Store Shelves Research Articles

Using positional sequence patterns to estimate the selectivity of SQL LIKE queries

Sequence patterns are frequently employed in many expert system applications in a wide range of domains from bioinformatics to smart homes and stock market analysis. Regular sequence patterns fail to express whether two consecutive items in a pattern are occurring right after each other in all pattern occurrences in an item database or not. Such a differentiation may be important for many intelligent system applications, for instance, to better address business questions like “should two frequently-bought-together items be located right next to each other on a retail store shelf, or is it ok to place them at some distance as long as they are in the same aisle?”. In this paper, we propose a novel type of sequence pattern, called “positional sequence patterns”, and illustrate its application on a special expert system, i.e., the query planner/optimizer of a database management system. Positional sequence patterns allow to accommodate extra information regarding whether a frequent ordered item pair always occurs next to each other without any gap in between in all pattern occurrences. Since positional sequence patterns are not considered by the existing sequence pattern mining algorithms, we also propose an algorithm to mine them. Next, we integrate the positional sequence patterns into the selectivity estimation component of the query optimizer as an expert system application. More specifically, in the knowledgebase of the query optimizer, a histogram-like structure of positional sequence patterns are created and stored. Then, during query optimization time, these histograms are utilized to infer the selectivity of flexible text queries that are enabled by the SQL LIKE operator. In particular, the proposed selectivity estimation method employs redundant pattern elimination based on pattern information content during histogram construction, and a partitioning-based matching scheme. The experimental results on a real dataset from DBLP show that the proposed approach outperforms the state of the art by around 20% improvement in error rates.

An improved scheme for determining top-revenue itemsets for placement in retail businesses

Utility mining has been emerging as an important area in data mining. While existing works on utility mining for retail businesses have primarily focused on the problem of finding high-utility itemsets from transactional databases, they implicitly assume that each item occupies only one slot. Here, the slot size of a given item is the number of (integer) slots occupied by that item on the retail store shelves. However, in many real-world scenarios, the number of slots consumed by different items typically varies. Hence, this paper considers that a given item may physically occupy any fixed (integer) number of slots. Thus, we address the problem of efficiently determining the top-utility itemsets when a given number of slots is specified as input. The key contributions of our work are three fold. First, we present an efficient framework to determine the top-utility itemsets for different user-specified number of slots that need to be filled. Second, we propose a novel flexible and efficient index, designated as Slot Type Utility (STU) index, for facilitating quick retrieval of the top-utility itemsets for a given number of slots. Third, we conducted an extensive performance evaluation using both real and synthetic datasets to demonstrate the overall effectiveness of the STU index in quickly retrieving the top-utility itemsets by considering a placement scheme in terms of execution time and utility (net revenue) as compared to recent existing schemes.

Continuity

Earlier this year in my neighborhood, teenagers were throwing toilet paper on their friends’ trees, leaving long strands of white draping into the street. A few weeks later, store shelves were empty, and even as I write this at the end of March, toilet paper is hard to find. Fear of coronavirus disease 2019 (COVID-19) has grown quickly, and it's still not certain how soon the situation in North America will improve. “At least there's water” has been said many times in the past few months, and while we're all thankful for the electricity, food, and security we rely on—especially in times of trouble—efforts to mitigate the pandemic couldn't even begin without safe and reliable water. It gives me great comfort that, for most of North America, our water and wastewater systems and the excellent professionals who support and operate them are essentially always there for their communities. The shock from COVID-19 means that we're currently facing limits on some supplies, such as protective gloves and masks and toilet paper. In the long term, the loss of jobs and the cost to businesses and governments will be incredible. But coronavirus has not been detected in drinking water, and based on current evidence, the risk to water supplies is low. We can continue to use our taps and showers as usual, and the challenges from COVID-19 for the water industry aren't so much technical as they are managerial. As modern society is tested, it's heartening to see how quickly the water industry responded once it was understood that this isn't just another virus. Once action was required, those who provide water, wastewater, reuse, and stormwater services stepped forward to double down on their commitment to continuity. Beyond ensuring that service goes uninterrupted, utilities acted early to address customer concerns, in some cases waving late fees and disconnects, all because a safe and continuous water supply is essential. While some can work from home, others are sequestered from their networks for weeks to work in shifts onsite at the plant. Research is still going on in some labs. Manufacturers have demonstrated their resilience by shoring up supplies and standing with their customers. Bound by constraints, consultants and bridge organizations are maintaining the connections that allow knowledge to transfer but keep the virus at bay. All of society's essential workers—the water industry included—can continue to operate because they have safe water. This year's Drinking Water Week is May 3–9; please take a moment to recognize how nice it is that tap water is there when you need it. And I hope you have toilet paper, too. This month's Journal AWWA features articles on Canada's approach to regulating lead in water, communicating about PFAS, and asset management. If you are interested in submitting an article, contact me at journaleditor@awwa.org.

