Sharper Supply Chains with Smart AI Modeling

Revolutionize Supply Chain Dynamics using Innovative AI and Data Analytics for Inventory Challenges Sharper Supply Chains With Smart AI Modeling

CASE STUDY

Introduction A Blunt Demand Planning Instrument

Our client, one of the largest global players in the manufacturing and industrial sector, faced a significant challenge in streamlining their inventory management process for their largest customer, an e-commerce giant. The client needed to overcome significant challenges in various SKU inventory levels fueled by an erratic demand while maintaining a 99% fill rate, leading to substantial excess inventory costs and customer service issues.

Challenge

Demand Planning Challenges on the Cutting Board

Cutting Down Inventory Carrying Costs Our client had been grappling with the issue of excess inventory costs. The conventional approach they’d been following at their warehouses involved maintaining an inventory level of between 14 and 21 days. This approach led to higher holding costs and lower fill rate, as well as operational inefficiencies resulting from overstocked warehouses. Dicing Demand Variability A significant challenge for our client was the erratic and unpredictable nature of demand across various SKUs from a prominent customer. Traditional forecasting methods, which often relied on historical data and established patterns, led to a heuristic approach to inventory stocking. As a result, our client struggled to cope with this level of demand variability that was marked by sharp fluctuations, seasonal variations, and unforeseen shifts in product demand preferences. Chopping Up Supply Chain Complexity Confronted by intricate distribution channels and a diverse product portfolio, our client realized that streamlining the supply chain was a top priority across all their SKUs. Without any data analytics, they struggled with tackling logistical hurdles, coordinating warehouse manpower, optimizing production, and managing deliveries. Their primary target was to achieve cost-effectiveness and responsiveness, ensuring they could meet the evolving demands of their customer effectively.

Solution A Recipe For Inventory Optimization Our solution followed a structured approach and comprised of three phases:

The first phase involved defining data requirements, exploring data sets, and conducting initial inventory and forecast analysis. Secondly, the team consolidated data from various sources, extensively analyzed forecast accuracy, demand patterns, highest revenue SKUs, and inventory costs, identified opportunities for improvement, and did an error analysis. In the final phase, the team constructed inventory models, automated data pipelines, and meticulously documented the entire process before handing it over to our client’s internal analytics team. These phases collectively drove the successful implementation of a data-driven inventory optimization solution.

The Min-Max Inventory analysis model developed for our client used a data-driven approach to determine optimal inventory levels. The main inputs into the model were the historical shipment data, the previous 13 weeks of fore- cast data, the expected demand for the next 13 weeks, the expected service level defined at 99%, and the inventory on hand at warehouses. By incorporating these parameters, the model calculates the minimum and maximum inventory thresholds for each SKU-Plant combination. This gave our client’s demand planners much-needed insight to maintain efficient stock levels, thereby reducing excess inventory carrying costs. The model is automated, facilitating real-time adjustments and enabling responsive inventory management to navi- gate demand fluctuations effectively. What we provided: A Finely Chopped Inventory Analysis Model

STRATEGY

AIM

RESULTS

Have a data-driven approach

To reduce inventory carrying costs

46% reduction in inventory levels

Solve demand variability management

To improve service level to 99%

Enhanced customer satisfaction

Real-time inventory control

To minimize stockouts or stock overflow To cater to different-sized retailers and distributors To lower operational inefficiency

Optimized inventory management

Higher percentage of satisfied customers

Scalable solution

Balancing costs effectively

Increased profit margins

Fractal’s in-depth knowledge in supply chain helped decode the specific challenges in the existing process and allowed for a more targeted and effective solution.

Priyasmita Ghose Engagement Manager, Consulting Industrials, Energy and Travel

Outcome

The client provided Fractal an NPS score of 10 and said that

A Slice of Clarity Through Inventory Transformation

the biggest impact of their partnership with Fractal was “improved supply chain efficiency”

The immediate impact By implementing the Min-Max Inventory analysis model, our client realized a remarkable 46% reduction in inventory levels. Simultaneously, the 99% service level was consistently maintained, enhancing customer satisfaction. The successful transformation of inventory management allowed the demand planners of our client’s largest customer to not only streamline warehouse operations but also significantly improve manpower utilization. Long-term benefits Fractal’s analysis model can be scaled and implemented seamlessly across our client’s customers – from small local retailers to massive global distributors. It acts as a blueprint, helping our client consistently align inventory levels with fluctuating demand, as well as optimize manpower utilization and streamline warehousing operations. Reduction in inventory levels Customer satisfaction delivered 46% 99% 12 100% Fill rate achieved Weeks to build the MVP

Enable better decisions with Fractal Fractal is one of the most prominent providers of Artificial Intelligence to Fortune 500® companies. Fractal’s vision is to power every human decision in the enterprise, and bring AI, engineering, and design to help the world’s most admired companies. Fractal’s businesses include Crux Intelligence (AI driven business intelligence), Eugenie.ai (AI for sustainability), Asper.ai (AI for revenue growth management) and Senseforth.ai (conversational AI for sales and customer service). Fractal incubated Qure.ai, a leading player in healthcare AI for detecting Tuberculosis and Lung cancer. Fractal currently has 4000+ employees across 16 global locations, including the United States, UK, Ukraine, India, Singapore, and Australia. Fractal has been recognized as ‘Great Workplace’ and ‘India’s Best Workplaces for Women’ in the top 100 (large) category by The Great Place to Work® Institute; featured as a leader in Customer Analytics Service Providers Wave™ 2021, Computer Vision Consultancies Wave™ 2020 & Specialized Insights Service Providers Wave™ 2020 by Forrester Research Inc., a leader in Analytics & AI Services Specialists Peak Matrix 2022 by Everest Group and recognized as an ‘Honorable Vendor’ in 2022 Magic Quadrant™ for data & analytics by Gartner Inc. For more information, visit fractal.ai

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