Data-driven Predictive Maintenance for Green Manufacturing
Harald Rødseth, Per Schjølberg
Available Online November 2016.
- https://doi.org/10.2991/iwama-16.2016.7How to use a DOI?
- green manufacturing; integrated planning; maintenance management; predictive maintenance
- With the current situation of high demand of sustainable manufacturing, different stakeholders have clear expectations for more environmental manufacturing and at the same time minimizing the operational costs. The role of maintenance plays a key role in the path towards sustainable manufacturing. For achieving green manufacturing, more data-driven predictive maintenance strategies is needed and is expected to reduce energy consumption, maintenance resources in terms of spare parts, and reduction of consumables in terms of example lubrication. The overall bottom-line for the predictive maintenance strategy is increased availability, reduction of maintenance hours in terms of reactive maintenance activities, and increased profit for the manufacturing business. For a predictive maintenance strategy, it is crucial to develop Key Performance Indicators (KPIs) for the maintenance management. Today, common KPIs such as availability and different indicators for maintenance cost has been developed. When aiming for more green manufacturing, a more integrated application of maintenance KPIs are needed. Today, the KPI Profit Loss Indicator (PLI) has been developed and demonstrated in the saw mill industry and is regarded to support a more integrated approach in terms of Integrated Planning (IPL). The aim of this article is develop a structured approach for data-driven predictive maintenance aligned with the concept of PLI. Through a case study, the approach is partly demonstrated for the manufacturing industry. The results in this demonstration shows that the data-driven maintenance strategy will have a positive impact of the PLI value and provide a sustainable manufacturing in long-term.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Harald Rødseth AU - Per Schjølberg PY - 2016/11 DA - 2016/11 TI - Data-driven Predictive Maintenance for Green Manufacturing BT - 6th International Workshop of Advanced Manufacturing and Automation PB - Atlantis Press SP - 36 EP - 41 SN - 2352-5428 UR - https://doi.org/10.2991/iwama-16.2016.7 DO - https://doi.org/10.2991/iwama-16.2016.7 ID - Rødseth2016/11 ER -