Predictive Analytics in Demand Forecasting: Improving Accuracy in Retail Supply Chains

Research Question

This thesis investigated how predictive analytics can improve demand forecasting in retail supply chains by addressing three main questions:

• How does predictive analytics affect supply chain agility and responsiveness?
• What techniques are most effective for applying predictive analytics to retail demand forecasting?
• How do predictive analytics compare with traditional forecasting methods?

2.

Methodology

A document analysis approach was used, reviewing academic literature, industry reports, and real-world case studies (e.g., Zara, Walmart). This allowed for a broad evaluation of predictive analytics without the need for primary data collection .

Key Findings

Improved accuracy: Predictive analytics outperforms traditional methods by integrating external factors such as weather, economic trends, and social media sentiment.

Real-world success: Companies like Zara and Walmart reduced stockouts and excess inventory while improving customer satisfaction by using predictive models.

Challenges: High setup costs, data quality issues, and the need for skilled personnel remain significant barriers.

Implications for Business

Predictive analytics enables retailers to:

• Reduce waste and inventory costs.
• Enhance customer satisfaction and retention.
• Use frameworks like CRISP-DM to ensure model quality and consistency.
• Build agile and responsive supply chains that adapt to real-time changes.

Recommendations for Practitioners

• Invest in robust IT infrastructure and data governance.
• Foster a data-driven culture with training for both technical and non-technical staff.
• Use frameworks like CRISP-DM to ensure model quality and consistency.
• Balance analytics with human judgment to avoid overreliance on models.

Conclusion

Predictive analytics is not just a technological upgrade — it is a strategic tool for transforming retail supply chains. While traditional methods still have their place, the future lies in combining machine learning with human expertise to deliver scalable, flexible, and accurate forecasting.