Proposal Intelligence
Managing vendor proposals and repair work orders manually in a high-volume environment leads to delays, inefficiencies, and missed cost-saving opportunities.
Retail club operations must automate vendor proposal reviews with AI to eliminate inefficiencies, cut unnecessary costs, and drive smarter, faster decisions across the business.
Empowering teams with a more efficient, customizable, and scalable way to manage data at scale through AI.
PROJECT TYPE
DATE
The Problem
The Club Facilities Maintenance processes 250,000 reactive repair work orders annually, with an operating expenditure (Opex) of over $100M. A significant portion of this spend—49%—was tied to vendor proposals, which stemmed from only 11% of repair work orders.
Manual review of vendor proposals causing delays and missed cost-saving opportunities.
A decision engine covering only 30% of trades, limiting the potential for decision automation.
No automated business rule checks were in place to ensure vendor proposals were evaluated efficiently, increasing costs and delays.
Historical proposal data was inconsistently formatted, making it difficult to conduct accurate cost analysis and comparisons.
Manual approval processes led to inefficiencies, high operational overhead, and excessive vendor charges.
The goal was to automate vendor proposal reviews, improve decision accuracy, and drive cost savings in Facilities Maintenance operations. We had 9 months to increase automation, optimize cost controls, and improve decision-making across 700+ daily work orders.
The Strategy
To optimize Facilities Maintenance proposal management, drive a data-driven and automation-focused transformation.
The Result
Cut $10 million in real estate operating costs by redesigning the user interface and enhancing the deci sion engine's machine learning algorithm.
Accelerated the onboarding of trades to decision engine.
Enriched data to optimize algorithms and improve accuracy.
Redesigned user interface and increase user engagement by 26%.
My Contribution
Discovery: Focused data standardization and decision optimization
Standardized historical proposal data, enabling structured cost analysis and vendor pricing benchmarks.
Developed an automated decision engine with predefined business rule checks, reducing reliance on manual approvals.
Integrated AI-driven insights, helping associates make informed proposal approvals or rejections.
Design: Process Automation & UI Enhancements focus
Conducted process mapping to identify inefficiencies in proposal handling and approval workflows.
Designed an intelligent UI that provided real-time cost insights, vendor comparisons, and recommended approvals.
Partnered with data science teams to refine machine learning algorithms, improving cost prediction and reducing overpayment risks.
Delivery: Change management and adoption focus
Socialized pilot results with leadership, demonstrating cost-saving potential and securing executive buy-in.
Launched a regional phased rollout, ensuring teams had the necessary training and support.
Monitored performance post-implementation, refining automation rules based on real-world usage data
