Supply Chain Analytics

Supply chain analytics can enhance the decision-making process by providing organizations with access to real-time data and insights. It provides greater visibility into the supply chain operations to identify patterns and make data-driven decisions. This visibility enables informed decisions on supply chain strategies, such as which suppliers to work with, what transportation modes to use, and how to optimize inventory levels. Supply chain management services help in proactively identifying potential risks and taking steps to mitigate them. Furthermore, by analyzing delivery and customer feedback data, organizations can gain a better understanding of customer preferences and make informed decisions about delivery schedules and inventory management.

Demand planning and forecasting are key to modern supply chains as it supports key operational procedures like demand-driven material resource planning (DDMRP), inbound logistics, production, financial planning, and risk assessment. Predictive analytics in supply chain can help organizations estimate the expected demand for products, plan their production schedules and procurement activities accordingly. It reduces the likelihood of overstocking or understocking, saving higher storage costs and inefficient use of resources. It also improves efficiency of supply chain operations and inventory planning by building ML models that identify shifts in consumer behaviour and enable SKU-level optimization for eCommerce.

AI-powered supply chain helps in inventory optimization by providing real-time data and insights into inventory levels and demand patterns. Analyzing data such as historical sales trends, lead times, and supplier performance can help make data-driven decisions to optimize the inventory levels. This includes setting appropriate safety stock levels, identifying slow-moving or excess inventory, and forecasting demand to ensure that inventory levels are sufficient to meet customer demand.