As we approach the end of the year, we’re taking a look at some of 2020’s factory failures within the Automotive sector, and ranking each one on its news, reputation and cost impact. As well as how preventable it may have been.
Massive fire at Ohio auto parts plant threatens jobs and livelihoods of workers1
In September, American Axle & Manufacturing’s plant in Ohio suffered a massive fire. Employees were evacuated safely and there were no reported injuries, but the fire caused extensive damage, shutting down the site. It also impacted the local area, with thick black smoke forcing some businesses and schools to close. As well as temporary water shortages due to water being redirected to fight the fire, leaving hundreds of local people out of work. It is thought the fire started with a hydraulic press sparking hydraulic fluid.
Hydraulic fluids can form dangerous mists or sprays from small leaks in hydraulic systems, which can become fiery torches when ignited – or a violent explosion if the oil mist is confined. Causes include abrasion or hydraulic oil leakage on or around hoses and fittings. Monitoring temperature, vibration and pressure in real time using a predictive maintenance solution can quickly detect and raise alerts on any movement away from the modelled norm before any damage occurs. Major incidents like this are very difficult to prevent with a preventative approach, using set schedules to perform prescribed maintenance, as it is not looking at the actual machinery health and its unique set of stressors.
Tesla Says Plant was Targeted by 'Sabotage'2
In October, Tesla’s California plant was subject to an attempted malicious sabotage attack from an employee. According to an email sent by Al Prescott, Tesla’s vice president of legal and acting general counsel: “Our IT and InfoSec teams determined that an employee had maliciously sabotaged a part of the factory. Their quick actions prevented further damage and production was running smoothly again a few hours later.”
Security is a major concern for any business, and Tesla’s experience here demonstrates why it is crucial to put in place a thorough and robust security strategy to minimize the risk of a cyber-attack – whether it comes from internal or external sources. Tesla appears to have dealt with the attack quickly to minimize disruption, but these sabotage attempts are becoming a regular feature at the company. Tesla is no stranger to staff and safety problems – but the public don’t seem to mind, with sales skyrocketing during 2020’s tumultuous year, bucking the general car buying trend over this period.
Massive fire destroys auto supplier company’s plant at Hinjewadi MIDC3
In February, automotive supplier Hinjewadi MIDC suffered a devastating fire at their 8-acre manufacturing plant in India. The plant manufactures lighting products and is part of Varroc Engineering Group. The fire broke out outside the manufacturing block and spread to the packaging area, spreading quickly across the site. It took over 10 hours to put the fire out.
This is an example of the importance of effective risk assessments and investment in cost-effective solutions. Whether it starts inside or outside the factory building, when a fire finds flammable material it spreads fast. For the entire site to be destroyed indicates a lack of fire suppression measures and a lack of effective alarm and containment equipment. While costly, this equipment is a fraction of the cost to reinstate a manufacturing site like this.
The winners and losers
So, which ranking did you choose to play with? The headlines may spark varying levels of interest, the reputation scores high and low, but the preventability scores across the board are all high.
Companies that carry out planned maintenance run the risk of equipment failing in between the fixed intervals, and something that would have been considered as a minor repair, such as the replacement of a faulty component, if left, could cause far greater consequences and damage. On the other hand, using a predictive maintenance approach that monitors real-time data and uses machine learning, historical data and manufacturer information can detect unusual behavior and predict problems before they present issues. For instance, Senseye PdM uses proprietary algorithms to spot subtle patterns in the behavior of industrial machinery and can often see a failure developing weeks, if not months, before it can impact on production. Users are then alerted when they need to fix something – ideally before it has an opportunity to cause a breakdown or otherwise impact on production. With cost-effective, cloud-based PdM solutions available, mass failures don’t need to happen.
And with digital transformation of the factory floor set to accelerate offering huge opportunities for production efficiency and visibility, it also brings a new operational risk – security. We've put together a list of 8 points that should be covered in your predictive maintenance deployment, as well as a general overview of security in this often-complex space. Download our white paper here.