Optimizing Supply Chain Inventory Management with Gen AI

John Doe
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5 min read

The "just-in-time" (JIT) model, once a holy grail of US supply chains, crumbled under the pressure of pandemic disruptions. The economic and industrial sectors are still struggling to find a new balance. This challenge is compounded by ongoing disruptions like the Russia-Ukraine conflict, the Israel-Hamas tensions impacting Red Sea and Suez Canal traffic, and capacity limitations at the Panama Canal due to water shortages.

Remember the empty shelves and long wait times for basic goods at the start of the pandemic? Those were stark reminders of how vulnerable our interconnected supply chains can be. 

While the global economy has shown signs of recovery, a strange phenomenon persists within US supply chains: inventory mismatches

This article aims to analyze inventory trends, identify root causes and provide you with a framework for understanding supply chain dynamics. More importantly, we’ll show you a few ways you can leverage Generative AI for inventory orchestration.

Analyzing inventory trends

Inventory mismatches are a puzzling issue. On one hand, businesses struggle with shortages of essential items, while on the other, warehouses are filled with unexpected surpluses.

To understand this situation better, let's look back to early 2020. Lockdowns caused production delays, reduced imports, and changed how people shopped. With more time at home, demand for durable goods soared, disrupting supply chains, especially those reliant on "just-in-time" inventory management from one of the most Covid-impacted zones in China.

JIT, while efficient, thrives on a predictable flow of goods. The pandemic shattered that predictability assumption by a long shot. In a desperate push to keep shelves stocked, businesses placed aggressive orders. The result? A wave of inventory buildup, particularly in sectors less affected by supply shortages.

Inventory buildup in sectors less affected by supply shortages

(Source: U.S. Census Bureau)

The wholesale inventory has surged to approximately two times its 2010 level, while the retail inventory, excluding auto-components/parts, has also risen to around 1.75 times the level seen during the same period.

Surge in wholesale inventory

(Source: U.S. Census Bureau; Plotted by Freightfox)

The current issue is not unique to US supply chains. Historically, during similar disruptive events such as the aftermath of 9/11, the 2008/09 Sub-prime crisis, and now the challenges posed by the COVID-19 pandemic in 2020, demand surges for consumer durables have been even more pronounced. This pattern is evident in the following analysis:

Disruptive events in US history.

(Source: U.S. Census Bureau; Plotted by Freightfox)

Pattern of demand surges for consumer durables

(Source: U.S. Census Bureau; Plotted by Freightfox)

Here are the key observations from the above chart:

  1. The retail inventory-to-sales ratio (IS Ratio) post-2020 is attempting to catch up with the wholesale and manufacturer's IS ratios. It hit its lowest point since 1992, bottoming out at just 1.10 in Apr-2021.
  1. Consumer durable inventory, which peaked at 2.12 in Apr-2020, dropped rapidly to 1.49 in Jan-2021 due to a surge in consumer demand for these products during the COVID-19 pandemic.
  1. As inflation rose and the US markets stabilized, this temporary demand decrease led to a sharp increase in the IS ratio to 1.86 in Apr-2023. Since Oct-2022, it has remained above the 1.75 mark.

Additionally, the fluctuations in inventory levels at the wholesale and manufacturers' levels concerning every month of inventory with retailers exhibit the significant challenges faced by organizations in managing their inventory effectively.

 Inventory movement across retail, wholesale, and manufacturer levels relative to retail inventory levels

(Source: U.S. Census Bureau; Plotted by Freightfox)

Identifying root causes

After 2020, manufacturers found themselves grappling with inventory stockpiles, while both wholesale and retailer networks struggled to meet actual consumer demand.

Let’s get to the root cause of this problem. 

There are 3-parts to this:

  1. High communication dwell time between information-exchanging nodes in the supply chains, influenced by both internal and external factors.

  2. Lack of comprehensive information exchange in a unified and rapidly consumable manner for various supply chain nodes to make automated decisions.

  3. Absence of advanced stochastic modeling and simulation for responsive decision-making and understanding the associated dollar value risk.

For all US organizations, this challenge manifests on two primary fronts:

  1. The need for highly accurate demand forecasts coupled with supply-side forecasts, accounting for possible constraints.

  2. Ensuring supply chain nodes can quickly relay real-time information for responsive decision-making across the supply chain.

