ThroughMark A. Barratt, Elliot Rabinovich und Annibal Camara Sodero · March 1, 2010
¹² In the retail sector, mixed retailers – i.e. H. Retailers who have multiple sales channels – Today, in the face of still stagnant sales, manage these inventories across multiple channels, both traditional retail (i.e. mortar) and direct (internet-based). A typical approach has been to offer complex product ranges to meet local consumer demand while trying to manage available stocks.
The advent of the internet as a supportive tool for online sales has only exacerbated the challenge. To cite just one example, buying behavior has changed dramatically as consumers use the direct channel to check product availability and assortment – for example, for subsequent in-store visits that lead to purchases through the retail channel. This puts a special emphasis on mixed retailers keeping accurate records of their inventory and highlights the dilemma of determining which range of products to make available in specific channels. A related, though less well-understood, challenge involves dealing with inventory data inaccuracy (IRI). Addressing – or attempting to mitigate – IRI is becoming increasingly important as multi-channel retailers rush to increase inventory availability through both their traditional retail channels and their newer, more direct channels.
This raises the question of whether blended retailers should allocate inventory separately to serve individual channels, or centralize inventory to meet aggregate demand regardless of channel. A related question is what are the IRI implications of each course of action? Blended retailers typically segregate inventory across channels to maximize product availability to consumers. By allocating inventory solely in their brick-and-mortar channel, they ensure internet sales don't impact in-store product availability. However, this strategy prevents retailers from realizing inventory efficiencies that can result from centralizing inventory across their channels. Such efficiencies have tremendous potential to reduce the cost of carrying safety stock, as well as the costs required to monitor and control inventory levels and their accuracy.
Retailers have also used technology to automate various critical systems ranging from ordering to forecasting to planning and replenishment. All of these tools use "system" inventory records to determine sets of parameters that optimize inventory control and influence both operational and financial decisions.³² Another connection to the technology is radio frequency identification (RFID) technology, which is becoming increasingly popular in retail . However, RFID remains an expensive option and is not yet feasible for all companies.³
In short, the ability to accurately determine how much inventory is on hand has become even more important than before. Without such accuracy, companies oscillate between the opposing risks of holding more inventory than necessary, or alternatively running out of inventory and being unable to meet customer demand. This has made IRI a relevant - albeit often underestimated - topic for many companies across industries from retail to defense.
Deficiencies in Cycle Counting
In their attempts to address and control the opposing risks of overstock and out-of-stock, most retailers and manufacturers employ some form of cycle counting, generally either by stock keeping unit (SKU) or by location. In the case of multi-channel retailers, the company's goal is to ensure an acceptable level of accuracy in their inventory (see Figure 1). When done properly, cycle counting is accompanied by an ongoing root cause analysis for sources of failure, leading to a continuous improvement approach that ultimately eliminates or significantly reduces the frequency of such cycle counting.
However, cycle counting is a static, predominantly financially oriented and periodically repeating measurement approach based on an ABC product classification.
Despite the fact that cycle counting has been in existence for over 30 years, there have not been any major improvements or breakthroughs in mitigating the inaccuracy of inventory records associated with this technique. Our main concern with cycle counting is that it overlooks the issue of physical product availability between counts; that is, what is happening on a dynamic basis in terms of product availability. Between “cycle counts”—whose timing varies on average from one month to six months, depending on the approach chosen—organizations strive to maintain availability without holding excessive inventories.
The cycle counting approach is based on accuracy measurements, which can be significantly outdated and contain serious errors. This could unnecessarily increase inventory or expose the business to stock-outs. Such a lack of transparency during the count creates potential bottlenecks in product availability, not to mention the adverse impact on the company's forecasting, planning, and reordering processes.
The reality is that companies don't understand the true impact of errors on their inventory records; As a result, they simply respond by holding more inventory at a higher cost than may be required.
Inaccuracies in inventory records abound
With companies still relying primarily on cycle counting to keep accurate records, both practitioners and academics have given special attention to IRI in the retail sector - where a lack of inventory is a major concern. In particular, research has focused on the causes and consequences of IRI at the store level. To illustrate the magnitude of the problem, a study by a major retailer found that 65 percent of 375,000 stock keeping units (SKU) records were incorrect.
