How we helped manufacturing giant reduce production cost because of inaccuracy in the forecasting process

Summary

Client: The case study revolves around a leading industrial manufacturer headquartered in the United States. With a robust global presence and an impressive annual revenue exceeding billions of dollars, the client operated a complex network of more than 20 manufacturing facilities and 70 warehouses worldwide. 

Challenges: The client faced significant challenges stemming from forecast inaccuracy, which resulted in a combination of stockouts and excess inventory accumulation. These issues collectively contributed to significant financial losses, amounting to approximately 20% of their overall business revenue. 

Solutions: 

• Advanced demand forecasting:

To address the forecast inaccuracy challenge, the client collaborated with Quantzig, a renowned analytics and consulting firm. Quantzig undertook a comprehensive analysis of the demand signals of various SKUs, establishing an effective SKU classification framework. This classification allowed a deeper understanding of customer behavior and market trends. 

• Machine Learning algorithms:

Quantzig enabled the client to leverage advanced machine learning algorithms, which analyzed the demand signals of individual SKUs. Multiple algorithms were deployed for each SKU, harnessing the power of predictive modeling techniques to enhance accuracy and robustness. 

• Automated model evaluation:

Quantzig automated the process of calculating the Mean Absolute Percentage Error (MAPE) for each forecasting model. This automation eliminated manual calculations, streamlining the evaluation process. By objectively comparing the performance of different models, Quantzig helped select the best-fit model for each SKU. 

Impact Delivered

All of the above initiatives resulted in:

• 75-90% improvement in forecast accuracy
• 80% reduction in forecasting process time
• Significant improvement in production planning

Industry Overview

• The USA industrial manufacturing industry is a dynamic and integral part of the nation’s economy, accounting for a substantial portion of its Gross Domestic Product (GDP). 
• Technological innovation is a hallmark of the USA manufacturing industry and it embraces cutting-edge technologies such as automation, robotics, the Internet of Things (IoT), artificial intelligence, and additive manufacturing to optimize processes and deliver innovative products. 

About the Client

• Our recent client is a leading industrial manufacturer headquartered in the USA, with staggering revenue of over $4 billion. The client was facing the daunting challenge of a complex manufacturing process and was operating across 20 manufacturing facilities and 70 warehouses globally.
• The repercussions of inaccurate forecasting loomed large as the client grappled with stockouts and the detrimental impact of Inventory Holding costs (IHC), collectively resulting in a staggering 20% loss to their bottom line. 
• Despite their significant market presence and revenue, the client’s profitability was under siege, necessitating urgent action to address the forecasting shortcomings and regain control over their operations. 
• By unlocking the power of accurate demand forecasting, the client aimed to eliminate stockouts, optimize inventory levels, and maximize operational efficiency, ultimately securing their position as an industry leader in the highly competitive global market. 
• The convergence of meticulous planning, advanced forecasting methodologies, forecasting process, and strategic decision-making would pave the way for the client to recapture lost revenue, streamline their manufacturing processes, and chart a path to sustainable growth. 

Challenges

• Stuck in a one-size-fits-all rut, the existing demand forecasting solution of the leading industrial manufacturer failed to tap into the invaluable signals emanating from both the marketing and demand side, leading to missed opportunities and costly oversights. Shackled by a rigid approach, the current forecasting solution overlooked the dynamic cues from marketing campaigns, consumer trends, and shifting demand patterns, leaving the client vulnerable to suboptimal inventory management and revenue losses. The client’s forecast inaccuracies proved to be a costly misstep, eroding their market position and hindering their ability to fulfill customer demands with precision and agility.
• Forecast inaccuracy plagued the client’s operations, resulting in a troubling scenario of excessive inventory pile-ups for some SKUs and frustrating stock-outs for others. The combined impact of inventory congestion and stock shortages inflicted a substantial financial toll, amounting to a strategic loss of over $200 million for the client. The severe consequences of forecast inaccuracy prompted the client to urgently seek a forecasting process that could rectify the inventory imbalances and minimize the crippling financial losses incurred. 
• Faced with the shortcomings of existing forecasting methods, the client urgently sought a groundbreaking solution capable of propelling demand prediction to new heights with a remarkable 20% boost in overall accuracy. Faced with the repercussions of forecast inaccuracies, the client was determined to bridge the gap between their current forecasting capabilities and the level of accuracy required for optimal decision-making. Intending to achieve this significant accuracy enhancement, the client embarked on a comprehensive search for a cutting-edge forecasting process that could harness advanced analytics, machine learning algorithms, and predictive modeling techniques.

Solutions

• To tackle the demand forecasting challenges head-on, Quantzig initiated the analysis of demand signals from a diverse range of SKUs, laying the foundation for a comprehensive understanding of their unique characteristics and patterns. Leveraging their expertise, Quantzig meticulously examined the demand signals of each SKU, unraveling the underlying factors that influence customer preferences, market trends, and seasonality. Through extensive data analysis and statistical modeling, Quantzig crafted an innovative SKU classification framework, enabling the client to categorize their product portfolio based on demand patterns, customer behavior, and other key variables. 
• Building upon the foundation of demand signal analysis, Quantzig took it a step further by integrating advanced machine learning algorithms into the forecasting process, enabling a more sophisticated and data-driven approach. It helped the client to unlock deeper insights from the demand signals of various SKUs, uncovering hidden patterns and trends that traditional methods might overlook. Quantzig implemented a systematic approach by running multiple algorithms on each SKU, harnessing the collective power of diverse forecasting models to enhance accuracy and robustness. 
• Taking automation to the next level, Quantzig implemented a streamlined process for calculating the Mean Absolute Percentage Error (MAPE) for each forecasting model, eliminating manual calculations and saving valuable time and resources. By automating the MAPE calculation, Quantzig ensured consistent and accurate evaluation of forecasting models for each SKU, enabling a more objective and data-driven selection process. Leveraging advanced algorithms and intelligent automation techniques, Quantzig seamlessly computed the MAPE for each model, providing the client with a standardized metric to compare and assess the performance of different forecasting approaches.