Data has been compared to fuel for businesses. However, it is not the dearth of data that poses a problem for an enterprise. Even if you discard all the valuable information hidden in unstructured data forms like images, videos, conversations, and textual reports, the amount of structured data available to enterprises is quite humongous. And the notion that companies can use data, both structured and unstructured to optimize different business processes, mitigate risk, and improve revenue, are also well founded.
However, there is a huge rift between owning data and using it effectively for the benefits of an enterprise. Data analytics deals with bridging the gap between data and predictive insights. And we are going to find out how predictive analytics can transform supply chain management for the better.
Predictive analytics at a glance
It can be simply defined as the process of predicting future outcomes by analyzing historical data.
A predictive analytics model may consist of decision trees, branching to show the possible outcomes. It can use regression technique – figuring out the relationships between variables. Or it can use neural networks to identify possible relationships within a data set.
What necessitates predictive analytics
Traditional reporting and descriptive data analytics prove insufficient in many cases. The increasingly volatile customer behavior and fast pace of the market make it difficult for a business to thrive with outdated insights.
Industries are blessed with better and cheaper computational prowess than ever, hence it is only reasonable to make the most of it and predict future trends pertaining to sales, demand, exchange rates and whatnot, and base important business decisions on those insights.
Predictive analytics in supply chain management
A supply chain is a complex entity – manufacturing, order management, inventory, shelving, logistics, resource management, all play significant parts to run the engine. The number of involved factors proportionately increase the number of things that can go wrong and the number of areas that can be optimized.
Predictive analytics entails creating a model that is trained with historical data and fine-tuned until it can forecast the past events based on the patterns in data. This means you can use predictive business analytics to determine what items to stock, which vendors to trust, which delivery truck to repair.
Case in point
A SaaS company operating in the healthcare sector created a platform to connect hospitals, healthcare professionals with suppliers. Their goal was to achieve absolute transparency on both ends of the supply chain. They wanted to create visibility of expenditure across the platform for all its users. They wanted to analyze the company’s spending patterns through spend and consumption analysis.
The company had a decade worth of invaluable structured and unstructured data waiting to be analyzed. They had over 160,000 invoices in Spanish. And the data has no lake of noise and uneven marks. The company also wanted an E-Commerce platform to remove the mediators between suppliers and buyers. The challenges involved in the endeavor inspired the solutions too.
The first step was to create a global database that would allow the handling of an enormous amount of data. It had three years’ worth of data which was standardized across all regions.
A catalog of major healthcare equipment was built by crawling across different sources.
A data warehouse designed to handle 2 million x 300 data points followers by an ETL pipeline capable of processing more than 10 million rows of items was built.
Better visualization of spend and consumption data was achieved through the application of machine learning on cleaned data. The solution featured a dashboard with custom filters based on user profiles.
The scanned images of handwritten invoices demanded a different approach. The handwritten invoices were transformed into machine encoded text with an inhouse optical character recognition system. The data was extracted from these invoices using the Google tesseract engine.
The end-to-end ECommerce platform designed for them featured live chat, automatic mail generation, and recommendation engine, among other capabilities.
The goal here was to achieve visibility, transparency, and ease of use while also making extensive use of automation. The goal was reached, and the results include major cost saving opportunities.
A sustainable and successful supply chain management system needs to get ahead of itself and adopt future ready decisions making processes. Predictive analytics is a crucial element in this regard.
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