How AI and Big Data Support Just-in-Time Manufacturers

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Managing supply chains often involves complex operations, predictions and inventory optimization challenges. Unstable markets, unreliable suppliers or even the slightest mishaps can create a devastating domino effect that can cost manufacturers huge amounts of resources and the respect of their customers.

This blog takes a look at how digital technologies such as AI and Big Data can support efficiency-oriented production and help organizations predict market shifts and protect themselves in the future.

In order to increase agility and adaptability, companies are increasingly turning to a new way of thinking – the so called “Lean” strategies. As explained by one of our blogs: “The key concept underlying lean principles is a systematic method for waste minimization within a manufacturing system without sacrificing productivity. The lean process involves evaluating every step to determine if it provides value to the client. If a process provides no value then steps must be taken to eliminate or minimize this step.”

Let’s take a quick history lesson to better understand how this kind of approach came to be. In post-war Japan, financial turmoil, lack of space for factories and inventory and limited natural resources meant production had to be done in the most effective way possible.  To counter the negative effects of these unstable circumstances, Japanese car manufacturer Toyota came up with an asset management technique called ‘Just-in-Time’ (JIT). In a nutshell, JIT involves ordering and receiving inventory for production only when an order is made by customers. This is what a Lean mindset does – it reduces wasteful business processes to a minimum.

JIT offers numerous advantages, the most important of which include being able to keep production runs short and innovate quickly if the need arises. Another compelling reason to apply JIT to manufacturing operations is the fact that companies can save huge amounts of resources by spending less on warehouse space, inventory storage, maintenance and raw materials. A freed up cash flow allows organizations to focus on innovating rather than keeping up with its own tempo.

While all this sounds great in theory, there are a number of risks manufacturers have to factor in when applying JIT and a Lean methodology to their business operations. Not being able to predict demand trends can often lead to stockouts – and if one link in the supply chain breaks, the entire system comes crashing down.

This is exactly where Artificial Intelligence and Big Data can be used to overcome operational challenges. Getting the right items in the right amounts to the right places at the right time is an extremely complicated matter. The solution lies in gathering large amounts of data and using advanced analytics and AI to process and turn it into highly accurate predictive analyses and demand forecasting tools.

Imagine an IoT network providing real-time, data-driven recommendations for improving supply chain processes and optimizing inventory assets on its own. Not only can technology reduce the risk of running out of stock or raw materials, but it can also improve through repetition and automation. Advances in AI and Machine Learning enable supply chain and inventory management systems to become better on their own.

The results speak for themselves. Toyota has managed to leverage digitalization to decrease costs by reducing overall inventory, freeing up assets and lowering obsolescence rates. A much more time- and cost-effective supply management process allows for constant improvement, while advanced data analytics and AI protect the organization during market shifts.

Learn how you can transform your business processes with the help of technology by visiting this page and, of course, browse our website for more insights about manufacturing.

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