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The purpose of this paper is to investigate the impact of backroom storage in supply chain replenishment decision parameters: the order quantity based on the well-established economic order quantity (EOQ) model.

The authors develop an EOQ-type model to investigate the operational cost impact of the order quantity with backroom storage. Because of the discrete and discontinuous nature of the problem, a modification of an existing algorithm is applied to obtain an optimal order quantity. Numerical experiments derived from a leading retailer in Thailand are used to study the cost impact of the backroom.

The paper shows that the backroom storage will significantly affect the decision regarding the order quantity. If its effect is ignored, the cost increase can be as high as 30 per cent. The costs and operations of additional shelf-refill trips from the backroom must be carefully analyzed and included in the decisions of replenishment operations.

The model is a simplified version of the actual replenishment process. Validation from a real-world setting should be used to confirm the results. There are many additional opportunities to further integrate other issues in this problem such as shelf space decisions or joint order quantity between vendors and retailers.

The insights gained from the model will help managers, both retailers and vendors or manufacturers, make better decisions with regard to the order quantity policy in the supply chain.

Problems with backroom storage have been qualitatively described in the literature in the past decade. This paper is an early attempt to develop a quantitative model to analytically study the cost impact of backroom on order quantity decisions.

The purpose of this paper is to investigate the impact of bias error resulted from using Monte Carlo simulation in evaluating the American-style option value.

The authors develop an analytical approximation formula to quantify the bias error under the assumption of conditionally independent and identically distributed samples of asset prices. The bias arises from the nested optimization and expectation calculation. The formula is then used to numerically quantify the bias and as an objective function for bias minimization for a given budget of samples.

Monte Carlo methods used in valuation of American-style options can results in bias error ranging from 2 to 10 per cent of the option value. The bias error can be reduced up to 50 per cent either by performing a better scheme for sampling or by efficiently allocating sample size.

The running time of the proposed procedure can be improved by using a specialized algorithm to solve the sample size allocation problem instead of using a commercially available subroutine MINOS. Other sampling procedures for bias reduction may be extended and applied to this multi-stage problem.

The methodology can help to more accurately approximate the option value.

The paper provides a method to develop an analytical approximation for bias error and provide a numerical experiment to test the methodology.

This study aims to investigate the impacts of geographical proximity on social capital development through the inter-relationship between three social capital dimensions (structural, relational and cognitive dimension) and the knowledge sharing between small- and medium-sized enterprises (SMEs). The authors empirically test a main hypothesis that the mechanism of social capital development that subsequently results in tacit knowledge sharing is different for SME buyer-supplier partners across their different geographical distances.

Multiple-group analysis in structural equation modeling (SEM) was conducted to test the research hypotheses using data collected from approximately 200 SMEs in Thailand’s food industry.

At a great geographical distance, the structural dimension impacts the cognitive dimension only in an indirect way through a relational dimension, which subsequently leads to knowledge sharing between SME buyer-supplier partners. At close geographical proximity, while the indirect impact of structural dimension on cognitive dimension through a relational dimension is still presented as it is in a great geographical distance, structural dimension has a positive and direct impact on the cognitive dimension as a complementary way to jointly reinforce knowledge sharing between SME partners. Among distant SME partners, the relational dimension shows a stronger impact on the cognitive dimension. In contrast, the direct influence of structural, relational and cognitive dimensions on knowledge sharing is identical, regardless of geographical distance.

The managers of SMEs can design their network-building approach in such a way that different location partners can enhance knowledge sharing. Policymakers could consider these results as a guideline when imposing SME development policies and geographical cluster policies in emerging economies.

This study provides empirical evidence that demonstrates how geographical proximity between SME partners in an emerging economy influences their social proximity through the lens of social capital development mechanism and thus leads to knowledge sharing between them.