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How a Series of ‘Quick Wins’ with AI Can Transform Businesses

By March 31, 2024Articles

by Kimberley Hagerty

Introduction

In the fast-paced world of supply chain management, the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies has brought about a revolution. Businesses are no longer confined to the constraints of traditional methodologies and are now equipped with powerful tools to navigate the complexities of modern commerce.

Demand Planning Revolution

Consider the scenario of demand planning, historically reliant on lagging historical data. Such an approach often led to inaccuracies and unreliability in forecasting inputs for production and material plans. However, with AI/ML-enabled Demand Sensing, the game has changed. This innovation provides near real-time insights and intelligent predictions, facilitating the automation of demand and capacity planning with dependability and consistency.

Supply Chain Management Evolution

The same is true for supply chain management. Legacy supply chain systems, characterized by their monolithic nature and batch-based heuristic processes, posed significant challenges to cross-functional data collaboration and holistic decision-making. AI/ML has permitted a decoupled microservices approach with the added benefit of surgically placed intelligent algorithms to collaborate across the end-to-end supply chain. This makes predictive and prescriptive business decisions possible dynamically across the value network regardless of system landscape.

Navigating Disruptions

But what about the tangible benefits of these technological advancements? We need to bear in mind that supply chain disruptions can strike at any moment, reverberating across the entire value network. While the physical impacts may be localized, the data and system effects are felt far and wide.

AI and ML as Indispensable Allies

Here, AI and ML emerge as indispensable allies, enabling businesses to understand the impact of disruptions and navigate the optimal path to recovery, all while safeguarding the interests of both customers and the companies’ financial bottom line.

Proactive Financial Impact Management

Business leaders can no longer afford to wait until disruptions occur to measure their financial impact. They need insights to protect the customer and their financial bottom line as quickly and seamlessly as possible. AI and ML provide the means to achieve such agility, offering “quick wins” in the form of immediate financial value. By harnessing accelerators to automate data capture and deliver intelligent insights at the point of disruption, reducing lead time to capture data from several weeks to near real time, they obtain optimized recommendations at the point of disruption across the value network, thus protecting the customer experience and the financial impact on the business in near real time.

Decision Intelligence in Supply Chains

AI contributes to decision intelligence in supply chains. A good example of decision-making processes that have been enhanced by AI is the Amazon Scan, Label, Apply & Manifest (SLAM) process. When a customer places an order, there are multiple microservices and intelligent algorithms that run to find the most optimal way to fulfill it, based on the customer promise and best financial business outcome. As there can be multiple disruptions that occur from the time an order is placed until it ships, AI/ML plays a critical role in dynamically protecting the customer and the financial business outcome all the way up until the SLAM process executes. Being able to dynamically adjust and change the most optimal delivery option when the shipping labels are printed at the SLAM process is truly game-changing and sets the standard on how intelligence plays a critical role in supply chain resiliency.

The Supply Chain Data Strategy Journey

This example might sound unique to Amazon, but any company can produce their own specific solution by following the four stages of the Supply Chain Data Strategy Journey: Crawl, when chaos and uncertainty prevail; Walk, with clarity and focus emerging; Run, when autonomy is achieved and, with intelligent insights, the next final phase of profit building is attained, or Fly.

Building Intelligent Collaboration and Orchestration

In the quest for competitive advantage, businesses must recognize the pivotal role AI and ML play in enhancing the end-to-end customer experience. Supply Chain is the only horizontal that covers the entire customer end to end value network. As supply chain digital transformation strategies evolve, the ability to build intelligent collaboration and orchestration capabilities becomes paramount. The result is a supply chain that is not only transparent, dependable, and reliable but also consistently delivers the desired customer experience, setting the stage for continued success in today’s dynamic marketplace.


About the Author

Kimberley Hagerty is Compass UOL’s Chief Supply Chain Officer.

  • Kimberley has more than 30 years of experience leading digital business strategies for manufacturing, supply chain, transportation, and logistics, including the last three years at AWS, where she was Head of the Americas, Supply Chain, Transportation & Logistics. 
  • At Compass UOL, Kimberley is leveraging the company’s Gen AI toolset and AWS-certified competencies to help customers with global logistics and transportation projects quickly optimize their digital transformation journeys.
  • Earlier this year she presented her unique approach at the Gartner Data & Analytics Summit, where she showed, with examples, the transformative impact of Gen AI and machine learning in supply chain management, the immediate benefits of bringing ‘quick wins’ by mitigating disruptions in near real-time and how to model a company’s supply chain data strategy journey to achieve them.

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