2 research outputs found
Exploring Internet of Things Adoption Challenges in Manufacturing Firms: A Fuzzy Analytical Hierarchy Process Approach
Innovation is crucial for sustainable success in today's fiercely competitive
global manufacturing landscape. Bangladesh's manufacturing sector must embrace
transformative technologies like the Internet of Things (IoT) to thrive in this
environment. This article addresses the vital task of identifying and
evaluating barriers to IoT adoption in Bangladesh's manufacturing industry.
Through synthesizing expert insights and carefully reviewing contemporary
literature, we explore the intricate landscape of IoT adoption challenges. Our
methodology combines the Delphi and Fuzzy Analytical Hierarchy Process,
systematically analyzing and prioritizing these challenges. This approach
harnesses expert knowledge and uses fuzzy logic to handle uncertainties. Our
findings highlight key obstacles, with "Lack of top management commitment to
new technology" (B10), "High initial implementation costs" (B9), and "Risks in
adopting a new business model" (B7) standing out as significant challenges that
demand immediate attention. These insights extend beyond academia, offering
practical guidance to industry leaders. With the knowledge gained from this
study, managers can develop tailored strategies, set informed priorities, and
embark on a transformative journey toward leveraging IoT's potential in
Bangladesh's industrial sector. This article provides a comprehensive
understanding of IoT adoption challenges and equips industry leaders to
navigate them effectively. This strategic navigation, in turn, enhances the
competitiveness and sustainability of Bangladesh's manufacturing sector in the
IoT era
Big Data - Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques
This article intends to systematically identify and comparatively analyze
state-of-the-art supply chain (SC) forecasting strategies and technologies. A
novel framework has been proposed incorporating Big Data Analytics in SC
Management (problem identification, data sources, exploratory data analysis,
machine-learning model training, hyperparameter tuning, performance evaluation,
and optimization), forecasting effects on human-workforce, inventory, and
overall SC. Initially, the need to collect data according to SC strategy and
how to collect them has been discussed. The article discusses the need for
different types of forecasting according to the period or SC objective. The SC
KPIs and the error-measurement systems have been recommended to optimize the
top-performing model. The adverse effects of phantom inventory on forecasting
and the dependence of managerial decisions on the SC KPIs for determining model
performance parameters and improving operations management, transparency, and
planning efficiency have been illustrated. The cyclic connection within the
framework introduces preprocessing optimization based on the post-process KPIs,
optimizing the overall control process (inventory management, workforce
determination, cost, production and capacity planning). The contribution of
this research lies in the standard SC process framework proposal, recommended
forecasting data analysis, forecasting effects on SC performance, machine
learning algorithms optimization followed, and in shedding light on future
research