The Continuing Journey towards the Digital Supply Chain

Stephen DeAngelis

November 16, 2020

Supply chain experts David Simchi-Levi (@davidsimchilevi) and Edith Simchi-Levi note, “The pandemic has exposed one of the major weaknesses of many supply chains: the inability to react to sudden, large-scale disruptions.”[1] This lack of agility is one reason many organizations are looking more urgently at supply chain digital transformation. Caitlin Caldwell, Senior Manager of Product and Sustainability at CompuCom, asserts, “The future of supply chain begins with digitalization, and the process has already begun. Digitalization that was expected to span over years and years, has taken place in just a few short months as businesses have started adapting to the current environment. … Digitalization can allow businesses the ability to react to events more quickly. Additionally in the aftermath of external events, as well as in day-to-day operations, digitalization offers businesses the ability to increase efficiencies, manage risks, and remain agile.”[2]

As Caldwell states, the journey towards supply chain digital transformation has begun; however, many organizations remain near the starting line. Two years ago, Vicki Powers, a Supply Chain Writer at the American Productivity & Quality Center (APQC), insisted many organizations remain in the “potential” stage. She wrote, “The supply chain management function is still operating in a world of digital potential.”[3] The dictionary defines potential this way: “having or showing the capacity to become or develop into something in the future.” Although having “potential” is a good thing, what often goes unspoken is the fact that something having potential means there may not be much to crow about at the present. According to Powers, “Organizations are incorporating social media, mobile technology, and cloud-based services as an entry point but will require more time to add technologies such as blockchain, simulation software, real-time big data and analytics, and Internet of Things (IoT) connectivity. Embracing these digital disruptors will enable organizations to stay competitive and boast an optimized, agile supply chain.”

The digital supply chain journey starts with data

Supply chains have historically been a source of data. For example, some of the earliest cuneiform tablets were used to document business transactions. Over time, data became more detailed as supply chains became more complex. We’ve now reached a point where there is so much data (much of it in digital form) that new methods are required to deal with it. Michael Schrage, a research fellow at the MIT Sloan School of Management’s Initiative on the Digital Economy, explains, “While many business operations can swiftly transform, other key processes defiantly resist digital acceleration. Supply chains are a case in point: Spreadsheet- and ERP-dependent supply chain operations had to radically revisit and revise expectations. Yesterday’s digital transformation road maps proved largely useless.”[4] The problem, insists Schrage, is that there is too much latency in analyzing data. The pandemic proved the case. Schrage explains, “Top management literally couldn’t see what was happening — or needed to happen — to ensure safe and reliable deliveries under duress. This came as a shock. Data, not digitalization, was their immediate problem. Legacy leadership teams need to understand that decisions around data — not digitalization — drive successful supply chain transformation.”

In today’s business world, most organizations are drowning in data; however, it might not be the right data. That’s why Schrage insists decisions about data are crucial; especially decisions about real-time or near-real-time data. That type of data is critical for making timely decisions. Journalist Bob Violino (@BobViolino) notes, “Effectively managing supply chains has perhaps never been more important for organizations.”[5] The amount of data needing to be analyzed in order to make rapid decisions dictates the use of cognitive technologies. Violino explains, “Some organizations are finding that data analytics and related technologies such as artificial intelligence (AI) and machine learning hold the key to supply chain management excellence.” Although Greg Brady, CEO and founder of One Network Enterprises, agrees that cognitive technologies are important, he stresses the importance of real-time data. He writes, “Most supply chains today attempt to execute plans using data that is days old, but this results in poor decision-making that sub-optimizes the supply chain, or requires manual user intervention to address. Without real-time information, an AI tool is just making bad decisions faster.”[6]

Advanced analytics in the digital supply chain

The digital supply chain relies on connectivity and collaboration. Brady notes, “The ability to access data outside of the enterprise or, more importantly, receive permission to see the data that is relevant to your trading community, must be made available to any type of AI, Deep Learning or Machine Learning algorithms. Unless the AI tool can see the forward-most demand and downstream supply, and all relevant constraints and capacities in the supply chain, the results will be no better than that of a traditional planning system. Unfortunately, this lack of visibility and access to real-time, community data is the norm in over 99 percent of all supply chains. Needless to say, this must change for an AI tool to be successful.” Fortunately, journalist Sneha Kumari notes, “Technology helps supply chain organizations to gather information from an expanding variety of sources.”[7]

The staff at CIO Review writes, “Big data analysis plays an essential role in the supply chain management sector. As the present day technology advances steadily, so does the supply chain activities.”[8] They go on to list five reasons why advanced analytics are crucial for supply chain management. They are:

1. Improved processes. Big data analytics are predicted to reshape supply chains by “improving the processes related to manufacturing, distribution and delivery.”
2. Better tracking. “One-third of organizations face product tracking issues due to environmental conditions. Big data analytics can help supply chain firms to improve their troubleshooting skills and enhance response rate by 40 percent.”
3. Actionable insights. Companies can utilize data to generate reports, optimize processes, and provide actionable insights to decision-makers.
4. Better corporate alignment. Corporate alignment can’t be achieved if internal teams work from siloed datasets. Cognitive technologies can gather and integrate data into a single version of the truth that can be shared among teams.
5. Improved customer service. “Customer service … becomes personalized and results in higher response rate and enhanced overall customer retention.”

Concluding thoughts

Rajiv Nayan, Head of Market Development (Americas) at Digitate, writes, “Enterprises need to be proactive and prescriptive to ensure that what is planned is happening on the ground.”[9] They also need to know what is happening “right now” in their supply chains so they can react to unfolding circumstances. Nayan explains, “Difficult circumstances spur change, and the pandemic has led to greater adoption of AI and automation within supply chain management. … Implementing these tools will help organizations succeed going forward. For supply chain management teams, the vendors they work with and the customers they serve, an intelligent supply chain is the approach that serves everyone and enhances business continuity goals.”[9] Chief among those tools are cognitive computing solutions. Brady concludes, “The beauty of AI-based solutions is that they learn and drive continuous improvement over time. They get more precise and sophisticated as they gather more data and more experience. The sooner you start, the better the results you’ll see in future, and the further ahead you will be. With the right AI solution in place, you can outpace your competitors today, and be well positioned for reaping even bigger rewards of AI’s promise tomorrow.”

Footnotes
[1] David Simchi-Levi and Edith Simchi-Levi, “Building Resilient Supply Chains Won’t Be Easy,” Harvard Business Review, 23 June 2020.
[2] Caitlin Caldwell, “Why Supply Chain Digitalization is What is Needed Now,” CompuCom Insights, August 2020.
[3] Vicki Powers, “The Power of Technology-Enabled Supply Chains,” SupplyChainBrain, 12 March 2018.
[4] Michael Schrage, “Data, Not Digitalization, Transforms the Post-Pandemic Supply Chain,” MIT Sloan Management Review, 29 July 2020.
[5] Bob Violino, “Analytics: Your supply chain’s competitive edge,” CIO, 3 September 2020.
[6] Greg Brady, “8 Fundamentals for Achieving AI Success in the Supply Chain,” Supply Chain Management Review, 18 October 2017.
[7] Sneha Kumari, “Incorporating Big Data Analytics in Supply Chain,” Analytics Insight, 4 February 2018.
[8] Staff, “The Rise of Big Data Analytics in Supply Chain Management,” CIO Review, 26 March 2018.
[9] Rajiv Nayan, “How AI and Automation Benefit the Supply Chain,” Manufacturing.net, 14 September 2020.