--- title: "Replace spreadsheets for tracking of your robot leasing operations" description: "Learn how spreadsheets fall short in managing leased robots and discover the benefits of transitioning to purpose-built asset management systems for RaaS." url: https://starhive.com/blog/when-spreadsheets-start-breaking-your-robot-leasing-operations --- # When spreadsheets start breaking your robot leasing operations Learn how spreadsheets fall short in managing leased robots and discover the benefits of transitioning to purpose-built asset management systems for RaaS. [Charlotte Nicolaou PhD](https://starhive.com/blog/author/charlotte-nicolaou)31/10/2025 · 3 min read ![A leased inspection robot assess the production line of a automative factory](https://starhive.com/images/blog/when-spreadsheets-start-breaking-your-robot-leasing-operations/pexels-hyundaimotorgroup-19319639.webp) So many of the robotics companies we work with at Starhive start tracking their robots in spreadsheets (or Confluence lists, or Notion databases, etc). And it makes sense, they're quick, flexible, and work perfectly fine when you only have a handful of prototype robots in the field. However, as you transition out of the R&D phase and your fleet and customer base expand, so do the cracks. Robots or components go missing. Maintenance schedules slip.  At first, it’s semi-manageable. Then one day, a customer gets frustrated as their robot has broken _again_. Engineers can't figure out why, as the maintenance records are scattered and incomplete and nobody is quite sure if the motor module is a prototype from R&D or not. The customer has had enough and cancels their contract. Spreadsheets eventually stop helping you run your business and start slowing down. This blog outlines why and what you can do to transition before it becomes an issue.  ### Spreadsheets can’t keep up with high volumes of leased robots Tracking leased robots is inherently complex. Each unit is a combination of different components that might not be standard across every robot. They are made from different combinations of sensors, motors, controllers, and other modules, plus every robot and module has its own unique maintenance history that you need to understand. You’re dealing with a level of granularity that spreadsheets were never designed for. And that's before you include tracking which customers have which robots.  To track all of this in a spreadsheet, you're talking multiple sheets that teams need to switch between, or a huge number of columns in a single sheet. And of course, duplicates get made, people copy or update cells erroneously, and nobody knows why certain changes were made and when. And because the data isn't relational and changes aren't tracked, context is lost. Your engineering team can’t see which robots are due for service, your R&D team can't see patterns in which components are failing regularly, and your business teams can't see which customers are at risk of becoming frustrated.  ### Lost data means lost control When asset data becomes inaccurate and inaccessible, you lose operational control. Robots end up in the wrong place, service intervals get missed, and contract renewals are delayed because no one is confident the information is accurate. This creates real business risk: - **Maintenance lapses** lead to unplanned downtime in the field. - **Lease overruns** mean revenue leakage or disputes with customers. - **Component swaps** aren’t logged properly, so you lose traceability on high-value parts. - **Customer trust** erodes when your team gives conflicting answers about their robots. The damage isn’t immediate. It builds slowly and every incorrect cell in a spreadsheet adds risk. And in a robotics business where uptime and reliability matter, that uncertainty costs money and reputation. ### Scaling makes the cracks visible In our experience working with  robotics companies (and with businesses in other industries where complex assets are leased to customers), issues arise as the number of robots increases from tens to hundreds. Manual tracking just isn't suitable anymore. Each new robot manufactured and every new lease agreement adds complexity: different components, new contract terms, and maintenance obligations. You can’t expand leasing programmes or offer advanced service models if your core asset data isn’t reliable. Scaling amplifies the weak spots, and by the time you notice, it’s often painful to untangle. ### What replaces the spreadsheet Purpose-built systems for leased asset management give robotics companies what spreadsheets can’t: structure and context. A modern platform lets you: - Concretely link each robot to its customer, contract, and service history so data is easier to find. - Create digital twins of every robot to understand precisely which modules and components are installed, and the history of each of those components to make better decisions on maintenance vs replacement. - Provide reminders and notifications when critical dates are approaching such as routine maintenance, inspections, or lease renewals. - Give every team, from ops to finance, access to the same trusted data - Connect live usage or telemetry data from robot APIs For robotics companies, this is critical to maximising profitability. You’re managing machines that operate in the real world, often across multiple sites, with high expectations from customers. A connected asset system turns scattered data into reliable insight so you can scale without chaos. ### Final thoughts Spreadsheets are a great starting point. But because they aren’t built for the complexity of large numbers of leased robots and all their various modules, they hit problems. As you scale, the real challenge isn’t adding more robots, it’s keeping control of the data behind them. Investing in structured asset management early pays back fast. It prevents operational blind spots, protects revenue, and keeps your customers confident in your service. ![Charlotte Nicolaou PhD](https://starhive.com/images/blog/authors/charlotte-1.webp) About the author Charlotte Nicolaou PhD Charlotte is a technical marketer at Starhive with many years of experience in asset management and database configuration. She has previously worked at Atlassian and Sinch and takes joy in simplifying technical concepts to something easier to digest. 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