A Guide to Implementing AI in your Manufacturing Business

Guide to implement AI in Manufacturing

Getting down to basics, the core of any learning is intelligence. Humans learn and use means of communication and depend on visual stimulation for cognitive intelligence enhancement; even for everyday tasks such as working on a computer or enjoying a movie. Much the same way, machines can be taught to perform tasks with the help of algorithms that put things in perspective for them. This, in simple words is Artificial Intelligence or AI.

Now AI has found numerous applications already and is capable of complex tasks. Its main advantage as a system is its adaptability to any kind of context – quite similar to a human being’s ability. A well taught AI system will be able to adapt and grow to just about any enterprise size needs, making it highly scalable and reducing the chances of any downtime.

Thanks to the Internet of Things (IoT), AI applications are well supported through the numerous devices that are interconnected to form a network. In the production line – machines, transport, supervisory devices carried by humans etc, all churn out massive amounts of data on a regular basis. It is this data that AI can be applied to, to help with predictive maintenance, as well as performance enhancement with precision level accuracy.

So if you are looking to take that next step and bring in AI into your manufacturing system, here are three steps to getting it done.

Step 1 – Incorporate AI APIs into Your Processes

Like we said right at the beginning, the strength of AI lies in its flexibility and adaptability. You do not have to think of beginning with a whole new way of AI for your business. You can start with your existing applications and turn them intelligent by integrating them with APIs that will work best for your business. This can be the likes of language comprehension, pattern recognition, language processing, and video based searches among others.

 Doing this will help with something as basic as evaluating customer sentiment in real time. It will help address issues as and when they occur, aid in making corrections instantly and placing in new protocols where required.

 Read: How Information Technology influences Productivity in Manufacturing Industry

Step 2 – Move you AI applications to the next level

Starting off with APIs is a great way to start bringing in AI, but in the longer run it can be limiting for organizations. Keeping in mind customer engagement is the next step. This means the organization will have to work on acquiring data from a range of sources to create a customized machine teaching model for your application. This will have to include it all – systems for data processing, creating appropriate algorithms, testing and implementing various teaching models and then deploying it all in your specific manufacturing business.

Once a business reaches this stage, it would be wise to consider bringing on board a Machine Learning as a Service (MLaaS) that will use the data gathered appropriately and define the final API that can be created. The data will have to be routed through a Cloud infrastructure to enable a seamless testing of models and the creation of the teaching system.

Step 3 – Invest in Infrastructure and teams to run efficiently

Once you have the entire system up and running, you will need to have your infrastructure built to suit your model and have the teams in place to run the models from local nodes. This is especially true if you are a manufacturing set-up that works on a great deal of customization and where customers have to go through elaborate processes in terms of policies relating to data confidentiality. Besides high-end infrastructure, as an organization making the transition, ensure that you have on-board data scientists to help run things smoothly in-house. Utilizing open source platforms for the purpose of Machine Learning and Deep Learning are also essential.

In the recent years, most successful businesses have made huge investments in operational platforms such as ERP as well as finance and billing systems. The large scale value of such productivity enhancing platforms has been seen. It is now time to ride the next wave – that of analytical platforms that include AI and machine learning to help take an organization to the next level.

Read: A Comparison of the Top Machine Learning Platforms : Amazon, Azure, Google, IBM

In the current scenario, AI is growing to become an essential part of modern day applications in manufacturing. While databases may be ruling the roost right now, and rightly so, they will slowly take a backseat in terms of prominence (without a decrease in their importance) and make way for the next level; that is AI. Just as one would with the idea of digitization of business, creating a road map that clearly charts out the way forward is absolutely essential. Maintaining an even pace in getting your organization on board is also equally essential.

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Author : Ruth Date : 02 Mar 2018