Using a Bipolar Choquet Neural Network to Locate a Retail Store

Machado, Maria Augusta Soares; Gomes, Luiz Flávio Autran Monteiro; Santos, Danilo Jusan; Caldeira, André Machado

doi:10.1016/j.procs.2015.07.163

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…For example, Wang et al [ 90 ] made a quantitative site selection, so that a very large number of possible store locations are reduced to a small number, which is then investigated further by qualified employees. Machado et al [ 53 ] followed a different approach, in which a pre-selection had already been made and the remaining five stores were evaluated by a neural network. The site selection was grouped into the shell for master data with the goal of creating a master data record for the store.…”

Section: Application Areas Of Machine Learning In Retailmentioning

confidence: 99%

Identifying Application Areas for Machine Learning in the Retail Sector

Brackmann,

Hütsch,

Wulfert

2023

SN COMPUT. SCI.

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Machine learning (ML) has the potential to take on a variety of routine and non-routine tasks in brick-and-mortar retail and e-commerce. Many tasks previously executed manually are amenable to computerization using ML. Although procedure models for the introduction of ML across industries exist, the tasks for which ML can be implemented in retail need to be determined. To identify these application areas, we followed a dual approach. First, we conducted a structured literature review of 225 research papers to identify possible ML application areas in retail, as well as develop the structure of a well-established information systems architecture. Second, we triangulated these preliminary application areas with the analysis of eight expert interviews. In total, we identified 21 application areas for ML in online and offline retail; these application areas mainly address decision-oriented and economic-operative tasks. We organized the application areas in a framework for practitioners and researchers to determine appropriate ML use in retail. As our interviewees provided information at the process level, we also explored the application of ML in two exemplary retail processes. Our analysis further reveals that, while ML applications in offline retail focus on the retail articles, in e-commerce the customer is central to the application areas of ML.

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Section: Application Areas Of Machine Learning In Retailmentioning

confidence: 99%

Identifying Application Areas for Machine Learning in the Retail Sector

Brackmann,

Hütsch,

Wulfert

2023

SN COMPUT. SCI.

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Selecting Working Teams for Information Technology Outsourcing Projects Through a Combination of Methodologies

2017

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ABSTRACT. This paper deals with a problem that Information Technology outsourcing suppliers generally face when selecting a working team technically capable for specific roles in software development projects. A combination of methodologies, interactively integrated, is proposed. They are Soft System Methodology to structure the problem, Repertory Grid for individual interviews and elicitation of the selection criteria, DRV Processes to assess the candidates and to generate knowledge and consensus on the selection process and Linear Programming to assign people to each position. This multimethodology allowed finding a more comprehensive solution than that initially requested by the company, since it helped to establish the necessary transformations for the selection model to operate in the right way, set the competencies to be considered as selection criteria, develop a consensus estimate of the weighted criteria, and award global values to candidates, optimizing the assignment of roles in the group for the project.

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Artificial Neural Networks and Their Applications in Business

Bihl

Young

Weckman

2018

Encyclopedia of Information Science and Technology, Fourth Edition

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Despite the natural advantage humans have for recognizing and interpreting patterns, large and complex datasets, as in Big Data, preclude efficient human analysis. Artificial neural networks (ANNs) provide a family of pattern recognition approaches for prediction, clustering and classification applicable to KDD with ANN model complexity ranging from simple (for small problems) highly complex (for large issues). To provide a starting point for readers, this chapter first describes foundational concepts that relate to ANNs. A listing of commonly used ANN methods, heuristics, and criteria for initializing ANNs is then discussed. Common pre- and post- data processing methods for dimensionality reduction and data quality issues are then described. The authors then provide a tutorial example of ANN analysis. Finally, the authors list and describe applications of ANNs to specific business related endeavors for further reading.

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Using a Bipolar Choquet Neural Network to Locate a Retail Store

Cited by 4 publications

References 5 publications

Identifying Application Areas for Machine Learning in the Retail Sector

Identifying Application Areas for Machine Learning in the Retail Sector

Selecting Working Teams for Information Technology Outsourcing Projects Through a Combination of Methodologies

Artificial Neural Networks and Their Applications in Business

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