Market Basket Analysis To Identify Stock Handling Patterns & Item Arrangement Patterns Using Apriori Algorithms

The process of managing the pattern of handling stock of goods and the pattern of arranging goods on store shelves requires an identification process by utilizing data from sales transaction results. Market basket analysis of sales transaction data using Apriori Algorithm stages produces an information in the form of association rules with a minimum support value of 50% and a minimum confidence of 60%. It can be a reference in the arrangement of items on store shelves by referring to a combination of items that are often bought by consumers simultaneously. In addition, the stock inventory pattern can take advantage of the results of determining the high frequency value in the combination pattern 1 - itemset C1 with a minimum support value of 50% which is compared with the initial inventory.

In-store Customer Shopping Behavior Analysis by Utilizing RFID-enabled Shelf and Multilayer Perceptron Model

Understanding customer shopping behavior in retail store is important to improve the customers’ relationship with the retailer, which can help to lift the revenue of the business. However, compared to online store, the customer browsing activities in the retail store is difficult to be analysed. Therefore, in this study the customer shopping behavior analysis (i.e., browsing activity) in retail store by utilizing radio frequency identification (RFID)-enabled shelf and machine learning model is proposed. First, the RFID technology is installed in the store shelf to monitor the movement tagged products. The dataset was gathered from receive signal strength (RSS) of the tags for different customer behavior scenario. The statistical features were extracted from RSS of tags. Finally, machine learning models were utilized to classify different customer shopping activities. The experiment result showed that the proposed model based on Multilayer Perceptron (MLP) outperformed other models by as much as 97.00%, 96.67%, 97.50%, and 96.57% for accuracy, precision, recall, and f-score, respectively. The proposed model can help the managers better understand what products customer interested in, so that can be utilized for product placement, promotion as well as relevant product recommendations to the customers.

Attitude-behaviour dissonance regarding the importance of food preservation for customers

The aim of the study was to identify the importance of individual food product attributes (the use of preservatives, processing method, shelf-life period and nutritional value) as well as their relation to the purchase behaviour. To achieve this, consumer preferences were decomposed in conditions of full access to information, and data was compared with actual consumer behaviour related to making purchasing decisions in front of the store shelf. Based on data from 338 respondents, conjoint analysis and repeated ANOVA measurements were carried out, allowing to eliminate individual behavioural patterns. The results showed a dissonance between the consumers attitudes towards the attributes and their shopping behaviour. The processing method was the most important declared attribute for consumers, meanwhile this information was the least searched for during purchase. At the same time, shelf-life period marked as the least important was the main information searched for by consumers when shopping. The results also show a clear dislike towards the use of microwaves as a preservation method, while traditional thermal preservation was marked as the most positive for consumers. In addition, consumers were classified into 3 heterogeneous groups, identifying differences in the preferences of food attributes. The obtained results have practical value relating not only to the preferences of individual attributes, but also to their various variants. The results can be a guide for companies to properly label food products. The indicated dissonance illustrates the educational gap and related insufficient motivation to get to know the product before buying it. This is extremely important in connection with technological development and new food processing methods.

