Service and After Sales
Service and after sales are the functional areas where manufacturers and their customers come together after the purchase. And this is exactly where one of the greatest potentials for IoT lies: those who want to know how their products are actually performing at the customer's site need to equip them with sensors. Those who want to prevent failures before the customer calls need remote monitoring. Those who want to reduce service costs need to equip technicians with the right information.
IoT makes service proactive rather than reactive. Machines report themselves before they fail. Technicians arrive prepared because they know real-time diagnostic data. Maintenance contracts evolve from time intervals to condition-based support. This reduces service costs, increases customer satisfaction, and opens up new business models.
On this page you will find verified real-world examples from the IoT Use Case network – for service organisations taking the next step towards data-driven service.
These challenges are driving IoT projects in service and after sales
Low first-fix rate and expensive service calls
Service technicians drive to the customer without knowing exactly what is defective. Wrong spare parts in the van, unnecessary journeys, multiple visits for one problem: these first-fix-rate issues cost money and frustrate customers. IoT-based remote monitoring and remote diagnostics enable the fault to be known before the visit and resolved at the first attempt.
Reactive instead of proactive service
Waiting until the machine fails is the most expensive service model. Predictive maintenance based on IoT sensor data detects wear indicators early – and triggers targeted service interventions before damage occurs. This reduces downtime costs for the customer and makes the service process plannable.
Lack of visibility into product behaviour in the field
How is the product performing at the customer's site? Which parameters are frequently exceeded or undercut? Which faults recur regularly? Without real-time field data, manufacturers develop their products from the desk rather than real operating behaviour. IoT telemetry continuously delivers field data for product development and quality improvement.
Outdated maintenance models without data foundation
Traditional maintenance contracts are based on time intervals – independent of actual wear. IoT data enables condition-based service models: maintenance happens exactly when it is needed. In addition, it opens up new business models such as outcome-based contracts, equipment-as-a-service, or performance guarantees.
Missing digital service history and machine records
When was the machine last serviced? Which parts were replaced? What faults occurred? Without structured digital service history, service teams lose valuable time on research. IoT-linked service portals automatically maintain complete machine records – with sensor data, service reports, and spare parts lists.
High travel costs and slow response times in service
Service technicians cannot be everywhere at once. IoT-supported remote service enables remote access to machines, transmission of real-time diagnostic data, and – combined with augmented reality – remote guidance directly through an AR headset. This reduces travel costs, shortens response times, and increases the capacity of the service team.
Real-world solution examples in the Service and After Sales functional area
Open source: data acquisition and visualisation for sustainable gas production
KROHNE Messtechnik GmbH: innovative IIoT platform for data analytics solutions
IoT in Service and After Sales: What Actually Works in Practice
Service was long reactive: customer calls, technician drives out, problem gets fixed. IoT reverses this sequence. Machines report themselves before the customer notices the failure. Technicians know before the visit what is defective. Manufacturers know the operating behaviour of their products in the field – and can improve their service and products on a data basis.
Typical Application Areas
Remote Monitoring and Remote Diagnostics
Sensors directly in or on the products at the customer continuously transmit operational and condition data. The service team sees in real time how every machine is running. Anomalies trigger automatic alerts. In many cases the problem can be resolved remotely – without a technician on site. If a visit is needed, the technician arrives with a complete diagnosis and the right spare parts.
Predictive Maintenance as a Service Product
Predictive maintenance is not just an internal tool – it is a service product. Manufacturers offering predictive maintenance services create a new, recurring revenue stream. Customers pay for failure prevention, not repairs. The shift from reactive to predictive service is one of the strongest levers for service profitability.
Digital Machine Records and Service History
Every machine receives a digital twin in the service portal: sensor values, error codes, maintenance reports, spare parts history, installed software version. Service technicians retrieve all relevant information before the deployment. New members of the service team are productive immediately. Escalations are resolved faster through complete history data.
New Business Models: Equipment-as-a-Service and Outcome-Based Contracts
IoT data enables usage-based billing: payment per operating hour, per part produced, per service delivered. Manufacturers who observe their products in the field via IoT can give availability guarantees – and align their service organisation to prevent failures rather than repair them.
IoT Telemetry for Product Development and Quality Improvement
Field data from IoT-connected products shows how machines are actually operated: what load conditions are real? Which parameters are permanently exceeded or undercut? What faults occur under which operating conditions? These insights flow directly into product development, quality assurance, and technical documentation.
What Sets IoT in Service Apart from Other Areas
Service is about trust. Customers grant access to their facilities and production data. Data security, data sovereignty, and transparency about what is collected and analysed are therefore not just technical but strategic business requirements. Manufacturers who operate transparently here build sustainable customer relationships.
Real-World Examples from the IoT Use Case Network
In our network you will find concrete, verified solution examples for IoT-supported service – from remote monitoring for industrial pumps and predictive maintenance platforms for machine tools to digital machine records and equipment-as-a-service models in mechanical engineering. Every example shows which technologies were used and what was concretely achieved in the end.
No marketing fluff. Only practice.
Building IoT-supported service – we can help
Are you building your service organisation with IoT, or do you want to become more visible as a solution provider in the after-sales space? We help you find the right partners and reach real end users.
Get in touch