Supply Chain Analytics Trends: The Internet of Things and More
Have you noticed how rare it is these days for a retail store to have empty shelves and tons of different items out of stock or on backorder?
“Empty shelves” (both literal and figurative) mean lost opportunities for businesses. So it’s unsurprising that in the past couple of decades, businesses of all sizes have started investing in technology that can forecast supply and demand, while pinpointing bottlenecks in manufacturing and distribution.
The evolving art and science of supply chain analytics plays a huge role in this.
With the right tools and people, organizations can now demystify every step in their supply chain—from the point when a raw material is sourced on the other side of the world to when it ends up as a finished widget in the hands of a consumer.
Indeed, supply chain analytics is only progressing and becoming more sophisticated—and here are some recent developments you need to know about.
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IoT and Analytics: A Match Made in Supply Chain Heaven
Data Unification: Bridging the Gaps Between Businesses
‘Hello, I’m Big Data.’ ‘And I’m Smart Data.’
For Supply Chain Professionals, School Is Never Out for Summer
IoT and Analytics: A Match Made in Supply Chain Heaven
The internet of things (IoT) might be yet another overused buzzword, but there is great potential for internet-connected sensors and devices in the realm of supply chain analytics.
In the field of supply chain management, the mantra has always been something to the effect of “get the right product to the right customer at the right time at the right price.” But what happens when the product breaks or malfunctions after the customer receives it?
Traditionally, organizations have had to rely on historical data in order to forecast how many spare parts to manufacture and stockpile, based on customer demand. But, this method can be inaccurate and costly.
Researchers at MIT, in conjunction with OnProcess Technology, developed a framework by which organizations can use data collected in real time from products equipped with IoT devices to more accurately predict failures.
This allows them to more precisely gauge how many spare parts they need to stock.
For example: When your computer crashes, you might be prompted to send a crash report to your operating system provider. This helps them discover bugs and allows them to better triage their resources to fix them.
Similarly, if you have an IoT-equipped washing machine that malfunctions, the manufacturer will be alerted in real time. They can then use that data not only to determine inventory for spare parts, but also to further improve their products—or theoretically, their planned obsolescence strategy.
“When dealing with supply chain management, the complexity and the challenges are daunting. The internet of things, when integrated with other analytics creates the potential for a real breakthrough across the supply chain. The logical place to start is with inventory planning with service parts. This is a real, innovative approach.”
Dan Gettens, chief analytics officer at OnProcess
Gettens thinks that the technology will be adopted first by firms that manufacture things such as medical devices and broadband and wireless networking hardware. He predicts it could see widespread implementation in those industries within the next five years.
While the research conducted by MIT and OnProcess is simply a proof of concept, IoT technology is already revolutionizing supply chain analytics in other industries.
When it comes to food safety, distributors and manufacturers must comply with strict USDA regulations. With IoT technology, they can automatically capture critical information such as temperature, humidity and vibration while food is in transit.
“The best organizations are correlating these automatic measurements with their physical audits. When combining both methods, suppliers and purchasers alike can have visibility into the quality of the entire chain.”
Tim Harris, vice president of product at RizePoint, a corporate compliance software vendor
Data Unification: Bridging the Gaps Between Businesses
An organization can have near total visibility into the inner workings within their own four walls. At the same time, it can be completely in the dark about the operations of its suppliers, subcontractors and other business partners. This is the biggest hindrance to supply chain visibility.
End-to-end supply chain visibility (dubbed E2ESCV by Gartner) is no longer a pipe dream for supply chain professionals.
More software vendors are providing tools—referred to as supply chain visibility software—that allow businesses to collaborate, communicate and more effectively share critical data with their business partners.
These tools work by unifying and standardizing data across different channels, keeping everyone involved in the supply chain on the same page in real time.
For example: A manufacturer who relies on a third-party distributor to ship its goods across the ocean would have improved visibility into the status of the shipment if the distributor has also implemented tools that relay shipment data in real time. This leads to more informed decision making and more profitable outcomes.
‘Hello, I’m Big Data.’ ‘And I’m Smart Data.’
Is too much data a bad thing?
The concept of “big data” has attracted some critics—and rightly so. There’s no doubt that big data—extremely large and complex data sets that require sophisticated software and algorithms to process and analyze—will play a strategic role in guiding certain operational processes.
But, some think the way organizations approach big data is misguided, as it can result in information overload and indecisiveness.
“The big data concept of ‘go to the internet and use deep machine learning to tell me what is going on’ is not practical or realistic,” says Gary Neights, senior director of product management at Elemica, a supply chain network solutions provider.
Instead, Neights argues, organizations should focus on smart data:
“Smart data is first identifying the business problem you want to solve, determine the source of that data, get the data and use it to solve a business problem. Applied to supply chain execution, this means near real-time data integration and processing of structured data feeds.”
Gary Neights, Senior Director of Product Management at Elemica
Essentially, the smart data approach is the spreadsheet equivalent to seeing the forest for the trees. In supply chain analytics, it’s crucial for practitioners to be able to step back and think critically about what problem they are trying to solve.
For Supply Chain Professionals, School Is Never Out for Summer
It’s telling that so many universities are investing in training students with the latest tools and techniques related to supply chain analytics. There’s even a push to start supply chain education as early as middle school.
Supply chain management used to be a subject worthy of a class or two for a degree in business administration.
But, with the growing importance of the field and the increasing reliance on analytics to drive decision making, it has evolved into a full-fledged interdisciplinary field of study that combines the fields of:
Business
Data science
Engineering
Information technology
It is continuing to evolve—and that means supply chain professionals must constantly stay on top of the game, and their employers should recognize the importance of continued education.
“What I’m seeing is that the supply chain managers are not keeping up with the demand from their own folks to provide training in data science and machine learning. I’m seeing a lot of people in the supply chain taking matters into their own hands and enrolling in online courses to learn this material. I suspect that companies will catch up and encourage this continuing education.”
Michael Watson, partner at Opex Analytics and adjunct professor at Northwestern University
Want to learn more about the latest in supply chain management software? Be sure to check out our Buyer’s Guide for more information.