Framework for understanding supply chain dynamics

If we strategically segment this across the supply chain, we can observe this as follows:

Freightfox framework for understanding supply chain dynamics

This FreightFox framework is designed to help organizations understand the impact of supply chain information exchange and forecasting accuracy on potential scenarios. It aids in identifying vulnerabilities and developing appropriate strategies using data.

Organizations should consider forecasting accuracy levels at different touchpoints — Finished Goods (FG), Wholesale, and Retail levels — through the lens of organizational internal visibility and control over vendor-partners in global supply chains.

By categorizing forecasting accuracy into High, Medium, and Low levels across various levels — Immediate Vendor (1-Echelon), Vendor of Vendor (2-Echelon), and Beyond Vendors of Vendors (Multi-Echelons) — we can create a clear matrix to assess and segment the current state of the supply chain.

Freightfox matrix to assess and segment the current state of the supply chain

This FreightFox Framework aims to establish the state of the supply chain for an organization and outline possible strategies at a detailed level to address various scenarios. 

While a supply chain does not remain static as depicted by the boundaries above, it operates as a continuum, constantly changing based on internal and external factors that impact supply chain performance either positively or negatively.

Leveraging Generative AI for inventory orchestration

As we explore advancements and innovations in inventory management, one standout technology inevitably pops up: Generative AI

To illustrate the potential of Gen AI in this use case, let's look at a practical example to understand how it can enhance inventory orchestration at the nodal level, leveraging distributed systems and context-centered communication.

Consider this scenario: A logistics company is tasked with managing inventory across multiple warehouses (nodes) to efficiently fulfill customer orders. Traditional methods rely on manual or semi-automatic data analysis from siloed systems like ERP (Enterprise Resource Planning) and TMS (Transportation Management System), leading to a fragmented decision-making process.

Let's explore how Generative AI can tackle this:

1. Data Aggregation and Preprocessing

Gen AI can ingest and gather real-time data from diverse sources such as ERP (stock levels, orders), TMS (shipment status), and historical sales data. It can analyze this data to identify patterns and relationships affecting inventory levels at each node.

2. Inventory Forecasting

The AI model can be trained to predict future demand at each node, considering factors like seasonality, promotions, and past sales trends. This enables proactive inventory allocation across the network. Additionally, these models can extract qualitative web-based text information to assess risks in our value chain.

3. Contextual Communication and Orchestration

Scenario 1: Demand Spike at a Node: When the AI detects a surge in demand at a specific node, it automatically:

  • Sends alerts to relevant personnel (warehouse manager, logistics planner) highlighting potential stockout risks.

  • Recommends replenishment options, considering lead time, transportation cost, and availability at nearby nodes in real time.

  • Initiates communication with the TMS to schedule expedited shipments from warehouses with surplus stock.

Scenario 2: Excess Inventory at a Node: If excess inventory is identified at a node, the AI:

  • Suggests potential transfers of excess stock to nodes with higher predicted demand, optimizing overall inventory distribution.

  • Facilitates communication with the sales team to explore promotional campaigns aimed at clearing excess stock at that specific node.

4. Continuous Learning and Improvement

Gen AI can continually monitor the performance of its recommendations, learning from successful outcomes. This iterative process allows it to refine its forecasting models and communication strategies over time.

* * *

By implementing Gen AI in supply chain management, organizations can unlock a range of benefits:

  • Improved inventory accuracy leading to reduced stockouts.

  • Optimization of inventory allocation, resulting in reduced transportation costs.

  • Enhanced agility in responding to demand fluctuations.

  • Improved collaboration between warehouse, logistics, and sales teams facilitated by contextual communication.

Moreover, Gen AI offers integration capabilities with existing ERP and TMS systems through APIs. This integration enables:

  • Access to real-time inventory data and order information.

  • Triggering automated actions such as generating shipment requests or updating stock levels.

Wrapping up,

The challenges faced by US supply chains, amplified by disruptions like the COVID-19 pandemic and geopolitical tensions, underscore the critical need for innovation and resilience. The prevalence of inventory mismatches, as highlighted, demands urgent attention and innovative solutions.

The application of Gen AI offers promising avenues for addressing these inventory mismatches. By leveraging real-time data aggregation, predictive analytics, and contextual communication, organizations can enhance inventory accuracy, optimize allocation, and improve responsiveness to market dynamics.

However, addressing these challenges requires collaborative efforts from businesses, policymakers, and other stakeholders. It's important to recognize the interconnectedness of supply chains and work together to build resilience. 

Embracing innovative solutions and fostering collaboration is a surefire way to ensure the stability and competitiveness of US supply chains in the face of evolving challenges.

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