Case Study
A UK wholesale distributor operating three warehouses replaced four legacy systems with Odoo ERP in a 28-day Softomate-led go-live, achieving 96.4% inventory accuracy post-deployment, eliminating GBP 38,000 in annual software costs, and reducing order-to-dispatch time by 41%.
1 min read By Deen Dayal Yadav, Founder & AI Automation Director
UK wholesale distributor, GBP 14M turnover, 38 staff, 3 warehouses
A UK wholesale distributor operating three warehouses replaced four legacy systems with Odoo ERP in a 28-day Softomate-led go-live, achieving 96.4% inventory accuracy post-deployment, eliminating GBP 38,000 in annual software costs, and reducing order-to-dispatch time by 41%.
UK wholesale distributor, GBP 14M turnover, 38 staff, 3 warehouses
A UK wholesale distributor operating three warehouses replaced four legacy systems with Odoo ERP in a 28-day Softomate-led go-live, achieving 96.4% inventory accuracy post-deployment, eliminating GBP 38,000 in annual software costs, and reducing order-to-dispatch time by 41%.
A UK-based wholesale distributor of industrial fasteners and fixings, turning over approximately ?14M annually across three warehouses (Birmingham, Manchester, Leeds) and 38 staff including warehouse operatives, sales account managers, purchasing, and finance, had reached a tipping point in its software stack. The business was running four separate systems that did not communicate cleanly with each other: a 2003-era warehouse management system (WMS) for stock control, Sage 50 for accounts, a homegrown Access database for customer pricing, and an Outlook-based system for sales order tracking. The mismatched architecture had grown organically over fifteen years and the cost of keeping it stitched together had become visible in operational metrics.
The most visible symptom was inventory accuracy. The legacy WMS held stock positions that diverged from physical reality at any given moment by an average of 11% across the 3,400 SKUs the company carried. The reasons were structural: stock movements were entered manually after-the-fact by warehouse operatives, internal transfers between warehouses were tracked on paper before being keyed in (often days later), and the system had no real-time integration with the sales-order system, meaning stock could be sold that did not physically exist or, conversely, stock could sit unsold because the system showed it as committed elsewhere when it was not.
The downstream effects of inventory inaccuracy compounded. The customer-service team estimated they spent roughly 18 hours per week handling exception cases driven by stock errors: cancelling orders for stock that did not exist, locating misallocated stock between warehouses, and explaining delays to customers who had been promised delivery dates that the WMS data could not actually support. The sales team had built informal workarounds (texting warehouse managers directly to confirm stock before committing on calls) that bypassed the system but added friction to every transaction.
The accounts integration was the second pain point. Sales orders entered via Outlook had to be re-keyed into Sage at month-end for invoicing, a process that consumed roughly 30 hours of the finance manager's time each month and introduced typing errors that the finance manager had measured at a rate of approximately 4% per re-keyed line. The error rate was the basis of an ongoing reconciliation workload that had reached the point where the finance manager was considering hiring an additional finance assistant whose primary role would have been to resolve those errors.
The pricing complexity added the third pressure. The business operated a complex tiered pricing structure with customer-specific discounts, volume breaks, and contract pricing for around 40 of its larger accounts. Pricing was held in the Access database, accessed by sales account managers via a desktop interface only available on three specific terminals. Quoting a customer required physically walking to one of those terminals, looking up the customer, checking applicable contracts, and copying the price. The managing director estimated, conservatively, that the firm was losing 30 to 40 hours of sales productivity per week to this single workflow.
The brief to Softomate was sharp. Replace the four systems with a single integrated ERP, achieve at least 95% inventory accuracy within 90 days of go-live, eliminate the month-end re-keying entirely, and make customer-specific pricing available to all sales staff from any device. Critically, the cutover window was constrained: the business could not afford a long parallel-running period because the warehouse operatives could not realistically be asked to enter every stock movement into two systems for weeks on end. The go-live needed to be fast and clean.
Softomate selected Odoo (Community edition with a small set of paid Enterprise modules) as the target platform after a one-week evaluation against three alternatives. The decision was driven primarily by Odoo's native integration of WMS, sales, purchasing, accounting, and CRM into a single data model, which meant the cross-system inconsistencies that had plagued the previous setup were architecturally impossible. The fact that Odoo is open-source also gave the business control over the future direction of customisation without vendor-lock dependency.
The 28-day build was structured into four week-long phases, each with a specific scope and explicit cutover criteria. Week one was discovery, configuration design, and data preparation. Week two was core module configuration (inventory, sales, purchasing, accounting) and the first wave of data migration. Week three was custom development for the tiered pricing logic (which required Odoo customisation since the existing structure was more complex than Odoo's native pricelist feature), warehouse-floor mobile interface configuration, and the integration with the firm's barcoded stock-picking hardware. Week four was the cutover itself: a Friday-Sunday cutover window with the new system going live for inbound stock movements on Monday morning.
