Evolving Business Intelligence in Supply Chains

As supply chains become more diverse and complex, so do their information systems and associated business intelligence systems. The need to build near-real-time visibility, data-driven culture, and digitalization are business imperatives that have forced organizations to invest in data and analytics tools and technologies.  

However, such tools still need to evolve technologically to address business complexities, even though technology and computing power have rapidly evolved in the last decade. Additionally, the rapid emergence of data science, Artificial Intelligence (AI) and Machine Learning (ML) algorithms and the proliferating adoption of cloud computing are disrupting the field of business analytics. And a category of analytics tool that is ripe for disruption is Business Intelligence (BI) tools.  

BI tools have been around for more than four decades, and like many other business analytics tools, they are evolving to address business trends in the digital age. Considering this evolution, many SAPinsider organizations who have been leveraging BI tools for a long time are asking, “What is going to be the future of BI tools?” 

Business intelligence tools have evolved in one key aspect, that is, from being leveraged for historical insights into “what happened?” to being used for getting answers to questions like, “What is happening?” and “What can happen?” And this is where operational intelligence, an evolving aspect of BI tools transformation, comes into play.  

In simple terms, operational intelligence or operational business intelligence is the outcome of BI tools evolution which produces near-real-time insights and enables better decisions and actions. However, the key aspect of differentiating operational intelligence from BI tools is the type of insights it generates.  

Traditional BI tools typically leverage descriptive analytics methodologies. Simply put, BI tools focus more on answering the question, “What happened?” Whereas, in operational intelligence, answering the question, “What happened?” does not work. Operational intelligence can answer three additional questions: 

1. What is happening in near-real-time? 

1. What can happen in the short term or long term? 

1. What can be done about it? 

Let us try to understand this using an example from supply chain management. If an organization is leveraging BI tools into its inventory data, traditional BI tools will provide insights into what went out of stock after an event. This means that the organization can find out the date on which an SKU went out of stock, where and when, after the stockout has already occurred.  

Now let us compare this with operational intelligence tools. With an operational intelligence tool, organizations can get on top of the stockout with recommendations on mitigating it. The operational intelligence tools go beyond descriptive information to provide insights like: 

• Exception-based alerts to SKUs needing attention 
• Prescribed inventory re-balancing 
• Business user-defined alerts to manage expiry-driven promotions 
• Real-time comparison inventory position of complete BOM to production schedules 

Tools with the features described above are the need of the hour. Irrespective of what terminology we assign to them, the fact is that analytics tools with the above-mentioned features are the need of the hour. With the proliferation of Generative AI and augmented technologies, we will see more tools with operational intelligence capabilities in the future.