What is big data analytics, and why is it needed?
The traditional Supply Chain is doomed! From rising fuel prices to global overnight markets, high customer expectations to low cost competitors, there are a host of emerging challenges that traditional supply chains are just not equipped to deal with. There is a very real and very pressing need for companies to rethink their approach to Logistics and the Supply Chain. This is where data Analytics comes into play.
Big Data Analytics is the science of studying large data sets and the factors that contribute to it. For a company to identify and make positive changes to their systems, they need to first understand how each factor affects the overall system, and how the rest of the system will respond to changes in these factors.
By implementing big data analytics, a firm could effectively create new products and services, provide better customer service, increase sales and revenue, and expand into new markets.
New data tools and the rise of analytics.
The recent spate of upgrades that are hitting supply chains, constitutes some of the most sweeping changes that have been applied to a backbone function of a business in the last 30 years. Even with fundamental changes to the way operations are run and problems are solved, for most companies the chief planning tool used is the excel sheet.
Statistical analysis and basic pattern recognition represent most of the analytical studies that supply chains are subjected to. Companies have been finding out the hard way that as the complexity of their supply chain increases, these classical tools are incapable of futher leveraging their data to any meaningful degree.
However, the development of big data and the tools for its analysis are bringing about new opportunities. An inherent understanding, that increasing the supply chain efficiency in small ways can contribute to very large cuts in wastage, is being developed and propagated through the market. These cuts in wastage translate into cuts in spending, a benefit that can be passed onto the end customer. The supply chain is suddenly a compelling factor that affects customer behavior, something it has never been in classical models.
Descriptions, predictions and prescriptions.
Big data analytics falls into 3 main categories, descriptive analytics, predictive analytics and prescriptive analytics. These 3 methods of analysis are applied to supply chains and logistics operations to help companies better understand their systems and what steps they should be taking to gear up for the future.
Descriptive Analysis gives you an in depth look of the current system and its governing factors. It also describes why your system is the way it is.
Predictive Analysis answers the question “What’s on the horizon?”. With the use of advanced modelling and complex mathematical tools, predictive analysis tries to answer where your system will be in the future and how it got there.
Prescriptive Analysis gives you the power to study the changes you need to make to your systems in order to attain your goals.
Don’t get left behind
The current scenario cannot stand as it has done. The wastage incurred in trying to fit traditional models into new requirement molds is huge. The current systems are not sustainable, especially considering newer and more nimble players that can eat away at your margins.
Investments into supply chain analytics will bolster your value proposition. With advanced modelling and predictive analysis paving the way forward, a shift in how companies view their data is fast approaching. Companies that invest heavily in supply chain analytics are already reaping the benefits with satisfied customers and better bottom lines.
