Reengineering for Efficiencies by Leveraging Process Mining

In highly regulated industries like aerospace or medical device manufacturing, compliance with industry standards and regulations is essential. Process Mining can help ensure manufacturing processes adhere to these standards and provide data for compliance reporting.

Discreet manufacturing involves complex and often highly customized production processes. Process Mining can help identify bottlenecks, inefficiencies, and areas for improvement in these processes. By visualizing the entire production workflow, organizations can optimize their operations, reduce lead times, and improve product quality.

For instance, research by Samsung Electro-Mechanics (SEM) Co. illustrates how process mining can offer a deeper understanding of machine task allocation and usage duration. This analysis revealed that specific machines were used more frequently and for more extended periods. By identifying these disparities, SEM could fine-tune their machinery allocation strategies, optimizing their production processes.

Process mining delivers insights into various operational aspects, including the speed of operations, throughput rates, and process frequency. Manufacturing companies can leverage this data to identify gaps in target processes, detect inventory shortages, rectify recurring production errors, and pinpoint bottlenecks that hinder cross-departmental workflows. Process mining acts as a diagnostic tool, enabling manufacturers to unearth the root causes of these issues and take corrective actions, thus preventing disruptions and slowdowns in organizational processes.

Insights collected from process mining are valuable not only for immediate operational improvements but also for long-term planning. Analyzing processes facilitates the identification of overburdened and underutilized production stations, allowing for the balanced allocation of resources. Redundant steps within production workflows can be identified and removed, significantly reducing cycle times, and enhancing overall efficiency. Leveraging data-driven scheduling, manufacturing companies can intelligently plan production runs during off-peak hours, effectively mitigating resource constraints and ensuring a more efficient and cost-effective operation.

Cost optimization is a critical concern for manufacturers, and process mining can be a powerful tool in this aspect. An example from the electronic manufacturing sector in the Netherlands showcases how process mining detected inefficiencies in the subcontracting process, leading to prolonged work. By analyzing the process, the company restructured the initial steps to eliminate the need for additional document requests later in the workflow. This simple adjustment reduced wasteful activity by a substantial 85%, resulting in cost savings and accelerated processes.

In a discreet manufacturing plant producing heavy machinery, unplanned equipment downtime can be costly. Process mining is applied to maintenance operations, revealing patterns in machine breakdowns and maintenance scheduling. Armed with this information, the manufacturing teams can implement a predictive maintenance program, ensuring that machines are serviced before they fail. The result is a significant reduction in unplanned downtime and improved machine reliability.

Organizations must adopt a comprehensive approach to unlock the full potential of Process Mining. This approach includes a systematic framework for implementation. Here are some ways in which Birlasoft can embed process mining in core solutions to help and support businesses optimize their manufacturing plants:

Objective-Centric Framework: Craft a strategic approach that closely aligns process mining with client objectives. Tailored solutions to address the specific goals of discreet manufacturing clients.

Data Refinement: Gather important information from various places in the ERP system, allowing organizations to find mistakes and spots where they can do better. This data helps leaders ensure that the right tasks are organized correctly and fix issues in dividing work across production lines. It ensures accountability and that the tasks are shared in a better way among different roles based on different areas of work.

Visualized Process Models: Leveraging cutting-edge process mining tools, they construct visual process models that bring the intricacies of real transactional data to life. These models illuminate the true flow of processes, capturing variations and alternate paths taken in different scenarios within the discreet manufacturing environment.

Analyze and develop: The process mining efforts are geared towards carefully examining the created process models. Thorough analysis covers a close inspection to uncover operational inefficiencies, bottlenecks, deviations from standard procedures, and other crucial areas of interest. They utilize process mining tools to provide essential measurements like process duration, frequency, and conformance.

Implementing and Revising: Support extends to carrying out the recommended changes. This involves adjusting the ERP system, redefining processes, integrating automation, or providing targeted user training. After the changes are in place, there's a return to the process mining phase to evaluate the impact and confirm that the modifications achieve the intended outcomes. This iterative approach is central to refining the processes.

Continuous Monitoring and Stakeholder Involvement: They emphasize ongoing monitoring of process efficiency and compliance. Regular application of process mining enables them to spot emerging areas for further improvement, ensuring that processes remain optimized over time. Birlasoft's approach values stakeholder engagement, involving process owners, management, ERP system administrators, and end-users. Their feedback is instrumental in refining the insights and directing the course of continued enhancements.

By harnessing the potential of process mining, organizations can lay down a clear roadmap for its implementation. This involves the collection and integration of process data from diverse sources, including enterprise systems, databases, and application logs. And investing in dedicated process mining tools and assembling a proficient team capable of scrutinizing and deciphering this data.