Additionally, recent anecdotal evidence suggests that IRI is still a major concern in the distribution center. For example, at a large electronics retailer, researchers found that before a new retail store opened, 25 percent of SKUs were already inaccurate, suggesting the likely source of these errors was the retailer's distribution center.
Inventory record inaccuracy remains a pervasive and largely unexplored problem across all industries, spanning various tiers of the supply chain. Not surprisingly, the consequences of data inaccuracy are poorly understood not only in physical retail stores, but also in distribution centers and beyond. IRI can produce different results as summarized in Figure 2. When the System Inventory Record (SIR) is higher than the actual physical inventory (i.e. a positive balance), this leads to a situation known as a "freeze".
If this situation is not recognized and corrected, depending on the size of the error relative to the reorder point maintained by a company, the SKU will be “frozen” once their stock is depleted, since the SIR will have a positive balance of items in inventory.
In a DC setting, this situation persists only until an order for the depleted inventory arrives and a zero physical inventory status is detected. In a brick-and-mortar or online retail environment, this situation becomes even more damaging. Since there is no physical inventory, customers cannot purchase the item. The fact that no customers are purchasing the item creates a potentially dangerous scenario. If there are no sales for the product, the product's forecast will be adjusted downwards over time before eventually being removed from the list.
When the SIR is lower than actual physical inventory - indicating a negative balance - this is referred to as "bloat". In such cases, the SIR will decrease until the reorder point is reached to replenish inventory. At this point, an automatic replenishment order is generated ahead of time. At first glance, this is a more desirable result than freezing. The reason: Most companies would prefer IRI to manifest itself as excess inventory rather than the risk of being out of stock. However, in a direct channel, this may be reflected in lower inventory availability that does not reflect actual inventory levels in the warehouse.
The pilot study
To understand IRI as a supply chain phenomenon, we first had to conduct an exploratory pilot study. Specifically, we examined inventory policies and practices in the DC of a national pet retailer (name withheld at company's request) to (1) better understand issues associated with sustained count periods and (2) gain insight into daily daily variability in inventory records.
By counting inventory for only seven consecutive days, the pilot study found significant evidence of very dramatic variability between overruns and shortages in a sample of 30 SKUs (see Figure 3). This sample consisted of 10 SKUs selected from each of the three main types of storage locations – fast moving, bulk and modular picking.
On the one hand, the fast-moving products like canned cat and dog food did not suffer from IRI. On the other hand, slow-moving, bulky and modular items showed considerable fluctuations (between excess and shortage) in the counting period. For the 20 SKUs in the bulk and module categories, we have also seen a clear trend that there is actually more inventory than the system inventory record indicates. If this were to continue year-round, the company would be holding more inventory than necessary and paying inventory costs in excess of what is necessary.
Of potentially more concern than excess inventory is the variance in tracking accuracy for some of the individual SKUs. Some have records that "jump" from accuracy to excess inventory and back again; other SKUs go from excess stock to shortage and back again. No transactions (such as goods receipts, putaways or picking) explained these variations, although we corrected any process delays related to how quickly products received from suppliers, for example, were credited to the system inventory record. This leads us to consider the system as inherently unstable.
Main Research: Multichannel DC
In order to examine the systemic conditions underlying IRI in a multichannel distribution center, we needed to find a retailer serving such channels from the same facility. Next, we studied an apparel retailer with annual sales of approximately $200 million in 2008. This retailer operates a national distribution center that serves both a traditional (brick-and-mortar) retail channel and a direct (web-based) channel. From a total of approximately 12,000 SKUs, we isolated those that were common to both channels. We ranked these popular SKUs by sales volume (in units) in 2008 within each product category to identify fast, medium and slow moving items. We then selected the two fastest selling and the two slowest selling products. That left us with 27 SKUs, including a selection of the three most popular product colors and sizes.
To assess IRI, we tracked physical inventory for the 27 SKUs in the distribution center for both retail and direct channels. At the same time, we compared this information with the data in the retailer's SIR. In tracking the retailer's physical inventory, we counted the number of items in stock for each channel each day for a period of ten consecutive business days. (We did not count inventory over the weekend as the DC only operated Monday through Friday.) The ten business days correspond to two calendar weeks in September 2008. These two weeks were chosen because we wanted to study IRI conditions without disrupting the Seasonality of demand for the retailer's products.
