Case Study 1

Global Supply Co. (GSC), a multinational manufacturing company, faced challenges in optimizing its supply chain operations to meet customer demand while minimizing costs and maximizing efficiency. Despite vast amounts of data on production schedules, inventory levels, transportation logistics, and supplier performance, GSC struggled to make optimal real-time decisions. GSC embarked on a prescriptive analytics initiative to address this issue by leveraging advanced modeling techniques and algorithms.

Analyse the critical prominence of prescriptive analytics to recommend the best action for improving supply chain performance.

Case Study 2

ABC Retail is a large chain of supermarkets with stores spread across multiple regions. With the increasing competition in the retail industry and the growing complexity of consumer behaviours, ABC Retail recognized the need to leverage advanced analytics to gain insights into customer preferences, optimize operations, and enhance profitability. To address these challenges, ABC Retail embarked on a project to implement machine learning (ML) techniques in their business analytics processes.

Learning Objectives

1. Customer Segmentation: ABC Retail struggled to effectively segment their customer base and tailor marketing strategies to meet diverse preferences and purchasing behaviours.

2. Inventory Management: The company faced challenges in managing inventory levels, leading to stockouts, excess inventory, and suboptimal allocation of resources.

3. Demand Forecasting: Accurate demand forecasting was crucial for optimizing procurement, minimizing waste, and ensuring sufficient stock availability to meet customer demands.

4. Promotion Optimization: ABC Retail sought to optimize promotional activities by identifying the most effective promotions for different customer segments and products.