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The purpose of the study is to evaluate whether the selection of accounting method used to value inventory increases or decreases the probability of a retail firm's ability to remain in existence.

This study employs a binary logistic regression model to predict group membership and the probability of failure. The study utilizes an unbalanced sample of US publicly traded failed and functioning retail firms over a ten-year period.

The results clearly support the conclusion that there is a difference in the probability of retail firm failure with respect to the accounting method used to value inventory. Merchants using a cost-based valuation method were 2.3 times more likely to fail than firms using a price-based method. The results also affirm existing bankruptcy literature by finding that profitability, liquidity, leverage, capital investment and cash flow are factors in retail failures.

The results suggest that traditional merchants cannot simply blame e-commerce or shifts in demographics for the retail Apocalypse; good management and proper valuation of stock still matter.

This study is the first to look at firm failure in the retail sector after the great recession of 2008, in an era known as the “retail Apocalypse.” In addition, this study differs from other firm failure literature by incorporating cost- and price-based inventory valuation methods as a variable in firm failure.

We examine the sample self-selection and the use of LIFO or FIFO inventory method. For this purpose, we apply the Heckman-Lee’s two-stage regression to the 1973–1981 data, a period of relatively high inflation, during which the incentive to adopt the LIFO inventory valuation method was most pronounced. The predicted coefficients based on the reduced-form probit (inventory choice model) and the tax functions are used to derive predicted tax savings in the structured probit. Specifically, the predicted tax savings are computed by comparing the actual LIFO (FIFO) taxes vs. predicted FIFO (LIFO) taxes. Thereafter, we estimate the dollar amount of tax savings under different regimes. The two-stage approach enables us to address not only the managerial choice of the inventory method but also the tax effect of this decision. Previous studies do not jointly consider the inventory choice decision and the tax effect of that decision. Hence, the approach we use is a contribution to the literature. Our results show that self-selection bias is present in our sample of LIFO and FIFO firms and correcting for the self-selection bias shows that the LIFO firms, on average, had $282 million of tax savings, which explains why a large number of firms adopted the LIFO inventory method during the seventies.

The study described here used data from a retailer of soft goods and housewares to evaluate various inventory‐grouping methods and to determine an optimal method considering inventory, transportation and consolidation costs. The specific objectives of this article are:

Traditional inventory replenishment decisions in distribution systems have been reactive, but the availability of information throughout the distribution system means that other methods should be considered.

An objective, two‐tier quantitative model has been developed for assessing the presence of total quality in an organisation, and for determining the effectiveness of a company’s total quality management initiatives. This was based on the hypothesis that, since the necessary and sufficient conditions for just‐in‐time inventory management and total quality management are almost identical, inventory performance should be a good indicator of quality achievement. A stand‐alone inventory rating method was used initially, and was later combined with return on capital and employee value indicators to create a model for more detailed evaluation. The two methods were tested on 48 companies. It was found that the inventory performance rating is a reliable indicator of a total quality organisation, and that the multifactor method is useful in identifying areas of success or failure. Both indicators predicted changes in overall business performance.

Quantitative forecasting techniques see their greatest application as part of production and inventory systems. Perhaps unfortunately, the problem has been left to systems analysts while the professional societies have contented themselves with exhortations to improve forecasting, neglecting recent developments from forecasting research. However, improvements in accuracy have a direct and often substantial financial impact. This article shows how the production and inventory control forecasting problem differs from other forecasting applications in its use of information and goes on to consider the characteristics of inventory type data. No single forecasting method is suited to all data series and the article then discusses how recent developments in forecasting methodology can improve accuracy. Considers approaches to extending the database beyond just the time‐series history of the data series. Concludes with a discussion of an “ideal” forecasting system and how far removed many popular programs used in production and inventory control are from this ideal.

This project attempts to present a space component inventory classification system for space inventory replenishment and management. The authors propose to adopt a classification system that can incorporate all the different variables in a multi-criteria configuration. Fuzzy logic is applied as an effective way for formulating classification problems in space inventory replenishment.