For each selected SKU, the SIR balance and SKU locations for both channels were downloaded from the DC's Warehouse Management System (WMS) on each counting day. In addition, additional data on product additions, daily orders, returns and any (revision) adjustments were collected daily for both channels. Data on pricing, product popularity in the marketplace, and inventory verification policies was collected from retailer records during the period that we collected SKU inventory data. It's important to note that for all SKUs in our study, the retailer followed guidelines for continuous review. These policies were based on a min-max approach to reordering and replenishing inventory of each SKU. When inventory reached a predetermined minimum level, the retailer rearranged the amount of inventory required to bring the level back up to a preset maximum.
The results show the dynamic IRI variability
The results of the research show significant dynamic IRI variability for both retail and direct channels. In particular, SKU records move from accuracy to positive and negative deviations from actual physical counts (i.e. freeze and bloat) - all within the 10-day counting period. The research shows some counterintuitive results compared to previous studies. These results support our contention that IRI poses different challenges depending on the organization's channel structure and position in the supply chain, supporting the contention that IRI should be studied in settings other than retail locations.
It is important to emphasize that the errors found in both channels could not be explained by transactions (receipt, putaway or picking). Again, this was true even when we corrected for process delays such as For example, how quickly products received from suppliers were credited to the system inventory record or how quickly returns were credited to the system inventory record.
We expected less inaccuracy at the higher end of the SKU price range, as we believed that pickers should be more careful to be accurate for high-priced items. Instead, as shown in Figure 4, we found most inaccuracies at both the high and low end of the price range in the direct channel and more inaccuracies at the mid and low end of the price range in the retail channel.
A program that effectively tackles IRI should aim to minimize inaccuracy on high-value items, where higher profit margins typically reside. One possible approach is to use incentives to draw managers' and employees' attention to operational activities related to these high-value items. However, such incentives must be balanced against the multitude of competing goals related to maintaining and increasing sales of these high value items and inventory management.
For high value (margin) items, it may be better to hold higher inventory than to focus too much on reducing the IRI on those particular SKUs.
Shrinkage - especially theft - can also be a significant factor for higher-priced items, which in turn leads to higher IRI values. Most retailers have focused on avoiding losses in their retail stores. You need to pay equal attention to direct channel shrinkage.
However, the overriding concern here is that the IRI errors occur at both the high and low end of the products in the direct channel. This suggests that there are some other potentially counter-intuitive issues at play. This is something that will be explored in the next stages of this ongoing research.
We would expect that the more transactions associated with a product, the more opportunities there are for inventory records to become inaccurate.
However, as we can see in Figure 5, there is significant inaccuracy regardless of the number of transactions in the direct channel. Unlike the previous pilot study we conducted, we saw many instances of IRI, particularly in the slower moving items. This was not an encouraging finding. And while the survey didn't show any obvious patterns across the 27 SKUs, we found slightly higher imprecision in the direct channel. This can be explained by the higher number of transactions in this channel compared to the retail channel. Also of note in Exhibit 5 are the many instances where SKU has recorded experienced conditions of Accuracy, Deficiency and Exceed - all within ten days.
Inventory review frequency was found to be negatively correlated with inventory record inaccuracy. The reason: Variances are easier to spot when repeat orders are placed or stocks are physically replenished. Inventory review policies where reorder points are close to maximum inventory levels (i.e., a small min/max gap) require frequent reviews. This is because inventory depletion will more often exceed the inventory reporting threshold as the difference between maximum carried inventory and inventory reorder points decreases.
Increasing the distance between the stock reporting point and the upper stock level reduces this frequency. Accordingly, we would expect that reducing the checking frequency would increase both the magnitude of the IRI and the number of inaccuracies in the timekeeping for the SKU. Accordingly, Figure 6 shows that an increasingly narrow min/max gap decreases the size of the IRI in the direct channel and thus increases the difference in IRI that exists between channels in a blended retailer.
