Predictive maintenance
Manufacturers, but also suppliers of spare parts, wears or consumables are constantly looking for new services designed to strengthen their customer loyalty and to support their unique position. Those who are digitally mature can leverage the Industrial Internet of Things (IIoT) and machine-to-machine communication to create predictive maintenance services.
What is predictive maintenance?
"Predictive" maintenance of machines means that service appointments are not triggered by general runtime hours or generic time frames. Predictive maintenance leverages measured data to find the perfect time to replace parts or stack up on consumables.
Wear components are used as long as possible, and are replaced (only) when needed to. This ensures machine availability, avoids downtimes while also keeping costs down. To realize this, digitized machines deliver millions of data via sensors to a decentralized network (edge computing) or directly to an IIoT hub for further processing. This data is then analyzed by the manufacturer and evaluated using machine learning technologies.
That’s what makes it "predictive": The evaluation determines the probability of failure of certain components within a defined period ahead. Accordingly, they are replaced at the right time, thereby reducing costs.
What is the main difference between predictive and preventive maintenance?
Predictive maintenance relies on actual equipment condition, using data and analytics to trigger maintenance before failure occurs. Preventive maintenance follows a set schedule, replacing parts or servicing equipment after a certain time, regardless of actual need.
What are the benefits of predictive maintenance?
When compared to other maintenance approaches, predictive maintenance offers some advantages. For example, to reactive maintenance: Here, downtimes are the trigger for a chain reaction to replace parts.
Contrasting this, preventive maintenance addresses issues well ahead: Here, "trouble makers" are replaced on suspicion at fixed intervals. This is often done prophylactically and earlier than necessary. Predictive maintenance is the way to avoid the mentioned disadvantages by calculating the optimum point in time for the job. This adds up to clear benefits of predictive maintenance:
- Machine downtime is kept to a minimum.
- Expensive, specific individual parts do nit have to be kept in stock.
- Production efficiency is increased.
The decisive advantage for suppliers is the interaction between predictive maintenance and the machine: Events such as a malfunctioning warning are generated from the machine and collected by sensor technology.
These events can then be used as the basis for new services and business models. They provide the customer with a considerable benefit – and the machine manufacturer with a competitive advantage.
Challenges and solutions of predictive maintenance
Challenge 1: Data integration and connectivity
Connecting various machines and legacy equipment to modern IoT platforms can be complex.
Solution: Utilizing scalable digital platforms, for example from Intershop, supports seamless integration of sensors and legacy devices, enabling unified data collection across varied assets.
Challenge 2: Data security and privacy
The management of physical products and digital services in a single system is often fragmented.
Solution: Advanced cloud-based solutions offer secure data storage and role-based access controls to protect maintenance and operational insights.
Challenge 3: Change management & skill gaps
Shifting to predictive maintenance may face resistance or require upskilling the workforce.
Solution: Comprehensive training programs and intuitive, user-friendly tools (with guided dashboards offered by Intershop customer portals) help teams adopt new procedures and maximize the value of predictive maintenance.
Challenge 4: High initial investment
Implementing sensors, software, and analytics tools requires upfront costs.
Solution: Digital commerce and service platforms often provide modular, scalable pricing and rapid ROI through reduced downtime. Many Intershop solutions enable a phased rollout, allowing value-driven expansion.
For tailored solutions and proven expertise in digital B2B processes, Intershop provides strategic support and implementation assistance.
Predictive maintenance use cases
Manufacturing:
A producer implements predictive maintenance on packaging machines. Real-time sensor data triggers service alerts before mechanical components wear out, ensuring uninterrupted production and optimal spare part stocking.
Wholesale & distribution:
Distributors monitor fleet vehicle health using IoT, preventing logistics delays and enhancing delivery reliability through data-driven maintenance.
Complex B2B scenarios:
Industrial equipment suppliers utilize predictive analytics within their digital customer portals, offering clients personalized maintenance recommendations and efficient spare parts ordering, as enabled by solutions like Intershop Commerce Platform.
A predictive maintenance example
Your machine is connected via the Industrial Internet of Things (IIoT). Sensors continuously measure vibration, temperature, and rotational speed, transmitting this data to a central IIoT platform.
Based on your expertise in system-critical components and supported by machine learning algorithms, the system continuously evaluates operational stability and calculates the probability and timing of potential component failures.
A modern commerce solution that seamlessly integrates your data sources, tools, and backend systems can make this information available to customers through a connected after-sales portal or mobile application.
By linking predictive insights with e-procurement capabilities, customer teams can immediately reorder critical components when needed. Service offerings are integrated as well: machine operators can directly schedule maintenance appointments with the manufacturer through the same portal.
The result is a comprehensive, value-added service ecosystem. Instead of delivering only a machine, you provide a connected, intelligent system designed for long-term reliability, efficiency, and performance.
Intershop customer case
Huisman leverages servitization by combining complex industrial products with digital services and aftermarket offerings, supported by a scalable B2B commerce platform.
Technology for implementing predictive maintenance
Predictive maintenance relies on an ecosystem of technologies:
- IoT sensors: Collect real-time data (e.g., vibration, temperature, run-time).
- Data analytics & AI: Machine learning models analyze trends and predict failures.
- Cloud infrastructure: Secures data storage, processing, and access from anywhere.
- Mobile apps & dashboards: Field teams get actionable insights instantly.
Digital customer portals as the front end for predictive maintenance
Monitoring machines takes place by displaying the machine as a digital twin in a dashboard within a customer portal. The user can take a look at general status information, so to speak the "heartbeat" of the system and easily gather information on potentially problematic components.
The dashboard is also the starting point for after-sales service. Events are picked up in a customer portal and linked to the commerce solution where parts, consumables or services can be directly purchased.
FAQs about predictive maintenance
What is the main goal of predictive maintenance?
The primary goal of predictive maintenance is to prevent equipment failures by determining the optimal time for maintenance activities. By identifying potential issues before they lead to breakdowns, companies can reduce unplanned downtime, extend asset lifespan, and optimize maintenance costs.
Which technologies are essential for implementing predictive maintenance?
Key technologies include IoT sensors, machine learning algorithms, cloud platforms, and integrated digital portals that collect and analyze asset performance data.
What are some common challenges in adopting predictive maintenance?
Adopting predictive maintenance can present several challenges. These often include integrating legacy equipment into modern digital infrastructures, managing and analyzing large volumes of data, ensuring robust cybersecurity, and developing the necessary digital skills within the organization.
Modern commerce and service platforms can help address these barriers by enabling seamless system integration, structured data management, and secure service workflows that support scalable, data-driven maintenance models.
What are the three types of predictive maintenance?
The three main types are condition-based monitoring (using real-time data from sensors), statistical process monitoring (analyzing trends and patterns in historical data), and machine learning-based prediction (using AI algorithms to forecast failures).
How do IoT sensors enable predictive maintenance?
IoT sensors collect real-time data on vibration, temperature, and other parameters, allowing systems to detect anomalies and predict failures before they happen.
Can predictive maintenance be applied to legacy equipment?
Yes, many solutions offer retrofit sensor kits and data integration platforms, making it possible to implement predictive maintenance even on older machinery.
How does predictive maintenance impact spare parts management?
Predictive maintenance improves spare parts management by forecasting when specific components will require replacement. This enables companies to align inventory levels with actual demand, reducing excess stock while minimizing the risk of shortages. As a result, businesses can lower carrying costs, improve supply chain planning, and ensure critical parts are available when needed.
Get started with predictive maintenance
Ready to unlock new service models, cut costs, and future-proof your operations? Contact our team today.
