This paper chronicles the evolution of load-sharing parameter estimation methodologies, with a particular focus on the significant contributions made by Kim and Kvam (2004) and Park (2012). Kim and Kvam's pioneering work underscored the inherent challenges in deriving closed-form solutions for load-share parameters, which necessitated the use of sophisticated numerical optimization techniques. Park's research, on the other hand, provided groundbreaking closed-form solutions and extended the theoretical framework to accommodate more general distributions of component lifetimes. This was achieved by incorporating EM-type methods for maximum likelihood estimation, which represented a significant advancement in the field. Unlike previous efforts, this paper zeroes in on the specific characteristics and advantages of closed-form solutions for load-share parameters within reliability systems. Much like the basic Economic Order Quantity (EOQ) model enhances the understanding of real-life inventory systems dynamics, our analysis aims to thoroughly explore the conditions under which these closed-form solutions are valid. We investigate their stability, robustness, and applicability to various types of systems. Through this comprehensive study, we aspire to provide a deep understanding of the practical implications and potential benefits of these solutions. Building on previous advancements, our research further examines the robustness of these solutions in diverse reliability contexts, aiming to shed light on their practical relevance and utility in real-world applications.

This paper studied the problem of determining the optimal inventory level to meet the customer service target level in a situation where the customer demand for each branch of a nationwide retailer is uncertain. To this end, ISR (In-Stock Ratio) was defined as a key management indicator (KPI) that can be used from the perspective of a nationwide retailer such as Samsung, LG, or Apple that sells goods at branches nationwide. An optimization model was established to allow the retailer to minimize the total amount of inventory held at each branch while meeting the customer service target level defined as the average ISR. This paper proves that there is always an optimal solution in the model and expresses the optimal solution in a generalized form using the Karush-Kuhn-Tucker condition regardless of the shape of the probability distribution of customer demand. In addition, this paper studied the case where customer demand follows a specific probability distribution such as a normal distribution, and an expression representing the optimal inventory level for this case was derived.

In this paper, the single-period inventory problem, what is called newsboy problem, has been revisited with two different conditions, uncertain supply and risk-averseness. Eeckhoudt et al. [5] investigated the effect of risk-averseness of a newsboy on the optimal order quantity in a stochastic demand setting. In contrary to Eeckhoudt et al. [5] this paper investigates the effect of risk-averseness in a stochastic supply setting. The findings from this investigation say that if  represents the optimal order quantity without risk-averseness then the risk-averse optimal order quantity can be greater than  and can be less than  as well.

This paper makes a detailed comparison between two metrics designed for measuring customer’s satisfaction in the retail industry. The first metric, which is called the customer service level, has not been widely used due to the intrinsic requirement on the parameter assumption(s) of the demand distribution. Unlike the customer service level metric the in stock ratio metric does not require any requirements on the demand distribution. And the in stock ratio metric is also very easy to understand the meaning. To develop the detailed planning activities for business with the in stock ratio metric on hand one should collect some information as following : 1) POS (Point of sales) data, 2) Inventory Data 3) Inventory Trend.

Convergence encompasses all those activities required to create additional or innovational values, involving realigning or restructuring among several industries, technologies and services. Those convergence strategies that fail to create these values or innovation will have very small probability of success. In other words, the effective strategies for successful convergence should be able to bring out new values or experiences to customers via product/service innovation or sometimes operational excellences. In 2005, Samsung SDS developed a 6sigma methodology called, SCM 6Sigma, with the similar motivation. In this paper, SCM 6Sigma will be applied to develop effective and concrete convergence strategies.