Consumers (Dis)Preference for Bitterness in Extra Virgin Olive Oil: A Field Experiment

Globally, bitterness in food is not a preferred attribute by consumers, for several reasons. In the case of Extra Virgin Olive Oil its presence is fundamental as it is a healthiness indicator, being strictly linked to the antioxidant content. The current study aimed to assess the extent of Italian consumers’ preferences for bitterness taste of Extra Virgin Olive Oil. Real choices made by shoppers at the store shelf of a hypermarket were recorded and then compared with the selected Extra Virgin Olive Oil sensory profiles evaluated by a professional panel. Findings reveal that bitterness exerts a negative willingness to pay equal to -1.18 €/liter. Furthermore, consumers tend to overestimate the perception of Extra Virgin Olive Oil bitterness compared to sensory experts. Results provide new evidences of consumer preferences for sweet sensory profile of Extra Virgin Olive Oil and bitter aversion.

Forecast and prospects for the formation of consumer preferences in the field of environmentally friendly products in the regional market

The article considers the formation of consumer preferences in the market of "environmentally friendly products" in the region. The Law "On Organic Products" introduces the concept of organic products, and for its producers it regulates the norms of production, storage, transportation, labeling and sales, and the law defines organic agriculture. The market condition was assessed in relation to Russians to quality products according to a number of criteria and the results were compared with similar indicators for the period from 2015 to 2019. The data are based on the socio-economic studies of Romir. The main directions of promoting biological products to the regional market are analyzed. Today, in the organic segment, basic products are mainly produced - drinking milk, cottage cheese, sour cream. Bioproducts are a more expensive product, therefore, it will not be available to everyone. About two-thirds of consumers are willing to pay more for organic and non-GMO products. The need to save overpowers the desire for a healthy diet, so 40% of respondents refuse to overpay for the quality of products, and this figure has been growing steadily over the past four years. The study revealed that the growing popularity of a healthy lifestyle leads to the fact that Russian consumers would like to see more natural products on store shelves and about 30% of them say that the labels on the packaging are “natural”, “organic”, “ECO” - are an additional incentive to shopping. Thus, the studies presented in the article allow forecasting the growth and development of this category of products in the future.

Mobile and stationary eye tracking comparison – package design and in-store results

PurposeThis paper aims to test the similarity of the results of on-screen eye tracking compared to mobile eye tracking in the context of first fixation location on stimuli.Design/methodology/approachThree studies were conducted altogether with 117 participants, where the authors compared both methods: stationary eye tracking (Tobii Pro X2-60) and mobile eye tracking (Tobii Pro Glasses 2).FindingsThe studies revealed that the reported average first fixation locations from stationary and mobile eye tracking are different. Stationary eye tracking is more affected by a centre fixation bias. Based on the research, it can be concluded that stationary eye tracking is not always suitable for studying consumer perception and behaviour because of the centre viewing bias.Research limitations/implicationsWhen interpreting the results, researchers should take into account that stationary eye tracking results are affected by a centre fixation bias. Previous stationary eye tracking research should be interpreted with the centre fixation bias in mind. Some of this previous work should be retested using mobile eye tracking. If possible small-scale pilot studies should be included in papers to show that the more appropriate method, less affected by attention biases, was chosen.Practical implicationsManagers should trust research where the ability of package design to attract attention on a shelf is tested using mobile eye tracking. The authors suggest using mobile eye tracking to optimise store shelf planograms, point-of-purchase materials, and shelf layouts. In package design, interpretations of research using stationary eye tracking should consider its centre fixation bias. Managers should also be cautious when interpreting previous stationary eye tracking research (both applied and scientific), knowing that stationary eye tracking is more prone to a centre fixation bias.Originality/valueWhile eye tracking research has become more and more popular as a marketing research method, the limitations of the method have not been fully understood by the field. This paper shows that the chosen eye tracking method can influence the results. No such comparative paper about mobile and stationary eye tracking research has been done in the marketing field.

