A multi-warehouse distribution business was tracking stock manually across locations, causing frequent stockouts and overstocking. DXDasia built a real-time inventory and order management system that cut stockouts 60%.
60% Reduction in Stockouts
Stock levels were tracked per-warehouse in separate spreadsheets, with no shared view across locations — making it nearly impossible to know true availability at any given moment.
We built a real-time inventory system that gives one accurate view of stock across every warehouse, with automation replacing the manual checking that was causing the delays.
The business moved from reactive, spreadsheet-driven stock management to a system that flags problems before they become stockouts:
Stockouts and overstocking are usually the same root problem wearing two faces: nobody has an accurate, real-time picture of inventory. Solving that one problem with live data fixed both symptoms at once.
Stock data syncs across warehouses in real time rather than on a batch schedule, so every location works from the same numbers the moment a sale or transfer happens. Reorder points are calculated per SKU based on historical movement rather than a single fixed threshold, so fast- and slow-moving items are handled differently instead of using one rule for everything. Barcode/SKU scanning ties physical stock movement directly to the system of record, removing the manual data entry that was the biggest source of the original discrepancies. Order processing checks live stock at the moment of order confirmation, not against a stale nightly count.
Tech Stack: React, Node.js, PostgreSQL with Redis for real-time sync, REST APIs (application programming interfaces) for warehouse and POS integrations.
Stockouts and overstock usually share a cause: the record of what is on hand is wrong. The best-known academic study is DeHoratius and Raman's 2008 paper in Management Science. According to a summary of it, nearly 370,000 records at 37 stores of one large US retailer were audited and 65% were inaccurate. It is one retailer, so do not treat 65% as a universal rate. The summary also reports that regular auditing reduced inaccuracy, while more complex store and distribution set-ups made it worse.
Spreadsheets per location make this worse. Each copy is updated by hand at a different time, so nobody can say what is available now, and sales are promised against numbers that are already stale.
The commercial cost shows at the till or checkout. An April 2026 survey of 1,000 US adults commissioned by DOSS found 45% would buy from a different retailer after a stockout and 32% would switch to a competing brand temporarily. That is a vendor survey of US shoppers, so Singapore figures may differ, but the direction is clear.
| Option | Suits | Main risk |
|---|---|---|
| Spreadsheets per site | One site, few SKUs, low order volume | Drift between copies; manual entry errors |
| Off-the-shelf inventory software | Standard workflows and common integrations | Workarounds when your process differs; per-user or per-location pricing |
| Custom system | Several warehouses, unusual workflows, specific POS or ERP links | Needs clear requirements, testing and ongoing support |
A decision matrix based on typical trade-offs, not a published benchmark.
First, one source of truth that updates when a sale, receipt or transfer happens, with scanning to remove retyping. Second, differentiated control: ABC analysis groups items by value, and as Wikipedia explains, A items get very tight control and accurate records while C items get the simplest controls. Its example splits have roughly 20% of items accounting for 70% of annual consumption value, though thresholds vary by business.
Third, reorder points set per item. The standard approach multiplies average demand by supplier lead time and adds safety stock sized to demand variability, lead-time variability and the service level you want. A single fixed threshold for every SKU over-orders slow items and under-orders fast ones.
The standard formula sets safety stock as a service-level factor multiplied by the square root of average lead time times demand variance plus squared average demand times lead-time variance, with reorder point equal to average lead-time demand plus safety stock. The same source warns that no universal formula exists, that it assumes independent, normally distributed demand, and that seasonal or trending demand causes stockouts in peaks and waste in troughs, so inputs need regular updating.
Singapore's IMDA logistics digital plan lists inventory solutions covering stock take, issuance, retrieval and search, with the stated benefits of greater visibility of inventory and assets, quicker decisions and lower risk of loss.
Fix the record first, then automate the decisions that depend on it.
Adding a new warehouse to the network is now a configuration change rather than a re-architecture — the same real-time sync that connects the existing locations extends to a new one without custom work. The movement data the system now captures is also feeding into demand forecasting for the first time, giving the business a data-backed basis for purchasing decisions instead of relying on gut feel and past experience. What started as a fix for stockouts has become the operational backbone the business now plans around, not just a stock-counting tool. The same real-time data is also changing how the purchasing team negotiates with suppliers — knowing exact turnover rates per SKU gives them a stronger position than the rough estimates they were working from before, and early conversations suggest it could open the door to better payment terms on the fastest-moving product lines.
Typically when you hold stock in more than one place, sell through more than one channel, or find staff correcting numbers daily. Those are signs the manual record cannot keep up. One site with few SKUs may be fine on a spreadsheet for longer.
Not always. Off-the-shelf suits standard processes and is quicker to start. Custom software makes sense when your workflows, warehouse network or integrations do not fit standard tools. Compare total cost over several years, including workarounds, not just licence fees.
Use ABC to decide. Count high-value A items most often, B items less often and C items rarely. The DeHoratius and Raman study, as summarised, found that audits reduced record inaccuracy, so regular counting is worthwhile.
Reorder point equals average demand during supplier lead time plus safety stock. Safety stock rises with demand variability, lead-time variability and your target service level. Work it out per SKU rather than using one rule for all items, and revisit it when demand is seasonal or a supplier changes, because the formula assumes stable, normally distributed demand.
It should confirm availability when an order is placed, reserve the stock, route the order to the right location and update every sales channel. It should also record returns and transfers, so the figure on screen matches what is physically on the shelf.
