Demand-based on-shelf availability - Integrating intraday store sales patterns in retail inventory management
Type
dissertation project
Start Date
April 1, 2011
End Date
April 1, 2012
Status
completed
Keywords
On-shelf availability
inventory management
demand modeling
Description
Providing items on retail shelves, i.e. on-shelf availability, lies at the core of retail supply chain management. Stocking too many items on retail shelves incurs excess handling cost, cost of capital, markdowns and write-offs. Stocking too little means risking stockouts (Sloot et al., 2005; Zentes et al., 2007). Stockouts have a negative impact on retailers and brand manufacturers, both directly on sales and profit and indirectly on customer satisfaction and store or brand loyalty respectively (Campo et al., 2000, 2003; Fitzsimons, 2000). For retailers, stockouts cause lost sales, dissatisfy consumers, diminish store loyalty and jeopardize marketing expenses (e.g. Marketing Online, 2004; EMFI, 2008).
Today, most retailers apply inventory management that target the on-shelf availability of any product at any time throughout store opening hours (Aastrup and Kotzab, 2009; Corsten and Gruen, 2003; McKinnon et al., 2007; Tan and Karabati, 2004). Yet, surveys on stockout rates over the past decade report that between 4 and 10 percent of the total stock-keeping units (SKU) at supermarkets are typically out of stock (EFMI, 2000; Gruen et al., 2002; ECR Europe, 2003; Roland Berger & Partner, 2003; IGD, 2004, 2005, 2006, 2007; Gruen & Corsten, 2008; Hofer, 2009). Despite efforts by retailers and manufacturers, these figures have remained constant over the past decade. During this time, scholarly and managerial efforts towards improving OSA have been directed primarily at overall improvements in stockout levels (Corsten & Gruen, 2003; ECR Europe, 2003; McKinnon et al., 2007; Aastrup & Kotzab, 2009). While this body of research has contributed significantly to our understanding of stockouts, the current advances treat demand as stationary (Tan & Karabati, 2004). This rigid assumption has led to the implementation of OSA policies that target undifferentiated on-shelf availability throughout store opening hours, as today's retailers protect themselves against unknown intraday demand variation. However, such policies seem inefficient considering the cost associated with maintaining a fixed level of on-shelf availability for all items, all day, all year. Also, OSA is only relevant when actual demand occurs. Shopper behavior research indicates the existence of differences in shopper purchasing habits over the day (e.g. Geiger, 2007; Reimers & Clulow, 2009), and the intraday effects of stockouts found on shopper purchasing behavior (Lee, 2004). This raises the question whether static daily OSA levels represent adequate targets for retail inventory management (van Woensel et al., 2007; van Donselaar et al., 2010).
To address this question, this study seeks to explore setting intraday OSA levels designed to ensure availability modelled on intraday store sales patterns. Thereby, this study's contribution is threefold. First, using econometric analysis, it aims to foster our understanding of intraday store sales patterns to identify at what point in time an item's OSA is relevant to shoppers and when it is less relevant. Second, it tries to identify logistically relevant attributes, such as case-pack size and sales velocity that affect these patterns. Third, the study takes a modelling approach to improve inventory control rules by accounting for intraday purchasing patterns, which previous research has not regarded. Theoretically, it applies service-dominant (S-D) logic as the foundation for designing on-shelf availability on customer purchasing patterns. Methodically, the study uses on econometrics and quantitative empirical modelling.
