In few of my previous posts, I have hypothesized scenarios where Machine Learning algorithms are managing key Supply Chain planning aspects. As the field of Artifical Intelligence evolves at a rapid pace, Machine learning’s “potential to deliver real-time optimization” is only starting to emerge, and evolution of the technology will accelerate over the next three years. And as we know, at its core, machine learning is driven by big data analytics and artificial intelligence.
These technologies can accomplish many things, but arguably one of the biggest benefits they will offer is streamlined and more efficient processes. Big data and analytics can train AI, which will then control and power automated systems. The automated systems will then augment — or in some cases, completely replace — human labor.
But how does this affect manufacturing, in particular, lean manufacturing? Below is my take on some ways Machine Learning is helping accelerate the evolution of lean manufacturing.
A major concern of lean manufacturing falls on the waste production and environmental impact one has during operation. Big data analytics, machine learning and simulation systems, can help organizations identify more economical and efficient opportunities while cutting down on the total amount of waste produced.
Furthermore, these technologies can improve processes, making them more efficient and reliable — a change that has a way of reducing waste, in and of itself. Overcoming weakness is a core principle of lean manufacturing.
The “Smart” lean factory
Factories and manufacturing facilities of the future will rely on automation for production and scalability. Schedules will be automated and directly influenced by supply and demand, set to produce more or less of a good depending on market changes. Because of this, big data, advanced analytics and artificial intelligence will become critical to the success of the industry.
Smart factories are multifunctional, reliable, diverse and more importantly lean.
Customer service and support are boorish in manufacturing, strictly in regards to the agility of feedback and influence. For example, if a large demographic of customers vocalize an issue with a product, in traditional manufacturing it takes a long time to implement the necessary improvements, even with incredibly lean processes in place.
Big data, analytics and predictive technologies especially can help brands and organizations respond much faster and more accurately to customer complaints and concerns. Effectively, the aforementioned technologies will streamline the entire process, which is a win-win for everyone — brands and consumers alike.
Preventative maintenance is essential for many reasons, especially when you need your plant or hardware to stay in operation. Your deadlines stay on point, and your development chain remains steady and reliable. But what if there was a way to truly know something was wrong with your equipment before it stalls or causes problems?
By outfitting manufacturing equipment with sensors, and syncing those devices up with analytics and machine learning platforms, preventative maintenance and hardware monitoring can become remarkably efficient. Downtimes will decrease immensely, if they don’t disappear altogether.
Sensors and monitoring tools are necessary for machinery and hardware that require compliance, transparency and traceability. Performance and training, usage scenarios and human-to-machine reliance can be tracked and analyzed. This affords many things, including an improvement in safety and security for workers, boosted performance and reliability and a true glance at overall production quality.
In healthcare, for instance, hospitals use systems such as this to identify physicians, nurses and professionals with a high rate of medical errors, so they can replace them accordingly. The systems we’re talking about have become commonplace across the manufacturing and development industries too, especially those concerned with “lean” practices.
Generally speaking, when you work to improve a process, system or series of mechanics, there’s a modicum of quality sacrificed in the process. This isn’t always true, but we’re not here to argue semantics. Awareness of it happening is the point.
The use of software and big data, however, can ensure that quality remains top-notch no matter what is changed, evolved or replaced. This is because you gain access to a more reliable series of reporting and monitoring systems, which work to provide an accurate picture of quality and performance throughout the entire supply and development chain.
Traditionally, you wouldn’t see these patterns or trends anywhere except the end of the production line. By that time, the damage is done, especially if the problems or complications are severe. Predictive analytics and big data can prevent such a thing from happening.