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Blog November 1, 2022
2 min read

Revitalizing Supply Chain Efficiency: How Optimization Transformed a Fortune 500 F&B Company

Discover how Tiger Analytics optimized a Fortune 500 Beverage and Snack company’s supply chain, reducing costs and enhancing visibility. Learn how Gurobi’s mathematical models streamlined product allocation, minimized expenses, and improved customer satisfaction amidst post-pandemic challenges.

As profitability wars heat up between brands, a resilient, efficient supply chain seems to provide a definite competitive advantage. Organizations are looking for more control over the information flow, visibility, and accessibility in their supply chains to help them meet their long-term, end-to-end efficiency and profitability goals.

Across the business spectrum, AI is used by companies in a wide array of off-the-shelf and custom-built solutions to determine the optimal configuration of their supply chains.

Optimizing the products’ flow in the network and putting the right products in the right place at the right time saves transportation costs, holding costs, and out-of-stock costs. It maximizes inventory utilization, reduces waste, and guarantees a short delivery time to the customers. It creates a win-win situation for the businesses and the customers too.

How Optimization Helped a Large Beverage and Snack Company Reduce Costs and Gain Visibility

Post-pandemic, a large Fortune 500 company in the Food and Beverage space was facing problems in its supply chain network. On one hand, gaps in the supply chain negatively impacted distribution – reducing the service level, increasing the product delivery time, leading to customer dissatisfaction and overall revenue loss; while on the other hand, visibility gaps in inventory management meant that products were piling up in some warehouses and could not reach customers, causing wastage.

At Tiger Analytics we designed a network optimization solution to help allocate products to the best node in the network and facilitate an equilibrium movement in the same layer of the warehouses to solve the existing long-days-on-hand problem. Using Gurobi’s mathematical optimization models, our solution:

  • Minimized the overall cost, including transportation, holding, and out-of-stock costs, and gradually managed the unbalanced product allocation issues.
  • Brought in a degree of visibility into the supply chain so that the operations team could track their products and manage the distribution through the network.
  • Helped meet customer demand effectively

The algorithm helped reduce the warehouse planners’ time and the global optimization flow decisions reduced the possibility of errors.

An optimized supply chain is critical to a business’s success. As new-age challenges crop up – so do opportunities and innovations.

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