Warehouse and transportation equipment is an essential part of modern logistics. However, unnecessary maintenance costs and premature new purchases are a burden on the economy. According to a study by McKinsey, the annual global maintenance costs for machines are around 50 billion euros, with billions being spent unnecessarily on neglected care and a lack of efficiency. Companies could save around 15-20% of these costs through preventive maintenance and efficient repairs.
Preventive maintenance: a proven concept
Preventive maintenance is a proven approach to maximizing machine performance and lifespan. Regular checks and targeted investments in spare parts prevent expensive breakdowns. Important components such as the hydraulic system or electronic controls require scheduled maintenance to avoid efficiency losses. High-quality spare parts are a key to cost-effective maintenance here.
This approach goes hand in hand with the credo of “doing more with less”. In our private lives, for example, the principle of extending the lifespan of household appliances through regular care and minor investments leads to long-term savings. Whether it’s equipping cars with high-quality, long-lasting tires or treating your own furniture with care, investing in quality often saves money and effort by reducing the need for repairs and new purchases.
Targeted investments for long-term efficiency in warehouse technology
In warehouse technology, for example, for Hyster forklift parts are needed to ensure the long-term functionality of these vehicles. With targeted investments in suitable spare parts and regular maintenance, machine wear can be significantly reduced and overall performance sustainably increased. The costs for spare parts are often significantly lower than the losses caused by unplanned machine downtime.
Reduce costs through predictive maintenance: A calculation example
Let’s assume that a company operates a production plant with annual maintenance costs of $100,000. By using predictive maintenance, these costs can be reduced by up to 30%, which corresponds to a saving of $30,000.
Without predictive maintenance, there is a risk of unplanned downtime. Such an outage could, for example, cost $50,000 to repair and result in $100,000 in lost production. The total cost would thus be $150,000.
By contrast, the investment costs for predictive maintenance, including sensors and software, amount to around $50,000. The total savings would therefore be $100,000.
In 2017, Tesla faced significant production challenges with its Model 3 assembly line. Unplanned machine downtime led to production delays, contributing to substantial financial losses. Implementing a predictive maintenance strategy could have potentially mitigated these issues by identifying equipment failures before they occurred, thereby maintaining production schedules and reducing associated costs.
Using artificial intelligence in maintenance planning and monitoring
Artificial intelligence is revolutionizing maintenance strategies in industry. By analyzing large amounts of machine operating data, AI can identify patterns that indicate impending failures. This enables predictive maintenance, in which maintenance work is carried out based on actual need rather than at fixed intervals. According to a study by McKinsey & Company, the use of AI in maintenance can reduce downtime by up to 50% and lower maintenance costs by 10 to 40%.
One practical example is General Electric (GE), which uses AI to optimize the maintenance of its aircraft engines. By analyzing sensor data in real time, GE can identify potential problems early and take proactive measures, increasing reliability and reducing costs.
