antifragile outcomes is limited, with very few examples of increasing performance with increases in uncertainty. This article presents an initial empirical exploration of how additional inventory can result in antifragility.
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Kaustav Kundu and Matteo Rossini
The purpose of this paper is to develop a simulation model to evaluate inventory and distribution decisions like lateral transshipments in a network with multiple products.
Abstract
Purpose
The purpose of this paper is to develop a simulation model to evaluate inventory and distribution decisions like lateral transshipments in a network with multiple products.
Design/methodology/approach
Data are collected from a company, and a discrete-event simulation in Python is developed to support the decision-making process of managers through different algorithms of lateral transshipments.
Findings
The numerical results show that the periodic delivery-continuous reorder policy is more robust than the others because the reorder process is not affected by the higher saturation that is achieved by periodic reorder–based policies. The new lateral transshipment algorithm will lead to huge savings in logistics costs for any company and increase truck saturation without causing a decrease in the service level.
Research limitations/implications
This paper provides a novel institutional perspective on a complex logistics issue where COVD-19 is believed to complicate the context.
Practical implications
This solution is devised for any company to achieve even greater benefits in terms of customer service improvement and logistics costs reduction.
Originality/value
The main contribution of this paper is the proposal of a new lateral transshipment algorithm that shows performance improvement by simulating distribution network processes according to different configurations.
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