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Supply Chain Mapping and Analysis

OEIS Research staff will work directly with you and members of your supply chain to define the end-to-end view of all supply chain entities, interfaces, processes and practices currently in place.

Our analysis will be performed to meet customer specific objectives, while employing proven supply chain mapping and analysis techniques to evaluate and support the improvement of total supply chain performance.

Utilizing enterprise supply chain readiness assessment criteria, OEIS researchers can assess the readiness of the supply chain according to how the supply chain is constructed and the supply chain management practices employed.

Total Supply Chain Visibility


Data Mining the Total Supply Chain

OEIS researchers can perform thorough analysis of the supply chain structure and performance. Being part of a non-profit university research center, our researchers can work with multiple entities within the supply chain to provide comprehensive analysis of performance metrics like lead time, inventory levels and supply chain management costs. 

In addition, our researchers can perform on site assessments and interviews with organizations at multiple tiers in the supply chain.

Critical Supply Chain Data


Discrete Event Simulation Modeling

Supply chain simulation modeling, allows the user to evaluate and predict supply chain performance based on multiple internal and external factors, environmental conditions and supply chain requirements. 

OEIS researchers will work directly with your team to develop and utilize computer generated simulation models to perform predictive analysis on supply chain performance, critical metrics and costs.

Discrete Event Simulation


The enterprise supply chain mapping and modeling approach provides the following benefits:

  • A concise view of the supply chain noting critical areas
  • Details of the multi-echelon supply and value chain supporting the articel or component
  • Detailed lead times and other performance requirements for each supplier
  • Critical paths and specific bottlenecks
  • Collaborative analysis and measurement of actual performance criteria from real data
  • Simulating and predicting the effects of multiple variables on supply chain performance