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Big Data - Befriending the Supply Chain Hitman

Big Data. The term conjures up some kind of scary hitman who's ready to take you down. "Watch your back, or Mister Big Data will get you!"

It's a warning many supply chain execs have taken to heart. The amazing quantity of information now available to inform and improve operations has truly gained their attention.

 

Recent research from Accenture [1] shows that 97 percent of supply chain executives understand that big data has the potential to improve and even transform supply chain operations. But only 17 percent claim their organization is using it.

Before we look at why those remaining 83 percent might want to get with the program, a brief background on big data.

 

Where it comes from

As you may recall from reading our whitepaper: "How technology is reshaping the modern supply chain", big data is one of several advances poised to transform the way supply chains operate.

Big data refers to the myriad data points that are now being generated and captured from connected devices and things (the Internet of Things, or IoT), GPS records from tracking products, along with the information gathered through traditional supply chain software like ERP and WMS, and even social media. One recent research paper [2] identified 52 different sources of data that might be collected through supply chain sources.

What's making these sources of data valuable is the confluence of three trends—volume, velocity and variety. There's more and more data thanks to the technologies noted above, it's being created faster and faster and it is no longer defined only in structured formats. [2]

Although lots of this data has been available for years, what’s new is our ability to leverage improved computing power to analyze it. This is known as Big Data Analytics.

Effectively, we can now turn data into business intelligence. And as you know—information is power.

 

What it's good for

The uses for big data are almost as varied as the data itself, and they range from the extremely specific to the nearly nebulous. The scope will depend on the number of sources integrated and how they are cross-referenced to create new insights that were not previously possible to achieve.

Looking at some real world examples helps to show the massive amounts of information that can be accessed.

For example, Amazon monitors and tracks the 1.5 billion items it holds in inventory in its 125+ DCs around the world. The data gleaned from keeping tabs on all this allows the company to use predictive analytics to ship items before they are ordered to the DC closest to the expected pending customer order.

Wal-Mart tracks customer transactions at a rate of more than one million per hour and keeps that info in a database. It's thought to be used for marketing and demand prediction, although Wal-Mart keeps the details close.

UPS uses location data collected through telematics systems on delivery trucks to streamline routing.

Big data analytics can be applied to improve operations across the four basic areas in supply chain management: marketing, procurement, transportation and warehousing. [1]

In marketing it's been suggested that communications can be analyzed to identify trends that indicate demand changes. Sophisticated tools can sift through reams of social media, Internet postings and direct communications to make these determinations.

On the supplier management side information about suppliers' other customer relationships can be gathered and leveraged, while geopolitical and financial risk can also be assessed and managed.

In DC operations real time location data, asset usage, dock scheduling information and labour allocation can be cross-referenced to improve operational efficiency.

And in transportation operations routing, scheduling, labour and equipment allocation can all be optimized using data collected from current and past operations and overlaid against traffic, weather, and mapping information.

Other potential applications include demand forecasting, improving customer relations and enhanced tracking to facilitate food and drug safety and recall procedures.

 

Why you should care

The applications of big data analytics are many and varied. And with each passing day—and the addition of terabytes of new data—the potential grows exponentially.

Those who are using it well—like Amazon, UPS and Wal-Mart—have been able to realize significant supply chain operations efficiencies. In fact, a recent survey showed that the companies who are best at leveraging big data analytics were up to three times more successful overall than industry competitors. [1]

A basic level of analytics use is now considered to be a business requirement. That means analytics alone is no longer a guaranteed advantage. [4] The challenge then is figuring out how to use the information gained from analysis to make better business decisions.

So, if you want to make big data work for you, so you don't have to worry about (Mr.) Big Data taking you down, it's time to take a hard look at your data sources and the ways that you might leverage what you are collecting into actionable information.  

white-paper-technology-supply-chain

 

Sources:

  1. "Big Data Analytics in Supply Chain: Hype or Here to Stay?" Accenture Global Operations Megatrends Study

 

  1. Big Data Analytics in Supply Chain Management: Trends and Related Research. Presented at 6th International Conference on Operations and Supply Chain Management, Bali, 2014, Ivan Varela Rozados & Benny Tjahjono, Supply Chain Research Centre, School of Management, Cranfield University, Cranfield, UK

  

  1. http://sloanreview.mit.edu/article/minding-the-analytics-gap/; Minding the Analytics Gap ; Magazine: Spring 2015Research Feature March 16, 2015; Sam Ransbotham, David Kiron and Pamela Kirk Prentice

  

  1. http://sloanreview.mit.edu/article/sustaining-an-analytics-advantage/; Sustaining an Analytics Advantage ; Magazine: Spring 2015 Research Highlight March 03, 2015 Peter C. Bell