Desperately seeking shelf availability: an examination of the extent, the causes, and the efforts to address retail out‐of‐stocks

Corsten, Daniel; Gruen, Thomas W.

doi:10.1108/09590550310507731

Cited by 242 publications

(277 citation statements)

References 6 publications

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“…A number of authors have reported on the disadvantages of using backroom inventory: increased labor costs due to the double handling of items, reduced service levels (Corsten and Gruen, 2003), and inventory inaccuracy (Raman et al, 2001). Therefore, under the objective of smoothing the workload during the week, SKUs with larger net shelf space are more suitable candidates to be ordered earlier.…”

Section: Drivers Of Order Advancementmentioning

confidence: 99%

Ordering Behavior in Retail Stores and Implications for Automated Replenishment

et al. 2010

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Retail store managers may not follow order advices generated by an automated inventory replenishment system if their incentives differ from the cost minimization objective of the system or if they perceive the system to be suboptimal. We study the ordering behavior of retail store managers in a supermarket chain to characterize such deviations in ordering behavior and investigate their potential drivers. Using orders, shipments, and POS data for 19, 417 item-store combinations over 5 stores, we find that store managers systematically modify automated order advices by advancing orders from peak to non-peak days. We show that order advancement is explained significantly by hypothesized product characteristics, such as case-pack size relative to average demand per item, net shelf space, product variety, demand uncertainty, and seasonality error. Our results suggest that store managers add value. They improve upon the automated replenishment system by incorporating two ignored factors: in-store handling costs and sales improvement potential through better in-stock. We test a heuristic procedure, based on our regression results, to modify order advices to mimic the behavior of store managers. Our method performs better than the store managers by achieving a more balanced handling workload with similar average days of inventory.

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Section: Drivers Of Order Advancementmentioning

confidence: 99%

Ordering Behavior in Retail Stores and Implications for Automated Replenishment

et al. 2010

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“…For example, the operations management literature includes numerous papers on inventory optimization that are applicable to setting planned inventory levels in a retail store. Recently, however, a few pioneering papers (Raman et al 2001a, 2001b, DeHoratius and Raman 2003, Ton and Raman 2004, Corsten and Gruen 2003, Ton and Huckman 2005, Van Donselaar et al 2006) have provided evidence of deficiencies in retail store execution, suggesting that optimized plans might be severely blunted by less than perfect execution. Although these papers have focused mostly on missing inventory, inventory record inaccuracy and inventory replenishment, it is reasonable to suspect that, given the high level of problems with inventories, other aspects of retail execution are imperfect also.…”

Section: Introductionmentioning

confidence: 99%

Retail Store Execution: An Empirical Study

2006

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We describe a methodology by which a retailer can identify action steps that are likely to increase sales and customer satisfaction and demonstrate the methodology using proprietary data from a large retailer with over 500 stores. We use monthly store-level data on a number of operational variables including in-stock rate, store staffing level as measured by payroll and store employee turnover, together with customer responses to satisfaction surveys. We develop a nested three-stage econometric model to analyze the marginal effects of various execution levers on sales, customer satisfaction and the percentage of customers who answer 'yes' to the question 'Did you find everything you were looking for?', which we term customer perceived in-stock. Our model explains approximately 75%, 97% and 71%, respectively, of the residual variation in sales, customer satisfaction, and customer perceived in-stock. We find that customer perceived in-stock is primarily driven by actual in-stock and customer rating of employee knowledge; overall customer satisfaction is primarily driven by customer perceived in-stock, payroll level, customer rating of employee knowledge and check-out efficiency; and sales is primarily driven by actual in-stock, overall satisfaction and payroll level. Finally, we estimate relative magnitudes of these effects, propose specific actions to improve sales and estimate the likely sales increase from those actions. Our results suggest that a modest reallocation of the payroll budget among stores could be expected to yield a 2-3% increase in sales with no increase in cost.

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“…As much as 25% of stock out situations are caused by issues in shelf replenishment [58]. Retailers try to make this process smoother to decrease the discrepancy between the stock data and what is on the shelf (I3E32).…”

Section: Demand Sensing and Segmentationmentioning

confidence: 99%

An Exploration of Big Data Practices in Retail Sector

Aktaş

Meng²

2017

Logistics

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Connected devices, sensors, and mobile apps make the retail sector a relevant testbed for big data tools and applications. We investigate how big data is, and can be used in retail operations. Based on our state-of-the-art literature review, we identify four themes for big data applications in retail logistics: availability, assortment, pricing, and layout planning. Our semi-structured interviews with retailers and academics suggest that historical sales data and loyalty schemes can be used to obtain customer insights for operational planning, but granular sales data can also benefit availability and assortment decisions. External data such as competitors' prices and weather conditions can be used for demand forecasting and pricing. However, the path to exploiting big data is not a bed of roses. Challenges include shortages of people with the right set of skills, the lack of support from suppliers, issues in IT integration, managerial concerns including information sharing and process integration, and physical capability of the supply chain to respond to real-time changes captured by big data. We propose a data maturity profile for retail businesses and highlight future research directions.

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Desperately seeking shelf availability: an examination of the extent, the causes, and the efforts to address retail out‐of‐stocks

Cited by 242 publications

References 6 publications

Ordering Behavior in Retail Stores and Implications for Automated Replenishment

Ordering Behavior in Retail Stores and Implications for Automated Replenishment

Retail Store Execution: An Empirical Study

An Exploration of Big Data Practices in Retail Sector

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