Developing a long term strategy for a warehouse network
Author(s)Johnson, Patrick (Patrick Allen)
Sloan School of Management.
Donald Rosenfield and David Simchi-Levi.
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This thesis addresses the question of how to create a pro-active, long term, network centric warehouse strategy. This thesis will present an inventory model built to understand the capacity needs of Amgen's warehouses over the time period of 2017-2023 to support this mission, along with recommendations based on scenario analysis from this model to analyze and quantify the impacts of multiple scenarios in support of an efficient, effective, nimble supply chain. With worldwide operations supporting a global customer base, Amgen's operational philosophy is to ensure serving "every patient, every time". Amgen's warehouses play a vital role with this mission, storing raw materials to ensure production with safety stock and various levels of Work in Progress (WIP) based not only on operational safety stock, but also strategic safety stock to ensure demand is always met, even with unforeseen risks. In order to understand the impacts of growth on warehouse utilization, a relational database inventory model was created and linked to the long range forecast of supply and demand. This inventory model linked the Bill of Materials (BOMs) to the product forecast in order to to understand the quantity of raw materials required to meet the supply. The database also calculates the WIP and finished product levels of Amgen's products. This model considers inefficiencies in the warehouses, as warehouse pallet spaces do not always store the maximum capacity of the material. This inventory model calculated the capacity required for each warehouse over the forecasted ranges of FY 2016 to FY 2023. The findings of this model were used to create Amgen's long term warehouse strategy. The model demonstrated a +- 10% accuracy to 2017 planning. We developed a strategy that mimics Amgen's operational strategy. Amgen's operational strategy is to reduce fixed costs, and focus on flexibility with variable based costs. Based on this, we found the best strategy was to work with 3rd party logistics providers (3PLs) to mitigate the capacity gaps in a variable based manner. This option is preferred over investing in expanding capacity at warehouses already in use for all three scenarios of optimistic, baseline, and pessimistic demand profiles. The biggest lever to gain warehouse capacity is to improve inventory policies and the flow of communication. Inventory policies whose aim is to reduce inventory can be viewed as a sensitive topic at a company like Amgen. But, if done in a scientific manner, and moving from a Months on Hand (MOH) approach to a scientifically calculated inventory, then moving to a multi-echelon inventory optimization, inventory and risk can be reduced. The following are ways that can be used to reduce inventory and risks. -- Track forecast error to understand variation of demand -- Lead time reduction of raw materials and work in progress -- Risk Pool Drug Product (DP) "nude" vials and decrease lead time from DP to customer -- Re-order point frequency increases -- Reduction of demand variability through: -- -- Better communication of demand forecasts between marketing, global supply chain and site supply chain teams. -- -- Reducing variability of manufacturing planning -- Seek commonality of raw materials to lower safety stock levels -- Multi-Echelon Inventory Optimization By accomplishing these activities, Amgen has a scope to reduce 3PL storage requirements by 20k pallet-year spaces over the same time period. This will lower the expense of 3PL costs, and overall risks, over the same time period by $11 M. Considerable work will have to be accomplished, but the benefits will outweigh the costs.
Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2017.Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 62-63).
DepartmentMassachusetts Institute of Technology. Engineering Systems Division; Massachusetts Institute of Technology. Institute for Data, Systems, and Society; Sloan School of Management
Massachusetts Institute of Technology
Institute for Data, Systems, and Society., Engineering Systems Division., Sloan School of Management.