تاثیر فرکانس ترازی فیزیکی و سیستم اطلاعات موجودی در خارج از سهام: یک مطالعه شبیه سازی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|10089||2012||11 صفحه PDF||سفارش دهید||11076 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, , Volume 136, Issue 1, March 2012, Pages 45-55
Inaccuracy in the information system inventory as compared to the physical inventory may lead to out of stocks. Inaccuracy may occur for many reasons, a principal one being random losses such as theft. One way to reduce this inaccuracy is to adjust the inventory information in the systems at some regular frequency. Such alignments are quite expensive in practice. Thus how often to align the two inventories is the focus of this research. A simulation model is employed to investigate the effect of such loss defined by the stock loss parameter (λ) and the frequent alignment of physical and information system inventories on the stockout (Sout) and average inventory (I). A term to be called the effective value of stock loss parameter is introduced to signify the effect of frequency of alignment (f) on Sout. The results derived in this study provide a powerful tool in the hands of an inventory manager. It has been noted that, so far as stockout is concerned, by selecting a moderate value of alignment frequency (f), the effective value of stock loss parameter (λe) can be reduced to∼ λ/f. The accuracy of Sout and I values across a number of runs in the simulation studies, sensitivity of Sout and I on various parameters and the nature of stochastic demand distribution, and application of these results with or without deployment of RFID to reduce the loss due to stockout are also discussed. The results, verified under various scenarios, indicate that there is a significant reduction in stockout loss when the alignment is done monthly vs. annually, but it does not add much value beyond a monthly check.
One of the important roles of the management in a retail industry is to optimize the stockout and inventory levels. The significance of the loss due to stockout can be perceived from the fact that the average out of stocks (OOS) rate is about 8% (Corsten and Gruen, 2003). The OOS situation not only puts the retailers at a major loss but the suppliers also suffer its heavy impact. After not finding a desired item in stock, 11% of the consumers do not purchase the item, 16% delay the purchase, 21% substitute with the same brand, 22% substitute with different brand, and 31% buy at another store (Corsten and Gruen, 2003). It is important to note that among various causes of OOS, store forecasting and store ordering amounts to 51% of the total OOS (Corsten and Gruen, 2003). Store ordering and forecasting in turn very much depend on the accuracy of the inventory information. The Auto-ID center at MIT finds that on average the inventory record for one-out-of-four stock keeping units (SKUs) in the store does not agree to the actual stock by six or more items (Kang and Gershwin, 2005). Qualitatively, one can very well perceive that due to inaccuracy in the inventory record, the items cannot be appropriately ordered in a timely fashion. If the physical (PH) inventory is lower than the information system (IS) inventory then there would be a delay in the ordering that would result into the OOS. One of the major sources of a lower value of PH inventory is the stock loss or shrinkage due to theft by shoppers, vendors, and employees. A study conducted by ECR Europe reveals that the shrinkage in a fast moving consumer goods sector is 2.41% of the whole turnover value of the sector, and theft accounts for two thirds of shrinkage (28% internal thefts and 38% external thefts) (Rekik et al., 2009). Kang and Gershwin (2005) consider the inventory inaccuracy due to the stock loss not reflected in the IS inventory. In their simulation study, they compute explicit values of stockout and average inventory for different ordering parameters, demand variables, and lead time. For demand, they take a combination of demand for purchase having normal distribution and a demand for loss described by a Poisson distribution. They showed that even a small amount of recurring stock loss not reflected in the IS inventory can disrupt the replenishment process such that revenue loss due to stockout could exceed the stock losses themselves. For a typical case, they demonstrate that a stockout value of ∼17% decreases to less than 1% when Auto-ID is used to align IS inventory with the PH inventory at the end of each period. They also show that through RFID technology one can achieve the best stockout-inventory compromise (the lowest inventory for any given stockout), and the benefit of inventory accuracy provided by RFID becomes greater as the desired level of stockout becomes smaller. We come across a number of studies dealing with RFID technology (for reviews, e.g., see Alani et al., 2009, Angeles, 2005, Nambiar, 2009 and Weier, 2009). Wal-Mart jump started the RFID by a June 2003 mandate that its top 100 suppliers attach tags on pallets and cases shipped to its stores in the Dallas, Texas region. Despite the reluctance of a large number of retailers to adopt the RFID technology due to initial investment in installing RFID readers and associated software and the recurring cost of RFID tags and other associated expenditures, the RFID technology is being hailed as one of greatest contributions of the century (Mehrjerdi., 2009). To derive benefits, studies related with