scholarly journals Semantic Digital Twins for Retail Logistics

2021 ◽  
pp. 129-153
Author(s):  
Michaela Kümpel ◽  
Christian A. Mueller ◽  
Michael Beetz
Keyword(s):  
Knowledge Base ◽  
Semantic Information ◽  
Retail Store ◽  
Scene Graph ◽  
Robot Systems ◽  
Semantically Enhanced ◽  
Online Stores ◽  
Visualization And Simulation ◽  
Store Environment ◽  
Stationary Retail

AbstractAs digitization advances, stationary retail is increasingly enabled to develop novel retail services aiming at enhancing efficiency of business processes ranging from in-store logistics to customer shopping experiences. In contrast to online stores, stationary retail digitization demands for an integration of various data like location information, product information, or semantic information in order to offer services such as customer shopping assistance, product placement recommendations, or robotic store assistance.We introduce the semantic Digital Twin (semDT) as a semantically enhanced virtual representation of a retail store environment, connecting a symbolic knowledge base with a scene graph. The ontology-based symbolic knowledge base incorporates various interchangeable knowledge sources, allowing for complex reasoning tasks that enhance daily processes in retail business. The scene graph provides a realistic 3D model of the store, which is enhanced with semantic information about the store, its shelf layout, and contained products. Thereby, the semDT knowledge base can be reasoned about and visualized and simulated in applications from web to robot systems. The semDT is demonstrated in three use cases showcasing disparate platforms interacting with the semDT: Optimization of product replenishment; customer support using AR applications; retail store visualization, and simulation in a virtual environment.

Store Format Influence on Customer Perception of the Store Environment

2012 ◽  
Vol 1 (4) ◽  
pp. 9-21
Author(s):  
Susana Henriques Marques ◽  
Maria Santos
Keyword(s):  
Current Trend ◽  
Retail Store ◽  
Retail Stores ◽  
Client Perceptions ◽  
Customer Perception ◽  
Store Attributes ◽  
Dependent Variables ◽  
Store Environment ◽  
Strategic Differentiation ◽  
Retail Formats

This study compares client perceptions of the global in-store environment applied to different retail store formats. Literature has shown that certain store attributes are important strategic differentiation tools for grocery retailers. A retail atmosphere can lead to success or failure of a business. Previous studies have neglected the current trend to the coexistence of different retail formats, under different brands but within the same organization. In these cases, a multi-banner company needs to customize the atmosphere to its customers in order to gain attention. This research is about the influence of the store format on the servicescape of the grocery retail stores. A survey was conducted of 302 hyper and supermarket customers. A range of atmospherics variables were considered, including some less studied, such as temperature and cleanliness. The results show that all the dependent variables are sensitive to store format, except cleanliness.

Attractive and facilitating store atmospheric stimuli

2020 ◽  
Vol 48 (4) ◽  
pp. 363-379 ◽  
Author(s):  
Gaurav Bhatt ◽  
Abhigyan Sarkar ◽  
Juhi Gahlot Sarkar
Keyword(s):  
Design Methodology ◽  
Consumer Psychology ◽  
Retail Store ◽  
Environmental Cues ◽  
Store Brand ◽  
Content Type ◽  
Development Method ◽  
Psychological Mechanism ◽  
Store Environment ◽  
Relevant Factors

PurposeThe majority of past studies on the physical store environment have focused on the impacts of distinct store environmental cues like music, crowding and décor on consumers' responses. However, recent research posits that consumer is more likely to experience several cues in a combination, rather than in isolation, i.e. different categories of store environmental cues are likely to impact consumer psychology holistically. Hence, our study aims to identify the relevant factors of store atmospheric cues impacting consumer's attitude in physical retail store context and validate scales to measure such factors.Design/methodology/approachThis research develops and validates psychometrically reliable scales to measure two broad store stimuli factors namely: attractive and facilitating store stimuli, following the scale development method suggested by Churchill (1979).FindingsThe study shows that attractive store stimuli predict affective and sensory store brand experiences. The facilitating store stimuli moderate the effects of attractive store stimuli on affective and sensory store brand experiences. Affective and sensory store brand experiences predict store satisfaction.Originality/valueThis research contributes to the existing body of store ambience research by empirically understanding the psychological mechanism through which customers perceive different store cues holistically leading to the elicitation of store satisfaction.

Supporting Semantic Capture During Kinesthetic Teaching of Collaborative Industrial Robots

2018 ◽  
Vol 12 (01) ◽  
pp. 167-186 ◽  
Author(s):  
Maj Stenmark ◽  
Mathias Haage ◽  
Elin A. Topp ◽  
Jacek Malec
Keyword(s):  
Domain Knowledge ◽  
Semantic Information ◽  
Industrial Robot ◽  
Semantic Context ◽  
Industrial Robots ◽  
Teaching Mode ◽  
Language Support ◽  
Semantic Data ◽  
Robot Systems ◽  
System Knowledge