A fuzzy-based approach with ABC classification is proposed to incorporate all the different variables in a multi-criteria configuration. Fuzzy logic is applied as an effective way for formulating classification problems in space inventory replenishment of the soil preparation system (SOPSYS) which is used in grinding and sifting Phobos rocks to sub-millimeter size in the Phobos-Grunt space mission. An information system was developed using the existing platform and was used to support the key aspects in performing inventory classification and purchasing optimization.

The proposed classification system was found to be able to classify the inventory and optimize the purchasing decision efficiency. Based on the information provided from the system, implementation plans for the SOPSYS project and related space projects can be proposed.

The paper addresses one of the main difficulties in handling qualitative or quantitative classification criteria. The model can be implemented using mathematical calculation tools and integrated into the existing inventory management system. The proposed model has important implications in optimizing the purchasing decisions to shorten the research and development of other space instruments in space missions.

Inventory management in the manufacture of space instruments is one of the major problems due to the complexity of the manufacturing process and the large variety of items. The classification system can optimize purchasing decision-making in the inventory management process. It is also designed to be flexible and can be implemented for the manufacture of other space mission instruments.

An uncertain product demand in online retailing leads to loss of opportunity cost and customer dissatisfaction due to instances of product unavailability. On the other hand, when e-retailers store excessive inventory of durable goods to fulfill uncertain demand, it results in significant inventory holding and obsolescence cost. In view of such overstocking/understocking situations, this study attempts to mitigate online demand risk by exploring novel e-retailing approaches considering the trade-offs between opportunity cost/customer dissatisfaction and inventory holding/obsolescence cost.

Four e-retailing approaches are introduced to mitigate uncertain demand and minimize the economic losses to e-retailer. Using three months of purchased history data of online consumers for durable goods, four proposed approaches are tested by developing product attribute based algorithm to calculate the economic loss to the e-retailer.

Mixed e-retailing method of selling unavailable products from collaborative e-retail partner and alternative product's suggestion from own e-retailing method is found to be best for mitigating uncertain demand as well as limiting customer dissatisfaction.

Limited numbers of risk factor have been considered in this study. In the future, others risk factors like fraudulent order of high demand products, long delivery time window risk, damage and return risk of popular products can be incorporated and handled to reduce the economic loss.

The analysis can minimize the economic losses to an e-retailer and also can maximize the profit of collaborative e-retailing partner.

The study proposes a retailer to retailer collaboration approach without sharing the forecasted products' demand information.

The inventory management system of a discount retail store was examined. A just‐in‐time inventory management model and a quantity discount model were used to determine the appropriateness of each model for the retail outlet. Based on the calculations performed, it was determined that utilizing a retail just‐in‐time (JIT) policy is unrealistic. Customer demands constantly change, and shortages due to stock‐outs can cause huge losses in profits, especially when customers are lost to competitors. Additionally, the quantity discount model provides the lowest total cost for a retail outlet. Not only are the prices cheaper when inventory is bought in large quantities, but shortages or stock‐outs are rare. The optimal solution for a retail store is implementing the quantity discount method.

To investigate the application of multi‐attribute utility theory (MAUT) to aid in the decision‐making process when performing benchmarking gap analysis.

MAUT is selected to identify the overall best‐in‐class (BIC) performer for performance metrics involving inventory record accuracy within a public sector warehouse. A traditional benchmarking analysis is conducted on 14 industry warehouse participants to determine industry best practices for the four critical warehouse metrics of picking and inventory accuracy, storage speed, and order cycle time. Inventory and picking tolerances are also investigated in the study. A gap analysis is performed on the critical metrics and the absolute BIC is used to measure performance gaps for each metric. The gap analysis results are then compared to the MAUT utility values, and a sensitivity analysis is performed to compare the two methods.

The results indicate that an approach based on MAUT is advantageous in its ability to consider all critical metrics in a benchmarking study. The MAUT approach allows the assignment of priorities and analyzes the subjectivity for these decisions, and provides a framework to identify one performer as best across all critical metrics.

This research study uses the additive utility theory (AUT) which is only one of multiple decision theory techniques.

A new approach to determine the best performer in a benchmarking study.

Traditional benchmarking studies use gap analysis to identify a BIC performer over a single critical metric. This research integrates a mathematically driven decision analysis technique to determine the overall best performer over multiple critical metrics.