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How to use big data analytics to transform and improve your supply chain

Big data is a disruptive technology in todays supply chains.

Its impact is so great, the global supply chain as we know it is being completely transformed…you could go so far as to call it a revolution.

However, not all companies are using big data analytics.

For those who are, it’s most often in a fragmented way.

The application of big data supply chain analytics is evolving so quickly you need to get on board or you run the risk being left behind.

If you are new to the use of big data supply chain analytics, this article is for you.

You’ll learn how it can transform your supply chain, making it more agile and efficient.

What is supply chain big data analytics?


A report by the McKinsey Global Institute, Big Data: the next frontier or innovation, competition and productivity, defines big data as “datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.”

Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights.

These insights lead to better and more efficient business decisions. (source: SAS).


Why is applying big data analytics challenging?

An Accenture report based on research findings from supply chain executives suggests 97 percent recognize the benefit of big data in the supply chain.

However, only 17 percent report implementing any findings from their big data analysis.

While these numbers may seem puzzling, it really comes down to a lack of knowledge.

The quantity of information can be overwhelming.

Many executives and planners don’t know where to begin.

It's a challenge to obtain information from analytics and apply it to supply chain forecasting and logistics planning.


How to apply big data analytics in the supply chain


A Boston Consulting Group (BCG) article, Making Big Data Work: Supply Chain Management, suggests companies optimize their supply chain data mining through powerful data-processing and analysis capabilities.

Specifically, the article suggests companies in the supply chain can use big data analytics to:

  • improve accuracy of demand forecasts
  • discover new demand patterns
  • develop new services by sharing data with the supply chain
  • engage in preventive maintenance of production assets and installed products
  • conduct near real-time supply planning using dynamic data feeds from production sensors and the Internet of Things.


Supply chain data optimization: 3 easy applications


It’s challenging for executives to know where to focus their time and resources when applying supply chain analytics.

BCG make it simple by condensing data application to three high potential opportunities:

1. Visualizing delivery routes

2. Pinpointing future demand

3. Simplifying distribution networks

According to BCG, companies exploiting these three areas of opportunity “can generate significant revenues and profits, as well as reduce costs markedly, lower cash requirements, and boost agility.”


Here is a summary of their three areas of opportunity:


1. Visualizing delivery routes

Geo-analytics or location-based data, combined with powerful analytics allows you to create a single map to pinpoint the best site locations for transportation routes. (source: GIS People)

According to BCG, when using geo-analytics to manage supply chain routes, it’s possible to reduce transportation costs by 15 to 20 percent.

It’s especially true when partners in the supply chain work together to coordinate deliveries.

An example is of two companies who merged detailed geographic location data onto delivery data. (see image below).

Image credit: Boston Consulting Group, Making Big Data Work: Supply Chain Management

This made it possible for them to visualize order density and identify pockets of overlap.

“The companies learned that they shared similar patterns of demand. Vehicle-routing software also enabled rapid scenario testing of dozens of route iterations and the development of individual routes for each truck,” says BCG.

The companies tested various scenarios.

The scenario testing allowed companies to identify unused delivery capacity on typical routes.

Real-time data with live traffic feeds, combined with past data, created new forecasts and eliminated hours of wasted time.


2. Pinpointing future demand

In today’s global supply chain, forecasting demand can be risky, cumbersome and time consuming.

BGC explains how managers often rely on inflexible systems and inaccurate estimates from the sales force to predict future demand.

BGC discusses how “companies can look at vast quantities of fast-moving data from customers, suppliers, and sensors.”

These companies can combine this information with things such as:

  • weather forecasts
  • competitive behaviour
  • pricing positions

In other words, external factors impacting demand.

Advanced big data analytical techniques and software are used to integrate data from a number of systems speaking different languages.

Examples of the systems with the different languages include:

  • enterprise resource planning
  • pricing
  • competitive-intelligence systems

The merging of these different languages allows managers a view of things they couldn’t see in the past.

Big data analytics does the forecasting.

Sales people are freed up to provide the raw intelligence about changes in the business environment and to convert sales leads.


3. Simplifying distribution networks

BCG explains how distribution networks evolved over time into dense networks of warehouses, manufacturing facilities and distribution centers.

These networks often cover huge territories.

They suggest many fixed networks have trouble adapting to the shifting flows of supply and demand.

According to BCG “big-data-style capabilities can help companies solve much more intricate optimization problems than in the past.”

"Leaders can study more variables and more scenarios than ever before, and they can integrate their analyses with many other interconnected business systems,” says BCG.

“Companies that use big data and advanced analytics to simplify distribution networks typically produce savings that range from 10 to 20 percent of freight and warehousing costs, in addition to large savings in inventories,” adds BCG.

A fast-moving consumer goods company is used as an example in the BCG article.

Using advanced big data analytical techniques, the company was able to downsize from 80 factories across 10 countries to 20 manufacturing facilities.

Yes, that’s a drop of 60 manufacturing facilities!

Efficiency and savings increased dramatically. Below is the model developed by BCG through the big data analysis.

Image credit: Boston Consulting Group, Making Big Data Work: Supply Chain Management


Supply chain has ample information for big data analytics


The scale and speed supply chains are generating digital data is next to impossible to keep up with.

There is no shortage of data for analysis.

As an example, the following graphic gives a summary 52 different sources of big data being generated in the supply chain.

The data is plotted on the graph with volume and velocity on the vertical axis and variety on the horizontal.

With structured data, a company has control over the data produced. There’s less control over unstructured data.

The graph illustrates that most supply chain data is generated outside of a company.

Companies need to move to an open culture of data sharing for accurate supply chain forecasting.

This means moving to a collaborative approach to big data sharing versus competitive approach.


(Source: Big Data Analytics in Supply Chain Management: Trends and Related Research. Presented at 6th International Conference on Operations and Supply Chain Management, Bali, 2014 )


On a final note

Big data is truly revolutionizing how supply chain networks are being formed and grow.

It is moving the supply chain into a collaborative economy where knowledge sharing is the norm and the foundation of new networks.

Future supply chain leader must know how to integrate big data analytics into business planning and risk analysis.

It needs to be done continuously as the pace for big data analytics continues to rapidly evolve.


Do you use big data analytics in your supply chain operations? How have you applied it to your business?

Listen to episode 42 of the Square One Supply Chain Podcast for our discussion on big data analytics for the supply chain:

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About The Author

Shari Fenn
Shari Fenn
Shari is a member of the Calgary Region's transportation, supply chain and logistics content development team. She is a communications strategist, storyteller, professional writer and champion for inbound content marketing. Shari has a professional background in communications that includes clients such as VIA Rail Canada and Calgary Regional Partnership's regional transit brand, On-It. Shari is passionate about developing relevant, remarkable and authentic content.