## 1 Introduction  
Spain is the tenth largest agri-food power in the world and the fourth largest agricultural area within the European Union [1]. In recent decades, the use of fertilizers has played a crucial role due to their great contribution to intensifying agricultural production in response to the increasing demand for food products by the growing global population [2]. In this context, the Spanish fertilizer industry has experienced a growing demand for its products, facing a continuing challenge to enhance production, efficiency, and product quality [3]. The manufacture of chemical fertilizers is subject to stringent regulatory standards that require consistent compositions of specified elements across all batches. The raw materials can be either solid or liquid, and the uniformity in their composition is crucial to ensure consistency in the final product content. However, achieving consistent product quality is often challenging due to the inherent variability in raw materials, manufacturing processes, and environmental conditions. Variability in product composition can have significant implications for both manufacturers and customers [4]. For manufacturers, it can result in increased production costs, rework, and waste, while for customers, it can lead to inconsistent product performance and reduced satisfaction [5]. Thus, maintaining the process within tolerance limits is essential to ensure consumer satisfaction and to thrive in a competitive market.  
A potential strategy that can be used to minimize process variation is the integration of Lean and Six Sigma (LSS) methodologies [6]. LSS is a data-driven approach that combinesthe fundamental principles of Lean and Six Sigma to assist an organization in maintaining a competitive position in the global market and achieving business goals and organizational excellence [7]. In short, Lean focuses on waste elimination and speed reduction, improving process efficiency and reducing overall costs, with a particular emphasis on customer satisfaction [8]. It provides a set of tools and techniques designed to minimize lead times, inventories, setup times, rework, and other hidden factory inefficiencies [9]. On the other hand, Six Sigma aims to reduce variations and defects through statistical analysis and rigorous problem-solving tools [10]. It is a project-oriented, statistically based methodology that provides data to drive solutions. Hence, each approach offers a unique set of tools, and their integration can maximize the potential that either approach could achieve independently. Large enterprises, such as Toyota, Texas Instruments, Kodak, General Electric, Bank of America, and Sony, have achieved impressive results because of the adoption of these methodologies [11; 12; 13].  
In chemical industries, such as fertilizer manufacturers, processes involve multiple variables, including large volumes of hazardous substances, strict regulations, energy consumption, and waste production. Consequently, this sector is suitable to fully benefit from these business strategies. This work presents a case study conducted in a Spanish chemical company focused on implementing an LSS framework in order to reduce variability in the final product composition. Three specific fertilizers exhibiting high composition variability were selected for analysis. A range of LSS tools, including process mapping (value stream mapping), root cause analysis (Ishikawa cause-effect diagram), project management (SIPOC and DMAIC), and statistical process control, was implemented in the company to monitor the impact of variability in production and its effect on inventory. Variability in product composition is a major factor in customer dissatisfaction and may jeopardize customer loyalty [14]. By minimizing it, companies can enhance not only customer satisfaction but also product quality and profits.