Overall, our research shows that SKUs are far more likely to be inaccurate in the direct (internet-based) channel than in the traditional channel. Specifically, the frequency of discrepancies between SIR and physical inventory was 58.1 percent in the direct channel, compared to 30.7 percent in the stationary channel.
In terms of magnitude of errors, SKUs in the stationary channel exhibit errors less frequently, but exhibit higher error magnitudes. The average size of the discrepancy between SIR and physical inventory is 31 units versus 3 units for the direct Internet-based channel. We also observed significant volatility in both channels during the 10 counting days, further suggesting a significant level of system instability.
What is behind these differences in frequency and magnitude between the direct and retail channels? In terms of frequency differences, there are many more transactions (albeit smaller in size) in the direct channel than in the retail channel. This sheer frequency of transactions seems to account for the increased frequency of IRI. Although the direct channel has the appearance of a nervous or "excited" state, it is relatively more stable than the retail channel.
In terms of size differences, there are significantly fewer – but significantly larger – transactions in the retail channel than in direct sales. These comparatively larger transactions appear to be responsible for the increased magnitude of IRI. While the brick-and-mortar retail channel is stable at times, it is actually more volatile when IRI occurs due to the scale.
Impact on management
The implications for management and the lessons learned from this research are far-reaching. Some of the most important are the following:
• The research shows that direct Internet-based channels may have more errors than brick-and-mortar channels, but to a lesser extent. This strongly suggests that managers need to develop specific control plans to more consistently track inventory levels and changes in their records over time for their online channels. These plans should include mechanisms to allow for adjustments in the records to reflect current inventory conditions. This feature is particularly important for those items that are part of brick-and-mortar distribution channels, where the discrepancy in the order of accuracy in inventory records tends to be high.
• The research also shows that cycle counting, the primary method currently used to manage and control inventory record accuracy, fails to capture the dynamic and often transient nature of inventory record inaccuracy in a DC environment. This shortage usually triggers an early order, resulting in overstocking. To ensure record accuracy, cycle counts must be supplemented with continuous inventory checks that track fluctuations in inventory levels to prevent premature or unnecessary restocking. Cycle counts can also overlook the occurrence of intermittent stockouts during times when stocks are not being checked. Using continuous review periods to track IRI over extended windows of time can identify these occurrences and help uncover their root causes.
• The research shows that managers need to address the problem of “process delays” – i.e. H. the time delays between physical actions and the updating of system records, which may increase or even decrease the existence and magnitude of some of the errors behind inaccuracies. Ideally, the warehouse management system (WMS) should be able to handle this situation. But based on evidence from this research, the pilot research, and other anecdotal evidence, it's not happening. This presents an opportunity for WMS providers, particularly given the increasing demand for real-time inventory visibility.
The options for handling inventory record inaccuracies are strong. When companies choose not to address the dynamic aspects of IRI, they expose themselves to numerous inefficiencies – too much or too little backordering, loss of customer goodwill due to phantom stock-outs, continuing to operate under high uncertainty, and more. If organizations choose to address the dynamic aspects of IRI, their efforts can take the form of introducing incentive programs, deploying a new WMS, implementing continuous improvement programs, redesigning the DC layout, and so on. It's important to recognize that with such initiatives come tradeoffs: the resulting increased availability, improved customer service, and overall improved inventory management require an investment. It could be the addition of new or more competent staff; an updated or completely new WMS; a new set of operations.
Perhaps most importantly, managers remember that any successful initiative to improve inventory record accuracy involves a major culture shift and requires the full support and involvement of top management.
Endnotes:
- Economic and Statistical Administration (2009).http://www.esa.doc.gov/ei.cfm.
- DeHoratius, N., A. Raman, 2008. Inventory record inaccuracy: An empirical analysis.management science54(4) 627-641.
- Özer, 2007. Unlocking the Value of RFID.Production and operational management16(1) 40-64.
- Piaseki, D.J. (2003) "Inventory Accuracy: People, Process, and Technology." Kenosha, Wisconsin. OPS release.
- DeHoratius, N., A. Raman, 2008. Inventory record inaccuracy: An empirical analysis.management science54(4) 627-641.
