Jan 22, 2024

Implementing an analytics-driven raw material purchasing process at a packaging industry

An holistic decision support system to enable the acquisition of raw materials

At a glance

Challenge

A packaging manufacturer needed to optimize its raw material purchasing process to reduce excess inventory while balancing procurement and holding costs for materials representing over 50% of COGS.

Solution

Developed an AI-driven purchasing optimization solution combining demand forecasting, safety stock optimization, and cost-efficient order planning to reduce inventory while minimizing procurement costs.

Results

The solution optimized raw-material purchasing through integrated analytics, reducing acquisition costs by 17%, lowering inventory by over two weeks, and improving forecast accuracy by 9 percentage points.

Players in the manufacturing industry are continuously pushed to reduce their operational costs to strive in a highly competitive global market. Under such demanding environment, our client, a packaging firm, struggled to achieve optimal inventory levels across its main raw material, whose acquisition costs account for more than 50% of the COGS (Cost of Goods Sold).

As a result, this project initiated to develop a methodology to support the company’s raw-material purchasing process, which contributed to the reduction of excessive raw-material stock, while ensuring the best possible trade-off between acquisition and inventory costs.

To leverage a more holistic and data-driven decision regarding the raw-material purchasing process, the devised solution followed a three-fold approach:

MRP-based forecast – Development of an end-to-end simulation of the downstream productive process to insightfully account stock levels throughout the manufacturing line and enable a more robust forecasting method of raw-material consumption. Such forecast aided the reduction of stock levels by allowing more accurate purchasing orders.
Replenishment policy – Definition of safety stock levels tailored to raw materials inherent specificities, such as demand volatility and volume, and considering supplier’s delivery performance. The new policy supported a smarter allocation of stock, reducing stock levels in materials with more favorable conditions.
Purchasing orders optimization – Application of an optimization model responsible to schedule and allocate the required purchasing orders according to suppliers’ conditions and the company’s cost structure. With the application of an analytical model, each purchasing order is designed to minimize the engaged

By providing an analytics-driven integrated view of the raw-material purchasing process and reducing the overall effort in placing clear-sighted purchasing orders, the project fostered the company’s monitoring culture and ensured effective management of its raw materials inventory.

The optimization of purchasing orders allowed the packaging firm to reduce its acquisition costs by 17% and enhance the management of its raw materials inventory through a decrease of more than 2 weeks of stock coverage. Moreover, apart from reinforcing the interconnection amongst distinct departments, the integration of the complex productive context induced a significant forecast accuracy improvement (of 9p.p) regarding raw materials consumption.

Case Studies