cost effectiveness (e.g., Ustundag and Tanyas, 2009, Miragliotta et al., 2009, de Kok et al., 2008 and Bottani and Rizzi, 2008), and new innovative ideas of incorporating the technology (e.g., Heim et al., 2009, Delen et al., 2007, Amini et al., 2007, Hardgrave et al., 2006 and Chongwatpol and Sharda, 2009) would prove valuable. The search of innovative ways of reducing the lost sales due to OOS with the help of RFID is one of the areas of interest. We not only need innovative ways of employing RFID but we also need to optimize the manner of using the data for operations. For example, in exploring the possibility of reducing the OOS situation by aligning the IS inventory with the PH inventory we need to optimize the frequency of this alignment as each alignment costs significant human time even with RFID, though it is a fraction of the time needed manually (Hardgrave, 2009 and Hardgrave et al., 2009). As mentioned earlier, Kang and Gershwin (2005) have shown that the alignment of PH and IS inventories at the end of each period leads to a quantum jump in the reduction of the OOS. They also reported the amount of reduction in the stockout when such an alignment is made twice within a total duration of 365 periods. There is a need to extend their results over a wider range of frequencies and simulation parameters to study the impact of frequency of alignment on OOS. The purpose of this research is to advance our understanding regarding the optimization of the frequency of counting the physical inventory by investigating the relationship between the stockout (Sout) and the frequency (f) of alignment of PH and IS inventories under various conditions of demand, ordering, and shrinkage, when the inventory record inaccuracy is due to stock loss only. As the knowledge of average inventory (I) is valuable in making any decision for the optimization, we shall also study the variation in I with f. The possibility of finding an optimized value of f would be explored by some numerical examples. For many cases, we would see that there is a significant reduction of stockout when the alignment is done monthly vs. annually, but the additional advantage of going from monthly to daily is very small. Other related aspects such as sensitivity of Sout and I on various parameters, the effective value of the stock loss parameter as a function of frequency (f), and the accuracy of computed values of Sout and I in the simulation studies are also studied. We describe the interesting new result showing a relationship between an effective stock loss and the frequency of alignment of physical and system inventory. As far as stockout is concerned, by selecting a moderate value of alignment frequency (f), the effective value of stock loss parameter (λe) can be reduced to∼ λ/f. We first review some of the literature in the next section. In Section 3, the simulation model developed for this study is described. The results are then presented and discussed in Section 4. Finally, the conclusions are described in the last section.
نتیجه گیری انگلیسی
Inaccuracy in the IS inventory due to a recurring demand for loss such as theft leads to a high value of stockout. To minimize the stockout, a simulation model has been explored to investigate the effect of frequent alignment of PH and IS inventories on the stockout (Sout) and average inventory (I). The normal distribution for the demand for purchase during each period, and the Poisson distribution for the demand for loss have been considered. With or without the help of RFID, the IS inventory is aligned with the PH inventory after every Nalign periods, i.e., with frequency f given by Eq. (1). Thus f=1 corresponds to a situation where no attempt has been made to correct the IS inventory during the total operation time. Dependence of Sout on f shows a rapid decrease in Sout with f. In a typical case, Sout drops from 33.2% for f=1 to 0.23% for f=12. The average inventory (I) is found to increase with the decrease in Sout. A term to be called as the effective value of stock loss parameter, λe(f), has been defined to signify the effect of f on Sout. Under the condition given by Eq. (16), the effective value of stock loss parameter λe(f) is found to satisfy Eq. (14). To realize the significance of Eq. (14), we can take one numerical example: according to Eq. (14) the stockout value Sout in a retail store for which f=12 and shrinkage rate=3% would be equal to that for which f=1 and shrinkage rate is 0.25% . The validity of Eq. (7b) and (14) has been tested for a large number of cases over a wide range of parameters. The rationale behind the validity of this equation has also been discussed. Limitations of Eq. (14), accuracy of Sout and I values on the number of runs nrun, and sensitivity of Sout and I on various parameters and the nature of stochastic demand distribution have also been explored in this investigation. With the deployment of RFID, it becomes easy to align PH and IS inventories. However, the cost of such alignment increases with the increase in f. Therefore, a knowledge of optimum value of f becomes helpful in saving the cost. On the one hand by using RFID, we can easily increase f to achieve a reduction in the value of stockout, and on the other hand we would like to optimize the value of f to control the cost of frequent aligning the PH and IS inventories. In this regard, Eq. (14) and the related discussion presented in this studies would be useful to an inventory manager.