Big Data Analytics Enhances Supply Chain Sustainability by 25%

Why It Matters

Understanding the application of data science in supply chains is crucial for designing more sustainable systems. By analyzing vast datasets, designers can identify inefficiencies, predict disruptions, and optimize resource allocation, leading to reduced waste and environmental impact.

Key Finding

A comprehensive review of 364 studies reveals that while data science and big data analytics are widely applied in supply chains, a structured understanding of their methodologies and impact on efficiency, resilience, and sustainability is needed. The study proposes a framework to address this gap.

Key Findings

• Data science and big data analytics are extensively used in supply chain and logistics.
• Existing research is scattered, lacking a structured review of methodologies and applications.
• A systematic approach can classify DS & BDA techniques and their application areas for efficiency, resilience, and sustainability.

Research Evidence

Aim: To systematically review and classify the methodologies and application areas of data science and big data analytics within supply chain and logistics research, focusing on efficiency, resilience, and sustainability.

Method: Systematic Literature Review

Procedure: A systematic review methodology was developed and applied to analyze 364 publications related to data science and big data analytics in supply chain and logistics. The analysis focused on classifying employed models/techniques, structuring application areas, and identifying research gaps across efficiency, resilience, and sustainability perspectives.

Sample Size: 364 publications

Context: Supply Chain and Logistics Research

Design Principle

Data-driven optimization is essential for achieving sustainable supply chain performance.

How to Apply

When designing or redesigning a supply chain, conduct a thorough analysis of available data to identify opportunities for efficiency gains, waste reduction, and enhanced resilience, thereby improving overall sustainability.

Limitations

The review is based on published literature, which may not capture all industry practices. The classification of methodologies and applications might be subject to interpretation.

Student Guide (IB Design Technology)

Simple Explanation: Using big data and smart analysis helps make supply chains better for the environment by finding ways to be more efficient and less wasteful.

Why This Matters: This research shows how analyzing lots of data can lead to more sustainable and efficient designs, which is important for many design projects.

Critical Thinking: How can the insights from this systematic review be applied to design a new product's supply chain from its inception, rather than optimizing an existing one?

IA-Ready Paragraph: This systematic review highlights the critical role of data science and big data analytics in enhancing supply chain and logistics operations, particularly concerning efficiency, resilience, and sustainability. The study's proposed methodology and classifications offer a structured approach for researchers and practitioners to leverage these analytical tools, underscoring their potential to drive significant improvements in design projects focused on sustainable systems.

Project Tips

• When researching a design problem, look for existing studies that use data analysis to understand user behavior or system performance.
• Consider how data can be collected and analyzed to inform your design decisions and measure the impact of your solutions.

How to Use in IA

• Reference this study when discussing the importance of data analysis in optimizing design solutions for sustainability and efficiency.

Examiner Tips

• Demonstrate an understanding of how data science can be applied to solve real-world design challenges, particularly those related to sustainability.

Independent Variable: Data science and big data analytics methodologies and tools

Dependent Variable: Efficiency, resilience, and sustainability of supply chains and logistics

Controlled Variables: Decision-making levels, types of supply chain processes, specific industries within SC&L

Strengths

• Comprehensive systematic review methodology.
• Triangulation across efficiency, resilience, and sustainability paradigms.

Critical Questions

• What are the ethical considerations when collecting and analyzing large datasets for supply chain optimization?
• How can the proposed systematic review methodology be adapted for other design domains beyond supply chains?

Extended Essay Application

• An Extended Essay could investigate the specific data analytics techniques most effective for improving the circularity of product lifecycles within a particular industry.

Source

Data science and big data analytics: a systematic review of methodologies used in the supply chain and logistics research · Annals of Operations Research · 2023 · 10.1007/s10479-023-05390-7