Literature Review papers

Literature Review in Manufacturing & AI

Seeking an experienced academic writer to create a comprehensive literature review for a research project focused on the intersection of artificial intelligence (AI), circular economy principles, and manufacturing efficiency
(Attached are my outline ideas). This literature review will critically analyze and synthesize insights from ten specific academic papers, exploring topics such as AI in operations management, circular economy in manufacturing, and the implementation of sustainable technologies in supply chains. It will be used to help me create my own literature review, your work provided will not be used or submitted.

Approx 2000 words / Harvard Citation Style. Can use Chat GPT to assist but would prefer academic writing style.

Job Responsibilities:
1. Thorough Analysis: Conduct a detailed analysis of each of the ten provided papers. Summarize key points, methodologies, results, and conclusions.
2. Synthesis: Integrate insights from these papers to provide a coherent view of current trends, gaps in the literature, and potential future research directions.
3. Critical Evaluation: Critically assess the methodologies and conclusions of the studies, discussing their implications for both theory and practical application in manufacturing.
4. Framework Development: Propose a theoretical framework based on the Resource-Based View and Institutional Theory that aligns with the themes from the reviewed papers.
5. Writing: Compile the findings into a well-structured literature review chapter, adhering to academic standards and citation guidelines.
Deliverables:
• A literature review document of approximately 1500-2000 words
• A summary presentation outlining key findings and theoretical implications.
Budget and Timeline:
• Please provide your quote and time deliverable

Additional Papers

How to use no-code artificial intelligence to predict and minimize the inventory distortions for resilient supply chains

The ASSISTANT project: AI for high level decisions in manufacturing

Unleashing the power of AI in manufacturing: Enhancing resilience and performance through cognitive insights, process automation, and cognitive engagement

Artificial intelligence and relocation of production activities: An empirical cross-national study

Artificial Intelligence (AI) and data sharing in manufacturing, production and operations management research

Assessing the impact of supplier benchmarking in manufacturing value chains: an Intelligent decision support system for original equipment manufacturers

Human-centric artificial intelligence architecture for industry 5.0 applications

AI-based decision making: combining strategies to improve operational performance

The interpretive model of manufacturing: a theoretical framework and research agenda for machine learning in manufacturing

Abstract

Manufacturing is undergoing a paradigmatic shift as it assimilates and is transformed by machine learning and other cognitive technologies. A new paradigm usually necessitates a new framework to comprehend it fully, organise extant knowledge, identify gaps in knowledge, guide future research and practice, and synthesise new knowledge. Paradoxically, such a framework to guide the research and practice of ML in manufacturing remains absent. This paper attempts to fill this gap by presenting the interpretive model of manufacturing as an integrative framework for ML in manufacturing. A systematic hybrid literature review approach has been adopted to conduct both thematic and conceptual synthesis of the literature. The descriptive literature review method has been used to conduct a thematic synthesis of the literature. The framework synthesis method has been used to complete a conceptual synthesis of the literature. The resultant framework, the interpretive model of manufacturing, is articulated as consisting of scan, store, interpret, execute, and learn as its purposive components. Research questions have been identified for each of these components, as well as at their interfaces, to develop a comprehensive and systematic research agenda. Additional areas for extending research have also been identified. Implications for manufacturing operations, manufacturing strategy, and manufacturing policy have been drawn out for practitioners and policy makers. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

Structure of paper

Research Question

How can artificial intelligence-driven solutions enhance the resilience and efficiency of manufacturing supply chains while supporting circular economy principles?

Objectives

1. To Assess the Current State of Ametek’s Manufacturing Operations:

1. • Conduct a detailed analysis of Ametek’s existing manufacturing processes and supply chain management practices.
   • Identify key areas where AI can be integrated to improve operational efficiency and resilience.

2. To Investigate AI Applications Relevant to Ametek:

1. • Review AI technologies and their potential applications in manufacturing, specifically focusing on areas pertinent to Ametek’s operations.
   • Examine case studies of similar companies that have successfully implemented AI solutions in their manufacturing processes.

3. To Propose an AI Implementation Framework for Ametek:

1. • Develop a step-by-step framework for integrating AI into Ametek’s manufacturing and supply chain operations.
   • Outline the necessary technological, organizational, and cultural changes required for successful AI implementation.

4. To Evaluate the Potential Benefits and Challenges of AI Integration:

1. • Analyze the expected benefits of AI implementation in terms of efficiency, resilience, and sustainability.
   • Identify potential challenges and risks associated with AI adoption and propose mitigation strategies.

5. To Align AI Implementation with Circular Economy Principles:

1. • Explore how AI can support Ametek’s transition towards a circular economy by enhancing resource efficiency, waste reduction, and product lifecycle management.
   • Propose specific AI-driven initiatives that align with circular economy principles.

6. To Develop a Case Study on AI Implementation at Ametek:

1. • Document the process of proposing and planning AI integration at Ametek.
   • Provide a detailed case study that can serve as a roadmap for other manufacturing companies looking to adopt AI solutions.

Structure of the State-of-the-Art Literature Review

1. Introduction

1. • Purpose: Explain the importance of AI in manufacturing and circular economy practices.
   • Scope: Define the scope of the review, including the key topics and questions addressed.
   • Objectives: Outline the main objectives of the review.

2. Theoretical Background

1. • AI in Manufacturing: Provide an overview of how AI is applied in manufacturing processes.
   • Circular Economy Principles: Explain the principles of the circular economy and their relevance to manufacturing.

3. Review of Key Topics

1. • AI Applications in Manufacturing: Discuss various AI applications in manufacturing, including predictive maintenance, process optimization, and quality control.
   • AI and Supply Chain Management: Explore how AI enhances supply chain management, focusing on efficiency, resilience, and sustainability.
   • Integration of Circular Economy Practices: Review how AI facilitates the implementation of circular economy practices in manufacturing supply chains.

4. Critical Analysis and Synthesis

1. • Trends and Developments: Highlight the latest trends and developments in AI and circular economy practices in manufacturing.
   • Gaps and Challenges: Identify gaps in the current research and challenges in integrating AI and circular economy principles.
   • Case Studies: Provide examples of successful AI implementations in manufacturing that support circular economy practices.

5. Research Agenda

1. • Future Research Directions: Propose areas for future research based on identified gaps and emerging trends.
   • Practical Implications: Discuss the implications of the research findings for practitioners and policymakers.

6. Conclusion

1. • Summary of Findings: Summarize the key insights from the review.
   • Recommendations: Provide recommendations for future research and practice.

Theoretical frameworks for paper

1. Resource-Based View (RBV)

The RBV emphasizes the strategic importance of a firm’s internal resources and capabilities in achieving competitive advantage. In the context of your study:

• AI as a Strategic Resource: AI technologies can be seen as valuable, rare, and difficult-to-imitate resources that enhance manufacturing efficiency and supply chain resilience.
• Circular Economy as a Capability: The ability to integrate circular economy principles can be viewed as a dynamic capability that enables firms to adapt to environmental and market changes.

3. Institutional Theory

Institutional theory examines how organizational structures, practices, and strategies are influenced by social, cultural, and regulatory norms. In the context of your study:

• Regulatory and Normative Pressures: The adoption of circular economy practices can be driven by regulatory requirements and societal expectations for sustainability.
• AI Adoption: Institutional pressures can also influence the adoption of AI technologies, as firms strive to conform to industry standards and gain legitimacy.