Cost Transformation

Case Study: EBITDA Improvement For A Food Ingredient Distributor

Revitalizing Value Sourcing and Supply Chains for EBITDA results

The Challenge

A large national distributor, recently acquired by a private equity firm, was looking to significantly boost their EBITDA through cost transformation.

They experienced significant cost competition across several categories. With volume declining year upon year, the company faced pressure to expand their margins as they attempted to increase customer penetration.

In addition, their distribution network, organically grown with several major customers, had left them with many facilities scattered across the country. Even if the current network provided robust coverage, the company could not match the service level requirements their customers were demanding.

Our team was engaged to broadly improve the company's EBITDA position, deploying its Value Sourcing and Supply Chain teams and expertise to address product margins and network design optimization.

Our Breakthrough

Our team first focused solely on improving the distributor’s product procurement. Each product category was prioritized based on the size of the opportunity and allocated to one of three sourcing waves.

Each wave consisted of one third of the product portfolio, and our consultants worked in tandem with the distributor's category managers to derive new strategies for each product. Levers utilized included spend consolidation, consortium buying, supplier diversification, index pricing, and various other strategies.

In parallel the team focused on network optimization. We implemented our proprietary BlueNet Supply Chain model to optimize the network footprint total cost, based on a bottom up simulation of freight, warehousing, and inventory costs.

Our Impact

The sourcing workstream addressed over 90 percent of the company's spend portfolio and led to a 10 percent increase of the distributor’s EBITDA.

The project team developed an optimal network design by reducing the number of warehouses, and also lowering freight costs. This led to an incremental 15 percent cost decrease, but still ensuring delivery of goods at an equal service level.

Lastly, through using tools such as our machine learning forecasting asset the network optimization team identified 30 percent inventory reduction opportunities and was already able to implement part of these in the first six months.