Smart shopping with the ADA Seal of Acceptance

Looking for a dental product to help keep your mouth healthy and your smile bright? The store shelves are stocked with options. How do you know which to try? The American Dental Association (ADA) can help. Look for the ADA Seal of Acceptance (Seal) (Figure). The Seal says that a dental product is safe and effective. Also, visit MouthHealthy.org or follow the ADA on social media. The ADA provides information on the science behind dental health claims. Through these outlets, the ADA also identifies ideas and trends that are not supported by science. And listen to your dental professionals. They are familiar with your needs and can point you in the right direction. Products with the Seal make it easy to identify what benefits have been scientifically proven according to Seal program requirements. Companies clearly state, in a bulleted list alongside the Seal, which benefits are supported by science. For example, if a toothpaste says that it helps with cavity prevention, dental sensitivity, enamel erosion, or whitening, the company has provided research showing that it does just that. And toothpastes are not the only products that carry the Seal. More than 200 over-the-counter products have the Seal, including toothbrushes (both powered and manual), mouthrinses, products that clean between your teeth, and products that help relieve oral pain or dry mouth. The Seal program even has categories for water filters, sports mouthguards, and sugar-free chewing gum. Companies cannot use the Seal without permission from the American Dental Association (ADA), and the ADA will not give that permission without sufficient proof that the product does what the manufacturer says it will do, safely and effectively. What qualifies as proof? Science. Companies must provide results from laboratory tests and clinical studies supporting each oral health claim made for a product. These studies have to be performed by laboratories and researchers who are not associated with the company. The ADA even has a voluntary program to make sure laboratories that conduct independent research in support of products with the ADA Seal of Acceptance can provide reliable results. In addition to reviewing study results and research sites, the ADA conducts its own research on products such as toothbrushes, toothpastes, and mouthrinses. The number of dental products in stores and online that make all kinds of promises seems almost limitless. Look for the ADA Seal of Acceptance to be sure that the claims made by the dental product you choose are backed by science.

A Study on Effect of Product Packaging Elements on Consumer Buying Behaviour

Today due to cutthroat competition among the companies and rising demands of customers, companies have to provide innovative products with unique presentations to satisfy the customers. Companies are now also focusing equal Weightage on presentation and packaging of products. A consumer's eye is capable of providing valuable information about how a package design is perceived on a store shelf versus its competitors. This study aims to investigate the role of various packaging tactics used by the companies to lure the customers. In this research, researchers try to identify the effect of various packaging elements (like packaging material, colour, design of wrapper, innovation etc.) on consumer buying behaviour. A questionnaire is developed with the help of a five point liker scale. Survey of 150 respondents has been done in Ahmedabad city. Structural Equation Modelling is used to identify the effect of various independent variables on dependent variables.

Beyond Victory Gardens: Bolstering Resilience in Food Crisis Response

The COVID-19 pandemic disrupted much of daily life, not the least of which was the nation’s food supply. Empty grocery store shelves, rotting produce in the fields, and gallons of milk dumped rather than sold manifested as symptoms of the fragile nature of the U.S. food system. Rectifying issues of resilience through the incorporation of local and regional food sources as supplementary to the existing channels of production and distribution may have prevented such a harsh shock to the system. This article identifies the weaknesses of the U.S.’s industrial and consolidated food supply chain that prioritizes extraction and economic gain over resiliency, and further describes the ways in which the COVID-19 pandemic exacerbated these points of failure. The article first depicts some of the disruptions to the food supply chain stemming from consequences of the pandemic such as issues with matching supply to demand, wasting large quantities of food, and exacerbating systemic food insecurity. The article then provides a comprehensive overview of existing government crisis and disaster planning with an eye toward how these plans and policies incorporate or ignore implementing local and regional food into the greater food supply. The article concludes with recommendations for how to integrate local and regional food sources into government planning, identifying local and regional private entities such as food policy councils, farmers markets, and food banks as the most promising vehicles of change.

