Established Reliability

• Proven Methods: Techniques like exponential smoothing and Bayesian models use historical data to predict future demand.

• Transparent & Interpretable: These models are easier to understand and ideal for stable demand patterns.

• Low Resource Needs: They require less computational power and are simpler to maintain than AI models.

  Limitations

• Narrow Data Use: Often ignore valuable external data such as social trends, weather, or economic indicators.

• Labour-Intensive: Some, like Bayesian models, require extensive data collection and tuning, especially in large-scale retail.

• Low Adaptability: Struggle with sudden changes like market shifts or supply chain disruptions.

Opportunities

• Real-Time Forecasting: AI/ML adapts quickly, using diverse data like sales trends, weather, and social media.

• Pattern Detection: Identifies complex trends to improve inventory accuracy and reduce stockouts.

Challenges

• Data Dependence: AI needs high-quality, well-understood data—poor data can worsen results.

• High Costs: Significant upfront investment in technology and expertise is required.

• Lack of Transparency: AI models can be hard to interpret compared to traditional methods.

Innovation and Legacy Data

UK retailers should balance innovation and proven methods when adopting AI and ML for demand forecasting. AI works well for volatile categories like fashion and tech by using real-time data and social trends to predict shifts in demand. Success depends on clean, well-understood data.

Introducing AI

Retailers can start by testing AI on high-impact products to evaluate its value. While traditional models may cost less upfront, AI offers greater long-term flexibility. A blended approach that combines AI for emerging trends and traditional models for stable patterns can offer the best results.

ESG and Uncertainty: AI & ML

Post-Brexit uncertainty, evolving consumer behaviour, and growing ESG goals make AI and ML increasingly valuable for UK retailers. These technologies help manage supply chain disruptions, track real-time online shopping trends, and support sustainability by reducing waste.

Challenges

Adoption is challenged by market volatility, data quality issues, and ROI concerns—while talent shortages and model transparency are seen as less critical barriers.