Operational Transformation

Overview

Ricoh Latam, Ricoh Japan's Latin American division, serves more than 16,000 clients across the region from its base in Miami. Recognized for its innovative printing and technology solutions, the company is looking to improve its margins through automation and artificial intelligence. To support this strategy, Ricoh partnered with us to improve service delivery, reduce costs, and optimize its internal processes.

Areas: Logistics & Operations, Sales, Finance, Supply Chain.

Timeline

• September 2024 — Launch of the AI-powered spare parts management project
• December 2024 — Launch of the trip and logistics automation project
• January 2025 — Development of AI-powered bank reconciliation and data processing tools
• February 2025 — Launch of GenAI projects for budgets, backlogs, and predictive models

AI-Powered Forecasting & Inventory

Ricoh faced significant fragmentation in inventory management, with information spread across available stock, in-transit orders, and commercial commitments. This created inefficiencies such as stockouts, overstock, and reactive decisions, making it difficult to align sales, planning, and operations.

We developed a comprehensive solution that unifies inventory, sales, and planning into a single dynamic system, consolidating information from AX and D365. The tool combines historical data, commercial commitments, and operational variables to:

• Predict future demand with greater accuracy.
• Optimize stock levels, avoiding shortages and overaccumulation.
• Provide real-time visibility for sales and planning teams.

Payment Reconciliation Project

Reconciling payments across multiple banks and their associated invoices was complex and required significant manual effort, even with rules already defined. The goal was to automate the process and lay the groundwork for incorporating AI that identifies new rules and improves matching rates.

We implemented rules-based automation to match payments and invoices. Although AI has not yet been applied, the current system lays the groundwork for its future adoption. Reconciliation time went from 30–40 minutes to 1 minute.

Travel Logistics Intelligence

Creating travel records (shipments from ports to logistics centers) required multiple documents — invoices, forms, and more — that had to be reviewed, consolidated, and manually loaded into the system. This process took more than 90 minutes per entry and generated frequent errors.

We automated the entire process using AI to detect and extract data from PDFs and other documents. What once required manual entry is now handled by an intelligent system, cutting processing time from 90 to just 5 minutes. This reduced human error and freed up time for higher-value tasks.

AI-Powered Spare Parts Management

Information on spare parts consumption came from multiple sources, making it difficult to unify into a single, clear view. This made tracking usage and projecting future needs complex and slow. The team needed a simpler, more centralized process.

We centralized and integrated all data sources into a single system. This significantly improved data quality and reduced the time needed to access consumption history. With a unified view, the team can now better anticipate demand and make faster, more accurate decisions.