The late Thomas Samuel Kuhn, an American philosopher of science, introduced the term “paradigm shift” in his 1962 The Structure of Scientific Revolutions. The term caught on. As Tania Lombrozo, a psychology professor at Princeton University, notes, “Talk of paradigms and paradigm shifts has since become commonplace — not only in science, but also in business, social movements and beyond.”[1] She adds, “A paradigm shift is defined as ‘an important change that happens when the usual way of thinking about or doing something is replaced by a new and different way.'” We all know that change is a constant feature of the business landscape; however, a paradigm shift represents a break in the ways things are changing. During periods where change is more-or-less predictable, experts talk about modernizing operations. When a paradigm shift takes place, modernization is an insufficient response.
Modernizing vs. Transforming
Most pundits insist that the Digital Age represents a paradigm shift in how businesses operate. As a result, they argue that industrial age organizations need to transform into digital enterprises supported by digital supply chains. Business consultant Bryce Boothby, Jr., insists, “Modernizing won’t provide the future value you’re looking for.”[2] He goes on to note that most business leaders seem to understand the importance of digital transformation. He writes, “91% of business leaders rate digital transformation as a current business priority to remain competitive.” He’s just not sure they really understand the difference between modernizing and transforming.
Jim Tompkins, Chairman at Tompkins International, writes, “I think people are underestimating the magnitude of what is happening today. … Are you telling me that for us to deal with digitalization, digital commerce and the pandemic all we need to do is change the way we do business? … Simply changing these processes is totally inadequate. You must throw out how things have been done in the past and reinvent your business, supply chain and logistics processes.”[3] Tompkins doesn’t think the word “transformation” captures how dramatically supply chains must change. Tricia Wang, a self-described Tech Ethnographer & Sociologist, believes it does. She explains, “A lot of companies treat digital as if they are ‘doing digital’ — this is ‘digitization’ at its worst — as if it’s some checklist of things to do. It’s very transactional, and people are so busy doing digital they don’t even know WHY they are doing it in the first place! Whereas [some companies] embrace ‘being digital’ — this is ‘digital transformation’ at its best — it’s a total paradigm shift in the culture and operations — it’s not just about buying the latest digital tool, but about creating a new system, new cadence, new mindset.”[4] In the end, I think they are saying the same thing — modernizing isn’t transforming.
The digital transformation journey begins by recognizing the importance of data. Supply chain expert Adrian Gonzalez asserts data is the supply chain’s most profitable 4-letter word.[5] He adds, “The lynchpin for any technology is data.” Yossi Sheffi, Director of the MIT Center for Transportation & Logistics, goes even further. He writes, “The well-worn adage that a company’s most valuable asset is its people needs an update. Today, it’s not people but data that tops the asset value list for companies.”[6] What they don’t say is that the value of data must be unlocked through analysis and the key that unlocks that value is artificial intelligence (AI).
Leveraging AI in the Digital Transformation Journey
Industry and tech journalist Joe McKendrick writes, “Despite the hype with rosy vendor and analyst pronouncements, the state of data and analytics is still dismal — hampered by latency and data silos. It’s time to look at a new way of organizing analytics environments.”[7] He adds, “While there has been endless excitement about high-level analytics delivered through artificial intelligence, it’s time to pay more attention to the front end of the process. That is, the business intelligence (BI) tools that bring all the insights together for digestion by business decision makers.” McKendrick isn’t alone in his call to integrate data in such a way that decision-makers obtain the actionable insights they need to move forward.
Joe Bellini, Chief Operating Officer at One Network, observes that a lot of companies are beginning to leverage AI; however, like McKendrick, he believes more can be done. He explains, “Your company is probably using AI, most likely on a correlation basis to improve forecasting, eliminate bias, determine inventory policies, or predict disruption due to external vectors like weather. … It is better to combine planning and execution using AI and prescriptive analytics to win the game with your system. … Using AI and prescriptive analytics on a causal basis to produce improved outcomes is a much better approach than many of the corporate applications of AI/ML today.”[8] He concludes, “Leveraging AI and prescriptive analytics in this way generates the best outcomes, but it requires combining planning and execution, along with the ability to remember decision-making patterns for future application.”
Decision-making is probably the most important thing any company does. As Bain analysts, Michael C. Mankins and Lori Sherer, assert, “The best way to understand any company’s operations is to view them as a series of decisions.”[9] They add, “We know from extensive research that decisions matter — a lot. Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.” That’s why Enterra Solutions® focuses on advancing Autonomous Decision Science™ (ADS®). As I noted in another article, “ADS technology can autonomously analyze data, generate insights and make subtle, contextually informed, judgment-based decisions quickly, accurately and with limited human intervention, and then learn from the results of those decisions. It can effectively reshape the way companies structure and optimize their value chain.”[10] In other words, ADS helps humans make decisions when that is the best course of action and, when decisions can be made better by the system, it can make those decisions autonomously.
Concluding Thoughts
“To cope with the seemingly never-ending supply chain crisis,” writes, Matt Comte, operations transformation practice leader at PwC, “business leaders are turning to artificial intelligence to make strategic business decisions. A recent survey by PwC found that 48% of business leaders use AI to drive supply chain decisions, and 54% of business leaders plan to use AI-driven simulations to enhance supply chain operations.”[11] He adds, “AI allows for simulations of vast amounts of data from suppliers, customers, competitors, and external factors like weather or geopolitical events. In the process, leaders can better predict supply chain dynamics and disruptions, and have the most up-to-date integrated business plans in place to navigate the complexities of a rapidly shifting business environment.” AI solutions, like the Enterra Global Insights and Decision Superiority System™ (EGIDS™), can help ensure that supply chain operations are transformed, not merely modernized, and are fit for purpose in the Digital Age.
Footnotes
[1] Tania Lombrozo, “What Is A Paradigm Shift, Anyway?” NPR, 18 July 2016.
[2] Bryce Boothby, “Modernizing Is Not Digitally Transforming. Are You on the Right Path?” Multi Party Orchestration, 28 March 2022.
[3] Jim Tompkins, “Transformation is Inadequate: Why Reinvention is the Only Option for Business Success,” Tompkins Blog, 15 September 2020.
[4] Trevor Miles, “Let’s be clear: Digitization is not the same as Digital Transformation,” Kinaxis Blog, 8 December 2017.
[5] Adrian Gonzalez, “Supply Chain’s Most Profitable Four-Letter Word: Data,” Talking Logistics, 16 May 2022.
[6] Yossi Sheffi, “What is a Company’s Most Valuable Asset? Not People,” LinkedIn, 19 December 2018.
[7] Joe McKendrick, “A Call for End-to-End Data Analytics Supply Chains,” RT Insights, 25 May 2022.
[8] Joe Bellini, “How to Make Artificial Intelligence Work in Your Supply Chain,” The Network Effect, 28 April 2022.
[9] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[10] Stephen DeAngelis, “Autonomous Decision Science™ Discussed on the Next Normal Show,” Enterra Insights, 29 July 2021.
[11] Matt Comte, “AI in Supply Chain: Five Things to Prioritize,” SupplyChainBrain, 27 May 2022.
