• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.110-118
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• 2020

It is important to have an appropriate amount of inventory for effective operations. This study seeks to develop a sustainable newsvendor model by incorporating environment-related costs into the existing well-known (single-period) newsvendor model. Since leftovers do more harm to environments than stockouts, the optimal order amount tends to be lower than the traditional quantity. However, this is not the case when a second buy is allowed. A second order opportunity is prevalent in industries where it improves demand forecast despite incurring extra costs. In this study, we conduct an extensive numerical analysis for a newsvendor situation with a permitted second buy. The results show that we can reduce inventory costs by considering sustainability concerns. The research idea of including sustainability considerations into existing inventory models can be extended to a more general case and provides managerial insights for better inventory decisions.

• Korean Journal of Mathematics
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• v.29 no.2
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• pp.371-386
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• 2021

This paper investigates two discrete time queueing inventory models with positive service time and lead time. Customers arrive according to a Bernoulli process and service time and lead time follow geometric distributions. The first model under discussion based on replenishment of order upto S policy where as the second model is based on order placement by a fixed quantity Q, where Q = S - s, whenever the inventory level falls to s. We analyse this queueing systems using the matrix geometric method and derive an explicit expression for the stability condition. We obtain the steady-state behaviour of these systems and several system performance measures. The influence of various parameters on the systems performance measures and comparison on the cost analysis are also discussed through numerical example.

• Journal of Korean Society of Forest Science
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• v.100 no.1
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• pp.62-69
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• 2011

Six commonly used non-linear growth functions were fitted to individual tree height-dbh data of eight major tree species measured by the $5^{th}$ National Forest Inventory in Chungcheong province. A total of 2,681 trees were collected from permanent sample plots across Chungcheong province. The available data for each species were randomly splitted into two sets: the majority (90%) was used to estimate model parameters and the remaining data (10%) were reserved to validate the models. The performance of the models was compared and evaluated by $R^2$, RMSE, mean difference (MD), absolute mean difference (AMD) and mean difference(MD) for diameter classes. The combined data (100%) were used for final model fitting. The results showed that these six sigmoidal models were able to capture the height-diameter relationships and fit the data equally well, but produced different asymptote estimates. Sigmoidal growth models such as Chapman-Richards, Weibull functions provided the most satisfactory height predictions. The effect of model performance on stem volume estimation was also investigated. Tree volumes of different species were computed by the Forest Resources Evaluation and Prediction Program using observed range of diameter and the predicted tree total height from the six models. For trees with diameter less than 30 cm, the six height-dbh models produced very similar results for all species, while more differentiation among the models was observed for large-sized trees.

• Korean Management Science Review
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• v.11 no.2
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• pp.99-131
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• 1994

This paper shows the direction in which logistics modellers should make their effort by examining the gap between desirable characteristics which logistics decision models should possess and deficiencies from which existing models suffer. For this purpose, we(1) categorized logistics models into facility planning, inventory management and transportation/delivery planning models, (2) carried out a wide survey of theoretical and industry models within each category and (3) assessed recent development of integrated logistics models.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.172-180
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• 2013

We herein consider a stochastic multi-item inventory management problem in which a warehouse sells multiple items with stochastic demand and periodic replenishment from a supplier. Inventory management requires the timing and amounts of orders to be determined. For inventory replenishment, trucks of finite capacity are available. Most inventory management models consider either a single item or assume that multiple items are ordered independently, and whether there is sufficient space in trucks. The order cost is commonly calculated based on the number of carriers and the usage fees of carriers. In this situation, we can reduce future shipments by supplementing items to an order, even if the item is not scheduled to be ordered. On the other hand, we can reduce the average number of items in storage by reducing the order volume and at the risk of running out of stock. The primary variables of interest in the present research are the average number of items in storage, the stock-out volume, and the number of carriers used. We formulate this problem as a multi-objective optimization problem. In a numerical experiment based on actual shipment data, we consider the item shipping characteristics and simulate the warehouse replenishing items coordinately. The results of the simulation indicate that applying a conventional ordering policy individually will not provide effective inventory management.

• Journal of applied mathematics & informatics
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• v.42 no.5
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• pp.1063-1076
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• 2024

This research introduces an innovative approach to revolutionize inventory management strategies amid unpredictable demand and uncertainties. Introducing a Fuzzy Economic Order Quantity (EOQ) model, enriched with the centroid defuzzification method and supervised machine learning, the study offers a comprehensive solution for optimized decision-making. The model transcends traditional inventory paradigms by seamlessly integrating fuzzy logic and advanced machine learning, emphasizing adaptability in fast-paced business landscapes. The research unfolds against the backdrop of agile inventory management advocacy, with key contributions including the centroid defuzzification method for crisp interpretation and the integration of linear regression for cost prediction. The study employs a real-life bakery scenario to demonstrate the efficacy of both crisp and fuzzy models, underscoring the latter's superiority in handling uncertainties. Comparative analysis reveals nuanced impacts of uncertainty on inventory decisions, while linear regression establishes statistical relationships for cost predictions. The findings underscore the pivotal role of fuzzy logic in optimizing inventory management, paving the way for future enhancements, advanced machine learning integration, and real-world validation. This research not only contributes to adaptive inventory management evolution but also sets the stage for further exploration and refinement in dynamic business landscapes.

