Strategies to Reduce Manufacturing Cycle Times
While many industries are now operating as they did prior to the COVID-19 pandemic, manufacturers continue to grapple with complex challenges like global supply chain issues, unpredictable product demand spikes, and delivery driver shortages. These challenges have extended lead times, which, in turn, has placed significant pressure on companies to improve manufacturing cycle times. This has resulted in manufacturers finding ways to streamline their processes, automate manual functions, and redesign products so they are easier to produce. So, what are some of the strategies followed by manufacturers to reduce cycle times? Let us dive deeper into this topic through this blog.
Accessing and Centralizing Data
Manufacturing plants have traditionally been structured in a way that facilitates operations but results in information silos and a lack of transparency. Data is frequently stored in multiple sources such as warehouse and inventory management, manufacturing resource planning (MRP), manufacturing execution systems (MES), and edge computing. Data management is one of the top manufacturing technology trends and it provides an end-to-end view that can guide decision-making and optimize a manufacturing operation. Increasingly, manufacturers are creating a data fabric to centralize their data. This virtualized architecture layer and tool set connect data across disparate systems and creates a unified view. It prevents unauthorized access with unique security controls and automatically optimizes data for performance. Additionally, teams can connect systems without being slowed down by the complexity of traditional data programming, which leads to rapid application development.
Maximize Machine Uptime
A significant contributor to manufacturing cycle time spikes is critical machines going offline. Improving machine uptime builds overall equipment effectiveness (OEE), which measures the amount of manufacturing time that is productive. Machine uptime depends on the quality of the maintenance of the equipment. Some manufacturers only fix machines when they break, others schedule regular maintenance, and some focus on proactive defect elimination. The most effective method is predictive maintenance, such as using AI in video surveillance to improve productivity. Connecting machines and IoT devices allow manufacturers to access data and use analytics to identify flagging performance issues and wear and tear before machines fail. It also ensures maintenance technicians focus on the most urgent problems. With predictive maintenance, manufacturers can identify performance declines before they negatively impact cycle times. It also increases the lifespan of machinery, which reduces the cost of equipment replacement. Predictive maintenance reduces breakdowns by an average of 70% and maintenance costs by 25%.
Identify and Eliminate Bottlenecks
Performance slowdowns can be caused by manual processes, poor work floor layout, or a design that could be improved to simplify product assembly. Even something as simple as extended pipe fabrication setup times can increase operational costs and bottleneck time-to-market. The two critical components to consider are process stability and process speed. Stability refers to the standardization of a process – how much variability is evident? If the average cycle time varies significantly, it will be challenging to make process improvements and accurately measure speed, or efficiency enhancements, so high variability must be the first issue addressed.
Once the average cycle time is stable, performance lags caused by bottlenecks or process deviation can be identified. One way to accomplish this is by utilizing process mining tools. Process mining examines how a process occurs in the real world from log data in software systems. For example, running process mining on water tank development process might identify that employees manually enter data that could be automated or captured via an application form or a photo scan. Or it might be observed that a single handoff between employee stations takes unnecessary additional time. This could indicate the need to change the operations floor layout to shave a few seconds off each handoff (which results in significant savings over time).
Concluding Remarks
While manufacturers can develop strategies to improve floor operations, the correct and appropriate implementation of these strategies is the differentiator between visible improvements and sunk costs. Also, a holistic approach is generally necessary to reduce manufacturing cycle time and quality remains king – after all, reduced cycle times do not help if quality is compromised!