Choosing the right AI to navigate evolving business conditions and unexpected market changes.
Throughout the industrial value chain, artificial intelligence (AI) and machine learning (ML) have been adopted at an accelerating rate. According to a 2021 Google Cloud report, 76% of manufacturers have turned to digital enablers and disruptive technologies such as data analytics, cloud, and AI due to the COVID-19 pandemic. Across industries, the value of AI and advanced data analytics has been reinforced by the need to address evolving customer expectations, system disruptions and rapidly changing market conditions, such as the COVID-19 pandemic. The global manufacturing industry is ground zero for this environment.
As AI-enabled tools continue to evolve, manufacturers need to implement the latest technology and management practices to remain competitive. AI has been around for over 60 years and in that period, the term has grown to encompass a wide variety of technology. It has become a buzzword that is often overattributed to define technologies that would be more correctly labeled as machine learning. The catch is that there is nothing artificial, nor intelligent, about most machine learning. Generally, machine learning is a powerful pattern recognition technology that is, however, a “black-box” without explanatory value and is fundamentally limited by its inability to understand the patterns it is recognizing and to reason like a human being.
Before implementing a new technology, it is imperative that manufacturers understand that a human-like reasoning AI and ML are different and complementary capabilities, and that the future of AI is taking data analysis one step further by combining computational intelligence with human-like reasoning and decision-making. This next wave is called Autonomous Decision Science (ADS). ADS technology can autonomously analyze data, generate insights and make subtle, contextually informed, judgement-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, including manufacturers, structure and optimize their value chain.
Using AI to navigate unknown variables
Disruptions to the global value chains and business ecosystems of manufacturing companies stem from a variety of reasons. These include: natural disasters, climate change, a global pandemic, geo-political uncertainty, inflation; the list is endless, and the exact repercussions are always unknown. These events have ripple effects that can impact supply and demand. For example, the Institute for Supply Management recently announced that its index of manufacturing activity dropped from month-to-month as a result of supply chain slowdowns decreasing overall demand for manufacturers.
Predicting all these disruptions and their ripple effects is impossible, so manufacturers are tasked with making their supply chain and trade promotion plans as agile and resilient as possible. ADS technology uses mathematical analysis and AI-enabled inference to enable manufacturers to be more dynamic and anticipatory by generating actionable insights from data in faster and more market responsive ways. At its core, ADS technology allows manufacturers to shift their strategic thinking and view these disruptions not as problems but opportunities to mitigate risk, exploit opportunity and optimize their business faster than their competitors.
Between business constraints and market supply and demand signal data, the number of variables and scenarios to consider are countless. Analyzing them manually, or with surface-level AI, is not a feasible option for timely decision-making. ADS technology breaks this barrier of variable dimensionality. It addresses scenarios where there is limited or no historic data and, as a result, it can make reliable strategic decisions at the speed of the market. By leveraging ADS technology, manufacturers take the next step forward in the ever-changing challenge of navigating uncertain market conditions.
Choosing the right AI technology
For manufacturers implementing the next level of AI into their business strategy, they should consider technology that includes the following attributes:
- End-to-End: Insights, optimization and decision-making capabilities should bi-directionally span the entire value chain and its interconnected nature, enabling a “common brain” to be the single source of truth across the enterprise.
- Outside In: The technology should be consumer-driven and include dynamic market sensing response capabilities.
- Ecosystem Expansive: Analysis of not only the manufacturing enterprise but retailers, consumers, suppliers and logistics providers to coordinate and optimize the complex dance amongst the players in the ecosystem and to realize the holistic impact of recommendations.
- Anticipatory: It should have the ability to anticipate the behavior of market participants with a high level of accuracy – providing a business sonar and radar to the participants.
The next wave of data analysis
Understanding what tools are available and how they differ can be complex, especially given the broad meaning “AI” has come to hold. Ultimately, AI in the form of ADS technology should streamline data, perform non-linear optimization and fundamentally enable judgement-based decision-making. With this capability, manufacturers can execute end-to-end value chain optimization and decision-making – leveraging insights from as deep and as broad as its data sources.
The impact this next wave of AI can have on value chain optimization is exponential. ADS technology provides the ability to holistically analyze data, transform business processes, generate insights, make decisions and learn from those decisions as if they were being made by the best human expert. With this level of real-time translation of data to insights to decisions, all at the speed of the market, manufacturers and retailers can turn market disruptions into growth opportunities.
About the Author:
Stephen DeAngelis is the Founder and CEO of Enterra Solutions®. As a technology entrepreneur and patent holder, DeAngelis has over 25 years of experience helping pioneer the application of advanced cognitive computing technologies and applied mathematics to commercial industries and government agencies. Stephen has held a range of academic and national laboratory appointments, including positions at the Department of Chemistry at Princeton University, the Software Engineering Institute at Carnegie Mellon University and the Oak Ridge National Laboratory. He also currently serves and has served on several Boards of Directors and Advisory Boards of private corporations, philanthropic and research-oriented organizations.