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In today’s retail sector, scalable sustainability initiatives are essential. This case follows a multi-year footwear recycling program spanning thousands of collections across disparate states and partner types. With data fragmentation and complex logistics, uncovering actionable insights had previously been a challenge. By applying Scoop’s agentic AI automation to historical program operational data, teams were able to isolate efficiency hotspots and correct systemic issues—demonstrating a repeatable approach for retailers seeking to maximize impact with minimal manual effort.
With Scoop’s automation, the recycling program team unlocked a granular view of operational effectiveness that had eluded manual reviews. Automated analysis revealed that efficiency was primarily determined by collection weight, yet also subject to major regional and partner-based variations—highlighting clear paths to optimize the program. By filtering performance data through AI modeling, line managers could justify adjusting collection thresholds, re-weighting booking protocols, and prioritizing specific collection methods. These improvements drove measurable efficiency gains, focused resources on high-impact partners and regions, and informed strategic planning for future expansions.
Represents all footwear pairs diverted from landfill through verified program pickups.
Tasmania achieved the highest documented collection efficiency, pointing to best practices for scaling bulk pickups elsewhere.
Tasmania achieved the highest documented collection efficiency, pointing to best practices for scaling bulk pickups elsewhere.
Major Retailers were responsible for nearly three-quarters of all pairs collected, validating a partnership-led approach.
Showed that the program maintained active engagement and reliable scheduling, with the majority of pickups confirmed.
Retailers globally face rising pressure to run large-scale sustainability programs, yet data for take-back or recycling efforts is siloed across partners, locations, and routes. This makes it difficult to answer critical questions: Which partners or regions are the most efficient? Where is operational waste hidden? In this footwear recycling initiative, program leaders contended with fragmented data flows—store bookings, collection weights, partner identities, and regional differences. Traditional business intelligence tools proved cumbersome when analyzing efficiency drivers across over a thousand pickup events and complex dimensions like partner type, region, and logistics method. As a result, identifying underperforming process segments or optimizing collection routes was a manual, slow, and error-prone process that delayed impact.
Automated dataset scanning & metadata inference: Scoop instantly profiled 1,076 collection events, auto-detecting key fields like weight, region, partner type, and method. This allowed immediate exposure of all operational dimensions, without the need for manual data wrangling.
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Key efficiency drivers emerged only through automated multi-factor analysis. Most notably, collection weight was found to be the dominant lever: efficiency tripled in extra-heavy (100+ kg) pickups, with certain hubs like Tasmania and the East Coast achieving rates more than double the baseline. Regional and method-based interplay exposed hidden optimization opportunities: for example, NSW stores using the national route achieved 30% efficiency at scale, a pattern masked within manual averages. Partner type introduced further nuance—Major Retailers outperformed others in lighter collections, while heavy and extra-heavy pickups benefitted from economies of scale and process standardization. Meanwhile, the Milkrun method emerged as a preferred option for frequency, though National routes sometimes yielded best-in-class results for large-volume events. These insights—difficult or impossible to isolate via traditional dashboards—empowered operators to focus on the scenarios proven to generate the highest returns. Notably, agentic ML flagged booking pipeline and seasonal dynamics, providing a framework for aligning future campaigns with observed mid-year and year-end surges, likely tied to retail inventory cycles.
Armed with actionable intelligence from Scoop, the program operations team rapidly refined collection tactics. Extra-heavy collections were prioritized in regions with demonstrated efficiency advantages, and underperforming combinations of method, region, and partner type were earmarked for process review. The findings justified targeting high-volume partners for expansion and tweaking booking thresholds to increase fulfillment rates. Planned next steps include using ongoing data feeds to trigger dynamic adjustments in collection routes and frequencies, systematizing best practices discovered in top-performing regions, and re-engaging partners in lower-efficiency categories with targeted support. With Scoop, the groundwork is now set for iterative, data-driven improvements—transforming recycling logistics from static reporting to continuous optimization.