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.
Albert Chan (LGO ’15)
Company: Li & Fung
Location: Hong Kong
Problem: The apparel industry is changing rapidly. Supply chains face faster development cycles, greater demands for environmental compliance, and lower prices. In such an environment, the ability to identify factories that can perform to exacting and evolving standards is a competitive advantage. With its network of more than 15,000 factories, Li & Fung wanted a way to use data analytics to support its sustainable sourcing process.
Approach: Albert evaluated whether metrics in product quality, order delivery, and factory compliance are historically correlated with factory longevity and annual spend. He also identified inherent factory attributes that are predictive of quality, delivery and compliance performance. Albert created a list of factory attributes most important to performance, including past product performance (e.g., quality and delivery metrics), human capacity (e.g., leadership experience and specific roles), and factory characteristics (e.g., firm size, compliance scores, and financial strength). He concluded:
- There is a direct relationship between product quality and long-term factory performance. To forecast quality performance, internal technical audits (initial evaluations performed on factories to gauge production readiness) appear to be a leading indicator of product quality performance.
- Compliance scores are not consistently predictive. Optimal compliance level depends on business-specific needs and goals.
- There is a tenuous connection between on-time delivery and annual spend. This is due to non-standardized definitions for on-time delivery.
Impact: Li & Fung plans to use these insights on better factory performance to begin aggregating the large amounts of data on factories and generating insights to support the development of a sustainable sourcing network.
Clararose Voigt (LGO ’16)
Company: Nike, Inc.
Location: Portland, OR
Problem: Nike is transitioning from a brick-and-mortar company to e-commerce, a significant change in its business model. Nike has historically operated as a wholesaler, but they anticipate major growth in retail sales through Nike.com. Nike asked Clara to develop a proof-of-concept Strategic What-IF Tool (SWIFT) for North American inventory, focusing on the digital market’s impact on product supply and demand.
Approach: Clara built a simulation tool that used adjustable parameters for variables like cancellation rates and Make-To-Order (MTO) vs. Make-To-Stock (MTS). Her results demonstrated that digital sales would have rapid revenue growth but also increasing liquidation units. The significant factor was liquidation units increase from Nike’s MTS business. While MTS allows Nike to be responsive to channel needs, it forces Nike to assume all inventory risk.
Clara tested two scenarios to develop a qualitative supply chain strategy. First, she increased Nike.com’s revenue CAGR 15%. Second, she increased MTS business by 10%. If Nike.com revenue CAGR increases more than 15%, marketplace units begin decreasing. Therefore, Clara recommended that Nike invest in a supply chain for premium customer service rather than capacity enhancements in a retail-driven marketplace.
Impact: Shifting NIKE’s business toward MTS increases liquidation units significantly. Therefore, if Nike wants to provide rapid response shipments, they should support that decision by investing in supply chains that effectively reduce liquidation risk such as time-to-market and lead-time reduction through on-shore/near-shore manufacturing.
Clara’s simulation established a framework that captures the interdependencies between Nike’s business channels. She helped Nike to rapidly test supply chain strategies within different revenue forecasts.
Shi Ying (Ariel) Chua (LGO ’15)
Location: Peoria, IL
Problem: Caterpillar manufactures mining and construction equipment. Both of these industries are cyclical in nature, which impacts Caterpillar’s business. To be competitive, Caterpillar needs to make capacity decisions with the cycles in mind. In Caterpillar’s history, there have been times when there was demand overload and times when facilities are over capacity. Caterpillar asked Ariel to establish a framework that enhances investment decision-making within the two- to six-year capacity planning horizon.
Approach: Ariel conducted a gap analysis and identified three major tactics to fulfill Caterpillar’s needs:
- Ariel created an augmented demand forecasting framework using existing long-term forecasts and time series analysis using historical data
- She developed an investment option evaluation tool using Monte Carlo simulations that allows for variability across industry trends, market share, prices, and margins.
- Finally, she introduced a multi-dimensional decision framework that captures the risk-return angle and Caterpillar’s corporate strategy goals.
Impact: The Caterpillar Production System team and corporate investment governance endorsed the framework. To implement the project, Caterpillar is collaborating with product groups to develop model parameters.