Demand forecasting plays a crucial role in Sales and Inventory Operations (SIOP). However, client forecasts often lack true customer demand. The existing method is biased and relies on a top-down approach, leading to questionable accuracy.
A large manufacturing company was facing significant challenges in their demand forecasting process. Their existing method was based on historical data and was not providing accurate predictions. This was leading to issues with inventory management, production planning, and ultimately affecting their bottom line.
As a result, the company was struggling to keep up with demand, which was leading to high levels of stock shortages and overstocking.
The company approached Cloudseed, a leading provider of demand forecasting and inventory management solutions, for assistance in improving their demand forecasting accuracy. Cloudseed recommended a machine learning-based approach, which would provide the company with a more accurate and real-time prediction of demand.
Cloudseed implemented their demand forecasting solution, which integrated with the company's existing ERP system. The solution used real-time data from various sources, such as customer orders, sales data, and market trends, to generate accurate demand forecasts.
The Cloudseed solution will enhance demand prediction for MTO and MTS SKUs by incorporating both internal (sales forecast) and external (consumer index, customer data, seasonality, special events, and macro-economic factors) factors that are crucial to the client's operations.
The Intelligent Forecasting system will accurately predict demand as closely as possible to the customer and develop a well-balanced demand and supply plan for a quarter, while also considering a 12-month outlook to enhance business outcomes and customer satisfaction.
The solution also provided the company with real-time insights into customer demand patterns, which helped the company to better understand their customers and make informed decisions about product development and production planning.
Key characteristics of Intelligent Forecasting include
After implementing the solution, the company experienced a significant improvement in their demand forecasting accuracy. They were now able to accurately predict demand, which allowed them to better manage their inventory levels and reduce the amount of stock shortages and overstocking.
In addition, the company was able to effectively track inventory levels and had a better understanding of customer demand patterns. This helped the company to make more informed decisions about product development and production planning, leading to improved production efficiency and reduced costs.
Benefits to Customer
Optimized inventory holding cost.
Reduced transportation expenses
Reduction in operational and manufacturing cost
Overall productivity improvement
Increased factory efficiency
Improved total cost of ownership
This led to a significant improvement in their demand forecasting accuracy and allowed the company to better manage their inventory levels, improve production efficiency, and reduce costs.
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