PENENTUAN POLA SEKUENSIAL DATA TRANSAKSI PENJUALAN MENGGUNAKAN ALGORITMA SEQUENTIAL PATTREN DISCOVERY USING EQUIVALENT CLASSES (SPADE)

Transaction data is customer or customer data at a commercial or non-commercial institution that contains the consumer id, transaction time, and transaction items. From transaction data such as supermarket transactions, sequential patterns can be found to determine the interrelationship between items or items. Data if further processed or analyzed will produce information or knowledge that is important and valuable as a support in decision making. This study aims to determine the consumption patterns owned by customers and provide information that can be used in determining the layout of new store shelves. The SPADE algorithm is an algorithm for finding sequential patterns to break down the main problem into sub-problems that can be solved separately. Based on the result obtained it can be concluded that the application of the SPADE algorithm has the highest minimum support value that can still form maximal frequent sequences is 29%. The highest minimum support value of the SPADE algorithm is 0.2% with a maximum minimum confidence value of 81% and the number of rules formed is 1,118 Rule, but confidence is taken 60% up so that there are only 15 Rule. Whereas the Apriori algorithm has the highest minimum support value that can still form the maximum frequent sequences is 25%. The highest minimum support value of the apriori algorithm which can still form a rule is 0.3% with a maximum value of 88% minimum confidence and the number of rules formed as many as 494 Rule, but confidence is taken 60% up so that there are only 29 Rule.

TemplateFree: product detection on retail store shelves

AbstractProduct recognition has a significant role because of its benefits to the compliant arrangements of stores, which further affects commercial contracts, customer satisfaction, and sale achievement. Automatic recognition systems have been proposed owing to the current high cost of manual inspection by clerks. Because of the difficult collection of product images, the systems are commonly one‐shot cases, in which the training data are actually template product images. However, despite the development of one‐shot recognition, the systems rarely utilize the special characteristics of products on retail store shelves, and the frequent updating of templates is still challenging. Furthermore, we consider that product detection can be the basis of product recognition. In this article, instead of the present workflow, we propose a novel product detection system, named TemplateFree, which combines product segmentation and zero‐shot learning. It detects products on retail store shelves by single store shelf images, that is, corresponding template product images are not necessary. TemplateFree concentrates on the characteristic that a store shelf can be segmented horizontally into layers and then vertically into products so that each product can be detected according to the segmentation. Double zero‐shot deep learning frameworks are used to improve the segmentation. In experiments, TemplateFree achieves better results than the present method. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

An Ensemble Filter for Indoor Positioning in a Retail Store Using Bluetooth Low Energy Beacons.

This paper has developed and deployed a Bluetooth Low Energy (BLE) beacon-based indoor positioning system in a two-floor retail store. The ultimate purpose of this study was to compare the different indoor positioning techniques towards achieving efficient position determination of moving customers in the retail store. The innovation of this research lies in its context (the retail store) and the fact that this is not a laboratory, controlled experiment. Retail stores are challenging environments with multiple sources of noise (e.g., shoppers’ moving) that impede indoor localization. To the best of the authors’ knowledge, this is the first work concerning indoor localization of consumers in a real retail store. This study proposes an ensemble filter with lower absolute mean and root mean squared errors than the random forest. Moreover, the localization error is approximately 2 m, while for the random forest, it is 2.5 m. In retail environments, even a 0.5 m deviation is significant because consumers may be positioned in front of different store shelves and, thus, different product categories. The more accurate the consumer localization, the more accurate and rich insights on the customers’ shopping behavior. Consequently, retailers can offer more effective customer location-based services (e.g., personalized offers) and, overall, better consumer localization can improve decision making in retailing.

Canola Breeding for HUMAN HEALTH

Canola Breeding for HUMAN HEALTH

Robust Shelf Monitoring Using Supervised Learning for Improving On-Shelf Availability in Retail Stores.

