Decision-making powered by AI can lead to incredible actionable insights. Mithun Nagabhairava, Manager â€“ Data Science, Kalypso, explores how expanding the role of AI helps enable autonomous decision-making, as well as augment the remaining human decision processes with context and decision support mechanisms.
As organizations lean further into artificial intelligence (AI) and machine learning (ML), they look to achieve more with less human input to reduce the increasing risks of over-reliance on human presence and human decision-making for business-critical operations. They call for practical, actionable, data-driven recommendations to help achieve autonomous decision-making capabilities in key areas such as supply chain, advanced planning and scheduling, inventory management, warehouse automation, resource allocation, and logistics.
Any company’s success depends highly on many effective decisions taken on time. However, in many instances, organizational decision-making has reached a complexity ceiling among businesses. The number of factors that come into play when making critical decisions and the complexity of the situations in which these decisions have to be made has far exceeded the human capacity to make the right choices consistently. Also, from what we have witnessed over the past couple of years, the COVID-19 pandemic has highlighted the liability human-dependent operations pose to business continuity and excellence.
To address these challenges, leading organizations prioritize adopting decision intelligence, which frames a wide range of decision-making techniques, bringing together advanced data science and multiple traditional disciplines to monitor, model, optimize, execute and maintain decision models & processes. Gartner recently named decision intelligence as one of its top technology trends for 2022Opens a new window and predicted that, by 2023, more than one-third of large organizations would use AI-enabled 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. Because the decision intelligence process is automated, it is quicker, non-biased, and rational. This means teams can evaluate recommendations, consider actions and potential outcomes, the risk-reward dynamic and improve their decision-making process by utilizing data they already have.Â
Enterprises across a wide variety of sectors have already expanded the role of AI to enable autonomous decision-making and augment the remaining human decision processes with context and decision support mechanisms. By leveraging existing digital operations and business data, enterprises can reach a new level of optimization, freeing up human resources and enabling an autonomous enterprise.
The Need for Improved Decision-Making
As more businesses accelerate their digital transformation efforts, the amount of data generated increases exponentially. Also, many of the critical decisions required to solve complex production challenges in this day and age are highly intertwined across all the levels of the organization’s ISA-95 model.Â
Take, for example, business planning decides what products to make, where, and with which materials from which suppliers based on the demand cycles that vary constantly.Â
The plant 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 seldom have real-time feedback on the constraints, disturbances, and bottlenecks that manifest in operations, something that directly impacts their business bottom line.
In execution, each line and its constituent unit operations have to be initialized with the best work instructions and recommended set points to optimize their performance to avoid reruns, rework, or scrap. Several critical production parameters and key quality attributes at each unit level have to be controlled with models and optimization processes that reject disturbances and continuously adapt to the circumstances.
The integration of decision intelligence models and feedback loops across all levels is a game-changer for organizations to unlock significant business value. While the vertically integrated capabilities are inspired by human input, they are constantly refined and optimized by learning algorithms up and down the stack. Integrating data, analytics, and AI together in this new way allows the creation of decision intelligence capabilities to support, augment, and automate decisions.
What the Process Looks Like
Many enterprises have already begun using enhanced visibility through â€œcontrol towers,â€ which provide decision-makers access to real-time manufacturing data aggregated across the organization. These insights enable timely and financially-sound decision making and provide intelligence on tradeoffs. Decision intelligence processes take this process further by applying AI to the â€œcontrol tower,â€ so that these decisions can be optimized without the need for manual human analysis that may introduce latencies or errors in key decisions that need to be made quickly. An example of this would be when a procurement team learns that an excipient supplier was used in manufacturing a drug and is unable to make their shipment as planned. Through decision intelligence, procurement professionals can quickly respond by sending instructions to the manufacturing floor to slow down production on the line of that drug product.
Implementing a unified decision-making strategy across the entire enterprise and the wider supply chain ecosystem can help modern organizations improve high-level business planning scenarios, drastically optimizing things like schedules and resource allocations to bolster the bottom line and stay competitive in the global business ecosystem.
Product-centric organizations can create a competitive edge in strategic product decisions by using decision intelligence to analyze competitor strategies and evaluate historic decisions.
Organizations should use decision intelligence in areas where business-critical decision-making must be improved with more data-driven support, AI-powered augmentation or where decisions can be scaled and accelerated with automation.Â
Decision intelligence promises to impact enterprise organizations positively very soon. From supply chain to unit operations, the autonomous capabilities powered by artificial intelligence & machine learning technologies are driving significant business outcomes for organizations and strengthening their competitive advantage for the long term.
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