How much raw material does your manufacturing plant need to meet demand this month, or this quarter, or during the holiday season? How much inventory should you stock and what is the optimum route to ship final products?

Decisions likes these are getting trickier, as supply chains become more complex and spread across the world. Companies need a warning as soon as a flood in Bangladesh threatens to slow down their supplier in Bangladesh and an alert before a certain sports event could suddenly increase demand for their products.

Big data can untangle complex chains

Several companies are turning to big data technologies to cope with the new complexities entering their supply chains.

Software tools like OpexAnalytics SCAN, use machine learning algorithms to help supply chain managers get new insights about their customer segments and distribution networks in order to plan transportation and inventory. Many use it to predict demand. For instance, when their customers order products, the tool can sense what else these buyers may be interested in. Even more intelligent systems are the way and could automate more complex supply chain decisions, from procurement to logistics and delivery.

No wonder, big data analytics are on everyone’s mind. Ninety-seven percent of senior executives at large global companies understand how big data analytics can benefit their supply chain, according to a recent Accenture survey. Numerous companies seem to be on the verge of making investments to develop mature analytics capabilities. More than one-third of executives reported being engaged in serious conversations to implement analytics in the supply chain, and three out of 10 already have an initiative in place to implement it.

It’s easy to see why data science is high on top managers’ priority lists. The smart supply chain ecosystem can:

  • Sense demand patterns: Analytics is no more restricted to reporting, but algorithms -- coupled with smart devices-- can now sense, test, and create a response to market. The idea is to use digital sensors in POS devices; chatter in supply and distribution channel and social media to sense market and then use cognitive reasoning to analyze structured, as well as unstructured, data to predict demand and customize products accordingly.
  • Optimize costs: Predictive analytics can create new levels of efficiency in distribution channels, like transportation systems. Take UPS, for instance. The world’s largest package shipping company is crunching data from customers, drivers, and vehicles in a new route guidance system to save fuel and time. Their latest project, ORION (On-Road Integrated Optimization and Navigation), uses fleet telematics and advanced algorithms to optimize tens of thousands of routes per minute based on real-time information. Savings can reach as high as $50 million, if the company reduces just one mile a day per driver.
  • Mitigate risks: Data can also help map out interlinks between various components of the multi-tiered supply chain, from raw material to the customer. It can be crucial in timely prediction and understanding of various geopolitical risks faced by companies at each step of the process. Automated systems can parse through structured and unstructured data and whenever they sense something unusual in channel, they can alert the right person at the right time to take the right action.

As more and more companies deploy sensor-based technologies, scanners, other mobile devices, real-time analytics could untangle several supply chain complexities and transform current inventory and logistics models.

About the author

Pragati Verma