Betting Big on Data in the Supply Chain

Connected devices in the supply chain collect vast amounts of data, but many organizations aren't using it to their full advantage.

It wasn't so long ago that companies were in the dark about the effectiveness of their supply chains. For many organizations, it was status quo to receive a monthly or even yearly update report. The utility of these reports was severely limited, as they recapped historical information with little or no prescriptive analysis. And far too often, it would be much too late to act upon any resulting insights.

That method of reporting doesn't fly anymore, and it shouldn't, thanks to the real-time visibility that's now at our fingertips. Connected devices used in the supply chain that take advantage of technology including sensors, GPS, and AutoID are collecting vast amounts of data, but unless you're regularly being sent that data, filtering it, analyzing it and incorporating it into your supply chain strategy, then it's safe to say this technology isn't being used to its full advantage.

Technologies like these, along with the resulting analysis, have transformed the supply chain from a black hole into a data-rich environment capable of delivering added value and competitive differentiation. Organizations can take advantage of real-time visibility to identify potentially damaging trends, like high return rates or shipping problems, (before any damage is done) in order to make changes that would remedy the situation. It offers the opportunity to shift priorities in the face of real-time events, inform customers of delays or quickly ramp up – or scale down – production to meet demand. Whatever the case may be, real-time visibility is an invaluable tool for your brand's global commerce operations.

Big data and its resulting real-time analysis complement the end-to-end visibility of your supply chain and enable you to act quickly enough to prevent problems resulting in revenue and profit loss at various points in the supply chain.

Here are the three areas of the supply chain I think will be most heavily impacted by real-time data analytics:

  • Forecasting : Inaccurate forecasting = trampled success. If you make too much, you're wasting cash and losing profit. If you make too little, you're missing revenue. If you make it at the wrong time, all three issues are probably hitting you simultaneously. By implementing fluid demand and supply plans that are updated in real-time, based on true demand signals, material availability and capacity, your revenue and profit potential is maximized.
  • Shipping : By incorporating real-time data analytics into your plan, you can mitigate the risk associated with a variety of disasters. By tracking products with GPS, you can know exactly where a shipment is in the event it needs to be re-routed or if it is lost. New routes can be identified quickly and efficiently. If a port strike happens as your products are entering port, you'll already know this due to your tracking technologies. Analytics can help determine the closest available port and route to take, simulating potential options and preventing your products from sitting stagnant for two weeks at sea.
  • Warehousing : Analytics on warehouse layout, product inventory and demand can help optimize operations within the warehouse, with simulations preventing physical resources from being expended before the plan is finalized. Once a solution is enacted in the physical warehouse, rules can be applied to ensure management is alerted to depleted inventory or potential roadblocks.

Supply chain professionals have begun to understand how better visibility through big data analysis can improve performance and efficiency, but many of them are just at the tip of the iceberg; there is so much more that can be done. Now is the time for companies to embrace these technologies, which will soon be the backbone of future supply chains.

0 comments on “Betting Big on Data in the Supply Chain

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.