Measuring the Effects of Automatic Replenishment on Product Availability in Retail Stores

Abstract

Maintaining product availability is one of the biggest challenges in retail business because it directly relates to lost sale and decrease in customer loyalty. A solution that ensures a more accurate prediction and eliminates costly stock-outs and wasteful overstocks is an automatic replenishment system. The goal of this paper is to measure the impact that the automatic replenishment system can have on product availability in retail business, especially when it comes to specific product and store related risk factors. A large quantitative study measured the performance of manual and automatic replenishment processes in a sample of 85 stores and 95 products of a major retail chain in Serbia. The study concluded that utilization of an automatic replenishment system can reduce stock-outs for the retail chains up to 60%. Specifically, when ordered through an automatic replenishment system, fast-selling products recorded 40% greater availability, products on promotion 48% higher availability, and products in a high-density retail stores 59% higher availability. The findings extend current understanding of automatic replenishment systems, and especially their performance related to high-risk retail conditions.

Inventory Management Model for Reducing Stockout rate by Applying Lean Warehousing and DDMRP Tools in a SMEs in the Commercial Sector

“…Adequate demand planning is essential to allow the availability of products at the right time and place, which has a direct impact on lost sales and above all on customer loyalty and satisfaction [2]. On the other hand, according to the author [3], the variability of demand is uncertain, which generates oversupply or stockouts, consequences that can be prevented with the application of methodologies and by implementing an optimal inventory management system.…”

“…The objective is to reduce the indicator to less than 7%. [2]. • Inventory obsolescenc: Percentage of damaged, obsolete and/or expired units of inventory.…”

Predicting Out-of-Stock Using Machine Learning: An Application in a Retail Packaged Foods Manufacturing Company

“…Recently published studies have addressed the OOS problem from different approaches: image processing methods [12], autonomous robotic system to shelf monitoring [13], mobile robot, depth cameras and neural networks to determine the occupancy of a shelf [14], deep learning OOS detection [15], shelf monitoring using supervised learning [16], and a combination of semi-supervised learning and deep learning to monitor on-shelf availability (OSA) [17]. In [18], the authors studied the impact of automatic replenishment on product availability.…”