Saxon AI

  Did you know that according to a survey by   Gartner , 34% of supply chain leaders believe that the topmost strategic change supply chain organizations will face in 5 years is adapting to new technologies? However, there is a continual struggle to transform massive amounts of data into insightful knowledge — insights that can propel timely and accurate decisions. This blog will explore Power BI, how to leverage it in supply chain analytics, and its use cases. Role of BI in Modern Supply Chain Analytics Interactive data visualization and analytics are hot topics for supply chain technologies, according to many leaders and decision-makers. Decision-makers can effectively manage the supply chain of their enterprise better by leveraging business intelligence and analytics to visualize data that makes it meaningful and readily accessible. Self-service features from platforms and tools like Power BI can enhance this. Power BI allows business and non-technical users to build their ...

  IT service management ( ITSM ) helps align IT processes with business objectives to deliver efficient, reliable, and customer-centric IT services. However, due to the push for digital transformation of late, IT teams often find themselves pressed for time. They are facing productivity challenges while battling to launch strategic initiatives. What’s creating this turbulence in ITSM? IT teams perform a lot of manual tasks in IT Service Management. For example, when an incident is reported, IT support personnel manually log the incident details into the ITSM system, including the description, priority, and affected services. They then triage the incident to determine its severity and assign it to the appropriate IT team for resolution. These tasks require human intervention and effort, inviting several challenges and limitations. Here are some of the most common challenges stemming from manual processes in ITSM: Inefficient incident management:  Manual incident management proc...

  Cognitive automation, also known as intelligent automation, applies artificial intelligence technologies such as machine learning and natural language processing to automate enterprise processes. This technology goes beyond  robotic process automation  (RPA), which uses a set of predefined rules to execute processes.  RPA requires human intervention when it encounters a case with no response instructions. The system does not learn from data. On the other hand, cognitive automation learns the context from the data using patterns. It analyzes and predicts the following action. Over time, the system can eliminate the need for human intervention and can function independently, just like a human does.  What makes cognitive automation powerful We already have some process automation technologies, such as digital process automation and robotic process automation. But what does make cognitive automation powerful? It’s the ability to leverage data to its full potential...

  Do you want your organization to be data-driven? Then we suggest that you consider having data governance in place. In this article, you will find out what exactly it is, why it is important, why it isn’t optional, and the best practices for it. Data governance is a structure that regulates who within an organization has the power to  manage data assets  and find out how to use them. It incorporates processes, people, and necessary technology to manage and safeguard data assets. Importance of data governance Finding data inconsistencies throughout an organization is no longer a concern when strong governance exists. For instance, account names may be altered or written differently between systems. This kind of situation can compromise data integrity and reduce the accuracy of analytics and business intelligence (BI). Below are a few advantages of  data governance . Data will be standardized and consistent throughout the organization, making decision-making easier. ...