Jun-Sik Kim

Bio: Jun-Sik Kim is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Optimal credit policy to increase supplier's profit with price dependent demand function = 가격과 수요의 관계를 고려한 최적 신용판매정책

Abstract: Abstract This paper deals with the problem of determining an optimal length of credit period from the perspective of supplier. We assume that a retailer jointly determines the unit retail price and order size to maximize profit when he/she purchases a product for which the supplier offers a trade credit. Two widely used demand functions are adopted for the study in which demands are decreasing functions of the retail price. A procedure is presented which shows how to achieve an optimal length of credit period for suppliers. The effects of credit period on the behaviour of retailers are also investigated using an example.

Study of a Fuzzy Production Inventory Model with Deterioration Under Marxian Principle

Abstract: The basic concern of the Capitalistic approach of manufacturing industry is to minimize costs and to maximize profit. Through uncontrolled rate of exploitation, the manufacturer creates large quanta of social surplus. This ultimately results in the economic injustice and inequality among social economic classes. This current article makes an initiation to develop an economic production quantity model with deterioration under Marxian approach of socio-political economy. Here, two Marxian economic production quantity models are developed aiming at the minimization of exploitation rate and reduction of social surplus. The notion of marginal profit is considered here to reduce the social surplus (Net profit) incurred in the production plant itself. Fuzzy system has also been studied to get the variability of the exploitation in the model. Sensitivity analysis, graphical illustrations are made to validate the model.

A credit period contract towards coordination of pharmaceutical supply chain:The case of inventory-level-dependent demand

Abstract: This paper considers a two stages pharmaceutical supply chain (PSC) consisting of a pharmaceutical manufacturer (pharma-manufacturer) that supplies one type of pharmaceutical product to a pharma-retailer. The customer demand rate for the pharmaceutical product is dependent on the pharma-retailer’s current-inventory level. The pharma-retailer determines the order quantity ( ) value as decision variable and the pharma-manufacturer uses EPQ system that usually the economic order quantity value of retailer is less than the optimal production quantity value of manufacturer. First, the problem is investigated in decentralized decision-making and accordingly, a coordination incentive based on credit payment period policy to coordinate the mentioned PSC in two structures is proposed: independent optimization and centralized model with credit policy. Moreover, numerical examples and sensitivity analysis are considered to illustrate the results of the presented coordination structures toward decentralized model.

A Learning-Based Optimal Decision Scenario for an Inventory Problem under a Price Discount Policy

Abstract: This paper aims to design an inventory model for a retail enterprise with a profit maximization objective using the opportunity for a price discount facility given by a supplier. In the profit maximization objective, the demand should be increased. The demand can be boosted by lowering the selling price. However, lowering the selling price may not always give the best profit. Impreciseness plays a vital role during such decision-making. The decision-making and managerial activities may be imprecise due to some decision variables. For instance, the selling price may not be deterministic. A vague selling price will make the retail decision imprecise. To achieve this goal, the retailer must minimize impreciseness as much as possible. Learning through repetition may be a practical approach in this regard. This paper investigates the impact of fuzzy impreciseness and triangular dense fuzzy setting, which dilutes the impreciseness involved with managerial decisions. Based on the mentioned objectives, this article considers an inventory model with price-dependent demand and time and a purchasing cost-dependent holding cost in an uncertain phenomenon. This paper incorporates the all-units discount policy into the unit purchase cost according to the order quantity. In this paper, the sense of learning is accounted for using a dense fuzzy set by considering the unit selling price as a triangular dense fuzzy number to lessen the impreciseness in the model. Four fuzzy optimization methods are used to obtain the usual extreme profit when searching for the optimal purchasing cost and sale price. It is perceived from the numerical outcomes that a dense fuzzy environment contributes the best results compared to a crisp and general fuzzy environment. Managerial insights from this paper are that learning from repeated dealing activities contributes to enhancing profitability by diluting impreciseness about the selling price and demand rate and taking the best opportunity from the discount facility while purchasing.

An Inventory Model with Price and Credit Installments-Dependent Demand

Abstract: Abstract Financial capability is one of the primary drivers for buyers to make purchases. Therefore, sellers must set an optimum selling price and consider trade credit facilities to attract more demand. This paper proposes an inventory decision model in which customer demand depends on the price and number of credit installments to serve low-abled buyers. This study has developed a demand function with a positive impact on installment policies and the effect of the selling price. Two models have been formulated to optimize the selling price and positive stock time, m total profit, with and without installment policies. Then, numerical examples and sensitivity analysis illustrate the proposed model for different cases. The study has found that the selling price and positive stock time can be optimized. Profits can be higher in the case of an installment facility than in the case without an installment facility. It shows positive responses from the buyer to the installment policy.

A Supply Chain Model with Learning Effect and Credit Financing Policy for Imperfect Quality Items under Fuzzy Environment

Abstract: In this paper, the seller offers a credit period to his buyer for more sales and the buyer accepts the seller’s policy to gain more profit, and it is assumed that the seller has defective and non-defective items. When the seller provides lots for sale to his buyer then, the buyer separates the whole lots with the help of inspection process into defective and perfect quality items. Further, in this scenario, the percentage of defective items present in the lot follows the S-shape learning curve and it is also considered that the demand rate is imprecise in nature. Here, the demand rate assumes a triangular fuzzy number due to the imprecise nature and it is the model assumption. Based on this assumption, we developed an inventory model with the effect of learning and trade credit strategy under a fuzzy environment for the buyer. The buyer’s total profit has been optimized concerning the order quantity in the fuzzy environment where order quantity has been assumed as a decision variable. The results of this model were verified with the help of numerical examples and sensitivity analysis. We compared the buyer’s total profit in a crisp and fuzzy environment and the buyer gained more profit in a fuzzy environment compared to the crisp environment. Moreover, we compared the results with and without the effect of learning and trade credit on the buyer’s ordering policy and obtained a positive effect on the ordering policy in the numerical section. We determined positive results from the sensitivity analysis, which proved that the trade credit policy will be beneficial for both partners of the supply chain.