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

QAmplifyNet: Pushing the Boundaries of Supply Chain Backorder Prediction Using Interpretable Hybrid Quantum - Classical Neural Network

Supply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, and enhancing customer satisfaction. However, traditional machine-learning models struggle with large-scale datasets and complex relationships, hindering real-world data collection. This research introduces a novel methodological framework for supply chain backorder prediction, addressing the challenge of handling large datasets. Our proposed model, QAmplifyNet, employs quantum-inspired techniques within a quantum-classical neural network to predict backorders effectively on short and imbalanced datasets. Experimental evaluations on a benchmark dataset demonstrate QAmplifyNet's superiority over classical models, quantum ensembles, quantum neural networks, and deep reinforcement learning. Its proficiency in handling short, imbalanced datasets makes it an ideal solution for supply chain management. To enhance model interpretability, we use Explainable Artificial Intelligence techniques. Practical implications include improved inventory control, reduced backorders, and enhanced operational efficiency. QAmplifyNet seamlessly integrates into real-world supply chain management systems, enabling proactive decision-making and efficient resource allocation. Future work involves exploring additional quantum-inspired techniques, expanding the dataset, and investigating other supply chain applications. This research unlocks the potential of quantum computing in supply chain optimization and paves the way for further exploration of quantum-inspired machine learning models in supply chain management. Our framework and QAmplifyNet model offer a breakthrough approach to supply chain backorder prediction, providing superior performance and opening new avenues for leveraging quantum-inspired techniques in supply chain management

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

Development of a recycling machine for constructing synthetic yarn from plastic waste

A major challenge in plastic recycling is to convert plastic waste into a useful product. For this transformation, sustainable technologies such as plastic recycling machines are required. Current technological concepts of plastic recycling are fairly similar. This study aims to develop a small and economical plastic recycling machine to enhance microenterprise by supplying simple equipment for recycling locally processed plastic waste into thread. Starting with a hopper at the input end, the machine incorporates an auger inside a barrel, which is then linked to a metallic perforated mold at the output end. With the help of the system, the plastic flakes melting process is facilitated by maintaining temperatures between 170 °C to 211 °C at equispaced locations of a uniform barrel, while the auger spin enables the flow of molten plastic forward towards the mold.The mold reshapes the liquid plastic into strings of thread. The machine exhibits higher efficiency, reaching approximately 75 % at a decreased screw speed, as low as 28 rpm. It also achieves an average throughput of 156 gm/hour at the lowest specific mechanical energy (SME) consumption. The prototype consumes 1.5 kW for an hour operation. The entire system requires minimal space, making it appealing to individuals with limited financial resources to start a new business venture. • A sustainable technology to recycle plastic waste into plastic thread. • This optimized, portable and robust system ensures safety and lower operating costs. • This system does not require prior knowledge for operation, hence encouraging small entrepreneurs