Case Study

Supply Chain Footprint Optimization $1.2B Oil Field Services Organization

"Leverage machine learning and decision science methods to plan, pressure test and compare go-forward models for global supply chain footprint requiring >$100m in investment"

Situation

  • Historically underinvested supply chain footprint not well positioned to service growing markets
  • Complex decision space required considering sourcing thousands of raw materials, intercontinental logistics routing, facility exit costs and capital investment
  • Qualitative issues such as political risk, intellectual property security and regulatory environment required additional consideration

Bespoke Solutions

  • Partnered with supply chain and operational leaders to review existing supply chain, understand growth markets and survey opportunities
  • Synthesized and re-engineered available logistics and manufacturing data to produce machine learning tool that maximizes footprint utility based on given constraints and inputs
  • Compiled and analyzed global raw material sourcing data to further supplement analyses
  • Compared competitive advantages and payoffs of capital investment in infrastructure across global portfolio

Leading With Results

  • Presented multi-dimensional comparison of wide decision space to support large investment decision
  • Recommended Condorcet options representing investment savings over $150m
  • Transitioned custom-built supply chain optimization tool to support on-going decision making
Case studies and expertise may represent projects and engagements performed by SLKone team members prior to their employment at SLKone
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