Human Centric Guidance and Troubleshooting for Customer Service

Stakeholders' Roles

ALMER is the AR/XR solution provider, while LNS the business owner contributing to design, deployment, and evaluation. SSF will have a role in training planning and pilot systems validation activities in its testbed

Motivation & Description

Maintenance and troubleshooting are critical to ensure smooth operations and avoid downtime. However, current maintenance and troubleshooting XR instructions are often not personalised, making it overwhelming for beginners and unnecessarily time-consuming for experienced personnel who need to sift through irrelevant information and steps. Additionally, troubleshooting can be stressful, especially when equipment is down. While XR glasses are used in the industry, personalisation is often lacking as creating personalised instructions is time-consuming. This pilot will illustrate how XR5.0 addresses the “limited personalisation” challenge through AI-driven functionalities that improve the personalisation, relevance, and accuracy of XR environments, including guidance for maintenance and troubleshooting. In this pilot, this guidance will be tailored to the needs of individuals e.g., to their level of experience, preferred interaction mode, and stress levels. The UCs will be based on ALMER’s AR glasses and software, as well as on the LNS bar feeder.


For Use Case 1: Time to complete the planned maintenance task reduced by > 35%; All planned maintenance tasks are automatically documented (for experts); Human satisfaction increased (measured by 5-scale rating of service technicians); Reduce onboarding time (new service technicians) by > 30%; Error rate reduced by 20% (new and medium experienced technicians); For the Use Case 2: Troubleshooting time reduced > 35% for new and medium experienced technicians; Reduction of documentation time for experienced technicians > 50%; Reduce troubleshooting learning time for new employees by > 20%.

Use Cases

This pilot will support planed predictive maintenance based on XR5.0 solutions. The service technicians are guided to the machines needing maintenance using AI-linked AR glasses knowing the 3D location of the machines. After verifying the machine, the AR glasses provide customised maintenance instructions based on expertise level: Beginners receiving detailed step by step guidance to experienced workers receiving only necessary information (e.g., replace “filter x”). Based on Human DTs, signs of stress or being overwhelmed are detected, prompting the AI system to adjust the guidance accordingly. This may include simplifying instructions or initiating human remote support if necessary. Finally, the system will adapt the quality assurance and documentation processes to match the employee's needs. For beginners, each step may need to be confirmed to ensure that the job is done correctly. Experienced workers will benefit from automated non-interactive and non-disturbing documentation of their maintenance activities.

This Use Case builds upon the first one, expanding its capabilities to exploit XR5.0 to perform troubleshooting for sudden malfunctions of machines. In such cases, it's often unclear what's causing the issue, making it necessary to identify the root cause before proceeding with maintenance / repairs (UC1). To efficiently identify potential root causes of sudden malfunctions, an AI system will generate adaptive (step-by-step) plans that prioritise troubleshooting. These plans take multiple factors into account, such as machine data, results from previous steps and the human experience and stress level (from the Human Digital Twin), to tailor the plan to the specific human and situation. Also, the representation on the XR glasses is tailorable to the preferences of the human (such as providing additional video support). The user has control over which personal data is collected and monitored by the AI system. Once the necessary repairs are clearly identified, the user can then proceed with repairs and documentation according to UC1.