Data Insight June 26, 2024

71% of Leaders Say AI Will Cut Inventory Waste. Here’s How It Can Cut Waste Everywhere 

The majority of supply chain professionals believe that, in the next three years, AI will have the biggest impact on inventory waste reduction. In fact, our research found companies are using AI to decrease waste of all kinds, from carbon to labor to material, across the entire supply chain. Here’s what they’re doing.

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Jalen Thibou

The Data

  1. 1

    When Zero100 asked supply chain leaders where AI will have the biggest impact in the next three years (specific to cost optimization), 71% said inventory waste.

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    But AI’s potential for waste reduction doesn’t stop there. Our AI Hub has identified use cases cutting labor, carbon, and material waste across the supply chain Loop.

Increasing Yields, Reducing Waste 

With global consumerism on the rise and lingering cost pressures, waste reduction across labor, material, and carbon is becoming a priority for those attempting to optimize operations. Practitioners are trying to crack the code on how AI can help – even the AI mega disruptor SHEIN is still trying to find an answer despite its astronomical growth in recent years.

Our annual AI survey revealed that, specific to cost optimization, 71% of supply chain leaders believe AI will have the biggest impact on the reduction of inventory waste in the next three years. The need for practical solutions, though, couldn’t be greater. 

Looking beyond inventory waste reduction, our analysis found overall waste reduction implementations across the entirety of The Loop: 

Table showing examples of AI uses to cut labor, carbon, and material waste across functions of the supply chain loop.
Source: Zero100

We dive deeper into a few of these examples from the AI Hub below. 

Waste Reduction in Action

Source: Sapporo Breweries has partnered with IBM Japan to develop an AI system that increases the speed of new product development. The system uses genAI to create over 1,200 product formulations from 700 raw materials. After considering Sapporo's existing product base, the system recommends combinations of raw materials, mixing amounts, formulations, and flavors to create a new product. Sapporo Breweries has been able to reduce formulation review times by 75% and product development cycle times by 50%. 

Make: Alibaba’s Xunxi Factory is designed for “made-to-sell” production informed by consumer insights and real-time trends aggregated from its e-commerce platforms. Technology permeates the factory: an AI-driven advanced planning system helps with scheduling and adjusting workflows; thousands of IoT touchpoints alert staff and operators of any issues; and physical equipment is fitted with digital capabilities for a more efficient and sustainable approach. For example, a laser cutting machine uses AI vision technologies to prevent errors while AI algorithms in washing machines have reduced the factory’s water consumption by 50%. In addition, lead times are 75% shorter, with a 30% reduction in safety stock needs. 

Sell: Lawson, a Japanese convenience store, is using AI to dynamically price perishable goods. Its software uses algorithms to set optimized prices based on store sales, delivery times, and other factors like the weather. To minimize food waste and costs, Lawson's system analyzes 270 SKUs in-store and can add discounted pricing based on the freshness of items like pastries and sandwiches.  

The Takeaway  

As you explore AI for productivity gains, don’t undervalue ROI in the form of waste reduction, increasing utilization rates, and improving the efficiency of materials already in your system. Reach out to us at to learn more about our AI framework, which highlights where a specific supply chain produces the most waste, following which we highlight key AI opportunities to prioritize the deployment of waste reduction initiatives. 


Zero100’s proprietary data and analytics are a combined effort between our data scientists and research analysts. We provide data-first insights matched with our own research-backed points of view and bring this analysis to life via real-world case examples being led by supply chain practitioners today.   

For this study, we analyzed 542 supply chain organizations and 600+ unique references of brands scaling AI implementations from the Zero100 AI Hub. We also surveyed 312 supply chain professionals, including CSCOs and COOs. Respondents include supply chain professionals from companies with an average company revenue of over $1 billion. The seniority of positions ranges from Senior Director to C-suite. Respondents work across all functions and all regions, with the majority based in the USA. We also analyzed 1,271 earnings calls from 150 brands from Q1 2024.