Build a system that helps you with inventory optimization and generates smart insights
Why inventory forecasting is important
Enterprises, especially manufacturers and retailers, are facing challenges in managing inventory effectively since with globalization, the supply chain has become much more complex. Materials are imported from all over the globe to make the supply meet demand across the enterprises. Due to this, making use of inventory data analytics to improve the supply chain’s effectiveness is becoming more important in the current global marketplace. Inventory optimization is the need of the hour, to eliminate the ambiguity of how to distribute the right inventory, in the right quantity, to the right locations, at the right time.
How we helped a company implement a smarter inventory prediction system
One of our clients, who is a major manufacturer of electronics, was experiencing challenges in gaining any valuable insights from their data, which can help them in demand forecasting and inventory planning. The client’s current inventory planning processes were ad-hoc in nature and every stage starting from data collection, reporting, till inventory analysis was done manually.
Our team of data scientists at Softweb analyzed the information regarding inventory transactions and identified the trends and patterns in inventory use. Inventory analytics anticipated the optimal inventory level which helped the organization in cutting overstocking costs, while also allowing them to classify the inventory materials based on their priority or popularity. Our system also ensured that the company never had stockouts, thus enhancing the customer relation and improving the overall profits.
Data science-driven smart inventory management system for manufacturers
- Traditional inventory management in manufacturing
- Limitations of traditional inventory management practices
- Problems manufacturers face with traditional inventory management
- What is Smart Inventory Management and how it overcomes the challenges that manufacturers face
- Real-life examples and use cases
- Core elements of this paradigm shift - Data Science and Machine Learning
- How to get started
- The company is on track to optimizing its inventory forecasting systems all over the globe and has experienced an increase in sales by 10% by making sure that supply meets the demand.
- The client is enjoying significant cost-savings with a reduction of excess inventory by 15%.
- The system that we built for the company now helps its inventory managers and decision makers keep track of where the goods are at any point of time.
How inventory prediction is useful to the manufacturing industry
- Helpful in reducing working capital requirements with inventory prediction solutions.
- Classify assets and products based on their priority or demand.
- Facilitates in identifying the accurate physical inventory for your supply chain.
- Easily determine optimal purchase levels to support production facilities.
- Analysis can help in considering alternative inventory models.
- Facilitates future consumption, sale or further processing/value addition.
- Data science can help you undertake preventive measures to reduce supply disruption.
- It can give demand-driven forecasting through a combination of structured and unstructured data.
- It can generate route optimization and more efficient transportation using GPS-enabled big data telematics.
- Calculate customer demand and provide detailed inventory insights.
- Useful in managing physical and virtual assets for peak profitability.