Orkla Home & Personal Care (OHPC)

Optimizing Procurement through Seamless RFQ Management

Fraud Detection Predictive Modeling

Developing a dedicated AI-based system to streamline supplier management, handle RFQs (Request for Quotations), and automate bid comparison for OHPC’s procurement needs.

Challenge Accepted!

Optimizing Procurement through Seamless RFQ Management

Process & Story

Orkla Home & Personal Care is a leading supplier of branded consumer goods and concept solutions to the grocery, specialized retail, pharmacy, and bakery sectors. OHPC focuses on products for personal hygiene, cleaning, and household care. They offer a wide range of products, including detergents, cleaning agents, personal care items, and hygiene products. OHPC operates primarily in the Nordic region but also has a presence in other European markets. The company is known for its strong portfolio of well-established brands that are trusted by consumers for their quality and effectiveness.

With their network of production plants, and each production plant ordering hundreds of materials, OHPC needed an efficient way to manage its procurement processes, especially when dealing with multiple suppliers.

Problem

OHPC needed a dedicated platform to streamline its procurement processes for two production plants. The challenge was to manage RFQs efficiently, automate supplier interactions, and provide seamless bid comparison and awarding processes. OHPC, despite utilizing SAP for broader business operations, heavily relied on custom spreadsheets and manual processes to manage procurement. The procurement team was tasked with juggling numerous RFQs and tracking communications with vendors through emails. Proposals submitted by suppliers had to be manually re-entered into spreadsheets for comparison, making it time-consuming and error-prone. Additionally, historical procurement data, such as past prices, had to be extracted from various sources and input manually to support decision-making.

This fragmented workflow created inefficiencies, slowed down the procurement cycle, and left the process vulnerable to human errors, making it difficult to manage multiple suppliers and ensure optimal pricing and supplier selection.

Solution

We developed a tailored, AI-powered application for OHPC, combining advanced data engineering, automation, and a user-focused interface to transform procurement processes from end to end.

Managing RFQs

The application enables users to efficiently create, publish, and track RFQs. Leveraging master data preparation and integration, it automates the RFQ creation process, dynamically matching materials to suitable suppliers based on material codes and historical interactions.

Dedicated vendor space

Suppliers access a secure, dedicated space within the application to submit proposals, upload documents, and respond to RFQs. Recognizing the challenge posed by varied, non-standardized response formats, we utilized multimodal AI to parse and structure unstructured data (e.g., PDFs, Excel files) in real time. This solution extracts relevant information — pricing, material specifications, vendor IDs — and standardizes it for seamless comparison, reducing data entry time and eliminating manual handling errors.

Automated proposal evaluation

The system incorporates sophisticated AI-driven data extraction techniques to conduct an initial evaluation of supplier proposals (to extract data points such as pricing, material specifications, and supplier history) and structuring them for easy comparison. This allows procurement teams to quickly assess bids and focus on high-priority decisions.

Supplier recommendations

The system provides real-time recommendations for optimal offers, supporting procurement teams in selecting the best suppliers based on detailed, side-by-side comparisons of pricing, material compatibility, and past performance. The platform’s advanced data pipeline is capable of processing hundreds of concurrent negotiations, drawing insights from historical data to highlight cost-effective choices, ensuring that procurement decisions are both informed and data-driven. As a result, OHPC is able to optimize its procurement operations, saving both time and costs, and ensuring that the best suppliers are consistently selected based on reliable, historical data.

Tools

Google Cloud Platform

Google Cloud Platform

Tensorflow

Tensorflow

Python

Python

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Challenges

Matching vendors and streamlining communication
The procurement process for OHPC is complex, involving hundreds of materials and numerous vendors, each with unique product specifications and formatting. A primary challenge was to prepare and structure the master data to ensure accurate vendor matching and efficient RFQ distribution. Through extensive data engineering, we standardized and enriched historical procurement data, organizing it by material codes, vendor profiles, and purchasing history. This preparation enabled the system to intelligently match vendors to specific RFQs based on compatibility and relevance, ensuring RFQs reached the most suitable suppliers efficiently, reducing manual workload and enhancing the speed and precision of the procurement workflow.

Automated proposal analysis based on Google Gemini AI
A key requirement was to ensure suppliers could easily submit their bids through the platform. One of the biggest challenges in procurement is processing the varied, unstructured formats of RFQ responses without manual intervention. Suppliers often include non-standardized elements like images, unique headers, and custom fonts to make their offers more appealing. By using Google’s Gemini, a state-of-the-art multimodal AI model, we developed a solution that can interpret diverse response formats. The model was trained to understand the context of OHPC’s procurement needs, materials, and product codes. This "memory" enables the AI to recognize relevant information from RFQ responses and link it to a database of procurement requirements. This intelligent processing allows procurement teams to analyze and compare offers without losing essential data from the original vendor submissions.

Historical data-driven recommendations
Leveraging historical procurement data, the platform highlights price differences and trends, allowing procurement teams to easily compare offers. This data-driven approach supports more informed decision-making, helping to quickly identify and select the most competitive proposals.

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