PROJECT: PROCUREMENT

Automation and AI in QSR supply chains

Challenges

  • National quick-service restaurant undertook business improvement project which revealed considerable costs, cycle times, delays, errors, rework and value loss in the procurement process.

  • There was often uncertainty about what the business rules were, which person can make decisions about purchase categories, what the company has previously paid (and agreed terms) for like categories, and the contracting process.

  • Creating RFIs, RFPs, assessing bids and commercials was manual and often didn’t refer to historical data.

  • Ave 132 total labour hrs per contract until commencement of deliverables, and 11 weeks cycle time.

Solution

  • Current state analysis to better understand the problem, root causes, goals, and relevant data and systems.

  • Clean, structure and optimise existing datasets.

  • Use a public model to assist draft RFI/RFP templates, tailor questions by category, summarise vendor responses and highlight non-compliance.

  • For bid responses we used a VLM to extract pricing, delivery timelines, and compliance statements from vendor submissions automatically.

  • To assess the bids we used an LLM based classification model trained on historical award data, and then score suppliers on cost, risk, quality, and other defined metrics.

  • Agentic AI tool analyses vendor documents for risk and compliance issues (e.g., sanctions, ESG policies, cyber posture, AML, Chain of Custody etc.) using Hugging Face NER pipelines and GPT-based RAG over vendor data.

  • Internal approvals used Power Automate, agentic AI and MCP servers to process mine and task automate. Identify bottlenecks, predict approval delays and route workflows dynamically.

  • Predict spend by category, supplier, or region, detect anomalies or non-compliant spend, using classical statistical approaches combined with fine-tuned LLM for post-analysis and Power BI.

  • Finally, creating a Procurement Assistant chatbot, RAG with text embeddings and our custom RAG fine-tuned LLM. E.g. “Show top suppliers by A, B and C.”

Impact

  • 70–80% reduction in manual labour from “we need to buy” until contract delivery start.

  • 55% faster cycle times from RFI to contract signed.

  • Full audit trail, risk visibility and decision consistency.

  • Accurate single source procurement spend forecast across whole group.

  • Labour average is 45hrs per contract (down from >130).

  • All FAQs are handled online by Q&A chat with only 12% going to human.