intralogistics

The lack of data-based decision-making prevents the creation of efficient, technically, and organizationally optimal material flows in intralogistics. This paper aims to develop a concept for a decision support system for optimizing forklift material flows based on sensor data. On the one hand, the efficiency and the data-drivenness of material flow planning shall be increased. On the other hand, a contribution to reducing logistics and material flow costs in manufacturing companies should be achieved. First, related works in sensor-based decision support for material flow planning are discussed. Then, a sensor-based concept of a decision support system for optimizing internal material flows of forklifts in manufacturing companies is developed. This concept consists of three layers. The data layer collects, prepares, and stores the required sensor data and additional constraints. The model layer integrates operations research methods to analyze, evaluate, and optimize internal material ...

For decades, different approaches have been designed and implemented for optimizing maintenance management systems in manufacturing, but maintenance is still known as a cost driver rather than an enabler or driver of value creation in production management. The challenges for companies facing this change are many, ranging from doubts about the economic efficiency of maintenance to lack of data and flexibility in the maintenance organization to shortage of skilled workers and lack of prioritization of sustainability and resilience. The vision of the Maintenance-Free-Factory (M2F) is to harness the potential of digitalisation as a driver of sustainability and resilience, enabling a fundamental transformation of maintenance. At the heart of the M2F is value-added production without maintenance disruption. Maintenance activities are carried out during unproductive production times. Therefore, five elements – digital maintenance processes, data-based maintenance, decision support ...