Supply Chain Analytics Bring Goods to You Faster, Safer

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Supply chain analytics are changing the way goods get from point A to point B.

In an era known for same day deliveries, next day shipping, in-store pickups of pre-ordered items and soon, drone delivery, supply chain analytics are proving to be the driving force in getting a product from point A to point B in a faster way than the same product made the journey last year.

Amazon provides the best-known example. Twenty years ago, most people wouldn’t dream of ordering clothes or jewelry online and having it delivered to their doorstep. Now, if they order off Amazon, they are unhappy if it doesn’t arrive in a few days.

You don’t need to have a desire to work in supply chain management to know that retailers have moved online and each is seeking the most efficient way to get products from the warehouse into customer’s hands.

Many are turning to Big Data for use in supply chains, which has led to a demand for data analysts in the field.

Massive Data Sets

Supply chain management lends itself to the application of data analytics because of the enormous amounts of data generated through the sale and delivery of a product.

One product journey can include a number of touch points, from initial online sales transactions, competitor’s pricing on the same item, the origin and destination of a package, the impact of weather on delivery, transportation costs and comments about products on social media, blogs and other sites. Forbes lists 52 data points for a single transaction on this chart.

That’s a lot to sort, which is why data analysts are playing an important role in the success of supply chains. Expert-level skills are needed on the collection and interpretation of data, as well as extracting actionable data from large amounts of information (and conversely, knowing what not to consider).

Although technology and data-driven strategies continue to evolve, many businesses have integrated Big Data into supply chain management to streamline the process.

The following strategies have been generated by data research.

Real Time Tracking

Businesses can now know exactly how sales are doing at any given point of the day rather than simply adding up sales at the end of the day. The internet of things (the placing of sensors on real objects that transmit data) allows for businesses to track the location of every mode of transportation along a supply chain, the availability of items in a warehouse and even weather that could impact transportation.

Online retailers also can track which day of the week garners the most customers for a sale, whether an email or social media post led to more business and how sales are tracking every minute of the day vs. previous sales. This can affect how much inventory is needed at certain times as well as staffing requirements.

Supplier Sourcing

Supply chain analytics allows companies to track each supplier involved in both the making and transportation of a product. In an example noted by Supply Chain Dive, a retailer of kosher products was able to inform others along the supply chain immediately when a Chinese manufacturer lost its license to make kosher products. That allowed for every company to make adjustments quickly.

A similar, but slightly different example, involves sensors that can track fish from the moment they are caught until they appear on the grocery store shelf. This allows consumers to know the supplier of the fish they eat, a growing concern as fears of fish-borne illnesses concern some consumers.

Processing Time Analytics

Companies using supply chain analytics focus on efficiency and speed, and like most analytics fields, supply chain’s use of Big Data is constantly evolving.

An article from Supply Chain 24/7 highlights an example from Hewlett Packard that embodies that evolution. In looking at why some orders arrive late, a company could simply look at what orders were not processed immediately or on time. However, data analysis showed that late processing of an order does not necessarily mean late delivery. HP’s analysts then looked at more granular data to find events that predict why an order arrives late. In the end, data revealed that a lack of advanced border clearance for the shipment caused significant delays.

The leveraging of supply chain analytics in these situations has led to better all-around performance. According to SCM World, 64% of managers in the industry think that Big Data is the most disruptive force in their industry.

Another 41% of companies say Big Data has increased reaction to supply chain issues and 36% say data has improved efficiency by 10% or more.

For supply chain professionals, data-driven strategies are the future. And for data analysts, supply chain management provides yet another area for a potential career.

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