- Raman, A., N. DeHoratius, Z. Ton, 2001. Execution: The Missing Link in Retail.California Management Review43(3) 136-152.
- DeHoratius, N., A. Raman, 2008. Inventory record inaccuracy: An empirical analysis.management science54(4) 627-641.
- Kang, Y., S.B. Gershwin, 2005. Information inaccuracy in inventory systems: inventory loss and stockouts. IIE transactions 37(9) 843-859).
- DeHoratius, N., A. Raman, 2007. Incentive design and retail performance for store managers: An exploratory investigation.manufacturing and service companies9(4) 518-534.
- Bernard, PA 1985. Cycle-Counting.Production Inventory Management26 27–41. DeHoratius, N
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ThroughMark A. Barratt, Elliot Rabinovich und Annibal Camara Sodero · March 1, 2010
¹² In the retail sector, mixed retailers – i.e. H. Retailers who have multiple sales channels – Today, in the face of still stagnant sales, manage these inventories across multiple channels, both traditional retail (i.e. mortar) and direct (internet-based). A typical approach has been to offer complex product ranges to meet local consumer demand while trying to manage available stocks. The advent of the internet as a supportive tool for online sales has only exacerbated the challenge. To cite just one example, buying behavior has changed dramatically as consumers use the direct channel to check product availability and assortment – for example, for subsequent in-store visits that lead to purchases through the retail channel. This puts a special emphasis on mixed retailers keeping accurate records of their inventory and highlights the dilemma of determining which range of products to make available in specific channels. A related, though less well-understood, challenge involves dealing with inventory data inaccuracy (IRI). Addressing – or attempting to mitigate – IRI is becoming increasingly important as multi-channel retailers rush to increase inventory availability through both their traditional retail channels and their newer, more direct channels. This raises the question of whether blended retailers should allocate inventory separately to serve individual channels, or centralize inventory to meet aggregate demand regardless of channel. A related question is what are the IRI implications of each course of action? Blended retailers typically segregate inventory across channels to maximize product availability to consumers. By allocating inventory solely in their brick-and-mortar channel, they ensure internet sales don't impact in-store product availability. However, this strategy prevents retailers from realizing inventory efficiencies that can result from centralizing inventory across their channels. Such efficiencies have tremendous potential to reduce the cost of carrying safety stock, as well as the costs required to monitor and control inventory levels and their accuracy. Retailers have also used technology to automate various critical systems ranging from ordering to forecasting to planning and replenishment. All of these tools use "system" inventory records to determine sets of parameters that optimize inventory control and influence both operational and financial decisions.³² Another connection to the technology is radio frequency identification (RFID) technology, which is becoming increasingly popular in retail . However, RFID remains an expensive option and is not yet feasible for all companies.³ In short, the ability to accurately determine how much inventory is on hand has become even more important than before. Without such accuracy, companies oscillate between the opposing risks of holding more inventory than necessary, or alternatively running out of inventory and being unable to meet customer demand. This has made IRI a relevant - albeit often underestimated - topic for many companies across industries from retail to defense. In their attempts to address and control the opposing risks of overstock and out-of-stock, most retailers and manufacturers employ some form of cycle counting, generally either by stock keeping unit (SKU) or by location. In the case of multi-channel retailers, the company's goal is to ensure an acceptable level of accuracy in their inventory (see Figure 1). When done properly, cycle counting is accompanied by an ongoing root cause analysis for sources of failure, leading to a continuous improvement approach that ultimately eliminates or significantly reduces the frequency of such cycle counting. However, cycle counting is a static, predominantly financially oriented and periodically repeating measurement approach based on an ABC product classification. Despite the fact that cycle counting has been in existence for over 30 years, there have not been any major improvements or breakthroughs in mitigating the inaccuracy of inventory records associated with this technique. Our main concern with cycle counting is that it overlooks the issue of physical product availability between counts; that is, what is happening on a dynamic basis in terms of product availability. Between “cycle counts”—whose timing varies on average from one month to six months, depending on the approach chosen—organizations strive to maintain availability without holding excessive inventories. The cycle counting approach is based on accuracy measurements, which can be significantly outdated and contain serious errors. This could unnecessarily increase inventory or expose the business to stock-outs. Such a lack of transparency during the count creates potential bottlenecks in product availability, not to mention the adverse impact on