The LGO program has many ways to focus your MBA on supply chain management. There is a defined track within Civil and Environmental Engineering for students who want to dive deep into supply chain coursework. Students then have multiple companies who regularly offer internship projects on global supply networks, which allow students interested in supply chain management to work in the field before taking a leadership role after graduation.
Optimizing Demand Re-Allocation under Fixed Capacity Commitments
Felipe Quintella Correia (LGO ‘22)
Problem: Nissan prides itself on offering a wide range of options to consumers and relies on options in order to maintain customer loyalty and trust. Felipe’s project intended to find opportunities for flexibility in Nissan sourcing strategies upstream of the manufacturing site.
Approach: Felipe brought in data from Nissan production schedules and sales information databases, as well as costs related to scenarios of over-capacity production. He also interviewed stakeholders across the company. Building on this, he created a use case for a specific vehicle model, using linear programming to enable demand allocation based on fixed supply constraints. His model allowed Nissan users to solve optimally for vehicle volumes in different trim levels in accordance to available supply capacity.
Impact: Felipe’s model allows Nissan to approach supply-chain flexibility with a range of different perspectives and goals for optimization. The results of the model showed that simulated use cases had 10% increase in volume and 17% increase in profit compared to a manual re-allocation process.
Inventory Modeling for Active Pharmaceutical Ingredient Supply Chains
Audrey Bazerghi (LGO ’20)
Problem: The Active Pharmaceutical Ingredient (API) is the small molecule that can be formulated into tablets and made into prescription drugs for therapy at AstraZeneca (AZ). AZ made the strategic choice to outsource close to 90% of its API production to focus capital investments on research and development. AZ’s current day success could be in part attributed to this shift in strategy; however, it has also locked API supply chains into long purchase lead times.
Approach: Audrey used inventory modeling to explore the trade-off between the attractive purchase price, and hidden costs of outsourcing for two API supply chains at AZ. She assessed the inventory levels recommended by a base stock policy with deterministic purchase order lead times at each contracted stage of the supply chains. The single-echelon calculations revealed that safety stock levels are not systematically inflated at individual stages.
Impact: Audrey demonstrated with a multi-echelon inventory optimization (MEIO) that a fully integrated supply chain would yield significant savings compared to a purely external supply chain. Today, AZ’s operating model allows it to partially coordinate with CMOs and capture up to 60% of the value left on the table by not being able to optimize the full chain. Audrey also proposed using cost premium frontiers to prioritize which contracts to renegotiate with CMOs in the future.
Integration of Advanced Analytics and Engineering Automation Solutions into Target’s End-to-End Supply Chain Modernization and Optimization Transformation
Durgesh Das (LGO ’20)
Problem: Target stores have been traditionally designed for a predominantly brick and mortar business fed by a push supply chain model. With the growing need for omni-channel sales fulfillment, supply chain engineering has become more complex. This project aimed to minimize backroom inventory and minimize in-store labor cost while meeting all customer demands and maintaining inventory above the presentation minimum.
Approach: Durgesh’s project consisted of three phases: analysis of current state sales and capacity, modeling inefficiencies in the supply chain and optimizing the inefficiencies in the supply chain. Sales floor weeks of demand, replenishment spillover, peak on-hand inventory, and daily back room inventory were the key metrics Durgesh used to help determine the potential of his optimized units of measure.
Impact: The outcome of the project revealed a large opportunity to optimize inventory holding along the axes of UOM, POG sizing and replenishment frequency. Durgesh also found that his study set the stage for a larger–scale optimization exercise. He recommended that the next step should dive into building a business case for optimization through deeper cost modeling, by extrapolating labor estimations and using actual time study data.
Virtual Pooling Approximation Using Longest Path Network Optimization
Kevin Schell (LGO ’18)
Problem: Caterpillar sells its products through a network of independently-owned dealers which own their inventory. Given long factory lead times, highly variable demand across geographies, and complex configurations, dealers are forced to maintain high inventory on their sites in order to keep service levels up. Kevin’s project aimed at exploring pooling arrangements among dealers to address these challenges, while taking into account the dealers’ reluctance to unilaterally give up equipment they might eventually sell.
Approach: Kevin proposed a model which Caterpillar dealers would maintain physical inventory in multiple locations and use a network swapping mechanism to create a virtual pool from which various dealerships could make and fulfill sales. He defined a commercial network model to simulate equipment ordering, inventory management, and sales across a sample dealer network, and ran the model to simulate results with and without network swapping implemented.
Impact: The baseline simulation results from Kevin’s model for a single class of Caterpillar vehicle suggest that network swapping could reduce inventory by over 12% and reduce customer back orders by over 17%. The net present value of the Caterpillar dealer network could be increased by over $3M. Kevin’s thesis addressed these results and outlined how this network swapping model could be further improved.