Freight Cost Reduction (Fortune 500 Manufacturer of printing Ink)

347 views

Published on

In this case study learn how BRIDGEi2i helped a Fortune 500 Printing Ink Manufacturer to develop an algorithm to maximize utilization of logistics spend and to develop a reliable estimate of the demand in each site by SKU and to save cost through optimization.

Published in: Data & Analytics
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
347
On SlideShare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
7
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Freight Cost Reduction (Fortune 500 Manufacturer of printing Ink)

  1. 1. A Case Study in Freight Cost Reduction A Fortune 500 manufacturer of Printing Ink Quick Context Objective a. 4 Ink suppliers, globally spread. Shipping Ink in Totes to 24 Cartridge Manufacturing sites; 120 different SKUs b. Shipping ink requires specialized 3PL services which are high cost • $5mn business impact due to Landed Cost savings • An understanding of Lanes for future demand based on Free Trade Agreements Impact • Freight decisions have long-term consequences – as many scenarios as necessary must be evaluated • Our ability to design these scenarios and evaluate them is key to scientific decision-making Key Success Elements Our Approach 4 Months 1 Year Client Project length Length of relationship with client • Data acquisition was a large part of the challenge • Internal data includes historical and forecasted shipment volumes, new planned products, vendor locations and dependency, current lane rates by air/ ocean/ road • External data includes lane rates from competing vendors, vendor managed inventory (VMI) fee etc. • 3 Scenarios were created based on planned volumes and future vendor locations • Each location was evaluated in detail for landed cost – ship cost + expedite cost + VMI + Taxes + Duties • A landed cost saving scenario with Current cost as baseline was developed based on several demand forecast scenarios • An MS Excel tool was developed to visualize the scenarios • Senior management would use simple filters in a pivot-like dashboard to assess the plausibility of each scenario • Landed cost savings would be the primary driver for end-state logistics vendor selection followed by their negotiations and agreements Data Management Algorithmic Play Operationalization a. To develop an algorithm to maximize utilization of logistics spend b. To develop a reliable estimate of the demand in each site by SKU and a Landed Cost saving due to the optimization

×