the company's forecasting, planning, and reordering processes. The reality is that companies don't understand the true impact of errors on their inventory records; As a result, they simply respond by holding more inventory at a higher cost than may be required. With companies still relying primarily on cycle counting to keep accurate records, both practitioners and academics have given special attention to IRI in the retail sector - where a lack of inventory is a major concern. In particular, research has focused on the causes and consequences of IRI at the store level. To illustrate the magnitude of the problem, a study by a major retailer found that 65 percent of 375,000 stock keeping units (SKU) records were incorrect. If this situation is not recognized and corrected, depending on the size of the error relative to the reorder point maintained by a company, the SKU will be “frozen” once their stock is depleted, since the SIR will have a positive balance of items in inventory. In a DC setting, this situation persists only until an order for the depleted inventory arrives and a zero physical inventory status is detected. In a brick-and-mortar or online retail environment, this situation becomes even more damaging. Since there is no physical inventory, customers cannot purchase the item. The fact that no customers are purchasing the item creates a potentially dangerous scenario. If there are no sales for the product, the product's forecast will be adjusted downwards over time before eventually being removed from the list. When the SIR is lower than actual physical inventory - indicating a negative balance - this is referred to as "bloat". In such cases, the SIR will decrease until the reorder point is reached to replenish inventory. At this point, an automatic replenishment order is generated ahead of time. At first glance, this is a more desirable result than freezing. The reason: Most companies would prefer IRI to manifest itself as excess inventory rather than the risk of being out of stock. However, in a direct channel, this may be reflected in lower inventory availability that does not reflect actual inventory levels in the warehouse. To understand IRI as a supply chain phenomenon, we first had to conduct an exploratory pilot study. Specifically, we examined inventory policies and practices in the DC of a national pet retailer (name withheld at company's request) to (1) better understand issues associated with sustained count periods and (2) gain insight into daily daily variability in inventory records. By counting inventory for only seven consecutive days, the pilot study found significant evidence of very dramatic variability between overruns and shortages in a sample of 30 SKUs (see Figure 3). This sample consisted of 10 SKUs selected from each of the three main types of storage locations – fast moving, bulk and modular picking. On the one hand, the fast-moving products like canned cat and dog food did not suffer from IRI. On the other hand, slow-moving, bulky and modular items showed considerable fluctuations (between excess and shortage) in the counting period. For the 20 SKUs in the bulk and module categories, we have also seen a clear trend that there is actually more inventory than the system inventory record indicates. If this were to continue year-round, the company would be holding more inventory than necessary and paying inventory costs in excess of what is necessary. Of potentially more concern than excess inventory is the variance in tracking accuracy for some of the individual SKUs. Some have records that "jump" from accuracy to excess inventory and back again; other SKUs go from excess stock to shortage and back again. No transactions (such as goods receipts, putaways or picking) explained these variations, although we corrected any process delays related to how quickly products received from suppliers, for example, were credited to the system inventory record. This leads us to consider the system as inherently unstable. In order to examine the systemic conditions underlying IRI in a multichannel distribution center, we needed to find a retailer serving such channels from the same facility. Next, we studied an apparel retailer with annual sales of approximately $200 million in 2008. This retailer operates a national distribution center that serves both a traditional (brick-and-mortar) retail channel and a direct (web-based) channel. From a total of approximately 12,000 SKUs, we isolated those that were common to both channels. We ranked these popular SKUs by sales volume (in units) in 2008 within each product category to identify fast, medium and slow moving items. We then selected the two fastest selling and the two slowest selling products. That left us with 27 SKUs, including a selection of the three most popular product colors and sizes. To assess IRI, we tracked physical inventory for the 27 SKUs in the distribution center for both retail and direct channels. At the same time, we compared this information with the data in the retailer's SIR. In tracking the retailer's physical inventory, we counted the number of items in stock for each channel each day for a period of ten consecutive business days. (We did not count inventory over the weekend as the DC only operated Monday through Friday.) The ten business days correspond to two calendar weeks in September 2008. These two weeks were chosen because we wanted to study IRI conditions without disrupting the Seasonality of demand for the retailer's products. For each selected SKU, the SIR balance and SKU locations for both channels