Balancing Exploration and Exploitation Through Customer Development Model

2021 ◽  
pp. 839-852
Author(s):  
Krishna Raj Bhandari
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Best Practices ◽  
Industry 4.0 ◽  
Development Model ◽  
Exploration And Exploitation ◽  
Customer Development

Balancing exploration and exploitation in entrepreneurial ventures enabled by Industry 4.0 has not been the focus of the existing literature. It is because the phenomenon is emerging and the focus has been to use practitioners' best practices in studying such phenomenon. In this chapter, the author combines the literature in balancing exploration and exploitation with the practitioners' best practices such as customer development model and lean startup. The author proposes that the existing models are good in principle but in order to really solve the problem in such an uncertain environment driven by big data, cloud computing, internet of things (IoT), and artificial intelligence, managers need to embed optimization algorithms in their decision making.

Balancing Exploration and Exploitation Through Customer Development Model

2018 ◽  
pp. 147-160 ◽  
Author(s):  
Krishna Raj Bhandari
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Best Practices ◽  
Industry 4.0 ◽  
Development Model ◽  
Exploration And Exploitation ◽  
Customer Development

Balancing exploration and exploitation in entrepreneurial ventures enabled by Industry 4.0 has not been the focus of the existing literature. It is because the phenomenon is emerging and the focus has been to use practitioners' best practices in studying such phenomenon. In this chapter, the author combines the literature in balancing exploration and exploitation with the practitioners' best practices such as customer development model and lean startup. The author proposes that the existing models are good in principle but in order to really solve the problem in such an uncertain environment driven by big data, cloud computing, internet of things (IoT), and artificial intelligence, managers need to embed optimization algorithms in their decision making.

scholarly journals Sustainable Industry 4.0 Wireless Networks, Machine Learning Algorithms, and Internet of Things-based Real-Time Production Logistics in Digital Twin-driven Smart Manufacturing

2021 ◽  
Vol 129 ◽  
pp. 04003
Author(s):  
Elvira Nica ◽  
Gheorghe H. Popescu ◽  
George Lăzăroiu
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Industry 4.0 ◽  
Production Systems ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Cyber Physical Production Systems

Research background: The aim of this paper is to synthesize and analyze existing evidence on artificial intelligence-based decision-making algorithms, industrial big data, and Internet of Things sensing networks in digital twin-driven smart manufacturing. Purpose of the article: Using and replicating data from Altair, Catapult, Deloitte, DHL, GAVS, PwC, and ZDNet we performed analyses and made estimates regarding cyber-physical system-based real-time monitoring, product decision-making information systems, and artificial intelligence data-driven Internet of Things systems in digital twin-based cyber-physical production systems. Methods: From the completed surveys, we calculated descriptive statistics of compiled data when appropriate. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. The precision of the online polls was measured using a Bayesian credibility interval. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing. Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. An Internet-based survey software program was utilized for the delivery and collection of responses. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau’s American Community Survey to reflect reliably and accurately the demographic composition of the United States. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Findings & Value added: The way Internet of Things-based decision support systems, artificial intelligence-driven big data analytics, and robotic wireless sensor networks configure digital twin-driven smart manufacturing and cyber-physical production systems in sustainable Industry 4.0.

scholarly journals A Supervised Learning Algorithm to Forecast Weather Conditions for Playing Cricket

2019 ◽  
Vol 9 (1) ◽  
pp. 1560-1565
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Weather Conditions ◽  
Redundant Data ◽  
Classification Technique

Now days, Machine learning is considered as the key technique in the field of technologies, such as, Internet of things (IOT), Cloud computing, Big data and Artificial Intelligence etc. As technology enhances, lots of incorrect and redundant data are collected from these fields. To make use of these data for a meaningful purpose, we have to apply mining or classification technique in the real world. In this paper, we have proposed two nobel approaches towards data classification by using supervised learning algorithm

Methodology for cognition of digital society

2020 ◽  
Vol 3 (2) ◽  
pp. 17-26
Author(s):  
N. N. Meshcheryakova
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Research Methods ◽  
Large Volume ◽  
Digital Transformation ◽  
Digital Society ◽  
Modern Information ◽  
Short Time

Digital sociology is a computational social science that uses modern information systems and technologies, has already formed. But the conflict with traditional sociology and its research methods has not yet been resolved. This conflict can be overcome if we remember that there is a common goal – the knowledge of the phenomena and processes of social life, which is primary in relation to the methods to be agreed upon. Digital transformation of sociology is essential, since 1) traditional sociological methods do not solve the problem of providing voluminous, reliable empirical data qualitatively and in a short time; 2) the transition from contact research methods to unobtrusive ones is in demand. The adaptation of four modern information technologies-cloud computing, big data, the Internet of things and artificial intelligence – for the purposes of sociology provides a qualitative transition in the methodology of knowledge of the digital society. Cloud computing provide researchers with tools, big data – research materials, Internet of things technology aimed at collecting indicators (receiving signals) in large volume, in real time, as direct, not indirect evidence of human behavior. The development of “artificial intelligence” technology expands the possibility of receiving processed signals of the quality of the social system without building a preliminary hypothesis, in a short time and on a large volume of processed data. Digital transformation of sociology does not mean abandoning the use of traditional methods of sociological analysis, but it involves expanding the competence of a sociologist, which requires a revision of University curricula. At the same time, combining the functions of an expert on the subject (sociologist) and data analyst in one specialist is assessed as unpromising, it is proposed to combine their professional competencies in working on unified research projects.

Smart Room Menggunakan Internet Of Things Untuk Efisiensi Biaya dan Keamanan Ruangan

2019 ◽  
Author(s):  
Robby Yuli Endra
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Smart Room

Revolus industry 4.0 tidak dapat dielakan, oleh sebab itu kitaharus mempersiapkan diri semaksimal mungkin. Beberapateknologi mutakhir di IR 4.0 contohnya Artificial Intelligence(kecerdasaan buatan), Big data dan Internet of Things. Buku Smart Room dengan menggunakan Internet of Things (IoT)buku yang membahas konsep otomatisasi ruangan denganmenggunakan sensor-sensor serta mikrokontroler Arduino sertapenggunaan Internet. Buku ini merupakan buku referensi hasildari penelitian penulis. Pada buku ini juga dijelaskan tahap-tahappembuatan prototype Smart Room dari tools-tools yangdigunakan, pengkodingan serta konsep dan arsitektur SmartRoom.Diharapkan buku referensi ini dapat bermanfaat di duniaakademis, sebagai bahan referensi ataupun bahan diskusi untukbelajar dan mengembang konsep Internet of Things (IoT) yanglebih luas lagi.Ucapan terima kasih tak lupa kami sampaikan kepada semua pihakyang telah membantu dalam penerbitan buku referensi ini. Tidakada gading yang tak retak, buku ini jauh dari kata sempurna olehsebab kami menerima masukan untuk penyempurnaan buku ini.

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