- Events, Blogs & News
We use artificial intelligence (AI) on a daily basis. Thanks to AI, our search queries get better answers every day, while media systems suggest films and series that might interest us. If we contact a customer service, online chatbots step in to help us. In the years ahead, this automation will only continue to increase, since we are on the eve of a new industrial revolution in which robots will be lending a helping hand.
The question is what AI is precisely; the algorithm is software that mimics human intelligence by conducting tasks. By using data during these procedures, they can improve processes, which means that they can learn. Various stages exist in such learning systems.
Software with limited AI conducts tasks much better and more quickly than humans do. Traditional chess computers have been consistently beating humans for years, while computers can translate basic texts in a flash which would take humans hours or even days. This type of intelligence is restricted to "if x happens, then y needs to be activated" scenarios. The business world is automating all sorts of processes using robotic process automation (RPA) in order to relinquish repetitive tasks. For instance, if stocks drop below a prescribed level, the system will immediately dispatch a purchase order. The intelligence that such systems have is derived from a programmer who transforms these connections into code, which provides predictable outcomes.
A next evolutionary step following RPA is intelligent process automation (IPA), which is fed by real self-learning systems. The brain is referred to as "machine learning." These algorithms learn without guidance from new input. Examples include spam filters, facial recognition, and self-driving vehicles. Machine learning is fueled by data, including big data. The algorithm is continually learning from the data which it is given, interpreting it, recognizing patterns, and using this input to generate new data that optimizes the algorithm. As a result, the outcomes change over time and with the input of more data.
Providing the algorithm with new data allows it to learn in order to make better and better decisions. It does require new data for this purpose, which is referred to as training data; for instance, test drives made by self-driving cars. Each trip provides new data and experiences, with the car subsequently being able to hit the road independently, while machine learning selects the route. The automatic analysis of various data sets provides insight into such things as future production demand. Thanks to machine learning, major improvements can be made to supply chain management, collaboration, logistics, and warehouse management.
Large amounts of data are required to train these types of systems. A first step is measuring all sorts of processes from the production chain or supply chain. By equipping machines and products with sensors, insight into processes can be gained. Using existing information supplemented with new information, colleagues can engage in automating processes with which they deal in their daily work themselves. They can use the JD Edwards Orchestrator to do so, without extensive experience with programming. The data can also feed machine learning tools, which in turn can make material handling systems and speech interfaces increasingly intelligent. Autonomously driving vehicles are another possibility, increasing efficiency and decreasing the chances of human error.
With AI, it should ultimately be possible to create an entirely self-organizing organization, although this future is still ahead of us. However, it is a good idea to already start considering how to deploy automation, harvest as much relevant data as possible, and think about how to further analyze this data. In any case, this process will yield a strategic advantage. By collecting this data and connecting machine learning tools to enterprise resource planning (ERP), you can lower costs and improve margins. So look into the possibilities and get started today!