were downloaded from the DC's Warehouse Management System (WMS) on each counting day. In addition, additional data on product additions, daily orders, returns and any (revision) adjustments were collected daily for both channels. Data on pricing, product popularity in the marketplace, and inventory verification policies was collected from retailer records during the period that we collected SKU inventory data. It's important to note that for all SKUs in our study, the retailer followed guidelines for continuous review. These policies were based on a min-max approach to reordering and replenishing inventory of each SKU. When inventory reached a predetermined minimum level, the retailer rearranged the amount of inventory required to bring the level back up to a preset maximum. The results of the research show significant dynamic IRI variability for both retail and direct channels. In particular, SKU records move from accuracy to positive and negative deviations from actual physical counts (i.e. freeze and bloat) - all within the 10-day counting period. The research shows some counterintuitive results compared to previous studies. These results support our contention that IRI poses different challenges depending on the organization's channel structure and position in the supply chain, supporting the contention that IRI should be studied in settings other than retail locations. It is important to emphasize that the errors found in both channels could not be explained by transactions (receipt, putaway or picking). Again, this was true even when we corrected for process delays such as For example, how quickly products received from suppliers were credited to the system inventory record or how quickly returns were credited to the system inventory record. A program that effectively tackles IRI should aim to minimize inaccuracy on high-value items, where higher profit margins typically reside. One possible approach is to use incentives to draw managers' and employees' attention to operational activities related to these high-value items. However, such incentives must be balanced against the multitude of competing goals related to maintaining and increasing sales of these high value items and inventory management. For high value (margin) items, it may be better to hold higher inventory than to focus too much on reducing the IRI on those particular SKUs. Shrinkage - especially theft - can also be a significant factor for higher-priced items, which in turn leads to higher IRI values. Most retailers have focused on avoiding losses in their retail stores. You need to pay equal attention to direct channel shrinkage. However, the overriding concern here is that the IRI errors occur at both the high and low end of the products in the direct channel. This suggests that there are some other potentially counter-intuitive issues at play. This is something that will be explored in the next stages of this ongoing research. However, as we can see in Figure 5, there is significant inaccuracy regardless of the number of transactions in the direct channel. Unlike the previous pilot study we conducted, we saw many instances of IRI, particularly in the slower moving items. This was not an encouraging finding. And while the survey didn't show any obvious patterns across the 27 SKUs, we found slightly higher imprecision in the direct channel. This can be explained by the higher number of transactions in this channel compared to the retail channel. Also of note in Exhibit 5 are the many instances where SKU has recorded experienced conditions of Accuracy, Deficiency and Exceed - all within ten days. Increasing the distance between the stock reporting point and the upper stock level reduces this frequency. Accordingly, we would expect that reducing the checking frequency would increase both the magnitude of the IRI and the number of inaccuracies in the timekeeping for the SKU. Accordingly, Figure 6 shows that an increasingly narrow min/max gap decreases the size of the IRI in the direct channel and thus increases the difference in IRI that exists between channels in a blended retailer. In terms of magnitude of errors, SKUs in the stationary channel exhibit errors less frequently, but exhibit higher error magnitudes. The average size of the discrepancy between SIR and physical inventory is 31 units versus 3 units for the direct Internet-based channel. We also observed significant volatility in both channels during the 10 counting days, further suggesting a significant level of system instability. What is behind these differences in frequency and magnitude between the direct and retail channels? In terms of frequency differences, there are many more transactions (albeit smaller in size) in the direct channel than in the retail channel. This sheer frequency of transactions seems to account for the increased frequency of IRI. Although the direct channel has the appearance of a nervous or "excited" state, it is relatively more stable than the retail channel. In terms of size differences, there are significantly fewer – but significantly larger – transactions in the retail channel than in direct sales. These comparatively larger transactions appear to be responsible for the increased magnitude of IRI. While the brick-and-mortar retail channel is stable at times, it is actually more volatile when IRI occurs due to the scale. The implications for management and the lessons learned from this research are far-reaching. Some of the most important are the following: • The research shows that direct Internet-based channels may have more errors than