scholarly journals Developing and Implementing Machine Learning Software at Home Depot

2021 ◽  
Vol 23 (4) ◽  
pp. 1-10
Author(s):  
Herbert Remidez ◽  
Sri Beldona
Keyword(s):  
Machine Learning ◽  
Technology Implementation ◽  
Supply Chain Disruptions ◽  
Product Availability ◽  
Use Of Technology ◽  
Development Lifecycle ◽  
Brick And Mortar ◽  
Home Depot ◽  
Learning Software ◽  
Learning Development

This teaching case explores the problem of shelfouts and the use of technology adoption to minimize it. Shelfout, wherein a product is not on the shelf when it is supposed to be, has received renewed interest especially given the fact that many brick-and-mortar stores shut down due to their inability to compete with online vendors. The coronavirus pandemic worsened this problem and companies continue to struggle with the resulting supply chain disruptions. Increasingly consumers are searching for products on the website to confirm product availability before traveling to the store. In this case we show how The Home Depot, Inc., (Home Depot), is addressing shelfouts and the process they went through in solving this problem. Instructors can use this case to introduce students to the machine learning development lifecycle, marketing courses discussing shelfouts, and courses with lessons related to technology implementation.

scholarly journals Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology

2021 ◽  
Vol 3 (2) ◽  
pp. 392-413
Author(s):  
Stefan Studer ◽  
Thanh Binh Bui ◽  
Christian Drescher ◽  
Alexander Hanuschkin ◽  
Ludwig Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Process Model ◽  
Practical Experience ◽  
Special Focus ◽  
Close Monitoring ◽  
Machine Learning Applications ◽  
Project Organizations ◽  
Considerable Impact ◽  
Learning Development

Machine learning is an established and frequently used technique in industry and academia, but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners face manifold challenges and risks when developing machine learning applications and have a need for guidance to meet business expectations. This paper therefore proposes a process model for the development of machine learning applications, covering six phases from defining the scope to maintaining the deployed machine learning application. Business and data understanding are executed simultaneously in the first phase, as both have considerable impact on the feasibility of the project. The next phases are comprised of data preparation, modeling, evaluation, and deployment. Special focus is applied to the last phase, as a model running in changing real-time environments requires close monitoring and maintenance to reduce the risk of performance degradation over time. With each task of the process, this work proposes quality assurance methodology that is suitable to address challenges in machine learning development that are identified in the form of risks. The methodology is drawn from practical experience and scientific literature, and has proven to be general and stable. The process model expands on CRISP-DM, a data mining process model that enjoys strong industry support, but fails to address machine learning specific tasks. The presented work proposes an industry- and application-neutral process model tailored for machine learning applications with a focus on technical tasks for quality assurance.

scholarly journals Diunsa: An Entrepreneurial Family’s Digital Response to COVID-19

2021 ◽  
pp. 251512742110331
Author(s):  
Lauri Union ◽  
Carmen Suen ◽  
Rubén Mancha
Keyword(s):  
Customer Service ◽  
Family Business ◽  
Service Operations ◽  
State Of Emergency ◽  
Supply Chain Disruptions ◽  
Online Retail ◽  
External Resources ◽  
The Third ◽  
The Family ◽  
Brick And Mortar

On March 15, 2020, in response to the Covid-19 pandemic, the Honduran government unexpectedly announced a state of emergency and mandated immediate closure of all businesses. Diunsa closed its six stores. The family-owned retailer had anticipated supply chain disruptions, stocked from alternative suppliers, and formed a crisis management team. Now, to keep the business afloat during the unexpected closure and retain all its employees on the payroll, the company had to move sales from the brick-and-mortar stores to an incomplete online retail site. The third generation in the family business—the Faraj siblings, all in their 20’s—led the critical transition online and response to setbacks. As digital-native millennials, they helped improve the website, customer service, operations, and delivery in a short amount of time and using external resources and various technologies. As the situation stabilized, Diunsa’s leadership asked: How will Diunsa build on the momentum for digital transformation and turn its tactical actions into a digital strategy? How can we continue to tap into the leadership of our up-and-coming generation to achieve these goals?

scholarly journals DECODING VIEWS AND SENTIMENTS OF PROGRAM HEADS TOWARDS THE SUPERVISION OF INSTRUCTION DURING THE COVID-19 PANDEMIC

