How Big Data Can Improve the Supply Chain

You’ve probably heard it said many times over that big data is essential for the efficient and effective operation of a modern supply chain. That explains why it’s sweeping the industry, and why many teams are adopting big data and related analytics tools into their regular routine. The global predictive analytics market is expected to rise to $14.95 billion by 2023, up from $3.89 billion in 2016.

The current adoption trends align with a 2017 report in which 82% of supply chain providers said big data analytics is especially important and disruptive for their current supply chain strategies. And forward-thinking manufacturers have already pushed 80% of their supplier network activity off-property, thanks to big data and cloud-based technologies.

Even with such fast rates of adoption for the technology, many wonder how the technology is actually leveraged. What benefits does big data really provide to supply chain providers? Is there anything you should know in regards to implementing the technology?

Improved Traceability for Your Supply Chain

For supply chain providers, the ability to identify and pinpoint where a product or good is along the chain is absolutely critical to a healthy operation. This is especially true of the food and beverage supply chain, where a particular line or series can be contaminated or simply require full monitoring.

There’s also the matter of knowing the current status of a product or good at any point along the supply chain. With food, you want to maintain appropriate storage temperatures, lest you risk contamination and the spreading of a serious foodborne illness.

Believe it or not, a concept like choosing the right air compressor for your cold storage systems can make or break operations. Should one fail along the transportation route, you could lose hundreds of pound of food or worse. This perfectly sums up why traceability is critical along the entire supply chain.

Without modern technologies, however, general traceability is difficult to achieve. Barcode scanners, radio frequency and IoT identification devices, GPS and location-based transmitters and much more facilitate the efficient collection of supply chain traceability data. But the beauty of big data and modern analytics is that all of that information can be fed into a wider database, which then gets sorted and leveraged to provide deeper insights.

Supplementing the Supply Chain With Relationship Manageability

For a supply chain to operate smoothly and without hindrance, companies must have a solid relationship with everyone involved, including:

  • Partners
  • Vendors
  • Distributors
  • Even customers

A business must know, for instance, what their customers want and when they want it. This allows the company to deliver products and goods to the appropriate region or demographic. But it also must communicate this information to vendors and distributors because if they slow the process for any reason, it ruins the timing.

Big data can be used to clue-in the many parties along a supply chain and keep in direct contact without requiring micro-management. The overseeing organization can watch a shipment of food being transported to a store. The entire time, it can see temperature, freshness and quality, and even routes taken to reach a destination.

When the goods finally reach store shelves, the same organization can see the status of those goods, which can be used to later discern performance — a product didn’t sell well because it wasn’t as fresh as it usually is. More importantly, these insights can be communicated and conveyed to partners to help improve the overall efficiency of a chain.

Demand Forecasting for the Supply Chain

We briefly touched upon the idea of knowing what your customers want, when they want it. The most pertinent way to gather and identify this information is through modern big data systems and predictive analytics. With a combination of the appropriate data and useful machine learning algorithms, big data systems can provide accurate, reliable forecasts about customer behavior.

It relies on the processing and review of performance and historical data, current environment stats, customer trends and communication, and even rival strategies. This information is bundled and sorted in order to extract actionable intel, which will allow you — the business at the source of the supply chain — to take action.

Seasonal campaigns and strategies are a great time for the use of predictive analytics, because customers generally want certain products or goods to coincide with the current environment. In retail, you wouldn’t deliver a winter jacket in the hot summer months, for instance. But that’s an obvious change that clearly doesn’t require big data and machine learning to identify. The kind of insights big data systems provide are those you wouldn’t otherwise know to follow or honor.

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