Supply Chain Visibility: More than a ‘Nice to Have’ Capability

Stephen DeAngelis

November 12, 2015

“As technology brings supply chain data into focus,” writes Gary Allen, Vice President of Supply Chain Solutions for Ryder System Inc., “the ability to access and interpret critical business information moves from being an added feature to a necessity. Better supply chain visibility has become more than a ‘nice to have’ as executives recognize the importance of gauging a shipment’s current location, delivery times, inventory levels and how demands can be satisfied.”[1] Of course, good supply chain visibility can do a lot more for your organization than simply determine the location of products. Perhaps its most important benefit is providing your company with better response times for dealing with emerging situations that could impact overall corporate operations and profits. Nevertheless, knowing where your products are is a good start. Smart label tracking is one way of improving this capability.

 

Mark Davenport, President of Mid-South RFID, reminds us, “One decade ago, Wal-Mart took an unprecedented step, asking suppliers to add radio frequency identification (RFID) tags to pallets and cases.”[2] Walmart’s actions, Davenport believes, ushered in the age of smart label tracking. He continues:

“Today, the retail industry is poised to make RFID technology and smart label tracking an integral part of the present and future. For producers to place products in retail stores during the age of smart label tracking, they will need RFID labels for pallets, cases and individual products. Smart label tracking is the practice of using RFID tags to track products throughout warehouses, shipping and retail stores. A tag, placed on items, cases and pallets, transmits a unique, identifying radio frequency that can only be picked up by an RFID reader. Next, a reader translates the signal into digital information that can sync up with an inventorying computer system. Smart label tracking with RFID technology is supplanting the existing technology of barcodes and barcode scanners. The reason? Instead of using the time-consuming process of scanning items individually, retailers, distributors and producers can automatically scan and track an entire warehouse of products with the right setup of RFID readers and computer software. Companies can process 20,000 items per hour instead of 250 items per hour with barcode scanners. The time and money savings alone make RFIDs appealing, and they also allow companies to cost-effectively capture more detailed and frequent data about where a product is in the logistics process — all while reducing the need for human intervention in product handling.”

Another benefit of using smart label tracking is that it can take advantage of the emerging Internet of Things (IoT) and provide manufacturers and retailers with data that can be used for more than just locating where products are in real-time. The data can help with inventory optimization, production schedules, and much more. To deal with all of this complexity, I believe that companies are going to increasingly turn to cognitive computing systems for solutions. Cognitive computing systems can deal with many more variables than previous computing systems; but, they also incorporate many other capabilities (like machine learning and predictive analytics) that have not been widely available. Dave Blanchard (@supplychainDave) explains, “Not that long ago, supply chain visibility tended to be an internally-focused process, one that allowed manufacturers to know when exactly they could expect to receive inbound goods and materials from their suppliers so they could plan and adjust their production schedules. While that’s still an important capability for companies to have, the Age of the Consumer has shifted the focus of visibility initiatives in the direction of the customers.”[3] That’s where predictive analytics enter the picture. Blanchard continues:

“Manufacturers still need a complete view of their supply chain as it exists now, of course, but just as crucial is being able to know where their supply chain needs to be. And that’s where predictive analytics come into play. ‘Predictive analytics are changing consumer buying behavior,’ notes Bill Abernathy, head of North America product supply logistics excellence, Bayer CropScience, ‘and supply chain professionals need to be able to satisfy the increasing demands of consumers who expect products delivered exactly when promised.’ … By applying advanced statistical analysis of structured and unstructured data sources (i.e., Big Data) to identify patterns and predict future events, manufacturers using predictive analytics gain the ability to make better decisions that anticipate what their customers are asking for now, and will be asking for in the future.”

It takes little imagination to understand how smart label tracking data can provide value-added for predictive analytics. As Blanchard notes, the ultimate goal of predictive analytics is to help business professionals make better decisions. The more complex the supply chain the more choices there are that have to be made. This kind of complex decision making can also benefit from cognitive computing. Cognitive computing systems can assume most of the routine decision making that needs to be accomplished and they can alert human decision makers to situations requiring their intervention. Tompkins International CEO Dr. Jim Tompkins (@jimtompkins) asserts, “Many people regard choices as a good thing. But, one must realize too few choices begets discomfort or unhappiness and too many choices leads to confusion, regret of alternatives not taken, and second guessing.”[4] Tompkins continues:

“Today, the business world is more complex than it has ever been. Adaptation to the marketplace is required in order to be successful. Therefore, choices must be made. Most companies are operating in an environment of choice overload. The mega-choices businesses are making to operate their supply chain will create greater success, status quo, or fail. Businesses must define what mega-choices need to be made, all involved must understand the choices being made, understand other businesses choices, and implement the choices correctly.”

Because choices made by cognitive computing systems follow codified rules, the understanding discussed by Tompkins is better achieved. Additionally, cognitive computing systems can deal with conflicts and ambiguities that inevitably arise as supply chain complexity increases. Melissa Clow (@MelisaClow), social media/public relations manager at Kinaxis, notes, “The ultimate goal is to have visibility into not just to what is happening within your own company but extended to all areas of your supply chain, including partners.”[5] She adds, “Supply chain visibility alone won’t yield effective supply chain orchestration; it is a prerequisite capability, among others.” Cognitive computing can help provide that orchestration and help break down information silos that have hindered companies for decades. Clow continues:

“As supply chains get longer and more global, there has been a significant increase in the number of supply chain nodes that need to be connected and the volume of data moving among these nodes. The complexity associated with connecting these nodes — both those internal and external to the organization — is a barrier to end-to-end supply chain visibility. Data harmonization across multiple systems of record also adds another layer of complexity.”

Clow concludes, “Despite these challenges, it is possible for organizations to achieve the higher levels of visibility. … And the benefits — which many believe include a more agile, resilient, competitive and profitable supply chain — are worth the effort.” A cognitive computing system is a multi-purpose tool in the kit that can help achieve better supply chain visibility.

 

Footnotes
[1] Gary Allen, “How Supply Chain Visibility Helps You Navigate Complexity,” Multichannel Merchant, 13 October 2015.
[2] Mark Davenport, “Preparing For The Age Of RFID Smart Label Tracking,” Industrial Distribution, 8 October 2015.
[3] Dave Blanchard, “Predictive Analytics Let Manufacturers See More Clearly into their Supply Chains,” IndustryWeek, 27 March 2015.
[4] “Supply Chain Clarity Crucial to Tackling Choice Overload,” ATN, 9 October 2015.
[5] Melissa Clow, “Overcoming the Challenges to Achieving End-to-End Supply Chain Visibility,” 21st Century Supply Chain Blog, 1 December 2014.