A manufacturing business eliminated production delays and grew revenue 16% monthly with automated supply chain coordination

The team manually calculated component requirements from production forecasts, figured out specific parts needed from different vendors, and hand-wrote individual purchase orders. This process consumed 10+ hours weekly and was riddled with calculation errors. Disconnected inventory data led to stockouts and production delays that directly impacted customer satisfaction.

The business had grown faster than its operational infrastructure. What had worked with five vendors and a handful of product lines had become unmanageable with twenty-three vendors, over 400 distinct components, and a product catalogue that expanded quarterly. The production planning manager, the single person who understood the full inventory picture, was spending more than a quarter of their working week just on purchase order generation.

The process itself was error-prone at every step. The planning manager would start with a production forecast in one spreadsheet, cross-reference it against an inventory count in a separate sheet that was updated manually each morning, calculate the delta for each component, then determine which vendor supplied each part by consulting a third document. Purchase orders were written in Word and emailed individually to each vendor. If the inventory count was stale by even a day, which it frequently was, given that goods arrived and were consumed without automated tracking, the calculations were wrong from the start.

The consequences of errors cascaded through the business. A shortage of a single component could halt an entire production line. When a line stopped, orders were delayed. When orders were delayed, customers complained. The company had lost two significant accounts in the previous year directly because of repeated late deliveries caused by supply chain disruptions that better inventory visibility would have prevented. The owner knew the problem was solvable with the right tooling, they just had not found a solution that did not require a complete operational overhaul.

We created a master dashboard with centralised inventory tracking. The client enters a two-week production forecast into a simple Google Sheet. The system instantly checks the forecast against live inventory, calculates exact components needed, and automatically generates and sends purchase order emails to the correct vendors. A master dashboard tracks inventory levels and the status of all purchase orders in real time.

The solution was designed around the team's existing habits rather than requiring them to adopt new tools. The production planning manager was already comfortable working in Google Sheets, so we kept that as the input interface. The two-week production forecast is entered into a structured sheet with product names, quantities, and scheduled production dates, the same format the team was already using, just standardised.

When the forecast sheet is updated, an automation pipeline triggers immediately. The system reads the forecast, breaks each finished product down into its component bill of materials using a centralised BOM database we built and populated in week one, and calculates the total quantity of each component required across the two-week window. It then checks these requirements against live inventory levels pulled from a barcode-scanning system we integrated with the warehouse floor. Every time a component is received or consumed, a warehouse team member scans a barcode, and the inventory database updates in seconds.

The gap between what is needed and what is on hand becomes a purchase order. The system identifies the correct vendor for each component using a vendor-component mapping table, calculates the order quantity (incorporating each vendor's minimum order quantities and lead times), and generates a formatted purchase order email. POs are sent automatically for components below a reorder threshold; for larger orders above a defined value, the system drafts the PO and queues it for one-click approval by the planning manager before sending.

The master dashboard shows the live state of all purchase orders, sent, acknowledged, in transit, received, alongside current inventory levels and a two-week forward view of component requirements. Alerts fire via email when inventory for any critical component drops below a safety stock level, giving the team time to reorder before a shortage occurs. The entire system was built using a combination of Google Apps Script, a lightweight Node.js API, and a PostgreSQL inventory database.

The team reclaimed 10+ hours of manual coordination work every week. Production delays were eliminated, directly contributing to 16% monthly revenue growth. Manual calculation errors were eradicated. A single real-time dashboard now monitors all vendor activity and inventory status.

The most immediate change was the elimination of the weekly planning marathon. What had previously consumed a full morning of the planning manager's week, pulling data from multiple sources, calculating requirements, writing POs, sending emails, and chasing confirmations, now took under fifteen minutes of oversight. The manager reviews the draft POs queued for approval, confirms anything above the auto-send threshold, and moves on. The rest runs automatically.

Production delays were eliminated within the first six weeks. The combination of real-time inventory tracking and automated reorder triggers meant that the company stopped running out of components mid-production. In the six months before deployment, there had been eleven production stoppages caused by component shortages. In the six months after, there were none. Vendors reported that the purchase orders they received were more accurate and better timed than before, which improved the business relationships and, in two cases, resulted in better pricing terms.

The 16% monthly revenue growth figure reflects a combination of factors: recovering the revenue that had been lost to late deliveries, taking on new orders that the team had previously been hesitant to accept due to capacity uncertainty, and processing the same volume of production with fewer operational bottlenecks. The owner was able to commit to faster delivery timelines in sales conversations, which improved win rates on new business.

Manual calculation errors were fully eradicated. The BOM-based calculation engine does not make arithmetic mistakes, and the live inventory feed means the baseline figures are always accurate. The planning manager reported that the shift from reactive firefighting to proactive planning was the most significant quality-of-life improvement, they could see potential shortages two weeks out and address them before they became crises.