Guest Blogger: Tiger Analytics, a Gurobi Alliance Partner
As the world continues to traverse through a pandemic, wars, climate change, and the advent of the metaverse, lines between the physical and digital world continue to blur. Retail and consumer packaged goods (CPG) companies are redefining the supply chain after every major world event.
With supply chains becoming more complex and global companies wanting more control over information flow, visibility, and accessibility, the need to have an agile supply chain that can prepare the organization for the future is increasing. Data and optimization are the key areas that can help achieve this transformation.
For decades, mathematical optimization has been the go-to tool for supply chain network design. This AI technology is used by companies across the business spectrum in a wide array of off-the-shelf and custom-built solutions to automatically determine the optimal configuration of their supply chains. With mathematical optimization, businesses can make the best possible decisions on how to design their supply chain networks to ensure long-term, end-to-end efficiency and profitability.
Optimization allows companies to put the right products in the right place at the right time, saving transportation costs, holding costs, and out-of-stock costs. In addition, it allows them to maximize the utilization of inventory, reduce waste, and guarantee fast delivery times for their customers.
A large, Fortune 500 beverage and snack company was facing problems in its supply chain network post-pandemic. Its broken and inefficient supply chain was slowing down the time it takes products to reach customers—reducing service levels and causing revenue loss. In addition, products were accumulating in some warehouses, causing waste.
Tiger Analytics designed a network optimization solution to help allocate the products to the best node in the network and enable rebalance movement in the same layer of the warehouses to solve the existing long days-on-hand problem. This solution helps with meeting customer demand while minimizing transportation costs, holding costs, and out-of-stock costs.
Thanks to the power of optimization, the problem of unbalanced product allocation is improved gradually. What’s more, the solution also adds to the supply chain’s visibility, enabling the operations team to know where the products are and their distribution in the network. The automation of the algorithm reduces the warehouse planners’ time and the global optimized flow decisions reduce the unoptimized mistakes.
An optimized supply chain is critical to a business’s success. Omnichannel, IoT, and other new concepts bring both challenges and opportunities to the new supply chain era, which has made mathematical optimization a more attractive solution than ever before.
If you’re interested in reaping the benefits of this technology, start with our Jupyter Notebook Modeling Examples—which are mathematical optimization models coded using the Gurobi Python API and implemented with Jupyter Notebooks. With this set of examples, you can learn how to solve a classic supply network design problem that involves finding the minimum cost flow through a network. We’ll show you how—given a set of factories, depots, and customers—you can use mathematical optimization to determine the best way to satisfy customer demand while minimizing shipping costs. The second example goes a step further, showing how to determine which depots to open or close in order to minimize overall costs.
Are you in the California area? Come see Tiger Analytics (a Gurobi Alliance Partner) and Gurobi at the Open Data Science Conference (ODSC) November 1 – 3, 2022, in Booth 11. We’d love to see you there and answer any questions you may have, so please stop by!
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