The data migration was the most operationally risky workstream. The legacy WMS held 3,400 SKU records with inconsistent product codes, several thousand customer records with format variation across the four systems, and approximately 11,000 historical transaction records that needed to be preserved for VAT compliance. Softomate built a structured migration pipeline that consolidated all four sources, with three sequential validation passes: format normalisation, cross-system reconciliation, and final review by the firm's operations manager. The operations manager flagged 187 specific records for manual handling during the second pass, which were resolved one-by-one before cutover.
The tiered pricing customisation was the most technically complex part of the build. Odoo's native pricelist feature could handle customer-specific pricing and volume breaks but not the contract-specific override logic the firm used for its top 40 accounts. Softomate built a custom pricelist engine that layered three levels of pricing logic: the base catalogue price, the customer tier discount (applied automatically based on customer classification), and the contract override (applied when the relevant customer had an active contract for the relevant product family). The engine was integrated with Odoo's native quote-and-invoice workflow so account managers could generate quotes from any device with the correct price automatically applied.
The barcoded stock-picking integration replaced the manual paper-based picking workflow with a barcode-scanner-driven workflow that wrote stock movements to Odoo in real time. The firm's existing hardware (Zebra MC3300 scanners) was compatible with Odoo's mobile barcode interface, so no new hardware was needed. Warehouse operatives received four hours of training per person before cutover, with on-site Softomate support during the first three days of live operation.
The cutover itself ran without major incident. Friday evening end-of-day stock was counted manually across all three warehouses (a process the firm did annually anyway), recorded on paper, and keyed into Odoo as the opening balance on Saturday morning. The legacy systems were taken offline at midnight Sunday. The new system was live for warehouse operations from 7am Monday. Three minor issues surfaced during the first 48 hours, all resolved within 4 hours each: a barcode-reader configuration issue at the Leeds warehouse, a pricing-tier mapping error for one customer account, and a Sage import format mismatch that affected one supplier's invoices.
Inventory accuracy, measured by physical stock count against system stock at the 30-day post-launch cycle count, was 96.4% across the 3,400 SKUs. By the 90-day cycle count it had risen to 97.8%. This exceeded the 95% target the managing director had set and was the single most operationally significant outcome of the project. The 18 hours per week of customer-service exception handling driven by stock errors fell to under 2 hours per week within the first month. The customer-service manager redeployed that recovered time into proactive customer outreach for the firm's top 80 accounts, a workstream the firm had wanted to run for years.
The month-end re-keying of sales orders into Sage was eliminated entirely from week one of go-live. The finance manager recovered approximately 30 hours per month from this single change, plus the downstream reconciliation work driven by re-keying errors (which had been running at roughly 12 hours per month). The planned additional finance assistant hire was cancelled. The finance manager redeployed the recovered time into management-information reporting that gave the managing director real-time visibility on margin by customer, by product family, and by warehouse for the first time in the firm's history.
The pricing-access bottleneck was resolved completely. Sales account managers generated quotes on laptops, tablets, and phones from any location, with the correct customer-specific price applied automatically. Quote-to-order conversion time dropped by approximately 35% because the friction of physically walking to a pricing terminal was gone. The managing director reported that the sales team's overall productivity, measured by quotes generated per account manager per week, rose by roughly 28% across the first quarter post-launch.
The total order-to-dispatch cycle time, from order entry to confirmed dispatch from warehouse, fell from an average of 2.4 working days to 1.4 working days, a 41% improvement. The reduction was driven by the elimination of manual stock-check steps, the real-time visibility of cross-warehouse stock for fulfilment optimisation, and the automated handoff of confirmed orders from the sales system to the warehouse pick list.
The four cancelled software subscriptions (legacy WMS support contract, Sage 50 standard tier, Access database licence, and a small per-user CRM the sales team had been using for prospect tracking) eliminated ?38,000 of annual software cost. Odoo's licensing cost, including the small paid Enterprise modules and Softomate's ongoing support retainer, came to roughly ?14,000 annually, producing a net annualised saving of ?24,000 on software alone.
Total Softomate engagement cost was recovered within 7 months of go-live, calculated against the combined value of avoided headcount cost, eliminated software subscriptions, and the productivity recovery measured in sales-team hours. The firm has since commissioned a second phase build to extend Odoo into its purchasing workflow, where the goal is to integrate live with the firm's top 12 suppliers via EDI for automated stock replenishment based on minimum-level triggers.
Related service:Odoo ERP Implementation London. Further reading:Odoo ERP Implementation UK Complete Guide, Odoo Implementation Cost UK and Odoo 19 for UK Wholesale Distributors.
Anonymised client engagement. Identifying details modified for confidentiality. Outcome ranges reflect typical results from similar projects.
Names withheld to preserve confidentiality.
Names withheld to preserve confidentiality.
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