Uncontrolled process variations, raw material inconsistencies, and equipment inefficiencies can lead to production bottlenecks, increased costs, and inconsistent product quality. However, advanced analytics is now enabling manufacturers to address these long-standing challenges by utilizing the process data that they most often already have available to them.
The Hidden Challenges in Batch Processing
While batch production is often viewed as simpler than a continuous production process, this statement doesn’t always hold true. Batch production can more often than not involve multiple variables— raw material properties, a dimension of process steps, temperature fluctuations, and more—all of which can impact the final outcome of production. Some of the most pressing issues faced in batch production include:
- Process stability: Inconsistent operating conditions lead to fluctuations in batch quality and production times. Comparing performance across different equipment or batches can be difficult.
- Product quality variability: Minor deviations in process conditions can result in quality issues. Identifying multi-factor causes of these variations is critical to improving consistency.
- Raw material & dosing variability: Changes in material properties and dosing precision directly impact batch-to-batch quality and processing times.
- Changeover & time losses: Inefficient transitions between batches can cause delays and reduce overall plant throughput.
- Equipment reliability & maintenance: Small, undetected deteriorations in equipment can gradually degrade efficiency, leading to increased downtime and production losses.
- Cost management: Over-specifying quality requirements can lead to excessive raw material costs, while inefficiencies can increase, e.g. waste and energy consumption.
Data and Analytics for Process Optimization
Static trend views have been the standard for process monitoring, but they often miss the subtle variations that occur within individual batches. Advanced analytics henceforth allows for easier comparisons between batches and enables automated anomaly detection. This provides manufacturers with the means to spot inefficiencies in real-time and take corrective action before the inefficiencies escalate.
Achieving optimal batch production requires a structured approach to identifying inefficiencies and implementing targeted improvements. Here’s how manufacturers can systematically enhance their processes using data-driven insights:
- Defining goals and identifying performance bottlenecks
The first step in process optimization is setting clear objectives. Suppose the goal is to shorten batch cycle times without compromising quality. To achieve this, we need to track key quality measurements and pinpoint the factors contributing to delays.
- Diagnosing the root cause of delays
Once performance issues are detected, statistical analysis helps uncover underlying causes. If batches consistently exceed target production times, comparing process data from shorter and longer batches can reveal correlations between specific quality parameters and production inefficiencies.
- Real-time monitoring
With a clear understanding of performance bottlenecks, we can implement software solutions such as FH Cycle to keep track of relevant metrics in real time. These real-time analytics give operators the means to make informed decisions that are based on data rather than gut-feeling, preventing costly inefficiencies before they escalate. This also frees up a significant amount of time that would otherwise be spent on exploratory analysis of historical trends.
- Seeing the benefits
While the main benefit is an improvement to the set objectives, the benefits that come along with process improvements are often greater than the sum of an individual part. For example, reducing batch production times doesn’t just improve throughput—it also lowers energy consumption, reduces equipment wear, and enhances overall plant efficiency. Even a modest efficiency gain can lead to substantial cost savings and improved profitability across the production line.
Smarter, Faster, On-Quality Batches
Batch production has always been a mix of science and operational expertise, but with the recent advancements in data analytics, manufacturers can now gain unprecedented control over their processes. By leveraging structured batch production data, manufacturers can ensure consistent product quality, reduce waste, and enhance overall production efficiency.
This blog post has been written based on our recent webinar: Controlling Batch Cycles. In the 40-minute on-demand webinar, Eero Peiponen, Development Manager at Kemira explores key challenges in controlling batch cycles, followed by Samuli Kortelainen who provides insights into how analytics can help address these challenges. To learn more, click on the button below to watch the webinar on-demand or book a demo with one of our experts.
