Powering Automobile Industry In Times Of Recall
The Industrial Internet of Things (IIoT) is quickly grabbing the attention of industry players and is being seen as a significant disruption to global industry and the world economy. Accenture estimates the Industrial Internet of Things (IIoT) could add $14.2 trillion to the global economy by 2030. Arguably the biggest driver of productivity and growth in the next decade, the Industrial Internet of Things will accelerate the reinvention of industrial sectors that account for almost two-thirds of world output.
The question however is, are companies prepared to take full advantage of this enormous opportunity that lies ahead? Barring a few pioneers of the IIoT, the widespread adoption is hampered by several challenges.
Roadblocks & Hurdles creating mounting pressure on the Automobile Worker
With the increase in auto recalls, automobile manufacturers are faced with challenges of high cost, apart from rolling back the manufacturing process to square one. This clearly implies that the entire process will put immense pressure on automobile industry workers. As per a recent article by Industry Week, “ Toyota announced on June 29, 2016, that it is recalling 3.37 million vehicles globally over a pair of defects, just the latest hit for a Japanese auto industry affected by fuel-efficiency scandals and an exploding airbag crisis. The world’s top automaker has forecast an extra 150 billion yen ($1.46 billion) in quality-related expenses for the fiscal year ending in March 2017.”
- Overcautious Equipment Maintenance = Steep Maintenance Costs + Low Productivity
With most of his time being spent on equipment maintenance, this leads to rising costs with reduced efficiencies
- Unplanned Maintenance Schedules= Increased mean-time-to-repair
With sporadic and ad-hoc maintenance timetables, the average time-to-repair shoots up
- Technical Equipment Failure= Unmet Delivery expectations
The time lag in fixing unexpected breakdowns creates unsatisfactory end user experiences due to unmet customer commitments
- Limited Data + Lack of Technical Knowledge= Increase in overall Complexity
Complexity of machine signals with limited technical knowledge only makes it more complicated to achieve a final solution
- Obsolete Data Models= Additional Pressure to Upgrade
Data patterns constantly change and keeping track of all this manually takes an enormous toll on the auto-industry worker leading to immense trauma and pressure.
The current set-up allows him only to react to a breakdown/downtime caused by recalls. However, had he been equipped with the insights that could point him in advance to the trigger of a failure/defect, he could have prevented them with the accurate prediction.
Time for the Automobile C-suite to switch the fast lane
So, can today’s automobile C-suite get past the current hurdles for their workers? How can they add value to these unanswered challenges experienced day-in-day-out by the workers in demanding physical conditions? What necessary steps should they take to prevent future dangers and protect their workers and their customers?
Imagine a scenario where the automotive industry worker can isolate signals from noise and identify the right signals hidden in the data scattered over millions of data points coming from different sensors.
Imagine if he can predict and identify the indicators of failure even before they occur at scale by quickly sifting through pre-defined data of what sensor readings lead to what issues.
Imagine if he can predict when a recall is likely to happen by combining data from the past automobile models and scale it to new and existing models in an organized and automated way.
Is it possible to find such an automotive industry worker who can truly identify the dark areas in manual predictive maintenance and ensure that recalls are a thing of the past?
Introducing Automobile Industry’s Superhero Sans The Cape | Auto Recall Acer #PdMSuperHeroes #IIoT Click To TweetMeet the Auto-Recall Acer, who can tackle the numerous visible challenges of manual predictive maintenance such as data overload and unknown failure states, with much ease. Armed with the right ammunition of dynamic feature learning, looping supervised and unsupervised learning, continuous learning ensembles and meta-learning, he takes predictive maintenance to the next level.
Auto-Recall Acer has the much-needed prowess to prevent and predict failures well in advance to comfortably break through multiple data readings and analyse the root cause of an issue, even before it makes an entrance to the general public.
- Zipping with Operational Efficiency – The Auto Recall Acer is armed with Cognitive Predictive Maintenance
With this new gen armour, he can tear through the data silos and swiftly sift through billions of machine data and sensor records, utilize his time and resources most effectively
Result- Accurately Predict what factors lead to failure.
- More Miles per Hour– Providing Actionable Insights for lesser defects
Nothing escapes his watchful eye as he intelligently predicts components which may be at risk of failing, to reduce equipment failure and damages. He can add new lines of revenue by outlining the optimum warranty periods, and decrease long-term maintenance costs.
Result- Drastic reduction in potential warranty claims.
- Slicing & Dicing Production Downtime – Automatic Streaming of Real-time Data
He identifies when a potential issue could crop up to address it well before it leads to a possible failure. By constantly examining the automobile’s condition, he supplies a work request to the planner, well in advance of the imminent failure.
Result– Smooth maintenance planning as if the problem never existedChanging the Machine Game | Redefining Human Potential #AutoRecallAcer #PdMSuperHeroes Click To Tweet
It’s time to change the machine game and unlock the true human potential. It’s time to improve the overall quality of life for workers and passengers and ensure no leaf is unturned when it comes to safety and efficiency.
Cognitive predictive maintenance is the game changer that holds the key to minimizing auto recall.
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