• Journal of the Korean Operations Research and Management Science Society
• /
• v.36 no.3
• /
• pp.91-105
• /
• 2011

In this paper, we analyze a logistic system involving a supplier who produces and delivers multiple types of items and a buyer who receives and sells the products to end customers. The buyer controls the inventory level by replenishing each product item up to a given order-up-to-level to cope with stochastic demand of end customers. In response to the buyer's order, the supplier produces or outsources the ordered item and delivers them at the start of each period. For the system described above, a mathematical model for a single type of item was developed from the buyer's perspective. Based on the model, an efficient method to find the cycle length and safety factor which correspond to a local minimum solution is proposed. This single product model was extended to cover a multiple item situation. From the model, algorithms to decide the base cycle length and order interval of each item were proposed. The results of the computational experiment show that the algorithms were able to determine the global optimum solution for all tested cases within a reasonable amount of time.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2004.05a
• /
• pp.441-445
• /
• 2004

In this paper, we deal with an inventory model of a multi-stage, serial supply chain system where a single product type and nonstationary customer demand pattern are considered. The retailer and suppliers place their orders according to an echelon-stock based replenishment control policy. We assume that the suppliers can access online information on the demand history and use this information when making their replenishment decisions. Using a reinforcement learning technique, the inventory control parameters are designed to adaptively change as the customer demand pattern is altered, in order to maintain a given target service level. Through a simulation based experiment, we verified that our approach is good for maintaining the target service level.

• Journal of Korean Institute of Industrial Engineers
• /
• v.28 no.3
• /
• pp.232-239
• /
• 2002

This paper analyzes how a buyer can determine the economic replenishment policy when the supplier allows delay in payments for an order of a product. For the analysis, it is assumed that the length of credit period is a function of the buyer's amount of purchase, and inventory is depleted not only by buyer's demand but also by deterioration. Investigation of the properties of the model developed allows us to develop an algorithm whose validity is illustrated using an example problem.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.34 no.4
• /
• pp.152-161
• /
• 2011

The operation of vending machine system presents a decision-making problem which consists of determining the product allocation to vending-machine storage compartments, replenishment intervals of vending machines, and vehicle routes, all of which have critical effects on system profit. Especially, it becomes more difficult to determine the operation variables optimally when demand for a product that is out-of-stock spills over to another product or is lost. In this paper, we propose a heuristic for solving the operation problem of the vending machine system and evaluate it by comparing with Yang's algorithm on various test problems with respect to system profit via a computer simulation. The results of computational experiments show a substantial profit increase of the proposed heuristic over Yang's algorithm. Sensitivity analysis indicates that some input variables impact the profit increase significantly.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.39 no.3
• /
• pp.170-179
• /
• 2016

In this paper we have tackled the outstanding inventory planning problems over new product launching period in a more holistic manner by addressing first the definition of efficient business rules to effectively control and reduce the inventory risks followed by the rigorous explanations on the implementation guide on suggested inventory planning rules. It is not unusual for many companies in the consumer electronics market to make a great effort to reduce the time to launch a new product because the ability to bring out higher performing products in such a short time period greatly increases the probability for them to remain competitive in the high tech market. Among so many newly developed products, those products with new features and technologies appeal to many potential customers while products which fail to win customers by design and prices rapidly disappear in the market. To adapt to this business environment, those companies have been trying to find the answer to minimize the inventory of old products so they can move to next generation products quickly with less obsolete material. In the experimental implementation of our rule-based inventory planning, Company 'S' reduced the inventory cost for the outgoing products as low as 49% of its peak level of its preceding product version in just 5 month after the adoption of rule-based inventory planning process and system. This paper concluded the subject with a suggestion that the best performance of rule-based inventory planning is guaranteed not from one-time campaign of process improvement along with system development but the decision maker's continuing support and attention even without seeing any upcoming business crisis.

• The Journal of Society for e-Business Studies
• /
• v.26 no.1
• /
• pp.67-78
• /
• 2021

The number of domestic e-commerce transactions has been breaking its own record by an annual average growth rate of over 20% based on volume for the past 5 years. Due to the rapid increase in e-commerce market, retail companies that have difficulty meeting consumers in person are in fierce competition to take the lead in the last mile service, which is the only point of contact with customers. Especially in the delivery area, where competition is most intense, the role of the fulfillment center is very important for service differentiation. It must be capable of fast product preparation ordered by consumers in accordance with the delivery service level. This study focuses on the order picking system for rapid order processing in the fulfillment center as an alternative for companies to gain competitive advantage in the e-commerce market. A mixed integer programming model was developed and implemented to optimize the stock replenishment in order picking facilities. The effectiveness was scientifically and objectively verified by simulation using the actual operation process and data.

