Aug' 23
The IUP Journal of Operations Management
| Article | Price (₹) | ||
| Improving Manufacturing Process Efficiency Using Boolean Algebra |
100
|
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| Optimization Techniques in Logistics Management Using Deep Learning Algorithms |
100
|
||
| Impact of RPA on Procurement Process of Service Industry MNCs in India |
100
|
||
| Risk Mitigation Using Interpretive Structural Modeling in Fertilizer Supply Chain |
100
|
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| Research Note Lean Practices in Circular Bioeconomy: Opportunities and Challenges |
100
|
||
Improving Manufacturing Process Efficiency Using Boolean Algebra
For many manufacturing firms, achieving process efficiency is a core objective which can only be achieved either by input minimization or by output maximization. Nevertheless, there are many constraints. To attain efficiency in the manufacturing process, many engineers have worked on cost reduction. However, its effects on quality cannot be ignored. Quality and cost have a direct relationship. This paper tries to develop an efficiency control system based on Boolean algebra. This control system may be related in many points to different control quality tools. However, the particularity of this system is the inclusion of cost factors into the process. The core focus of apprehension is directed toward a responsive system. The system is based on intrusions by understanding each parameter of efficiency. The application of efficiency control is produced through the correlation between various crucial concepts.
Optimization Techniques in Logistics Management Using Deep Learning Algorithms
Logistics management plays a crucial role in supply chain operations, encompassing various tasks such as transportation, inventory management, and order fulfillment. The application of deep learning algorithms has emerged as a promising approach to optimize logistics processes and enhance efficiency. This paper explores the utilization of deep learning algorithms for optimization in logistics management, focusing on transportation routing, warehouse management, and demand forecasting. The study discusses the key deep learning algorithms employed in these areas, including deep neural networks, convolutional neural networks, recurrent neural networks, and reinforcement learning. Furthermore, it presents case studies and empirical evidence that demonstrate the effectiveness of deep learning techniques in improving logistics operations. The paper provides an overview of logistics management and the motivation for employing deep learning algorithms for optimization.
Impact of RPA on Procurement Process of Service Industry MNCs in India
The popularity of Robotic Process Automation (RPA) is increasing, as it helps reduce costs, restructure processing and drives better customer experiences. It is easy to use in business units, does not require any individual to additionally learn new tools or ask IT teams for support, and does not require any changes to be made to the existing IT infrastructure of an organization. Further, it is important to recognize that in contemporary times, the procurement process has become much more than purchasing goods and services on day-to-day basis. The procurement process provides added value in the long run. The paper assesses the impact of RPA on procurement process in MNCs operating in the Indian service industry, specifically in the Delhi NCR region in India.
Risk Mitigation Using Interpretive Structural Modeling in Fertilizer Supply Chain
Supply chains are the lifeline of businesses and economies. Today, with increased globalization and outsourcing, supply chains have become prone to many risks. The management of supply chain risks is a major area of research. It is important to understand the various risks inherent in a supply chain and the interactions between them before trying to manage/mitigate them. This paper seeks to understand the risks inherent in a fertilizer supply chain and model the risks using interpretive structural modeling. The dependence power and driving power of each risk is also calculated.
Research Note
Lean Practices in Circular Bioeconomy: Opportunities and Challenges
Starting from automobile
manufacturing, the Lean system
has reached almost all major
sectors like pharmaceutical, construction,
apparel, aeronautical, banking,
healthcare, etc. However, the Lean
principles have not been widely applied
in bioeconomy. In this note, the author has
tried to highlight this opportunity,
especially in the context of circular
bioeconomy
Lean and Circular Bioeconomy
Lean philosophy inspires organizations to
move towards excellence through
continuous improvement. It aims to
simplify processes to make them more
dynamic and adaptable to any changes.
Lean tools help to identify and eliminate
waste (Islam, 2023a; 2023b) and promote
a culture of continuous improvement
within the entire organization (Sa et al.,
2023).
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