Artificial Intelligence (AI) & Machine Learning (ML) in Supply Chain Planning (Part 2 of 2)

Capabilities Available Soon

According to a recent International Data Corporation (IDC) Digital Economy Model, by 2023 over 50% of all worldwide nominal gross domestic product will be driven by digitally transformed enterprises. And by 2025 at least 90% of new enterprise apps will have embedded AI capabilities.1 Gartner estimates that by 2021 AI augmentation will generate $US 2.9 trillion in business value and recover 6.2 billion hours of worker productivity.2

In study after study there is evidence of the growing use of AI & ML across all business functions, including supply chain planning. There are AI/ML capabilities available today being used to enhance supply chain planning operations. (See Part 1 of this two-part blog, “Capabilities Available Now”). What AI/ML capabilities are advanced supply chain solution providers actively developing that will be available in the near future?

Cognitive Analytics, the most advanced type of analytics, enable users to identify ‘New Insights’ through the use of AI, Machine Learning, and Natural Language Processing. Cognitive analytics also enable autonomous analysis and response, freeing up manpower to work on more value-adding activities. Additional cognitive analytic capabilities are being developed to automatically sense, analyze and respond to unplanned disruptions and opportunities, helping to minimize risk and maximize company benefits.

Scenario Selection Augmentation utilizes advanced cognitive capabilities to develop new insights and augment a planner’s ability to make fast, well-informed decisions. Utilizing ‘What-if’ scenarios leads to better decisions. However, identifying the right information needed to make a decision can be difficult and building ‘what-if’ scenarios can be time-consuming. An AI-powered scenario selection augmentation solution can autonomously search for the best solutions for disruptions and opportunities and can provide the planner with a set of the best alternatives to accelerate decision making.

Demand for new products is often difficult to forecast. Product Life-Cycle Profile Optimization improves forecast accuracy for items through ML-powered attribute-based modeling techniques. Attribute-based modeling involves creating demand profiles, assigning a profile to a new item, continually assessing profile accuracy and automatically revising the profiles. Product life-cycle profile optimization solutions learn from previous product introductions to optimize the profile shape and volume for new product launches.

Supply Parameter Optimization is a new capability similar to the forecast parameter optimization solutions currently available from advanced solution providers like Logility. Supply parameter optimization solutions continuously sense, analyze and update supply planning parameters to improve supply optimization and help ensure the supply chain operates at peak performance. As with the demand side, supply planning parameters are rarely reviewed or adjusted to reflect the actual state of the physical supply chain. This AI-powered capability autonomously analyzes the current supply chain state against current supply planning parameters populated in the supply chain ‘digital twin’ and automatically updates these parameters to optimize supply chain response.

Probabilistic Supply Simulations is an advanced ML-powered capability very similar to current probabilistic demand simulation capabilities. These solutions use machine learning to understand variability in supply capacity to build a range of possible supply response capabilities. Randomized supply capacities are used when running simulations. Product level revenue and profit data can be incorporated to enable financial risk assessment within the expected range of supply possibilities.

The first wave of Gen Z’s are entering the workforce and they expect to have the same Natural Language Interface and AI/ML automation capabilities available to them in the workplace that they grew up with at home and in school. Companies need to adopt supply chain software solutions with natural language interfaces and AI/ML automation capabilities that will allow users to converse with and use their supply chain platform as easily as they use mobile devices and home assistants.

Summary: 

The introduction of AI & ML into supply chain operations can propel your business into the future—harnessing automation, optimizing supply chain planning, and evaluating multiple scenario outcomes to boost your confidence in decision-making. Building a strong foundation of people, process, data and solutions, and taking advantage of purpose-built, industry-leading supply chain technologies, like those offered by Logility, can build your expertise and accelerate your move up the AI/ML maturity curve.

Henry Canitz

Written by

Henry Canitz

Short bio

Product Marketing Director Hank brings more than 25 years of experience building high performance supply chains. This experience includes evaluating, selecting, implementing, using and marketing supply chain technology. Hank’s graduate degree in SCM from Michigan State, numerous SCM certifications, diverse experience as a supply chain practitioner and experience in senior marketing roles with leading supply chain solution providers helps him to bring a unique perspective on supply chain best practices and supporting technology to the Voyager Blog. Supply Chain Brief

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