The ABC-XYZ analysis modified for data with outliers

Zenkova, Zhanna N.; Кабанова, Т. В.

doi:10.1109/gol.2018.8378073

Cited by 14 publications

(9 citation statements)

References 2 publications

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“…Note that formula (3) can be applicable only for a sufficiently stable demand for a product [27], i.e., actually for goods from group X according to the classification by the method of XYZ analysis [3,4,6]. In other words, the formula is applicable for such goods for which the coefficient of variation (CV) of a number of demand (sales) values does not exceed 10%.…”

Section: Economic Order Quantity With Additional Information About Thmentioning

confidence: 99%

“…In other words, the formula is applicable for such goods for which the coefficient of variation (CV) of a number of demand (sales) values does not exceed 10%. In this case, CV is expressed by the formula 4In the formula (4) 5represents the average level of demand (sales), (6) is the sample variance [28], X 1 , X 2 , ..., X N are sales (demand) volumes for a product during the year, N -the number of considered periods during the year.…”

Section: Economic Order Quantity With Additional Information About Thmentioning

confidence: 99%

“…Note that the equality of the levels of deficit risks R D and surplus R S is due to the symmetry of the standard normal distribution: the probability of "hitting" to the right of the upper boundary of the confidence interval is equal to "hitting" to the left of the lower boundary. Recall that the consideration here only of the groups AX, BX and CX, obtained according to the classification by the method of joint ABC-XYZ analysis [4,6], is due to the fact that the calculation of the economic order quantity EOQ is permissible only for goods with stable demand from group X.…”

Section: Lower Boundmentioning

confidence: 99%

“…Note that in this case trade is also considered as production. Therefore, in modern logistics, in order to search for the best estimate of potential demand, all kinds of mathematical models of various levels of complexity [5][6][7][8] are used, including those that involve additional information to reduce the forecasting error and improve the quality of assessment [9][10][11] and product classification [6,12]. This paper proposes a new, more accurate method for calculating the economic order size and its confidence intervals using additional information about the quantile of the sales volume cumulative distribution function (CDF).…”

mentioning

confidence: 99%

“…Restrictive parameters are also taken into account, for example, the maximum volume of the warehouse and the observed frequency of cases of its overflow. Usually such knowledge is ignored, although it can be used to advantage, since it has long been known that additional information helps to improve the quality of statistical procedures [6][7][8][9][10][11][12][13]. It is also worth noting that the use of information about the quantile has already been considered in a number of works [13][14][15][16][17][18], using the method of projecting the estimate of the distribution function into an a priori class.…”

mentioning

confidence: 99%

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The economic order quantity taking into account additional information about the known quantile of the cumulative distribution function of the product’s sales volume

Zenkova¹,

Musoni²

2020

Bus. Inform.

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In modern logistics and supply chain management, the task of inventory management is paramount. The total costs of the enterprise and consequently, its profit, directly depend on the accuracy of calculating the volumes and terms of orders. In this work, the problem of increasing the accuracy of calculating the economic order quantity for a product was solved by involving additional information about the known quantile of a given level of the distribution function of the volume of product's demand. The quantile information was used to recalculate the annual demand for the product, based on a modified estimator of the sales expectation for the period. The modified estimator is asymptotically unbiased, normal, and more accurate than the traditional sample mean in the sense of mean squared error. New formulas for calculating the economic order quantity and its confidence interval were presented and tested on real data on the monthly sales volumes of goods of a large retail store network over two years. It is shown that the classic way of mean calculation led to an underestimation of the volume of the economic order quantity, which in turn increased the risk of a shortage, and hence a drop in the quality of logistics services. The new calculation method also showed that the period between orders should be one day shorter. The work is practically significant; according to its results, recommendations are given to the enterprise.

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Section: Economic Order Quantity With Additional Information About Thmentioning

confidence: 99%

Section: Economic Order Quantity With Additional Information About Thmentioning

confidence: 99%

Section: Lower Boundmentioning

confidence: 99%

mentioning

confidence: 99%