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The Critical Union of Operational Excellence and Reverse Logistics

Reverse logistics is a critical component of our evolving and expanding supply chain. Unfortunately, the core tasks typically associated with this business model (see process map below) are classified as rework by the best business practices, fraught with waste and non-value-added activities (for example, unpackaging then later repackaging items). Companies tend to compensate for this by increasing inventory buffers and increasing staffing (includes temporary workers and overtime efforts).



This presents unique challenges for reverse logistics firms attempting to achieve or increase margins (or to survive), and these challenges are amplified by reduced predictability. “Blind” buys, bulk purchases, and retail returns can impose several “unknowns” on the management team: unknown (but estimated) incoming volumes, unknown incoming product mix, and unknown labor requirements per product. This can make labor allocation and cost planning a nightmare. See the scatter plot on the right. It’s extremely difficult to plan labor costs when the cost per unit (CPU) for a shift with 100 associates ranges from $0.20 to $0.80.


We utilize Lean and Six Sigma to provide order and create value for our reverse logistics clientele. These are the most widely used and proven business improvement approaches practiced today. These methodologies address business performance by maximizing productivity and efficiency and eliminating waste. By implementing sustainable and measurable initiatives, companies have saved millions of dollars and boosted their revenue generation.


We supported a national multi-million-dollar third-party reverse logistics provider with multiple processing and distribution facilities and retail locations spread across a regional footprint. The company was in a state of distress following two consecutive years of loss. Per the owner's request, Roxtar Consulting implemented all recommended improvements and achieved the benefits in excess of $15M per year. We:


  • Reduced labor costs by over 50% for seven major processes

  • Reduced external trailers and associated dwell costs by 90%

  • Improved Facility A capacity by 32% and Facility B capacity by 55%

  • Improved Packing throughput by 239% and reduced staffing by 44%

  • Managed multi-facility consolidation and the elimination of 140K square foot warehouse

  • Eliminated 2,661 excess quality management labor hours per year

  • Expanded processing diversity by 275% per production cell while reducing cell footprints by over 15% (contributing to increased capacity and throughput)


We also designed and deployed an integrated data collection and tailorable electronic reporting tool that employs machine learning to align production and planning with continuously (hourly) updated predictive analytics (throughput, capacity, and staffing) and other useful business intelligence.

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