Supply chains are becoming increasingly complex and, as a result, supply chain professionals are constantly facing information overload. Fortunately, cognitive computing solutions can help professionals deal with both information overload and complexity. When IBM coined the term “cognitive computing,” the company had information overload in mind. Ginni Rometty, former CEO of IBM, explained, “The idea was to help you and I make better decisions amid cognitive overload. … If I considered the initials AI, I would have preferred augmented intelligence. It’s the idea that each of us are going to need help on all important decisions.”[1] Since the introduction of cognitive computing solutions, they have slowly found their way into supply chain operations. Debra N. Phillips, Manager of Marketing at Emerge, notes, “Supply chain professionals receive massive volumes of internal and external data streams.”[2] She adds, “There is little doubt that using analytics for supply chain decision-making helps businesses improve operational, strategic, and tactical efficiency.”
What is Cognitive Computing?
Because the term “cognitive computing” was coined by IBM, some experts believe it is just a marketing term and prefer the terms “artificial intelligence” (AI) or “machine learning” (ML). All those terms are related and machine learning plays and important role in cognitive computing. Tejal Sushir, a Senior Content Executive at Ruglas, writes, “New buzzwords and terms emerge almost daily in this digital age. One such term is Cognitive Computing, which is gaining immense popularity among individuals and organizations. A cognitive system learns human behavior and reasoning at scale to naturally interact with them. In simple terms, it’s a computer science field that aims at creating intelligent machines capable of learning, reasoning, and understanding like humans.”[3]
Supply chain professionals understand that decisions must often be made with incomplete or ambiguous information. Cognitive computing shines in this environment. Jye Sawtell-Rickson, a senior data scientist at Meta, explains, “Cognitive computing is designed to simulate the human thought process in complex situations, particularly where the answers may be ambiguous or uncertain. By combining artificial intelligence and its many underlying technologies, and constantly ingesting new information in the form of vast amounts of data, cognitive computing systems can be taught and, by extension, ‘think’ about problems to come up with plausible solutions.”[4] At Enterra Solutions®, we talk about our solutions being able to Sense, Think, Act, and Learn®.
Addressing Complexity and Information Overload
As explained at the beginning of this article, the value of cognitive computing in the supply chain field is derived from solutions that address information overload and supply chain complexity. One of the ultimate objectives of cognitive computing is to improve decision-making and, in some cases, take over decision-making. Sushir explains, “Cognitive computing platforms combine Machine Learning, Natural Language Processing (NLP), reasoning, human-computer interaction, speech and vision recognition, and more robust technologies to simulate human thinking and interaction and improve decision-making. … In simple words, cognitive computing is a system that understands and interprets large volumes of data and uses them to make decisions, solve problems, and improve business outcomes.” The staff at CIO Review adds, “[Cognitive Computing’s] data collection and analysis capabilities enable more informed, strategic decision-making and business intelligence As a result, business processes can be more efficient, financial decisions can be more informed, and overall cost savings can be achieved.”[5]
In an essay entitled “A responsibility to judge carefully in the era of prediction decision machines,” Harvard University Professor David Parkes wrote, “Machines need to be able to predict to decide, but decision making requires much more. Decision making requires bringing together and reconciling multiple points of view. Decision making requires leadership in advocating and explaining a path forward. Decision making requires dialogue. It is decisions, not predictions, that have consequences.”[6] Humans need to be involved in the most consequential decisions. However, not all decisions require human intervention. One way to deal with information overload is to let a cognitive platform make routine decisions just like the best experts a company employs. To this end, Enterra® is focusing on advancing Autonomous Decision Science™ (ADS®). ADS is the next step in the journey beyond data science. Using ADS, the machine plays the role of the data scientist or subject matter expert to help optimize businesses and help them run at the speed of the marketplace. As a result, supply chain professionals can make decisions that take advantage of market opportunities as quickly as possible.
Augmented Intelligence, via a cognitive computing system, can play an important role in reducing information overload and improving decision-making. Bain analysts, Michael C. Mankins and Lori Sherer, explain, “The best way to understand any company’s operations is to view them as a series of decisions.”[7] 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.”
Concluding Thoughts
According to JT Kostman, CEO of ProtectedBy.AI and a leading expert in applied artificial intelligence and cognitive computing, “We are entering an age where cognitive computing in particular will unburden us and allow people to become more quintessentially human. And I’m not talking about the far future. It’s already begun, and we’re going to see that accelerate rapidly.”[8] Part of the “unburdening” to which Kostman refers involves using cognitive computing’s ability to deal with complexity and information overload. As Phillips notes, “Analytics can profoundly impact supply chains if practitioners understand what the data is telling them and how to use it to improve, prevent problems, or implement strategies that will yield positive results.”
Footnotes
[1] Megan Murphy, “Ginni Rometty on the End of Programming,” Bloomberg BusinessWeek, 20 September 2017.
[2] Debra N. Phillips, “Information Overload: What Does It Mean? How Can You Gain Control?” Talking Logistics, 8 September 2022.
[3] Tejal Sushir, “Cognitive Computing Explained in 5 Minutes or Less,” Geekflare, 2 March 2023.
[4] Jye Sawtell-Rickson, “What Is Cognitive Computing?” Built In, 29 September 2022.
[5] Staff, “Key Advantages of Cognitive Computing,” CIO Review, 31 January 2023.
[6] David Parkes, “A responsibility to judge carefully in the era of decision machines,” Harvard University Digital Initiative, 2 December 2019.
[7] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[8] Sawtell-Rickson, op cit.
