Supply Chain Optimization
SiCepat Ekspres needed to replace a monolithic SQL Server logistics platform that struggled with real-time order and delivery data as Indonesia’s e-commerce growth drove a rapid increase in package volume.
Reported impact
SiCepat scaled deliveries to two million packages per day, cut processing time from hours to minutes, supported 40 million monthly deliveries, and freed engineers to keep modernizing logistics systems across the wider group.
SiCepat Ekspres moved key logistics workloads from a monolithic SQL Server architecture on AWS to MongoDB Atlas so it could support microservices, real-time tracking, and rapid feature delivery across its nationwide logistics network. Atlas now manages nearly a third of the company’s data, including the core application that coordinates orders from e-commerce partners, pickups, branches, and drivers in real time. The team uses MongoDB indexing to keep operational queries fast, Atlas Online Archive to move older delivery records into lower-cost object storage while keeping them queryable, and continuous cloud backups for localized recovery. MongoDB Realm supports the mobile side of the application, and the company is exploring Atlas Device Sync to keep device and server data aligned. Atlas has also become the default for new innovations such as an executive chatbot that answers daily operational questions from live company data.
Order and billing data from e-commerce partners, branches, and drivers are written to MongoDB Atlas, operational queries are accelerated through indexes, older delivery records are moved to Atlas Online Archive, and mobile workflows are served through MongoDB Realm.
The business has been able to quadruple the number of packages it is capable of delivering to as many as two million per day.
“Data is everything for us – from day-to-day business decision making, through to customer experience and on the ground operations.”
“MongoDB has been fantastic and really delivered for us operationally.”
“We’d heard good things about MongoDB’s fully managed Atlas service, so we decided to test this for our partners' order data and some billing applications. It was an immediate success.”
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Related
In-house Sample Jun 3, 2026 Urban-Transporte Warehouse staff handling KION spare parts orders faced intense end-of-day workload peaks because containers could not be closed until it was clear whether customers would add more items before the shipping cutoff,… accuracy rate of around 96% Extreme Gradient Boosting (XGBoost) 
Snowflake Sample Archived · Feb 11, 2026 GLS GLS needed to replace a high-maintenance on-prem analytics platform that could explain past delays but could not predict future delivery problems, and whose slow queries, outages, and operational overhead limited the… 21 months Amazon Web Services (AWS) · Snowflake AI Data Cloud 
Baidu AI Cloud Sample Oct 13, 2025 Shandong Port Group (山东省港口集团有限公司) Shandong Port Group needed a better way to serve many kinds of logistics customers because information was fragmented, multimodal transport coordination was weak, and inconsistent service processes limited logistics… Intelligent Customs Clearance · Intelligent Tracking · Port Encyclopedia 
