AI-powered decision making can lead to incredible actionable insights. Mithun Nagabhairava, Head – Data Science, Kalypso, explores how expanding the role of AI helps enable autonomous decision-making, as well as augmenting remaining human decision-making processes with contextual and decision-support mechanisms.
As organizations lean more into artificial intelligence (AI) and machine learning (ML), they seek to do more with less human intervention to reduce the growing risks of over-reliance on human presence and human decision making for critical business operations. They call for practical, actionable, data-driven recommendations to help achieve self-directed decision-making capabilities in key areas such as supply chain, advanced planning and scheduling, inventory management, automation, resource allocation and logistics.
The success of any business highly depends on many effective decisions being made on time. However, in many cases, organizational decision-making has reached a ceiling of complexity among companies. The number of factors that come into play when making critical decisions and the complexity of the situations in which these decisions must be made have far exceeded the human capacity to consistently make the right choices. Additionally, from what we have seen over the past two years, the COVID-19 pandemic has highlighted the responsibility of human-dependent operations for business continuity and excellence.
To address these challenges, leading organizations are prioritizing the adoption of business intelligence, which frames a wide range of decision-making techniques, bringing together advanced data science and several traditional disciplines to monitor, model, optimize, run and maintain decision models and processes. Gartner recently named Business Intelligence as one of its top tech trends for 2022 and predicted that, by 2023, more than a third of large organizations will use AI-based decision intelligence technology, including decision modeling.
Decision intelligence is designed to drive smarter decision-making by leveraging AI and ML to observe and analyze data, explore the chain of cause and effect relationships governing the system, and understand how actions lead to outcomes. results. Because the decision intelligence process is automated, it is faster, unbiased, and streamlined. This means teams can evaluate recommendations, consider potential actions and outcomes, risk-reward dynamics, and improve their decision-making process using the data they already have.
Companies from a wide variety of industries have already expanded the role of AI to enable autonomous decision-making and augment remaining human decision-making processes with contextual and decision-support mechanisms. By leveraging existing digital operations and business data, companies can achieve a new level of optimization, freeing up human resources and enabling self-sustaining business.
Learn more: How Artificial Intelligence Affects Businesses
The need to improve decision-making
As more businesses accelerate their digital transformation efforts, the amount of data being generated increases exponentially. Additionally, many of the critical decisions needed to solve today’s complex production challenges are tightly tied to all levels of the organization’s ISA-95 model.
Take, for example, business planning, which decides what products to make, where, and with what materials from which suppliers based on constantly changing demand cycles.
The factory then determines the production plan for the month, week and day accordingly, including the allocation of materials, assets and tooling, human resources and production schedules. However, they rarely have real-time insights into the stresses, disruptions, and bottlenecks manifesting in operations, which directly impacts their business results.
At runtime, each line and its constituent unit operations should be initialized with the best work instructions and recommended setpoints to optimize their performance to avoid reruns, rework, or scrap. Several critical production parameters and key quality attributes at each unit level must be controlled with optimization models and processes that reject disturbances and continuously adapt to circumstances.
Integrating business intelligence models and feedback loops at all levels is a game-changer for organizations to unlock significant business value. While vertically integrated capabilities are inspired by human input, they are constantly refined and optimized by learning algorithms from top to bottom of the stack. Integrating data, analytics, and AI in this new way enables the creation of business intelligence capabilities to support, augment, and automate decisions.
What does the process look like
Many companies have already started using enhanced visibility through “control towers,” which give decision makers access to real-time manufacturing data aggregated across the organization. This information enables timely and financially sound decision-making and provides information on trade-offs. Decision intelligence processes go further by applying AI to the “control tower”, so that these decisions can be optimized without the need for manual human analysis which can introduce latencies or errors in key decisions. which must be taken quickly. An example of this would be when a procurement team learns that an excipient supplier has been used in the manufacture of a drug and is unable to complete their shipment as expected. Using actionable intelligence, procurement professionals can respond quickly by sending instructions to the manufacturing floor to slow down production on the line of that drug.
Implementing a unified decision-making strategy across the enterprise and across the supply chain ecosystem can help modern organizations improve business planning scenarios high level, significantly optimizing things like schedules and resource allocations to drive results and stay competitive in the global business ecosystem. .
Product-centric organizations can create competitive advantage in strategic product decisions by using business intelligence to analyze competitors’ strategies and evaluate historical decisions.
Organizations should use business intelligence in areas where business-critical decision-making needs to be enhanced with more data-driven support, AI-powered augmentation, or where decisions can be scaled. scale and accelerated through automation.
Business intelligence promises to have a positive impact on enterprise organizations very soon. From supply chain to unit operations, autonomous capabilities powered by artificial intelligence and machine learning technologies are driving meaningful business outcomes for organizations and building their long-term competitive advantage.
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