Improving replenishment practices at the store level to minimize out-of-stock levels: a case study in a portuguese grocery retailer

The extent of the out-of-stocks (OOS) in the European grocery retail industry continues to be a major problem, ranging from 7% to 10%, reflecting poor on-shelf availability (OSA), which is a key performance indicator of customer service. This calls for the need for solutions to overcome this pressing issue, as it has consequences for all supply chain actors. The main causes of OOS are originated at the store level, being those related to ordering, replenishment and planning practices. The following research emphasizes the importance of addressing the leading causes of poor replenishment practices in the context of a hypermarket of a Portuguese grocery retailer, to which lack of replenishment represents the cause that contributes the most to OOS. The case study followed a qualitative approach, where multiple data collection techniques were used, such as direct observation, interviews, and data collected from the company, aiming at identifying the main problems and inefficiencies with the current procedures taking place at the store, in order to make improvement suggestions to address the problems identified, so the store can reduce the current OOS rate motivated by lack of replenishment. After understanding the store’s operations and the causes of the poor replenishment practices, some solutions were proposed. These solutions can be summarized in the need of improving planogram compliance by creating awareness of the importance of OSA, training the employees to improve their performance over the daily activities, redesigning the shelf replenishment process, and introducing technology-based solutions for OOS detection.Na Europa, o problema das roturas na indústria do retalho alimentar continua a ser bastante significativo, variando entre os 7% e 10%. Este é um reflexo da indisponibilidade de producto nas prateleiras, um indicador-chave na avaliação do serviço ao cliente. Este problema remete para a necessidade de encontrar soluções devido às consequências negativas para todos os elementos da cadeia de abastecimento. As principais causas das roturas encontram-se na loja, estando relacionadas com as práticas de colocação de encomenda, reposição e planeamento. A presente investigação enfatiza a importância de abordar as principais causas dos problemas relacionados com as práticas de reposição num hipermercado de um retalhista português, para o qual a falta de reposição representa a principal causa de roturas. O caso de estudo caso seguiu uma abordagem qualitativa, onde foram utilizadas várias técnicas de recolha de dados, como observação direta, entrevistas, e dados da empresa, com o objetivo de identificar os principais problemas e ineficiências nos processos em vigor, e propor respetivas melhorias, de modo a que a loja possa reduzir a taxa de roturas por falta de reposição. Após a compreensão das operações da loja e das causas relacionadas com as práticas de reposição, foram propostas algumas soluções. Estas soluções passam pela necessidade de melhorar o cumprimento do planograma através da sensibilização para a importância da disponibilidade de produto, da formação dos funcionários para melhorar o seu desempenho, do redesenho do processo de reposição, e da introdução de soluções tecnológicas para a deteção de roturas

Computer Vision and Deep Learning for retail store management

The management of a supermarket or retail store is a quite complex process that requires the coordinated execution of many different tasks (\eg, shelves management, inventory, surveillance, customer support\dots). Thanks to recent advancements of technology, many of those repetitive tasks can be completely or partially automated. One key technology requirement is the ability to understand a scene based only on information acquired by a camera, for this reason, we will focus on computer vision techniques to solve management problems inside a grocery retail store. We will address two main problems: (a) how to detect and recognize automatically products exposed on store shelves and (b) how to obtain a reliable 3D reconstruction of an environment using only information coming from a camera. We will tackle (a) both in a constrained version where the objective is to verify the compliance of observed items to a planned disposition, as well as an unconstrained one where no assumption on the observed scenes are considered. As for (b), a good solution represents one of the first crucial steps for the development and deployment of low-cost autonomous agents able to safely navigate inside the store either to carry out management jobs or to help customers (\eg, autonomous cart or shopping assistant). We believe that algorithms for depth prediction from stereo or mono camera are good candidates for the solution of this problem. The current state of the art algorithms, however, rely heavily on machine learning and can be hardly applied in the retail environment due to problems arising from the domain shift between data used to train them (usually synthetic images) and the deployment scenario (real indoor images). We will introduce techniques to adapt those algorithms to unseen environments without the need of costly ground truth data and in real time

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

Emerging technology influences on the merchandise practises of a retailer: a study of Massbuild South Africa.

Masters Degree. University of KwaZulu-Natal, Durban.Background: Retailers across the globe are embracing technological advancements in their merchandise and distribution processes. Technology is changing the way every retailer conducts business by helping to create efficiencies, save money, and provide better products and services. Retail companies are also adopting technology to their advantage. Purpose: The purpose of this research is to understand the impact newer technology implementation has on a retailer’s processes, specifically regarding merchandise and distribution. The study examined the current technology available to Massbuild and how these factors impact its daily processes. A prominent challenge in retail is the implementation phase of adopting newer technology, which requires management decision-making. Methodology: This research study is exploratory. The methodology was qualitative and utilised a semi-structured in-depth interview approach with twelve senior management employees at Massbuild. A purposive sampling method was used to help select participants who fit the criterion. The empirical findings provide insightful and vital information on the benefits and challenges of technology on merchandise and distribution processes. Findings: The research findings highlight the emerging technologies that will help a retailer focus on improving existing merchandise and distribution processes. The research participants interviewed emphasised particular technologies used by Massbuild and its evolutionary change over the past ten years. There has been a strong emphasis on automation, artificial intelligence and assortment optimisation within Massbuild. Regarding implementation of technology, interview participants provided insight into possible solutions to challenges they encounter within their respective employment roles. Contribution: Technology is at the forefront of retail and is continually evolving. There are in-depth studies available regarding technology in retail, especially with the influence of the fourth industrial revolution. This research provides fresh insights into the retail field of merchandise and distribution, and provides fruitful insight for future researchers

Misplaced product detection using sensor data without planograms

Accurate and timely provisioning of products to the customers is essential in retail environments to avoid missed sales opportunities. One cause for missed sales is that products are misplaced in the store. This can be addressed by fast and accurately detecting those misplacements. A problem of current detection methods for misplaced products is their reliance on up-to-date planogram information, which is often missing in practice. This paper investigates the effectiveness and efficiency of outlier detection methods for finding misplaced products without planograms. To that end, we conduct simulation studies with realistic parameters for different store parameters and sensor infrastructure settings. We also evaluate the detection methods in a real setting with an RFID inventory robot. The findings indicate that our proposed MiProD aggregation of individual detection methods consistently outperforms individual techniques in detecting misplaced products