Enterprise Resource Platform
A mid-size manufacturing company with over 150 employees and three production facilities had grown through acquisition over the past decade. Each acquisition brought its own software stack, and the resulting patchwork of disconnected systems was quietly eroding operational efficiency across every department.
The Challenge
When we first met with the operations team, they walked us through a process that had become painfully familiar: a sales order entered in the CRM had to be manually re-keyed into the production scheduling spreadsheet, then again into the inventory management system, and finally into the dispatch and logistics platform. Four systems, four manual entry points, and no reliable way to reconcile the data between them.
The consequences were significant. Production scheduling ran on a 72-hour lag because the team could only batch-process orders once the data had been verified across systems. Inventory counts were perpetually inaccurate, with the warehouse team spending the first two hours of every Monday morning conducting spot checks to correct discrepancies. Customer service had no real-time visibility into order status and frequently provided incorrect delivery estimates.
Beyond the operational friction, the company was losing money. Duplicate data entry errors led to roughly 12% of orders requiring some form of manual correction, and the lack of integrated reporting meant management decisions were based on data that was already days old.
Our Solution
We recommended a phased migration to a unified Odoo platform, customised to match the company's specific manufacturing workflows rather than forcing the business to adapt to generic out-of-the-box processes.
The first phase focused on data architecture. We designed a PostgreSQL-backed data model that consolidated customer, product, inventory, and order data into a single source of truth. Migration scripts written in Python handled the extraction and transformation of records from all four legacy systems, with automated reconciliation checks to catch discrepancies before they entered the new platform.
The second phase delivered the core workflow automation. Using Odoo's workflow engine with custom Python modules, we built automated pipelines that connected the sales process directly to production scheduling. When a sales order is confirmed, the system automatically checks raw material availability, reserves inventory, generates a production order, and slots it into the schedule based on capacity and priority -- reducing the order-to-production cycle from 72 hours to under 4 hours.
We containerised the entire deployment using Docker, making it straightforward to manage updates and scale across the company's three facilities. REST API integrations connected the new platform to the company's existing shipping provider and accounting software, ensuring a smooth transition without requiring those teams to change their tools.
Results
Within the first quarter of operation, order processing time dropped by 40% and manual data re-entry between departments was completely eliminated. The production scheduling team now works from real-time data, and customer service can provide accurate delivery estimates directly from the system. Inventory accuracy improved from approximately 82% to over 98%, and the Monday morning spot-check ritual was retired permanently. The management team now has access to a unified dashboard showing live operational metrics across all three facilities for the first time in the company's history.