Today’s market is more competitive than ever, and businesses need to know as much as they can about where they stand in it. This includes not just a business’s relationship with consumers, but also the processes driving how that business operates. The right analytical tools can give your business an edge on both fronts; one such tool is supply chain analytics software.
What is Supply Chain Analytics?
Broadly speaking, supply chain analytics covers any form of data analysis regarding a business’s supply chain, its efficacy over time and how to manage or optimize it. The supply chain—the organizations, resources and infrastructure involved in creating and selling a product, from raw materials to the end-user—relies on a steady flow of information to coordinate every step, which becomes exponentially more complex as the supply chain grows. Analytics tools and software allow this information to be more easily organized, visualized and applied towards identifying problem areas in the chain, predicting and mitigating risks and enabling stronger collaboration.
The Development of Supply Chain Analytics
Big Data has become an increasingly vital aspect of how modern companies function, quickly expanding within just the past decade as new computing technologies made it possible. While the supply chain’s importance has been known for a long time and statistics and performance data have been applied towards managing the chain, it’s only within recent years that the tools to feasibly compile all the relevant data have become available. Much of this data is unstructured and not always easily quantifiable, so traditional statistics become unable to provide answers. Machine learning algorithms and expert analysts that know how to employ them, however, are changing this.
How Can Supply Chain Analytics Help My Business?
The ability to collate the information stream associated with the supply chain—including ‘messier’ data regarding weather, social media and consumer responses—enables better decision making and more efficient performance. More significantly, applying recent advances in artificial intelligence and machine learning makes predictive, and even prescriptive, analysis possible. Analytics operatives aided by such AI can assess known or potential risks and predict possible outcomes of new policies or management decisions. In time, analytical algorithms and new cognitive technologies will become capable of interpreting patterns and using them to alert organizations so they can act swiftly and decisively. The sum result is improved return on investment, better cooperation between warehouses and partners, a stronger capacity to plan ahead and the ability to create and adapt to new business models.