Today, most retailers apply inventory management that target the on-shelf availability of any product at any time throughout store opening hours (Aastrup and Kotzab, 2009; Corsten and Gruen, 2003; McKinnon et al., 2007; Tan and Karabati, 2004). Yet, surveys on stockout rates over the past decade report that between 4 and 10 percent of the total stock-keeping units (SKU) at supermarkets are typically out of stock (EFMI, 2000; Gruen et al., 2002; ECR Europe, 2003; Roland Berger & Partner, 2003; IGD, 2004, 2005, 2006, 2007; Gruen & Corsten, 2008; Hofer, 2009). Despite efforts by retailers and manufacturers, these figures have remained constant over the past decade. During this time, scholarly and managerial efforts towards improving OSA have been directed primarily at overall improvements in stockout levels (Corsten & Gruen, 2003; ECR Europe, 2003; McKinnon et al., 2007; Aastrup & Kotzab, 2009). While this body of research has contributed significantly to our understanding of stockouts, the current advances treat demand as stationary (Tan & Karabati, 2004). This rigid assumption has led to the implementation of OSA policies that target undifferentiated on-shelf availability throughout store opening hours, as today's retailers protect themselves against unknown intraday demand variation. However, such policies seem inefficient considering the cost associated with maintaining a fixed level of on-shelf availability for all items, all day, all year. Also, OSA is only relevant when actual demand occurs. Shopper behavior research indicates the existence of differences in shopper purchasing habits over the day (e.g. Geiger, 2007; Reimers & Clulow, 2009), and the intraday effects of stockouts found on shopper purchasing behavior (Lee, 2004). This raises the question whether static daily OSA levels represent adequate targets for retail inventory management (van Woensel et al., 2007; van Donselaar et al., 2010).
To address this question, this study seeks to explore setting intraday OSA levels designed to ensure availability modelled on intraday store sales patterns. Thereby, this study's contribution is threefold. First, using econometric analysis, it aims to foster our understanding of intraday store sales patterns to identify at what point in time an item's OSA is relevant to shoppers and when it is less relevant. Second, it tries to identify logistically relevant attributes, such as case-pack size and sales velocity that affect these patterns. Third, the study takes a modelling approach to improve inventory control rules by accounting for intraday purchasing patterns, which previous research has not regarded. Theoretically, it applies service-dominant (S-D) logic as the foundation for designing on-shelf availability on customer purchasing patterns. Methodically, the study uses on econometrics and quantitative empirical modelling.
Leader contributor(s)
Ehrenthal, Joachim C.F.
Partner(s)
[http://www.uccs.edu/~tgruen/ Thomas W. Gruen, Ph.D.], Professor of Marketing, College of Business & Administration, University of Colorado at Colorado Springs ; [http://home.tm.tue.nl/tvwoense/ dr. Tom Van Woensel], Associate Professor of Operations Mana
Funder
Topic(s)
[http://www.sdlogic.net/ Service-Dominant logic]
retail supply chain management
and operations
Method(s)
Literature review
derivative propositions
time-series analysis
modeling
Range
HSG Internal
Range (De)
HSG Intern
Eprints ID
72427
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Item type:Publication, St.Gallen Seasonality Monitor (SSM) : On-shelf availability as required by your customers(LOG-HSG, 2011)Ehrenthal, Joachim C.F.As a retail customer, you walk into a store one evening and find your favorite brand of bread out of stock. For you, that just causes the hassle of having to walk across the street to buy the same bread at a competitor's store. For retailers, this is a huge problem. They must get the timing right of when to hold items available. Otherwise, retailers lose big money. Replenishing a brand of bread that is not sold in the following hours means wasting labor resources that could be put to better use elsewhere. If the bread does not sell, it may have to be marked down, or worse, thrown away. Both is very costly to the retailer and not sustainable. The question therefore is: At what time of the day do the products have to be on the shelves? This is where the St.Gallen Seasonality Monitor (SSM) helps. Using readily available sales data, this easy-to-use MS Excel tool visualizes patterns and trends in your sales by month, week, day, and hour of the day. To immediately view your results, follow these three steps: 1) Download the latest version of SSM from this website. 2) Input your sales data as shown in the file. 3) Select the item or category of interest and view the results. You can use the results to adjust instore logistics to match customer demand: Replenish in times of low sales to be prepared for the upcoming sales peak. In doing so, you ensure availability when it is required by your customers. Implementation will simplify store workload scheduling and improve handling efficiency. The St.Gallen Seasonality Monitor (SSM) is tried and tested in retailing. However, it can be used in other service environments where demand exhibits intraday fluctuations. These areas include call centers, shared service centers (e.g. accounting services), and hospital emergency departments. The St.Gallen Seasonality Monitor (SSM) is available under the creative commons license CC [http://creativecommons.org/licenses/by-nc/3.0/ BY-NC]Type:case study - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Value-attenuation in Service-Dominant LogicThis conceptual article examines the effects of out-of-stock items through the Service-Dominant (S-D) logic lens.Type:working paper