Nasser Safaie; Ali Nazeri; Anita Mottakiani
Abstract
In this study, the relationship between supply chain management functions, supply chain performance, competitive advantage, and organizational performance in the hospitality were investigated. The objective of this study was to assess the performance of supply chains and management in the hospitality. ...
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In this study, the relationship between supply chain management functions, supply chain performance, competitive advantage, and organizational performance in the hospitality were investigated. The objective of this study was to assess the performance of supply chains and management in the hospitality. For this purpose, a model consisting of 4 variables and 5 hypotheses was created. The statistical population of this study included all administrative staff of the 4 and 5-star hotels in Tehran. Sampling among senior and middle managers was done randomly. A total of 199 samples were collected and the designed model was tested using a structural equation approach. All study hypotheses were approved. The results indicated that using the dimensions of the supply chain management function, we can observe the positive and comprehensive impact of these factors on organizational performance, supply chain performance, and competitive advantage. In addition, supply chain performance and competitive advantage were found to have a positive and significant effect on organizational performance.
Arman Sajedinejad; Erfan Hassannayebi; Mohammad Saviz Asadi Lari
Abstract
It is scientifically challenging to determine the inventory level all through the supply chain in such a way that the desired objectives such as effectiveness and responsiveness of the supply chain system can be obtained. Simulation is a means for solving various problems which cannot be solved ...
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It is scientifically challenging to determine the inventory level all through the supply chain in such a way that the desired objectives such as effectiveness and responsiveness of the supply chain system can be obtained. Simulation is a means for solving various problems which cannot be solved by regular exact models such as mathematical ones due to their complexity. The present paper is aimed at simulating lean multi-product supply chain system as well as optimization of the objectives of supply chain. Variables of the simulation model include two types of Kanbans namely withdrawal, and production to determine the inventory level, and batch size of delivery parts for each stage of supply chain. So, in this paper simulation model was developed for supply chains, taking into consideration the different production scenarios and were modeled and compared. A production scenario is adopted for each level of the chain in order to achieve the objectives. The use of meta-heuristic techniques leads us to optimization of these variables which helps decrease delay of both product delivery and inventory level of supply chain. In this case, Genetic Algorithm has applied to find the best variable values of each scenario (included in the right number of each Kanbans), aimed at decreasing the costs and delivery delays. An example based on a case study is given to illustrate the efficiency of the proposed approach. Considering each level of supply chain, the ratio between and among cost, inventory, and delivery delay variables were obtained.
Masoud Rabbani; Leyla Aliabadi; Razieh Heidari; Hamed Farrokhi-Asl
Abstract
This study considers a reliable location – inventory problem for a supply chain system comprising one supplier, multiple distribution centers (DCs), and multiple retailers in which we determine DCs location, inventory replenishment decisions and assignment retailers to DCs, simultaneously. Each ...
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This study considers a reliable location – inventory problem for a supply chain system comprising one supplier, multiple distribution centers (DCs), and multiple retailers in which we determine DCs location, inventory replenishment decisions and assignment retailers to DCs, simultaneously. Each DC is managed through a continuous review (S, Q) inventory policy. For tackling real world conditions, we consider the risk of probabilistic distribution center disruptions, and also uncertain demand and lead times, which follow Poisson and Exponential distributions, respectively. A new mathematical formulation is proposed and we model the proposed problem in two steps, in the first step, a queuing system is applied to calculate mean inventory and mean reorder rate of steady-state condition for each DC. Next, regarding the results obtained from the first step, we formulate a mixed integer nonlinear programming model which minimizes the total expected cost of inventory, location and transportation and can be solved efficiently by means of LINGO software. Finally, several test problems and sensitivity analysis of key parameters are conducted in order to illustrate the effectiveness of the proposed model.