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More than 40% of workers devotes at least a quarter of his workweek to repetitive manual tasks: data entry, information crossing, system updates. In Supply Chain, that number is much worse. McKinsey documents that enter 60% and 70% of a planner's time is wasted in manual data aggregation, record cleaning and spreadsheet adjustments before any decision can be made.
According to our study of trends in the supply chain In LATAM, by 2026, only 36.7% of supply chain leaders have the implementation of RPA in their operations among their priorities. A relatively low percentage compared to Anglo-Saxon countries, but it reflects the operational reality of the region.
What is RPA in practical terms? It is a software that mimics what a human user does on existing digital interfaces (ERP, WMS, TMS, email, spreadsheets) following predefined rules for reading data, filling out forms, launching transactions and sending notifications. It doesn't think, it doesn't predict, it doesn't learn alone, it's just execution. Its value lies in making it consistent, fast and with an error rate close to zero.
In this article, we'll tell you what RPA specifically automates in the Supply Chain, what are the processes where it generates real ROI and (this is key) what it can't do, because that's where the work of intelligent planning begins.
RPA (Robotic Process Automation) are software robots that replicate human actions on existing digital systems. They do not require modifying the ERP or the WMS. The bot “sees” screens just like you, clicks, copies data, validates rules and executes transactions, only in seconds and without typing errors.
The most important distinction to be clear about: RPA isn't AI. He doesn't decide, he doesn't recommend, he doesn't learn patterns. If the rule says “when the stock falls below 50 units, generate an OC to supplier X for 200 units”, the bot executes that faithfully. If the condition changes or an exception appears outside the programmed rules, the bot escalates the human team.
The answer lies in the characteristics of the work:
McKinsey estimates2 that up to 80-90% of planning tasks in Supply Chain can be automated with advanced systems, leaving planners only exceptions and decisions of strategic value. And companies that have taken that route have felt it in their costs: Deloitte reports that 78% of companies that implement RPA achieve average reductions of 25-50% in operating costs from automated processes.
Before, the planner manually reviewed inventory levels, then calculated in Excel when and how much it needs to be replenished. I sent the reorder request to the system, and to the vendor by mail. This process can take 20 to 30 minutes, According to studies3, if you multiply it by the times you do it per week or month, are full days of manual administrative work only
With RPA, the bot can automatically identify that inventory level of products and select those that need to be reordered. Generate the reorder and send a draft to the system, and also generate the purchase order to the supplier by mail, ready for the planner to only verify the information and approve the operation. This speeds up manual management, giving the planner more time to focus on what's important.
UiPath Document cases where the automation of order processing reduces cycle time from hours to minutes, automating more than 90% of the steps between receiving the need and issuing the order. A case in the high-tech industry reported a 60% reduction in end-to-end processing time and freed up more than 75 million dollars in cash flow thanks to lower inventories resulting from more agile replenishment cycles.
For inventory reconciliation, craftsmanship doesn't change much. The common process is to export the data from the WMS, and also export the data from the ERP, the person is responsible for manually crossing them into Excel, in order to detect any faults or voids and to be able to create a report. This has a weekly recurrence, so decisions could be made with the wrong data for a full week. A study done by CAPS Research, shows that the average inventory accuracy with manual processes is around 65-91%.
By applying RPA to this task, it can be delegated to a bot that runs an automatic reconciliation every night. Compare the SKU by balance and location, contrasting with the ERP and automatically generate reports for review in the morning. This considerably increases the accuracy of the inventory.
How does this impact inventory management? MRP and planning studies indicate that accuracies below 98% significantly increase stock failures or force overestimation of safety inventory to maintain the fill rate. Raising accuracy from 90— 92% to 98— 99% reduces the safety stock without losing fill rate, which directly translates into less fixed capital and fewer product bankruptcies.
Registering a new supplier is a process that, before being automated, involves capturing the same information manually in multiple systems: the ERP, the accounts payable system, compliance tools, supplier portals and quality databases. Each system separately, often with redundant or incomplete data that forces the team to keep constant monitoring.
With RPA, the bot can automatically read the supplier's onboarding form, validate their tax and compliance data against external bases, and upload the records to all the required systems applying the defined approval rules. All this without manual intervention, leaving the team only the task of reviewing and approving.
The manual shipment tracking process is one of the most repetitive in the logistics area. The planner or customer service team enters each carrier's portals one by one, extracts the status of shipments, updates the ERP or TMS and answers emails from customers asking about their orders. All this, several times a day. Inquiries such as “Where is my order?” they represent between 40% and 60% of all customer service contacts in e-commerce, According to Gartner. Studies have indicated that 67% of customers don't buy again with a retailer or supplier that didn't notify them in time.
With RPA, the bot connects to carrier portals, APIs or EDI, extracts the status of each shipment and automatically consolidates it into internal systems. In the event of any exceptional event, it triggers alerts to the team and proactive notifications to the customer via email or SMS, without anyone having to intervene manually.
A case documented by Adastra describes how an RPA shipment tracking bot saved 5,600 man-hours per year and processed data requests 20 times faster for a global automotive group. Retailers that implemented unified tracking and automated notifications reported reductions of between 25% and 50% in order tracking inquiries, with specific cases where that reduction reached 50%. Fewer incoming inquiries, less operational burden, and better-informed customers.
