Re-engineering of Industrial Processes Using Business Process Modeling and Notation: A Case Study of Production Planning in the Tire Manufacturing Industry

Introduction and Objectives: The tire manufacturing industry holds a pivotal position in Iran's economy due to its critical role in automotive applications, transportation networks, and its heavy reliance on petroleum-derived raw materials. Over 60% of tire production inputs—such as synthetic rubber, carbon black, and chemical additives—are sourced from petroleum derivatives. Iran’s abundant oil reserves position it as a strategic player in this sector, yet inefficiencies in production planning processes often hinder its competitive potential. This study aimed to address these challenges by enhancing the efficiency and agility of production planning through business process re-engineering (BPR). The primary objective was to establish integrated data flows and real-time information accessibility to streamline decision-making, accelerate responsiveness to market fluctuations, and ensure timely production adjustments, thereby safeguarding organizational competitiveness. The innovative core of this research lies in its application of Business Process Model and Notation (BPMN)—a globally recognized standard for process visualization—to redesign production planning workflows in tire manufacturing. While BPMN has been widely adopted in industries like finance and logistics, its utilization in tire manufacturing, particularly within emerging economies like Iran, remains underexplored. This study bridges this gap by proposing a structured framework that emphasizes data integration, elimination of informational bottlenecks, and enhanced system agility, offering a replicable model for similar manufacturing sectors.

Methods: This applied research employed a hybrid methodology combining business process modeling with qualitative data collection techniques. BPMN was used to map and redesign workflows, while participatory observation and semi-structured interviews with stakeholders (e.g., production managers, IT specialists, and sales teams) provided insights into operational pain points. The study adhered to the Manganelli-Klein five-stage re-engineering model, a systematic approach comprising: 1. Objective Definition: Clarifying goals, such as reducing planning delays and improving data accuracy. 2. Process Selection: Identifying the production planning process as the focal point due to its cross-functional impact. 3. Current State Analysis: Documenting inefficiencies, including manual data entry, siloed departmental communication, and reactive decision-making. 4. IT Solution Design: Developing an Enterprise Resource Planning (ERP) system to automate data aggregation, capacity assessments, and real-time reporting. 5. Implementation: Deploying the redesigned processes and training staff to ensure seamless adoption.

Findings: The analysis revealed systemic challenges in the traditional production planning process. Key issues included misalignment between marketing forecasts and production capacities, fragmented data systems (e.g., sales teams relying on outdated inventory records), and delays in adapting to market shifts. For instance, manual data transfers between departments often led to errors, causing overproduction or stockouts. The re-engineered model addressed these shortcomings through a centralized ERP system. Market demands submitted by the sales unit are now automatically routed to the ERP platform, where they undergo validation, capacity analysis, and prioritization. Monthly production plans are dynamically adjusted using real-time data on machine availability, raw material stocks, and workforce constraints. These plans are decomposed into daily schedules, which are disseminated to production units via automated workflows. A critical feature of the system is its adaptive feedback loop: when market demands change, the ERP triggers a recalibration of production targets, ensuring alignment with current requirements. This flexibility minimizes bottlenecks and mitigates disruptions caused by sudden demand fluctuations.

Conclusion: This study demonstrates that integrating information technology with BPR significantly enhances production planning efficiency in tire manufacturing. The ERP system automated critical tasks such as capacity assessments, workstation scheduling, and data synchronization, reducing manual intervention and errors. Key outcomes include accelerated decision-making cycles (e.g., faster response to supply chain disruptions), improved cross-departmental coordination, and a reduction in planning delays. For example, centralized data access eliminated redundancies, enabling managers to monitor inventory levels and machine utilization in real time. The re-engineered processes not only refine production scheduling but also align operational workflows with strategic objectives, such as cost reduction and customer satisfaction. By minimizing interdepartmental conflicts and optimizing resource allocation, the framework strengthens the organization’s competitive edge in domestic and global markets. This research provides a validated blueprint for tire manufacturers seeking modernization, illustrating how BPMN and ERP systems can synergize to address industry-specific challenges. Future studies could explore the scalability of this model in other resource-intensive sectors or assess its integration with emerging technologies like AI-driven predictive analytics.