January 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

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

At a glance

Challenge

In the highly competitive manufacturing industry, our client, a packaging firm, faced challenges managing optimal inventory levels for raw materials, whose acquisition costs represented over 50% of COGS. To address this, a methodology was developed to optimize the raw-material purchasing process, reducing excess stock while balancing acquisition and inventory costs.

Solution

To create a more holistic, data-driven approach to raw-material purchasing, the solution employed a three-fold approach:

  1. MRP-based forecast: Developed an end-to-end simulation to accurately forecast raw-material consumption, reducing stock levels through precise purchasing orders.
  2. Replenishment policy: Established safety stock levels tailored to raw materials, factoring in demand volatility and supplier delivery performance, optimizing stock allocation.
  3. Purchasing orders optimization: Applied an analytical model to minimize costs by efficiently scheduling and allocating purchasing orders based on supplier conditions and the company’s cost structure.

Results

The project provided an analytics-driven, integrated view of the raw-material purchasing process, reducing effort and fostering effective inventory management. By optimizing purchasing orders, the packaging firm cut acquisition costs by 17% and reduced raw materials stock coverage by over 2 weeks. Additionally, the solution improved interdepartmental collaboration and boosted forecast accuracy for raw-material consumption by 9 percentage points.

Challenge

In the highly competitive manufacturing industry, our client, a packaging firm, faced challenges managing optimal inventory levels for raw materials, whose acquisition costs represented over 50% of COGS. To address this, a methodology was developed to optimize the raw-material purchasing process, reducing excess stock while balancing acquisition and inventory costs.

Approach

Solution

To create a more holistic, data-driven approach to raw-material purchasing, the solution employed a three-fold approach:

  1. MRP-based forecast: Developed an end-to-end simulation to accurately forecast raw-material consumption, reducing stock levels through precise purchasing orders.
  2. Replenishment policy: Established safety stock levels tailored to raw materials, factoring in demand volatility and supplier delivery performance, optimizing stock allocation.
  3. Purchasing orders optimization: Applied an analytical model to minimize costs by efficiently scheduling and allocating purchasing orders based on supplier conditions and the company’s cost structure.

Results

The project provided an analytics-driven, integrated view of the raw-material purchasing process, reducing effort and fostering effective inventory management. By optimizing purchasing orders, the packaging firm cut acquisition costs by 17% and reduced raw materials stock coverage by over 2 weeks. Additionally, the solution improved interdepartmental collaboration and boosted forecast accuracy for raw-material consumption by 9 percentage points.

Our
AI-generated
summary

Our AI-generated summary

Our AI-generated summary

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:

  1. 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.
  2. 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.
  3. 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

Our AI-generated summary

Our AI-generated summary

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.

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.

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