Supply chains have been around since people realized they had things worth trading. As new technologies emerged, supply chains transformed to take advantage of these technologies. As a result, transformation has been and remains an essential characteristic of supply chains. Today’s pace of change is breathtaking. In the manufacturing arena, we are at the beginning of an industrial revolution labeled Industry 4.0 and the supply chain is an essential part of this revolution. Scott Fawcett, Divisional Managing Director at Essentra Components, explains, “The Industry 4.0 revolution is well and truly underway and is redefining traditional manufacturing processes once and for all. The manufacturing industry is moving towards a more digitized, automated, agile and, ultimately, efficient operation and there is no better example of this than in the supply chain network. In today’s world, the supply chain is a multi-faceted ecosystem linking product development, manufacturing and distribution networks into one fully transparent and digitized system.”[1] Although the term “supply chain” invokes images of a linear system, supply chain professionals are well aware of the networked nature of the modern supply chain (i.e., the multi-faceted ecosystem to which Fawcett refers).
Technologies Driving Supply Chain Transformation
Although supply chain operations still involve procurement, manufacturing, and logistics, emerging technologies are dramatically affecting how these processes are implemented. Jill Beadle notes, “For years there has been an ongoing debate in the supply chain industry as to whether technology is, or should be, a competitive advantage. That debate may well be over. In a recent Gartner survey of supply chain professionals, 65% said the answer is yes.”[2] Technologies having the biggest impact on supply chain transformation are briefly discussed below.
Big Data and Advanced Analytics. You can’t intelligently discuss the Digital Age without bringing data into the discussion. The amount of data being generated and the insights being gained from big data by using advanced analytics is what makes the Digital Age different from past eras. Megan Ray Nichols observes, “Data analytics play a major role in the modern supply chain. Real-time data processing and monitoring result in new tools for today’s supply chain manager — but they have to know how to interpret them. … While the recent explosion of data is a boon for tech-savvy analysts, the amount of data makes it difficult to separate meaningful facts and statistics from useless or irrelevant information.”[3] That’s where artificial intelligence (AI) comes into play. AI systems (like cognitive computing platforms), can analyze vast amounts of data (both structured and unstructured) to generate actionable insights for decision-makers. Nichols notes three examples where cognitive computing and advanced analytics can make a difference: Supply Assurance (to track production timelines and milestones, enforce safety and compliance standards, identify market patterns and more); product lifecycle management (to mitigate risk, optimize processes, and gain insights); and order visibility (to track & verify transactions and to determine a shipment’s exact location or even its current condition).
Cognitive Computing. As noted above, cognitive technologies are essential for making sense of the oceans of data being generated each and every day by connected systems. Gartner analysts predict a lack of talent could stall AI in the years ahead. Beadle explains, “AI’s promise to do more with less has made it a key initiative for manufacturers. Although some AI eliminates basic human tasks, other components are highly cognitive and require support from employees with specialist capabilities, such as content curation or data ingestion.” Some of the skills gap can actually be closed by cognitive systems themselves thanks to embedded expertise. Cognitive technologies empower the business expert by automating the statistical expert’s and data expert’s knowledge and functions, so the ideation cycle can be dramatically shortened and more insights can be auto-generated. Cognitive computing is the key that unlocks data democratization for companies. David Weldon (@DWeldon646) reports, “The adoption of self-service analytics in many industries and by government agencies is on such a brisk pace that by 2019 the analytics output of business users with self-service capabilities will surpass that of formal data scientists.”[4]
Robotics and Automation. The emergence of e-commerce and omnichannel fulfillment strategies created new supply chain challenges which significantly increased the use of robotics and automation. Beadle reports, “By 2021, one in 10 warehouse workers in established economies will be replaced by autonomous mobile robots (AMRs). AMRs offer innovative, intelligent platforms to replace the ‘dumb’ automated guide vehicles used in warehouses and factories since the 1950s.” Robotic Process Automation (RPA) is also playing a role as it assumes tedious, rules-based, swivel chair work previously performed manually. RPA reduces costs and decreases errors. Driver shortages have increased the transportation industry’s interest in autonomous vehicles. Most major truck manufacturing are working on such vehicles. Experiments with truck platoons (three vehicles, one manned and two autonomous) are currently being conducted in North Carolina. Rio Tinto is using autonomous trucks in some of its mining operations. And autonomous cargo ships are also being planned.