Industrial robot systems being deployed today do not contain domain knowledge to aid robot operators in setup and operational use. To gather such knowledge in a robotic context requires mechanisms for entering and capturing semantic data. Such mechanisms would allow a system to gradually build a working vocabulary while interacting with the environment and operators, valuable for the bootstrapping system knowledge and ensuring the data collection over time. This paper presents a prototype user interface that assists the kinesthetic teaching mode of a collaborative industrial robot, allowing for the capture of semantic information while working with the robot in day-to-day use. Two modalities, graphical point-and-click and natural language, support capture of semantic context and the building of a working vocabulary of the environment while modifying or creating robot programs. A semantic capture experiment illustrates the approach.

scholarly journals The controllable elements of the total retail experience: A study of clothing shoppers

2003 ◽  
Vol 6 (1) ◽  
pp. 143-158 ◽  
Author(s):  
N. S. Terblanche ◽  
C. Boshoff
Keyword(s):  
Data Analysis ◽  
Scale Development ◽  
Retail Store ◽  
Major Objective ◽  
Five Dimensions ◽  
Personal Interaction ◽  
Complaint Handling ◽  
Shopping Experience ◽  
Clothing Store ◽  
Store Environment

Retail clothing stores continually have to adapt to marketplace demands to remain competitive. Customer retention has become a major objective for many clothing retailers. This study combines the management of a number of the controllable personal and non-personal elements that a customer are exposed to and interacts within a retail store, as part of the shopping experience. The data analysis procedures closely followed the guidelines for scale development suggested by Churchill (1979). The empirical results suggest that there are five dimensions considered important by consumers when assessing their satisfaction with a total retail experience in a clothing store. These are: merchandise value, internal store environment, personal interaction with staff, merchandise variety and complaint handling.

Researching in-store, at home: Using virtual reality within quantitative surveys

2018 ◽  
Vol 60 (4) ◽  
pp. 344-351 ◽  
Author(s):  
Ian Bramley ◽  
Alastair Goode ◽  
Laura Anderson ◽  
Elisabeth Mary
Keyword(s):  
Virtual Reality ◽  
Online Survey ◽  
Retail Store ◽  
Test Point ◽  
Failure Rates ◽  
Virtual Reality Technology ◽  
Point Of Sale ◽  
Future Potential ◽  
Store Environment ◽  
Traditional Approaches

This article reviews the experience, steps taken, and lessons learnt from including a virtual reality film within a mobile online survey. The survey was designed to test point-of-sale displays within a retail store environment, with respondents exposed to the store using virtual reality within the survey, rather than being a shown a static image or a standard film of the store’s interior. The results show how incorporating a virtual reality film within a survey can significantly add to the survey enjoyment compared to traditional approaches. The findings show how the uniqueness of the virtual reality experience can help engage respondents, offering a modern and relevant way to provide a more realistic survey experience that respondents are receptive to. The study also demonstrates that it is technically feasible to incorporate a virtual reality experience into an online survey among typical panelists, without high failure rates or the need to over-incentivise to participate. This article discusses the use of virtual reality within surveys and the practical steps taken to incorporate the virtual reality film, as well as the key learnings generated from the experience. The future potential for the application of virtual reality technology within research is also explored.

Learning from Approximate Human Decisions by a Robot

2007 ◽  
Vol 19 (1) ◽  
pp. 68-76 ◽  
Author(s):  
Chandimal Jayawardena ◽  
◽  
Keigo Watanabe ◽  
Kiyotaka Izumi
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Natural Language ◽  
Knowledge Base ◽  
Probabilistic Neural Network ◽  
Robotic Manipulator ◽  
Human Aspects ◽  
Pick And Place ◽  
Robot Systems ◽  
Related Aspect

Robot systems operating under natural-language commands must be able to infer the meaning intended by the issuer. Despite some successful research, however, an important related aspect not yet addressed has been the possibility of learning from natural-language commands. Such commands, generated by human users, contain valuable information. The inherent subjectivity of natural language, however, complicates potential learning from such commands and their interpretation. We propose decision making for robots operating under natural-language commands influenced by human aspects of decision making. Under our proposed concept, demonstrated in experiments conducted using a robotic manipulator, the robot is controlled using natural-language commands to conduct pick-and-place operations, during which the robot builds a knowledge base. After this learning, which uses a probabilistic neural network, the robot conducts similar tasks based on approximate decisions from the knowledge gained.

Ontology Self-Perfect Based Lexical Chain for IE

2014 ◽  
Vol 644-650 ◽  
pp. 1972-1975
Author(s):  
Rui Gao ◽  
Yan Zhang ◽  
Hua Deng ◽  
Jin Si ◽  
Xiao Meng
Keyword(s):  
Information Extraction ◽  
Knowledge Base ◽  
Semantic Information ◽  
Series Of Experiments ◽  
Lexical Chain

The perfection of an ontology knowledge base is essential to the research of ontology-based information extraction (IE). Information extraction in short documents with sparse vocabularies in the ontology will cause the problem of semantic deviation. That will affect the indexes of information extraction in short documents. In this paper, we propose a method of using lexical chain to perfect the ontology knowledge base automatically in order to cover the shortage of manual constructed ontology. We can solve the problem of semantic information deficiency caused by the sparse vocabulary in the ontology effectively through the use of this method. We proved the validity of our method through the series of experiments we conducted.

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