Solution Challenge

Client was looking for a solution to optimise and predict optimal inventory levels.

Summary

The challenge involved designing a solution that leverages machine learning and predictive analytics and forecast the optimal levels of inventory, thus controlling the production or purchase of materials. The solution would be based on data ingested from various sources of data including market segments, customer types, and sales regions. The goal is to provide accurate revenue projections to support strategic decision-making, resource allocation, and financial planning.

Preconditions

The following preconditions had to be met.

• Historical data on sales, demand patterns, lead times, and inventory levels are available.
• Integration with ERP and supply chain management systems is established.

Solution Flow

The solution consisted of a series of stages in which data was modelled, made available for analysis, and perform predictive analysis, before a forecast could be arrived at.

• Hanelytics ingested historical data on sales, demand patterns, lead times, inventory levels, and other relevant factors from various sources (ERP, SCM systems, etc.). It performs data preprocessing, cleaning, and feature engineering to prepare the data for analysis.
• Using advanced machine learning algorithms (e.g., time series forecasting, regression, deep Hanelytics analyzed the data to identify patterns, trends, and correlations. Hanelytics generates predictions for future demand, taking into account factors such as seasonality, promotions, market conditions, and external events.
• Based on demand predictions and constraints (e.g., lead times, safety stock levels, service level agreements), the system calculates the optimal inventory levels for each product at different nodes of the supply chain network. Hanelytics provided recommendations for inventory adjustments, including suggestions for replenishment orders, stock transfers, or inventory rebalancing across locations.
• Inventory planners can review the recommendations and make informed decisions regarding inventory optimization strategies. Hanelytics continues to monitor and adjust predictions as new data becomes available.

Alternative Flows

• If the data quality is poor or incomplete, the system may request additional data or manual intervention for data cleaning and preprocessing.
• If the predicted demand or recommended inventory levels deviate significantly from historical patterns or business constraints, the system may trigger alerts and provide explanations for further analysis.

Potential Benefits

• Inventory levels across the supply chain are optimized, leading to reduced carrying costs, improved asset utilization, and better responsiveness to customer demand.
• Supply chain operations are more efficient, with reduced stockouts and excess inventory.
• Continuous monitoring and adjustment of inventory levels based on updated data and predictions.
• Improved customer service levels and reduced stockouts.
• Increased supply chain agility and responsiveness to demand fluctuations.
• Better inventory visibility and control across the supply chain network.

By leveraging Hanelytics, organizations can proactively manage their inventory levels, streamline supply chain operations, and enhance overall business performance.