Big Data Analytics are Revolutionizing Supply Chain Management

Stephen DeAngelis

September 15, 2015

In his two decades of consulting on supply chain management matters, Steve Banker (@steve_scm), Service Director for SCM at ARC Advisory Group, claims to have a seen a number of “revolutions” advance the field.[1] Among those revolutions have been a broadening of the field from logistics to the more inclusive supply chain (and now value chain); the application of Lean and Six Sigma approaches to reduce supply chain costs; the adoption of sales and operations planning (S&OP) which has grown into integrated business planning (IBP); and the emergence of supply chain risk management. As supply chains have expanded around the globe, they have also increased in complexity; which is why Banker believes the next revolution will involve supply chain control towers. Supply Chain control towers bring together, in a new way, concepts introduced during past revolutions to improve overall supply chain visibility. Banker asserts the following characteristics will be found in a good control tower:

“[First,] enhanced risk management capabilities. … Minutes after a major catastrophe or impactful but less severe event occurs, a company should be able to draw a perimeter around an event epicenter and answer the following questions: What suppliers are included inside the perimeter? What components do I source from them? What products do they go in? Which customers will be impacted? What is my revenue at risk? [Second,] quick corrective actions designed to rebalance supply and demand as profitably and quickly as is possible. These corrective actions will be based on prebuilt playbooks, supply and demand simulation, and the use of social network collaboration.”

The term “control tower” is, of course, drawn from the aviation community. At an airport, the control tower sits high above the field so that controllers can see aircraft on the ground as well as well as aircraft departing or approaching the landing strips. It’s all about visibility. The same holds true for supply chain control towers — it’s all about the visibility. Supply chain visibility is achieved through data rather than elevation. It’s clear from Banker’s description of what he thinks a control tower should be capable of doing that big data analytics is at the heart of the revolution he sees fomenting. He believes that three technologies are required to make the control tower revolution successful. They are:

 

  1. Granular track and trace based upon a many-to-many, public cloud architecture that is built with common network master data. Further, far more types of sensor data will be used to provide visibility and there will be less reliance on EDI.
  2. A new generation of more powerful supply chain applications.
  3. New methods of handling Big Data, real time analytics, and better technologies for visualizing data.

 

Banker isn’t the only pundit who sees big data as a transformational driver of supply chain management. Louis Columbus (@LouisColumbus), Vice President, Marketing at iBASEt, suggests ten ways that big data is revolutionizing the supply chain.[2] Larry Alton (@LarryAlton3) offers sevens ways that big data us redefining supply chain management.[3] And Michele Brocca, Partner and Managing Director of Boston Consulting Group, writes about three ways that big data can improve your supply chain.[4] Let’s first look at the ten ways Columbus suggests big data is revolutionizing the supply chain.

 

1. The scale, scope and depth of data supply chains are generating today is accelerating, providing ample data sets to drive contextual intelligence.

2. Enabling more complex supplier networks that focus on knowledge sharing and collaboration as the value-add over just completing transactions.

3. Big data and advanced analytics are being integrated into optimization tools, demand forecasting, integrated business planning and supplier collaboration & risk analytics at a quickening pace.

4. 64% of supply chain executives consider big data analytics a disruptive and important technology, setting the foundation for long-term change management in their organizations.

5. Using geoanalytics based on big data to merge and optimize delivery networks.

6. Big data is having an impact on organizations’ reaction time to supply chain issues (41%), increased supply chain efficiency of 10% or greater (36%), and greater integration across the supply chain (36%).

7. Embedding big data analytics in operations leads to a 4.25x improvement in order-to-cycle delivery times, and a 2.6x improvement in supply chain efficiency of 10% or greater.

8. Greater contextual intelligence of how supply chain tactics, strategies and operations are influencing financial objectives.