This paper proposes a method to robustly monitor shelves in retail stores using supervised learning for improving on-shelf availability. To ensure high on-shelf availability, which is a key factor for improving profits in retail stores, we focus on understanding changes in products regarding increases/decreases in product amounts on the shelves. Our method first detects changed regions of products in an image by using background subtraction followed by moving object removal. It then classifies the detected change regions into several classes representing the actual changes on the shelves, such as “product taken (decrease)” and “product replenished/returned (increase)”, by supervised learning using convolutional neural networks. It finally updates the shelf condition representing the presence/absence of products using classification results and computes the product amount visible in the image as on-shelf availability using the updated shelf condition. Three experiments were conducted using two videos captured from a surveillance camera on the ceiling in a real store. Results of the first and second experiments show the effectiveness of the product change classification in our method. Results of the third experiment show that our method achieves a success rate of 89.6% for on-shelf availability when an error margin is within one product. With high accuracy, store clerks can maintain high on-shelf availability, enabling retail stores to increase profits.

CVS details supplement testing

Retailer CVS has released details about its Tested to Be Trusted program, which requires dietary supplement makers and distributors to submit their products to third-party testing before the company will offer the supplements in their stores or online. The tests do not evaluate any product health claims. Instead, they confirm the identity and amounts of the active ingredients in the supplemental label panel. Products are also screened for contaminants such as heavy metals, yeast, mold, pesticides, biological pathogens, and industrial compounds like polychlorinated biphenyls. Currently, supplements—vitamins, minerals, herbs, and more—don’t need approval from the US Food and Drug Administration to reach store shelves. The FDA may take action by issuing public warning letters to manufacturers or recalling items if it determines through product sampling that a supplement has been adulterated. CVS first announced the program in 2017 and has since tested 1,400 supplements from over 150 suppliers. The retailer will

Effects of personal dispositions, familiarity and consumption situation on Western brands’ packaging

PurposeSupermarket scans in the contextual non-Western markets (Pakistan and China) revealed that the majority Western consumer products are available in both global (English) and the vernacular language (Urdu, Chinese) packaging. The availability of two different packaging raises a question – which is more effective and under what situation. This study aims to address this question by investigating the roles of an internal-oriented disposition (concern for quality) and an external-oriented disposition (social consciousness) on packaging choice, under different consumption situations (conspicuous versus inconspicuous). The conceptual model tested concern for quality as a mediator on the influence of social consciousness on packaging choice, under the moderating effects of conspicuous versus inconspicuous consumption situation.Design/methodology/approachTwo in-store choice experiments were conducted in Pakistan and China. Shoppers who agreed to participate were first primed to either of the two conditions (conspicuous and inconspicuous). They were then shown two packaging types (global versus local language) on store shelves and asked to pick the preferred packaging. Finally, the participants completed a paper-based questionnaire containing the scale items and demographics variables.FindingsParticipants with high social consciousness preferred the global (English) packaging. The interaction effect of social consciousness and consumption situation moderated the choice. Concern for quality mediated the influence of social consciousness on global packaging preference.Originality/valueThis study extends the research into packaging by considering the mediating relationship and conceptualisation between external-oriented personal antecedent (social consciousness) and internal-oriented disposition (concern for quality), under different consumption (conspicuous versus inconspicuous) situation. The findings will inform brand managers’ packaging strategies in non-Western markets.

Robotic retail surveying by deep learning visual and textual data

Autonomous systems for monitoring and surveying are increasingly used in retail stores, since they improve the overall performance of the store and reduce the manpower cost. Moreover, an automated system improves the accuracy of collected data by avoiding human-related factors. This paper presents ROCKy, a mobile robot for data collection and surveying in a retail store that autonomously navigates and monitors store shelves based on real-time store heat maps; ROCKy is designed to automatically detect Shelf Out of Stock (SOOS) and Promotional Activities (PA) based on Deep Convolutional Neural Networks (DCNNs). The deep learning approach evaluates visual and textual content of an image simultaneously to classify and map SOOS and PA events during working hours. The proposed approach was applied and tested on several real scenarios, presenting a new public dataset with more than 14.000 annotated shelves images. Experimental results confirmed the effectiveness of the approach, showing high accuracy (up to 87%) in comparison with the existing state of the art SOOS and PA monitoring solutions, and a signification reduction of retail surveying time (45%).