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How Emerging Technologies are Changing Supply Chain Management

August 26, 2019


The world is changing at such a frantic pace some people would like to call a time out. Unfortunately for them, change is a relentless characteristic of modern life. The 19th Century French journalist Jean-Baptiste Alphonse Karr, coined the adage, “The more things change, the more they stay the same.” Doug Braun, CEO of IBS, is not so sure Karr’s proverb remains true. He writes, “The proverb … suggests that change does not affect reality on a deeper level but only cements the status quo. In our era, however, change is not only happening, the evolution of change continues to be fast.”[1] He insists change is occurring in the supply chain as fast as it is in other areas.


Payson Johnston (@PaysonJ), CEO & Co-Founder of Crowdz, writes, “Supply-chain management cannot be done in isolation. Industries shift, trends change, media hype comes and goes, and customers go elsewhere. Watch the trends and modify or change or even disrupt your supply-chains strategy in new directions, so you don’t get left behind.”[2] Kevin O’Marah (@komarah), Chief Content Officer at SCMWorld, adds, “The paradox of supply chain is that we constantly try to remove variability even as we embrace change. If supply chain becomes boring it means you’ve found something — a process, task, job, or just about anything else — that can be automated, and it’s time to move on. The learning should never stop.”[3] Today, there is a lot of talk about supply chain transformation and the emerging technologies driving that transformation. Two of the most oft-mentioned technologies are machine learning (a form of artificial intelligence) and blockchain.


Machine learning and supply chain management


Supply chain professionals understand how complex the supply chain can be. Jon Chavez writes, “In theory, a business supply chain is a fairly simple concept. … But in practice, supply chains become highly complex as a business grows.”[4] Fortunately, emerging cognitive technologies (i.e., different forms of artificial intelligence) are helping supply chain managers deal with complexity. Ironically, the massive amount of data generated by the supply chain is one of the key elements helping address the complexity challenge. Chavez observes, “Using analytics to manage a supply chain can turn it into a valuable provider of key insights and significant advantages. … Analytics — the ability to find meaningful patterns in data — can help manage costs, lead to efficiency and better decisions, increase services, and make better use of capital.”

When someone talks about finding meaningful patterns in data, they are talking about leveraging machine learning. Abhishek Bansal (@abheshekbansal), Cofounder & CEO of Shadowfax, writes, “Machine learning is a direct application of artificial intelligence which enables a system to learn from data recorded from actions and experiences for better future experiences. Machine learning incorporates learning arising from the combination of different variables enabling better consumer experiences.”[5] He adds, “The logistics industry and its supply chain management are affected by a high number of variables and uncertainties like inadequate area mapping or imbalance between demand and resources availability or vehicle breakdown or even the vagaries of weather. Determining innovative patterns in supply chain data through machine learning enabling excellent customer experiences can transform the prospects of most logistics businesses.” He goes on to identify three ways machine learning can benefit supply chain operations in last-mile delivery situations:


1. Enhancing Last-Mile Delivery Experience. Bansal notes, “Matching delivery time with customer’s convenience has always been a challenge in last-mile delivery. … The application of AI in logistics has reinvented the last-mile-delivery experiences. … We use machine learning to identify the type of delivery address — whether it is office or home — and the system automatically figures out the best time to make the delivery attempt. … ML also helps to keep the supply chain updated about weather forecasts, traffic situations and other important factors directly or indirectly impacting the delivery schedule.”


2. Identifying the Right Delivery Locations. According to Bansal, “Locating unstructured addresses is a tough job for delivery personnel.” He notes this particularly the case in developing countries like India “where non-standardized [addresses] are hard to decipher and locate.” He writes, “Machine Learning especially comes in handy here. We look at historical delivery data and use machine learning models to triangulate the approximate geolocation where the address lies.”


3. Enabling Field Staff to Take Smart Decisions. Bansal writes, “In the logistics industry, the on-ground variables are many and situations can change rapidly. … Using machine learning and advanced analytics, managers can quickly learn best case and worst possible scenarios. It uses complex algorithms to suggest optimal solutions to field personnel for best decisions sans much error.”


He concludes, “Machine learning and AI-based techniques form the foundation which will sustain the next-generation logistics and supply chain ecosystem in the market.”


Blockchain and the future of supply chain management


Most analysts will tell you blockchain (or distributed ledger) technology remains in its nascent stages. In an interview with the staff at the Wharton School at the University of Pennsylvania, Stefan Gstettner, partner and associate director at Boston Consulting Group, insisted blockchain will, in the years ahead, have a significant impact in dispersed networks with many participants.[6] Before discussing blockchain per se, Gstettner discussed supply chain management. He stated, “Supply chain management isn’t well defined across industries. I like to put it is as ‘end-to-end synchronization of entire value chains.’ There are two new words in this — ‘end-to-end’ and ‘synchronization.’ Both are important. End-to-end means we need to think through supply chains from the end-customer perspective. … It’s complex and end-to-end, not restricting our search to one company. Synchronization means reacting to changes in the market. In the volatile world that we all live in, it’s essential that supply chains react to changes in the market and be synchronized with the market.”


As noted above, Gstettner believes blockchain technology can help with both end-to-end supply chain visibility and synchronization. Concerning blockchain, he noted, “Blockchain is a different animal. It’s not really a new concept. It’s a technology that enables users to store data in a decentralized way. … The advantage is that information is available multiple times in the network, which means that it cannot easily be changed. It’s immutable, it’s trustworthy, and can be used by many parties.” Although he believes blockchain can have a significant positive impact in the right circumstances, he adds, “The assumption is that blockchain is the only solution and that it is good for everything in supply chain management. I think this is not at all true. … There must be very good reasons to look for blockchain applications to achieve what competing technologies can achieve.” Gstettner concludes, “Companies have a long transformation journey ahead of them with traditional digital technologies like machine learning, robotics and other automation opportunities. Only after that will blockchain unfold its true potential.”


Concluding thoughts


If there is one point to be drawn from the above discussion, it is that the supply chain is complex and that complexity is growing. Emerging technologies, like machine learning and blockchain, can help address supply chain complexity. I like the point made by Gstettner that multiple technologies need to be applied because no single technology can address every supply chain issue. He notes, “We are in an ongoing wave of companies thinking through their supply chain vision. There is still a long way to go for many companies to introduce technologies like artificial intelligence. How can I have a machine learning-driven supply chain? There are also other buzz words like self-driving supply chain and many companies are in their transformation process. I do believe there will be a development with what we call the bionic supply chain, which is the machine-augmented but still human-driven supply chain, and that is happening now.”


[1] Doug Braun, “Evolution of Change in the Supply Chain Swirling at Breathtaking Pace,” SupplyChainBrain, 20 January 2014.
[2] Payson Johnston, “12 Essentials of Supply-Chain Management,” LinkedIn, 6 January 2017.
[3] Kevin O’Marah, “Ten Timeless Truths of Supply Chain,” Forbes, 10 August 2017.
[4] Jon Chavez, “Big data untangles supply chain complexity,” The Blade, 27 October 2017.
[5] Abhishek Bansal, “How is Machine Learning Influencing Supply Chain Management?Entrepreneur, 30 July 2019.
[6] Stefan Gstettner, “How Blockchain Will Redefine Supply Chain Management,” Knowledge@Wharton, 30 July 2019.

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