2021 ◽  
pp. 323-329
Author(s):  
Jesus M. Meneses ◽  
Karen W. Cantilang ◽  
Delbert A. Dala ◽  
Jovito B. Madeja
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Latent Semantic Indexing ◽  
State University ◽  
Written Responses ◽  
Strategic Role ◽  
To Come ◽  
Learning Software ◽  
Supervision Of Instruction

The purpose of this study was to decode the hidden views and sentiments from the collated written responses of Eastern Samar State University’s Program Heads regarding supervision of instructions amidst the COVID-19 pandemic. This study utilized Exploratory Sequential Mixed Method to explore and understand the perspective or sentiments of Eastern Samar State University program heads towards supervision of instruction in the midst of the COVID-19 pandemic. Data were collected/collated from the participants indirectly using an interview questionnaire containing an open-ended question. The same were processed and analyzed using an open-source machine learning software called Orange toolbox (Demsar et al., 2013) wherein pre-processing, sentiment analysis and topic modelling built-in tools were utilized. The results showed that the most prominent words generated by the machine learning tool from the text file of responses are the words pandemic, performance, program, learning, difficult, supervision, instruction, internet, faculty, online students, teaching, delivery confusing, challenging, poor and connectivity. The dominant sentiment associated thereof lean towards negative polarity which implicate negative sentiments. Hidden topics were automatically generated by the machine which allowed the researchers to come up with the following related themes: “Impact of pandemic in the supervision of instruction of faculty and learning of students”, “Challenges in the delivery of instruction and supervision due to poor internet connectivity”, and “Strategic role of online modalities and connectivity in supervision and delivery of instruction”. There are limited researches navigating in text mining and sentiment analysis with the use of Orange toolbox particularly those that deals with supervision of instruction in a Philippine State University. There are related studies using machine learning software, but nothing like this study directed towards a specific gap in specific locale. KEYWORDS: Pandemic, Latent Semantic Indexing, Orange Toolbox, Sentiment Analysis, Thematic Analysis.

Defining Professional Development for Technology

2014 ◽  
pp. 376-395
Author(s):  
Madelon Reed Gruich
Keyword(s):  
Professional Development ◽  
Learning Outcomes ◽  
Technology Implementation ◽  
Instructional Programs ◽  
Optimal Learning ◽  
Use Of Technology ◽  
Levels Of Use ◽  
Systematic Training ◽  
Quality Learning ◽  
Training Opportunities

Professional development for technology implementation is a critical component of achieving successful learning outcomes in educational settings. The use of technology in all teaching disciplines and administration requires the systematic training of every individual within the organization. Technology tools often provide the catalyst for skill development and attainment of expertise to ensure organizational successes. Through proven and research-based training opportunities, administrators and instructors can receive and ultimately share quality learning experiences that guarantee optimal learning achievement for school districts and specific instructional programs as technology is integrated into curricula. Planning professional development that creates seamless technology assimilation at all levels of use helps to guarantee that instructional design parallels desired learning outcomes.

Use of Estimation and Control Techniques for Increasing of Efficiency Training for Public Officers in Russia

2020 ◽  
pp. 815-829
Author(s):  
Sergey Nedelko ◽  
Ekaterina Eremina ◽  
Yulia Lukanina ◽  
Artem Lukanin ◽  
Alexander Osteshkov ◽  
...  
Keyword(s):  
Professional Development ◽  
Professional Education ◽  
Technology Implementation ◽  
Information Science ◽  
Rapid Development ◽  
Educational Process ◽  
Continuous Professional Development ◽  
Use Of Technology ◽  
Estimation And Control ◽  
And Control

This chapter focuses on the use of technology estimation and control applied in Modern E-Learning Systems for training of public officers in Russia. Due to the rapid development of innovative technology, implementation of information science and technology in the educational process, it becomes obvious problem of interaction between the participants in the educational process and organizations - employers. This problem is particularly acute, and has its own specific characteristics in the field of continuing professional education of public officers in Russia. The authors propose to solve the problem of increasing the effectiveness of the training creation of a system of continuous professional development, the improvement of information and technical support activities for continuous professional development, including through the creation of a single information resource, including an updated bank of basic programs and additional professional Bank methodical, analytical and informational materials on the most pressing issues of implementation of the state policy for self-education.

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