9. Traceability and recalls are by nature data-intensive, making big data’s contribution potentially significant.

10. Increasing supplier quality from supplier audit to inbound inspection and final assembly with big data.

 

Columbus’ list offers an excellent overview of how supply chains can benefit from the implementation of big data analytics. Each of the topics on his list fit neatly into Banker’s control tower framework. Armed with that kind of information at your fingertips, the benefits of a big data driven supply chain are easy to see. As you might imagine, the seven ways that Alton suggests big data is redefining supply chain management covers much of the same territory already discussed by Columbus. He writes, “Business both large and small will never be the same again thanks to big data. It continues to revolutionize business processes with more insights and deeper intelligence shared across all business avenues. One of the areas where this data has the most influence is in supply chain management.” His seven ways that big data is revolutionizing the supply chain include:

 

1. Driving contextual data.
2. Improving transparency
3. Pinpointing focus (i.e., providing actionable insights)
4. Facilitating more complex supplier networks
5. Enhancing collaboration
6. Improving reaction time
7. Staying ahead of the curve

 

Brocca notes that past approaches to supply chain management need updating. “In recent decades,” she writes, “companies have looked to technology, lean manufacturing, and global production to increase efficiency and reduce costs. But these tactics are leading to diminishing returns.” Fortunately, big data analytics has come of age to help at just the right time. Brocca explains, “The combination of large, fast-moving, and varied streams of big data and advanced tools and techniques such as geoanalytics represents the next frontier of supply chain innovation.” She also correctly notes that the topic of “big data” is broad and can be daunting for any business executive looking to take advantage of this technology. “With so much available data and so many improvable processes,” Brocca writes, “it can be challenging for executives to determine where they should focus their limited time and resources.” To help business leaders determine where to start, she offers the following three suggestions.

 

1. Visualizing Delivery Routes. Logistics management challenges all but the most sophisticated specialists in ‘last-mile delivery.’ Traditional routing software at advanced delivery companies can show drivers exactly where and how they should drive in order to reduce fuel costs and maximize efficiency. The most flexible systems can plan a truck’s route each day on the basis of historical traffic patterns. … Recent advances in geoanalytical mapping techniques, paired with the availability of large amounts of location data and cheap, fast, cloud-based computing power, allow companies to dynamically analyze millions of data points and model hundreds of potential truck-route scenarios. The result is a compelling visualization of delivery routes — route by route and stop by stop. …

 

2. Pinpointing Future Demand. Forecasting demand in a sprawling manufacturing operation can be cumbersome and time consuming. Many managers have to rely on inflexible systems and inaccurate estimates from the sales force to predict the future. And forecasting has grown even more complicated in the current era of greater volatility in demand and increasing complexity in product portfolios. Now, companies can look at vast quantities of fast-moving data from customers, suppliers, and sensors. They can combine that information with contextual factors such as weather forecasts, competitive behavior, pricing positions, and other external factors to determine which factors have a strong correlation with demand and then quickly adapt to the current reality. …

 

3. Simplifying Distribution Networks. Many manufacturers’ distribution networks have evolved over time into dense webs of warehouses, factories, and distribution centers sprawling across huge territories. Over time, many such fixed networks have trouble adapting to the shifting flows of supplies to factories and of finished goods to market. … But today’s 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. 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.”

 

Obviously, the kinds of analytics discussed by Brocca involve artificial intelligence capabilities. Because so many confounding variables can affect supply operations, I predict that cutting edge companies will quickly implement cognitive computing systems that can handle the complexity found into today’s extended value chains. Such systems will be both the head and the heart of the control towers discussed by Banker. In Accenture’s latest technology vision entitled “From Digitally Disrupted to Digital Disrupter,” Accenture analysts state that cognitive computing will provide the “ultimate long-term solution” for many business challenges.

 

Footnotes
[1] Steve Banker, “The Next Revolution in Supply Chain Management,” Forbes, 12 August 2015.
[2] Louis Columbus, “Ten Ways Big Data Is Revolutionizing Supply Chain Management,” Forbes, 13 August 2015.
[3] Larry Alton, “7 Ways Big Data Redefines Supply Chain Management,” Small Business Computing.com, 7 August 2015.
[4] Michele Brocca, “3 ways big data can improve your supply chain,” World Economic Forum, 4 May 2015.