Poster
Client Context
This semester, we worked with the 200,000 sq. ft. distribution center for Tiffany and Co., located in Parsippany, NJ. This warehouse houses 12,000 SKUS and ships both sold orders and replenishment orders to their 326 retail stores worldwide. Due to their recent acquisition by LVMH, as well as their change in business strategy, Tiffany & Co. was looking to restructure their warehouse and lower high cycle times.
Project Objective
After doing some data cleaning and mining, we found that the roots of the issue were adhoc slottingand a build-upat theValue-added Services station of the warehouse, where items go through various necessary alterations, such as laser engraving and price-tagging. In order to tackle this bottleneck, as well as the overall issue of high cycle times, the team found two opportunities: re-slotting the items in the warehouse and wave planning.
Design Strategy
We created an optimization model, with 1.6 million decision variables and 1.7 million constraints, and a heuristic model to allow the client to dynamically find the most optimal location for each item.After re-slotting items based on their frequencies of getting picked, we created an application for the client to run these models themselves once a year.Alongside re-slotting in the picking zones of the warehouse, we recommended additional criteria for the waves of items moving through the warehouse at a time, such as grouping by shipping carriers and adding a capacity on the number of items that need to go through Value-added Services. All of these conditions were consolidated into a simulation that mimicked the current warehouse set-up, ensuring that we were testing our recommendations in a system indicative of the current processes.
Deliverables
There are a number of deliverables we will beproviding to our client. One is a simple, yet powerful tool that allows Tiffany to re-slot their inventory as frequently as they wish, though we recommend annual reslotting in June to due observed seasonality trends. We all also provide a report with both our slotting and wave planning recommendations containing details and support for our decisions. Finally, we will also deliver our simulation and sequencetable to theclient to justify our methodology and provide them with data they may need for future decisions.
Project Information
Student Team
Shreya Desai, Ozashwee Ghimire, Fares Hasan, Saman Muhammad, Will Reich, Neha Srivatsa, Asli Yucebilgin
Faculty Advisor
Faculty Evaluator