Manual invoice processing is one of the most costly and error-prone tasks within accounts payable. The usual flow involves receiving the invoice from the supplier, manually searching for the purchase order in the ERP, locating the merchandise receipt, crossing quantities and prices, validating tolerances and approving — or scaling for review. Industry studies estimate that the manual processing of an invoice costs between $10 and $16 dollars and takes between 8 and 10 days. With a manual error rate of between 1% and 5%, each discrepancy becomes a dispute that adds more cost and more time to the cycle.
With RPA, the bot captures the invoice data, automatically compares it against the OC and the receipt recorded in the ERP, applies the defined tolerance rules and only sends the exceptions that don't match for human review. The rest flows directly to payment, without manual intervention.
In a manual planning process, the planner extracts sales data from the ERP, the CRM, the e-commerce platform and external distributors. It opens them in Excel, cleans them, removes duplicates and consolidates them manually before loading them into the planning tool. McKinsey points out that this burden limits the frequency of planning cycles and the quality of decisions, because 70% of the time is spent cleaning data and operational activities before being able to analyze the information.
Now, with automation and RPA, a bot connects to all data sources (databases, APIs, files, emails), extracts demand series, automatically cleans them and uploads them to the demand planning tool or APS.
RPA can aggregate and deliver clean and timely data. But it doesn't make the forecast. It does not detect seasonality, it does not model the impact of a promotion, it does not estimate safety stock with demand variability. That's where RPA ends and intelligent planning begins.
They now exist demand planning software that don't even need data cleansing. They only connect to your ERP or data source, and the rest is done by the software, making it even easier to operate and make decisions with the least amount of human error possible.
If you are interested in this type of solution for your company, you can explore Datup, a software specialized in supply chain operation, which not only performs the Demand forecasting powered by AI, but it offers portfolio classification by profitability, rotation speed and stability, intelligent inventory optimization, and of your distribution points. All this, together with AlAia, a conversational assistant that you can consult about your operation 24/7.
Each return that arrives triggers a chain of manual captures in four or five different systems: the WMS to record the entry of the product, the ERP to adjust the inventory, the payment system to manage the refund, and the CRM or OMS to update the order status. Each one separately, often on different screens, with information that can become inconsistent between systems if any step is omitted or captured late. Returns represent between 14.5% and 16.9% of retail sales in the U.S. , According to industry data, and the cost of processing each return can range from $10 to $40, a volume that, at scale, represents a significant operating burden.
With RPA, the bot automatically detects the return trigger. Update the inventory in the WMS and the ERP, start the reimbursement flow and record the corresponding credit notes. Exceptions come to the team for review; the standard flow runs on its own, without manual intervention.
Cases documented by QSTRAT indicate that RPA can reduce errors in return processes by up to 90%, maintaining inventory accuracy close to 99%. Total processing costs report reductions of between 30% and 70%, turning one of the most fragmented processes in the logistics operation into an agile and traceable flow.
According to Deloitte, 25 to 50% are saved in automated processes. Organizations mature in intelligent automation report an average reduction of 32% in costs.
The error rate in manual capture is around 1 to 5%. Automated systems reduce that rate to less than 0.1%.
Tasks that take a person 15—30 minutes are completed in seconds. El Hackett Group documents that companies that automate OCs processing do so 76% faster and at 55% less cost than manual methods.
The same RPA infrastructure handles 2x or 5x the volume during Black Friday or the end of the month without increasing processing time.
Every bot action is recorded in an automatic log. This simplifies SOX audits, improves traceability in regulated sectors, and reduces the risk of fraud. Organizations with high levels of AP automation have 59% lower exception rates.
By automating capture and reconciliation tasks, procurement and supply chain teams can spend more of their time on strategic initiatives. Logistics cases report savings of tens of thousands of man-hours annually — 74,000 hours in SF Supply Chain, 5,600 in a global automotive group — redirected to continuous improvement and risk management.
RPA is great at executing repetitive rules on structured data. What you can't do:
The best Supply Chain stacks in 2025 combine both layers: RPA for operational automation (data collection, OCs execution, reconciliation, tracking) + an intelligent demand planning platform for the Forecast layer and inventory optimization.
In that stack, RPA feeds the models with complete and timely data, and the models make the decisions of how much to order, when and where. It's complementarity, not competition. Platforms like Datup operate exactly on that second layer: we take the operational data produced by your infrastructure and convert it into demand forecasts, safety stock recommendations and purchasing decisions.
RPA eliminates operational noise, fewer hours spent capturing data, fewer errors in OCs, better visibility of shipments, and reconcilations that run on their own while the team sleeps. But once your operation is clean and automated, the next question is the one that really matters: what do you do with all that data?
That's where Datup lives. We take the aggregated and clean operational data produced by your infrastructure (whether RPA, ERP, WMS or TMS) and convert it into demand forecasts, inventory recommendations and purchasing decisions. With machine learning models that incorporate seasonality, promotions and external variables such as inflation and climate. With a platform that your team can use from day one, without the need for a dedicated data team, implemented in an average of 5 weeks.
If you manage hundreds or thousands of SKUs in multiple locations and want to see what this would look like in your specific operation, Schedule a personalized demo. On the call, we showed you the live platform and shared the estimated ROI for your case from the start.
Felipe has specialized in the application of artificial intelligence to optimize supply chains, helping companies to predict demand, manage inventories and determine the ideal times to buy raw materials.
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