brick-and-mortar channels, but to a lesser extent. This strongly suggests that managers need to develop specific control plans to more consistently track inventory levels and changes in their records over time for their online channels. These plans should include mechanisms to allow for adjustments in the records to reflect current inventory conditions. This feature is particularly important for those items that are part of brick-and-mortar distribution channels, where the discrepancy in the order of accuracy in inventory records tends to be high. • The research also shows that cycle counting, the primary method currently used to manage and control inventory record accuracy, fails to capture the dynamic and often transient nature of inventory record inaccuracy in a DC environment. This shortage usually triggers an early order, resulting in overstocking. To ensure record accuracy, cycle counts must be supplemented with continuous inventory checks that track fluctuations in inventory levels to prevent premature or unnecessary restocking. Cycle counts can also overlook the occurrence of intermittent stockouts during times when stocks are not being checked. Using continuous review periods to track IRI over extended windows of time can identify these occurrences and help uncover their root causes. • The research shows that managers need to address the problem of “process delays” – i.e. H. the time delays between physical actions and the updating of system records, which may increase or even decrease the existence and magnitude of some of the errors behind inaccuracies. Ideally, the warehouse management system (WMS) should be able to handle this situation. But based on evidence from this research, the pilot research, and other anecdotal evidence, it's not happening. This presents an opportunity for WMS providers, particularly given the increasing demand for real-time inventory visibility. The options for handling inventory record inaccuracies are strong. When companies choose not to address the dynamic aspects of IRI, they expose themselves to numerous inefficiencies – too much or too little backordering, loss of customer goodwill due to phantom stock-outs, continuing to operate under high uncertainty, and more. If organizations choose to address the dynamic aspects of IRI, their efforts can take the form of introducing incentive programs, deploying a new WMS, implementing continuous improvement programs, redesigning the DC layout, and so on. It's important to recognize that with such initiatives come tradeoffs: the resulting increased availability, improved customer service, and overall improved inventory management require an investment. It could be the addition of new or more competent staff; an updated or completely new WMS; a new set of operations. Perhaps most importantly, managers remember that any successful initiative to improve inventory record accuracy involves a major culture shift and requires the full support and involvement of top management.Download Article PDF Deficiencies in Cycle Counting
Inaccuracies in inventory records abound
Additionally, recent anecdotal evidence suggests that IRI is still a major concern in the distribution center. For example, at a large electronics retailer, researchers found that before a new retail store opened, 25 percent of SKUs were already inaccurate, suggesting the likely source of these errors was the retailer's distribution center.
Inventory record inaccuracy remains a pervasive and largely unexplored problem across all industries, spanning various tiers of the supply chain. Not surprisingly, the consequences of data inaccuracy are poorly understood not only in physical retail stores, but also in distribution centers and beyond. IRI can produce different results as summarized in Figure 2. When the System Inventory Record (SIR) is higher than the actual physical inventory (i.e. a positive balance), this leads to a situation known as a "freeze".The pilot study
Main Research: Multichannel DC
The results show the dynamic IRI variability
We expected less inaccuracy at the higher end of the SKU price range, as we believed that pickers should be more careful to be accurate for high-priced items. Instead, as shown in Figure 4, we found most inaccuracies at both the high and low end of the price range in the direct channel and more inaccuracies at the mid and low end of the price range in the retail channel.
We would expect that the more transactions associated with a product, the more opportunities there are for inventory records to become inaccurate.
Inventory review frequency was found to be negatively correlated with inventory record inaccuracy. The reason: Variances are easier to spot when repeat orders are placed or stocks are physically replenished. Inventory review policies where reorder points are close to maximum inventory levels (i.e., a small min/max gap) require frequent reviews. This is because inventory depletion will more often exceed the inventory reporting threshold as the difference between maximum carried inventory and inventory reorder points decreases.
Overall, our research shows that SKUs are far more likely to be inaccurate in the direct (internet-based) channel than in the traditional channel. Specifically, the frequency of discrepancies between SIR and physical inventory was 58.1 percent in the direct channel, compared to 30.7 percent in the stationary channel.Impact on management
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Article Topics
finance·inventory management·MarchApril 2010·
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