Predictive Inventory Management System for Retail - Silverspace Inc.

A manufacturing company that depends on the operation of a large group of industrial machines for their production processes. Unplanned equipment failure

1. Case Study: Predictive Inventory
Management System for Retail

2. About Project
A manufacturing company that depends on the operation of a large group of
industrial machines for their production processes. Unplanned equipment failure
downtime that results in slowed production, higher maintenance costs, and
reduced overall effectiveness isa serious problem. In the future, predictive
maintenance using AI and machine learning is planned by the company to detect
any potential failures before they occur along with the scheduling of maintenance.
They are developing and deploying this solution with the help ofSilverspaceInc.
Key Challenges
A large retail chain with multiple stores across regions faces challenges
inoptimizinginventory levels. The existing inventory management
system relies heavily on manual forecasting and ordering processes,
leading to frequentstockouts, overstock situations, and suboptimal
shelf availability.

3. Optimize Inventory Levels
Reduce instances of stockouts and overstock situations while
maintaining adequate product availabili
Improve Forecast Accuracy
Implement a more accurate forecasting model to predict demand at a
granular level.
Solution Approach
The solution involves developing a predictive inventory management system using
AI/ML techniques. Here’s how we approach solving the problem:
1.Data Collection and Preparation:
Data Sources: Gather historical sales data from all retail stores, including SKU-level
transactions, seasonal trends, promotions, and external factors (e.g., weather data,
economic indicators).
Data Cleaning: Clean and preprocess the data to handle missing values, outliers,
and inconsistencies.
Feature Engineering: Create relevant features such as seasonality indicators, day-of-
week effects, and lagged sales data to capture patterns and trends.
Automate Replenishment
Introduce automation in inventory replenishment to streamline
operations and reduce manual effort.
Model Training: Train models on historical data, validate performance using
cross-validation techniques, and select the best-performing model based on
accuracy metrics (e.g., MAE, RMSE).
2.Model Selection and Training:
Demand Forecasting Models: Evaluate different time-series forecasting models
such as ARIMA, Exponential Smoothing, and advanced machine learning models
like LSTM (Long Short-Term Memory) networks.

4. 4.Automated Replenishment System:
•Decision Support System: Develop a decision support system that
generates optimal reorder points and quantities based on predicted
demand, desired service levels, and lead times.
•Integration with ERP: Integrate with the existing ERP (Enterprise
Resource Planning) system to automate purchase orders and
replenishment processes.
5.Monitoring and Evaluation:
•Performance Metrics: Define KPIs (Key Performance Indicators) such as
inventory turnover rate, service level, and forecast accuracy.
•Continuous Improvement: Monitor system performance regularly, gather
feedback from store managers, and refine models as needed to improve accuracy
and efficiency.

5. What Our Clients Say
SilverspaceInc started handling our
equipment maintenance and the
transformation has been unreal. The
AI-powered predictive maintenance
solution has made a big difference in
our operational efficiency and reduced
downtime in the plant. The information
and tools shared have been crucial for
us to improve our production planning
and extend our equipment's life.
John MManufacturingCo
John M
Manufacturing Co

6. [phone]
[email protected]
https://silverspaceinc.com/