• Journal of Preventive Medicine and Public Health
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• v.21 no.1 s.23
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• pp.195-206
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• 1988

Two major issues of the blood bank management are quality assurance and inventory control. Recently, in Korea blood donation has gained popularity increasingly to allow considerable improvement of the quality assurance with respect to blood collection, transportation, storage, component preparation skills and hematological tests. Nevertheless the inventory control, the other issue of blood bank management, has been neglected so far. For the supply of blood by donation barely meets the demand, the blood bank policy on the inventory control has been 'the more the better.' The shortage itself by no means unnecessitate inventory control. In fact, in spite of shortage, no small amount of blood is outdated. The efficient blood inventory control makes it possible to economize the blood usage in the practice of state-of-the-art medical care. For the efficient blood inventory control in Korean hospitals, this tudy is to develop formulae forecasting the standard blood inventory level and suggest a set of policies improving the blood inventory control. For this study informations of $A^+$ whole bloods and packed cells inventory control were collected from a University Hospital and the Central Blood Bank of the Korean Red Cross. Using this informations, 1,461 daily blood inventory records were formulated.48 varieties of blood inventory control environment were identified on the basis of selected combinations of 4 inventory control variables-crossmatch, transfusion, inhospital donation and age of bloods from external supply. In order to decide the optimal blood inventory level for each environment, simulation models were designed to calculate the measures of performance of each environment. After the decision of 48 optimal blood inventory levels, stepwise multiple regression analysis was started where the independent variables were 4 inventory control variables and the dependent variable was optimal inventory level of each environment. Finally the standard blood inventory level decision rule was developed using the backward elimination procedure to select the best regression equation. And the effective alternatives of the issuing policy and crossmatch release period were suggested according to the measures of performance under the condition of the standard blood inventory level. The results of this study' were as follows ; 1. The formulae to calculate the standard blood inventory level($S^*$)was $S^*=2.8617X(d)^{0.9342}$ where d is the mean daily crossmatch(demand) for a blood type. 2. The measures of performace - outdate rate, average period of storage, mean age of transfused bloods, and mean daily available inventory level - were improved after maintenance of the standard inventory level in comparison with the present system. 3. Issuing policy of First In-First Out(FIFO) decreased the outdate rate, while Last In-First Out(LIFO) decreased the mean age of transfused bloods. The decrease of the crossmatch release period reduced the outdate rate and the mean age of transfused bloods.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.10a
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• pp.105-105
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• 1993

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.4
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• pp.382-392
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• 2006

Vendor-managed inventory policy(VMIP) is a supply-chain initiative where the supplier is authorized to manage inventories of items at retail locations. In VMIP, the supplier monitors sales and stock information at retail locations and makes decisions of inventory replenishment and transportation simultaneously. VMIP has been known as an effective supply chain strategy that can realize many of benefits obtainable only in a fully integrated supply chain. However, VMIP does not always lead to lower the supply chain cost. It sometimes generates the total supply chain cost higher than the traditional retailer-managed inventory policy (RMIP). In this paper, we perform a comparison study on RMIP and VMIP in the retail supply chain which consists of a single supplier and a number of retailers. We formulate mixed integer programming models for both RMIP and VMIP with vehicle routing problems and perform computational experiments on various test problems. Furthermore, we derive the conditions which guarantee the dominant position for VMIP with respect to total supply chain cost in the simple retail supply chain.

• Journal of Korean Institute of Industrial Engineers
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• v.3 no.1
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• pp.39-47
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• 1977

Most of the current literature on inventory theory has been devoted to the study of single stage models. A class of inventory problems which is of great interest is the multistage inventory system which involves a series and hierarchical sequence of stations. This study analyzes some aspect of the series type and multi-stage inventory system, using the fixed cycle ordering which bas a modificatory control function in the system equations. The objective of this study is to clarify the dynamic behavior of the system. The author has derived the theoretical formulas of variation of ordering quantity and stock fluctuation of each stage due to power spectral density function. Influence of parameters such as, (1) intensity of autocorrelation of demand sequence ($\lambda$), (2) forecasting exponential smoothing factors of each stage (${\alpha}_1,\;{\alpha}_2,\;{\alpha}_3$) and (3) production control factor of the 3rd stage ($\gamma$), as operators of the system on the variation of ordering quantity and stock fluctuation of the system. is also clarified. As a result of this study, the relations between the variation of ordering quantity, stock fluctuation and the parameters of the system, have been found. The principles and the theorical analysis presented here will be applicable to more complex type of discrete control systems in constructing the specific condition of the system to minimize inventory variances.