Blockchain. Blockchain technology, first associated with crypto-currencies like Bitcoin, are receiving a lot of press in the supply chain arena. Nichols explains, “Supply chain managers are beginning to realize the value in blockchain technology within the modern supply chain. … IBM announced a new partnership with Maersk in January 2018, which will implement a blockchain-backed electronic shipping system on an international scale. The system has the potential to cut billions of dollars in annual spending on behalf of the global shipping industry. A key selling point of blockchain is the indisputable record — complete with a timestamp — that lets all parties verify transactions. It’s a highly efficient means of tracking nearly all forms of data — shipping information or otherwise — in the digital age.” Nevertheless Gartner analysts believe it’s too early to declare blockchain a supply chain success. Challenges remain. Beadle explains, “While supply chain interest in ‘blockchain’ grows, the required technology has not yet been fully developed. What has emerged in its place are multiple pilots and proofs of concept (POCs) across the supply chain in various industries, geographies and product categories. Until significant barriers can be overcome — including business ecosystems, business rules and governance, and layers of physical and digital authentication — Gartner expects that organizations’ supply chain blockchain initiatives will remain POCs.”
Additive Manufacturing. Additive manufacturing (aka 3D printing) has been touted as a disruptive technology. Although it will undoubtedly alter some supply chains, especially in the parts business, additive processes remain too slow for mass production operations. Additive manufacturing benefits include reduced transportation costs (parts can be made at near locations needing parts) and design capabilities previously impossible to execute. Nichols reports, “GE already has a plan to 3D-print 40,000 jet fuel nozzles by 2020. They’re so confident in the future of 3D printing that they’ve invested $1 billion into the technology in 2016 alone — and they’re planning to invest another $1 billion over the next few years. Other brands — from nearly every industry imaginable — are also exploring 3D printing. UPS is in the midst of launching more than 60 facilities across the U.S. to fulfill a new parts-on-demand printing service.”
Customer Service. Gartner predicts, “By 2021, 20% of all customer service interactions will be handled by virtual customer assistants and chatbots.” This unique use of AI will provide customers with personalized (if occasionally frustrating) help. Beadle reports, “Today, virtual customer assistants (VCAs) and chatbots handle 2% of customer service interactions. In four years, they will handle 10 times as much. Virtual agents will also be more empathetic, understanding, and able to manage high-volume complex interactions without human referral. The transformation will be visible as early as 2018, when industry leaders such as Amazon, IBM and Salesforce bring to market a new, general-purpose conversational AI platform. It will offer organizations a genuine alternative to human agents that is less costly and faster to deploy than what is currently available.”
Summary
As technologies continue to emerge, supply chains will continue to transform. In the years ahead, Fawcett observes, “With so many layers involved in the supply chain ecosystem, a transparent and digitized network will bridge the gap between supply and demand. Every facet of the supply chain network will support a fully visible feedback system, reporting on the needs and challenges of the ecosystem. Any changes or developments, from a sudden increase in customer demand to a breakdown of a key manufacturing system, can be signaled at any point and will travel immediately throughout the network allowing the appropriate adaptations to be made, some of which will be entirely autonomous.” Nichols adds that emerging technologies, like those discussed above, “aren’t just trying to disrupt the supply chain — they’re looking to revolutionize it.” As a result, she writes, “From the impending embrace of blockchain technology to the industrialization of next-gen 3D printing, supply chain managers will have their hands full in the coming weeks, months and years.”
Footnotes
[1] Scott Fawcett, “What does Industry 4.0 mean for the supply chain network?” Supply Chain Digital, 21 April 2018.
[2] Jill Beadle, “Gartner Predictions for the Future of Supply Chain Operations in 2018,” Smarter with Gartner, 21 December 2017.
[3] Megan Ray Nichols, “5 Technologies Disrupting the Supply Chain,” Manufacturing.net, 25 April 2018.
[4] David Weldon, “Self-service analytics and BI outpacing the output of data scientists,” Information Management, 5 March 2018.
