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Adding Intelligence to the Digital Supply Chain

August 25, 2017


The digital supply chain is often touted as the next step in supply chain evolution. Many pundits, however, are looking beyond evolution to revolution and are writing about an intelligent supply chain. An intelligent, or smart, supply chain is one orchestrated primarily by artificial intelligence (AI) or cognitive computing platforms. Lora Cecere (@lcecere), founder and CEO of Supply Chain Insights writes, “Cognitive learning’s potential to drive value is exciting. … Supply chains will learn as we sleep, and the insights will be deeper and richer with fewer people. It is coming. It is real. Are you ready?”[1] The intelligent supply chain must be built on the foundation of the digital supply chain. George W. Prest (@mhi_ceo), CEO of MHI, believes the time has passed when companies can take a wait-and-see approach to digitizing their supply chain. He claims the digital supply chain is already the new reality. “Consumers are driving this trend as they demand more buying options and faster service from supply chains that are always-on,” he writes. “Sensors that enable the collection of massive amounts of supply chain data, coupled with automation, cloud computing and predictive analytics are creating continuously operating supply chains that are more efficient and more cost-effective than traditional models.”[2]


Beyond the Digital Supply Chain


Prest reports only 16% of companies currently claim their supply chains are digital; however, 80% of company executives believe digital supply chains will be the norm within the next decade. Many analysts believe planning processes will be the first to transform to digital and then to cognitive. Cecere predicts, “Within five years we will have a redefinition of supply chain planning.” Marcus Malinen, Vice-President of Europe, Middle East, Africa and Russia at Quintiq, agrees. He writes, “Conventional planning is no longer sufficient to meet the dynamic requirements of manufacturing supply chains. Machine learning technology is a growing necessity due to functionality that allows analysis of real-time data and the ability to quickly respond to deviation in processes. By allowing the supply chain to learn from business reality, advanced analytics is offering a level of accuracy and insight once thought impossible. … With knowledge that corresponds to reality, the estimates used in planning becomes more accurate. As operational efficiency improves and planners have reliable decision-making support, customer satisfaction improves, the use of assets and resources is optimized and profitability increases.”[3] Puneet Saxena, Group Vice President for Product Strategy and Supply Chain Planning at JDA Software, notes, “Today, decision and data science algorithms can sift through structured data (such as customer ratings) and unstructured data (customer comments), and predict the impact those ratings, trends and social sentiment will have on future demand.”[4] He goes on to note that planning and pricing will combine in cognitive systems to maximize profits. He explains:

“Machine-learning algorithms that can optimize product and service pricing are already available in the market today. Additionally, technology for incorporating structured and unstructured data into predicting customer demand, and automatically integrating that demand with price optimization, is in advanced stages of research in the labs of leading supply chain software companies, who see the value of applying machine learning to the subject of demand shaping and price optimization.”

With advances in technology coming faster than many people anticipated, digital supply chains won’t be the dominant model for very long. Dr. Ravi Prakash Mathur, Senior Director of Supply Chain Management and Head of Logistics and Central Planning at Dr. Reddy’s Laboratories Ltd., asserts the next step will be the algorithmic supply chain. He explains, “In the supply chain management function, people talk about degrees of autonomy in the planning process. From use of historical data for planning, it goes through use of automation that can be overridden and ends at nonoptional automation, where planners cannot review the recommendations of the algorithms. The algorithmic supply chain requires organizational maturity and cultural readiness to embed and regularly rely on systems.”[5] Mathur predicts that evolutionary stage will also be short-lived as the intelligent supply chain emerges. “The concept of an intelligent supply chain,” he writes, “goes a step further by incorporating self-learning capabilities of the machine to make better supply-chain decisions.”


