An offshoot application of artificial intelligence (AI) is machine learning. The focus is on the successful development of computer programs that can access data and use it for themselves. It provides systems the ability to automatically learn and improve based on past experiences without being pre-programmed.
This learning process begins with making observations and collecting data from direct experiences and instructions and then looking for patterns in said data to optimize processes and make better decisions in the future. The entire cycle requires no human input or intervention along any step of the way.
What frameworks are used under machine learning?
Supervised machine learning algorithms can be used to apply what has been learned from past experiences onto new data and predict future events. It starts analysis from a known training dataset, producing an inferred function that makes a prediction output. This kind of system can provide targets for any new dataset once it has enough experience. It can also compare its output to the real output and adjust its model accordingly.
Unsupervised machine learning study how systems can infer a function to identify a hidden structure derived from unlabeled data. The focus is not on the output. Instead, it is on the hidden structures found by exploring the unlabeled and non-classified dataset.
Semi-supervised falls on the spectrum between the first two types. Here, they are used for both labeled and unlabeled data, with the quantity of the latter being greater. This framework is generally chosen when the acquired labeled data requires skilled and relevant resources to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.
Lastly, reinforcement machine learning algorithms is a method that interacts with its surroundings and produces actions to find out risks and rewards. Delayed rewards and trial and error searches are the most common uses of this type of learning. This method also allows machines and software to automatically determine the correct pathway in a certain context to maximize output and performance.
How does this apply to SAP Business One?
This is a huge area of impact to the ERP and SAP Business One users as one of the programs that use this machine learning is the Time Recording Chatbot. It allows employees to record time for projects whenever they are working on them. This works by synchronizing all the data collected at SAP Business One portals to the SAP Cloud Conversational AI to keep it aware of the project specifics. The master data is stored in the cloud, however. The software then uses a list of available employees to send to training, who then use face recognition to start a conversation with the AI chatbot. This chatbot will ask for details about a time recording for which project at what stage on which date and for how many hours while the prototype gained from the open-source code monitors the time.
Other applications can be in RPA or Robot Process Automation, where you could have your AP Invoices being sent to a generic email address, and the invoices is scanned, process, and uploaded automatically into SAP Business One as a draft for approval, making the relationship with the PO, and the GRPO.
Contact us if you would like to investigate areas where this technology could be implemented to achieve major savings in your company.