• The Journal of Information Technology
• /
• v.9 no.1
• /
• pp.65-79
• /
• 2006

e-Marketplace is a business concept which is importantly considered in the area of marketing. e-Marketplace provides the public field in which multi-purchasers can efficiently trade with multi-suppliers. Supply Chain Management(SCM) is being widely acknowledged by the development of information technology. SCM is well known as one of the key issues lately. The complexity of supply chains and the associated potential opportunities for gaining a competitive edge by designing a product and process to support supply chain management can be significant. In order to achieve successful implementation of supply chain management system, companies should understand some barriers in implementation and set up effective and integrated information system. Standardization of logistics is needed through the unification of EDI, Bar Code, Pallet and so on. It's effect is magnified on Efficient Consumer Response such as efficient store assortment, efficient replenishment, efficient promotion and efficient product introduction. International logistics management is the integration of key business processes from original suppliers to end user that provides products, services and information that add value for customers. e-Logistics is being used in managing the international logistics. In this study, three basic e-SCM models for the strategic implementation of supply chain management are suggested. Among them, the virtual company can be the best one we can develop in order to cope with the individualized customer needs.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.30 no.2
• /
• pp.105-115
• /
• 2005

By committing to a long-term replenishment contract, buyers can purchase a product at a lower price from a supplier who is less pressured to find new customers due to the long-term contract and can charge a discounted price. We develop an analytical model from the buyer's perspective to investigate a capacity reservation contract. We are considering the system with a single supplier and a buyer. The buyer can purchases any desired amount from a spot market at a higher price in addition to the contracted amount. For such a system, we propose an algorithm to derive the optimal contract terms. The result of computational experiments shows that the algorithm finds the global optimum solution in a resonable amount of time.

• Journal of Distribution Research
• /
• v.12 no.3
• /
• pp.23-48
• /
• 2007

Category management has been implemented to enhance competitiveness in the food distribution industry since 2000 in Korea. This study helps to understand why suppliers achieve better or worse performance than competitors in a category. The major objective of this article is to explore which category tactics are effective to have influence on category performance when suppliers as a category captain implement category management with variety enhancer categories like shampoo, toothpaste, and detergent. The Nielsen data were analyzed using regression and Chow test. The empirical results that were varied upon the store type and market position found out which specific actions on product assortments, pricing, shelving, and product replenishment can increase category sales. Specifically, in the case of market leader in large supermarket, the significant indicators of category sales with respect to category tactics are the out-of-stock rate, the variance across brand shares, the forward inventory, and the days supply of a product. However, in the case of follower in large supermarket, the significant indicators of category sales are the variance across brand shares, the forward inventory, and the days supply of a product. On the other hand, in the case of small supermarket, the significant factors on category sales for both market leader and follower are the retail distribution rate, the variance across brand shares, the forward inventory, and the days supply of a product category. In sum, regardless of the store type and market position, dominant brands in a category, the forward inventory, and short days supply of a product improved performance in all categories. Critical difference is that the out-of-stock rate acted as a key ingredient for the market leader between large and small supermarket and the retail distribution rate for the follower between large and small supermarket. This article presents some theoretical and managerial implications of the empirical results and finalizes the paper by addressing limitations and future research directions.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.3
• /
• pp.528-550
• /
• 2024

Warehousing demand prediction is an essential part of the supply chain, providing a fundamental basis for product manufacturing, replenishment, warehouse planning, etc. Existing forecasting methods cannot produce accurate forecasts since warehouse demand is affected by external factors such as holidays and seasons. Some aspects, such as consumer psychology and producer reputation, are challenging to quantify. The data can fluctuate widely or do not show obvious trend cycles. We introduce a new model for warehouse demand prediction called MAGRU, which stands for Multi-layer Attention with GRU. In the model, firstly, we perform the embedding operation on the input sequence to quantify the external influences; after that, we implement an encoder using GRU and the attention mechanism. The hidden state of GRU captures essential time series. In the decoder, we use attention again to select the key hidden states among all-time slices as the data to be fed into the GRU network. Experimental results show that this model has higher accuracy than RNN, LSTM, GRU, Prophet, XGboost, and DARNN. Using mean absolute error (MAE) and symmetric mean absolute percentage error(SMAPE) to evaluate the experimental results, MAGRU's MAE, RMSE, and SMAPE decreased by 7.65%, 10.03%, and 8.87% over GRU-LSTM, the current best model for solving this type of problem.