Linda Mandyu, Client Success Manager at Argility Technology Group, emphasizes the growing challenges of demand forecasting in the retail sector due to rapid market changes. Traditional methods, often reliant on complex Excel spreadsheets, are proving inadequate for category managers.
Mandyu highlights that while some retailers are integrating statistical analysis and business intelligence tools, the need for skilled statistical analysts remains critical. He describes the demand for 'unicorns'—experts uniquely qualified in retail analytics.
Argility leverages advanced predictive and prescriptive modeling on the Google Cloud platform, utilizing machine learning and AI to enhance demand forecasting capabilities. This approach allows retailers to optimize stock levels and pricing strategies, ultimately increasing sales and customer retention.
The company's 'demand forecasting as a service' model helps clients analyze large datasets to uncover insights, such as competitive pricing strategies and optimal order levels. This not only accelerates data analysis but also frees up resources for more strategic tasks.
An example cited by Mandyu involves a client in the FMCG sector, which is projected to save millions by improving its demand forecasting processes, reducing product returns significantly. By shifting from human estimation to AI-driven insights, the retailer aims to achieve a reduction in returns by 2% to 5%.
Mandyu concludes that combining human expertise with AI offers significant opportunities for solving complex retail analytics challenges, leading to faster and more accurate data-driven decisions.