Susan Fourtané (@SusanFourtane) insists, “The evolution of the supply chain requires the adoption and adaptation of its leaders to the emerging technologies that are revolutionizing the way supply chains operate. Productivity and sustainability must go together on today’s supply chain manager’s agenda. The next-generation supply chain demands efficiency, agility, and flexibility. A successful and balanced combination of automation and digital technologies is key to driving superior supply chain performance.”[6] She continues:

“Disruptive technologies born in this digital era include robotics and automation; predictive analytics; Internet of Things (IoT); autonomous vehicles and drones; sensors and automatic identification, inventory and network optimization tools; and wearables and mobile technologies. Each has the potential to transform industries and change consumer expectation. Cloud computing and blockchain will continue to support ongoing improvements. Consider this: more data has been created in the past two years than has been made in all previous years combined. Next-generation supply chains will use this trend to develop new digital capabilities such as the strategic placement and use of sensors and artificial intelligence. Available data (scrubbed for accuracy) provides a solid foundation for real-time visibility and information sharing. When filtered through a layer of analytics and communicated across the entire supply chain, good data allows for proactive and full-sighted operations.”

We all know technology is going to change how things are done. Robert Handfield (@Robhandfield), Bank of America University Distinguished Professor of Supply Chain Management at North Carolina State University and Director of the Supply Chain Resource Cooperative, believes many of the scenarios being painted today go too far. He explains, “Scenarios are being painted of computers operating in a real-time environment, using blockchain to process transactions, relying on Internet of Things to order goods and services, which are delivered by drones to consumers’ homes. The reality of this scenario is far-fetched indeed, primarily because futurists are dramatically underestimating the degree to which people organizations must change to deal with these technologies.”[7] Although he questions the timing of supply chain transformation, he doesn’t question the imperative for companies to change. “Change management is not just [an] option,” he writes, “but truly [an] imperative in a period when so many new technologies are coming on-line. Waiting for something to happen will be result in failure to adapt, and ultimately, to extinction.”




Supply chain professionals are quick to note that supply chains aren’t just ancillary business functions but represent the very heart of a business. Mathur writes, “Common wisdom tells us that organizations compete on the strength of their supply chain ecosystems. Future organizations [will] compete on the strength of intelligence embedded in their systems. Ultimately, the winner will be the supply chain that learns most quickly with greatest precision.” Malinen agrees that the adoption of advanced technologies will be critical for survival in the years ahead. He explains, “Gartner reports that 70% of companies surveyed are in the earliest stage of analytics (descriptive), which looks to the past to describe what has happened. Only 30% are in the advanced stages: 15 – 25% practice predictive analytics and 1 – 5% prescriptive. This low adoption of advanced analytics can really affect an organization’s competitiveness. Companies that master data and knowledge have supply chains that are more resilient, responsive and far ahead of the competition. Those not using it struggle with inaccurate estimates, inefficient planning and wastage.” Although many companies are still in the early stages of digital transformation, it’s not too early to be looking beyond the digital supply chain to the era of the intelligent supply chain.


[1] Lora Cecere, “Not the Jetsons: Ten Use Cases for Cognitive Learning in Supply Chain,” Supply Chain Shaman, 7 December 2016.
[2] George W. Prest, “Digital, Always-on Supply Chains are the New Reality,” MHI, 13 June 2017.
[3] Marcus Malinen, “The self-learning supply chain: Turning manufacturing into a self-adjusting process,” Manufacturing & Logistics IT, 27 July 2017.
[4] Puneet Saxena, “Powering Your Digital Supply Chain: Learning from Structured & Unstructured Data,” EBN, 31 July 2017.
[5] Ravi Prakash Mathur, “The Intelligent Supply Chain: A Use Case For Artificial Intelligence,” D!gitalist, 26 July 2017.
[6] Susan Fourtané, “Make Way for Digital, On-Demand & Always-On Supply Chains,” EBN, 25 May 2017.
[7] Robert Handfield, “The Digital Supply Chain Will Change Everything… Ask Gary Kasparov!” Supply Chain View from the Field, 21 June 2017.

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