4 Strategies for Manufacturing Cycle Time Reduction

In recent years, manufacturers faced major disruptions. Supply chain issues, unexpected product demand spikes, and delivery driver shortages led to longer lead times. While you can’t completely control external factors like these, you have far more control over your own internal manufacturing cycle times.

This post will cover what cycle times are, the benefits of reducing them, and four strategies to help with cycle time reduction.

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For this article, manufacturing cycle time is defined as the activities required to create and build a product within a manufacturing company. This can include the time it takes to load materials, assemble products on the line, complete quality control, and more.

Cycle time is not lead time, which refers to the time it takes from order placement to fulfillment for the customer. Lead time includes cycle times, but also includes the time it takes to receive materials for manufacturing and to ship products. Lead times are directly felt by customers, and can be influenced by factors like material availability or shipping delays. But shortening cycle times will also reduce lead times.

Shorter cycle times translate into a number of benefits:

• Increased efficiency leading to lower operational costs.
• Improved customer satisfaction due to greater on-time deliveries.
• Less scrap and rework.
• Improved productivity from workers.
• Greater predictability in developing finished products.
• Increased agility for when customer demand spikes.

For many manufacturing companies, production tasks have already been fairly well optimized. But there’s always room for improvement in your average cycle time—whether that’s streamlining a process, redesigning a product to be easier to produce, or automating a task that used to be manual.

Even incremental improvements in manufacturing production can lead to significant financial gains.

Before you start anything, it helps to take stock of the data you have available. Using hard data can guide your decisions and make a measurable impact on your ability to optimize manufacturing operations.

The truth is that data will come from multiple sources—warehouse and inventory management, manufacturing resource planning (MRP), manufacturing execution systems (MES), and edge computing, to name a few. You’ll need to centralize this information to achieve an end-to-end view before analyzing and improving your cycle times.

One solution here comes in the form of a data fabric. Data fabrics give teams a complete picture of the organization at scale so you get real-time insights to make data-driven decisions fast. It does this by allowing IT teams to discover and model data, unify the data from across systems, prevent unauthorized access with unique security controls, and automatically optimize the data for performance. This allows your teams to connect systems without getting mired in the complexity of traditional data programming, leading to rapid application development.

Start by looking for performance slowdowns. This can come from a number of different potential issues—manual processes, poor work floor layout, or a design that could be improved to simplify product assembly. Even something as simple as extended machine setup times can run up operational costs and bottleneck time-to-market.

There are two important factors to consider here: process stability and process speed. Stability refers to the standardization of a process. In short, how much variability is there? If your average cycle time varies significantly, it’ll be challenging to make process improvements and accurately measure speed or efficiency enhancements. If you have high variability, tackle that first. Note: Stabilizing these processes will also alert you to issues sooner because a huge spike in processing time will indicate a problem you can fix.

Assuming your average cycle time is stable, your next step is to find performance lags due to bottlenecks or process deviations. Process mining tools help here. Process mining examines the way a given process occurs in the real world from log data in software systems. For example, if you run process mining on a given process, you might find that employees manually enter data that could be automated or captured via an application form or a photo scan. Or you might find that a single handoff between employee stations takes extra time than it needs. This could indicate the need to change your floor layout to shave a few seconds off each handoff (which adds up to a lot of savings over time).

Quality is not only critical for happy customers, it also has an effect on cycle time. Quality issues can lead to scrap and rework, increasing processing times and costing your business in terms of wasted materials and labor time.

While it was mentioned in the previous section, standardizing processes and developing stability can help reduce scrap and rework. If someone does the same process the same way each time, this reduces variance and can decrease mistakes and flaws.

Process automation can also help improve quality. This can come from mechanical automation like shop floor robots or IoT devices or it can occur in the form of process automation via tools like artificial intelligence or intelligent document processing. Automating processes reduce human error, leading to fewer product defects. Plus, automation helps speed up the process when a machine can do the work quicker than a human, which leads to an overall shorter cycle time.

Nothing spikes cycle time quite like a critical machine going offline. Downtime hurts productivity and causes product delays. Improving machine uptime is critical for building overall equipment effectiveness (OEE), which measures the amount of manufacturing time that is truly productive.

Manufacturers sit along a continuum in how they handle maintenance. Some reactively only fix machines when they break, others schedule maintenance on a regular cadence, and some may focus on proactive defect elimination.

The most effective model is predictive maintenance. With the advent of IoT devices and connected machines, manufacturers can access data and use analytics to note flagging performance issues or wear and tear before the machine breaks down. More importantly, it allows you to prioritize work assignments for maintenance technicians based on the most pressing issues. With predictive maintenance, you can catch performance declines sooner to prevent decreased cycle times. As an added bonus, this can keep your equipment in working order for longer, reducing the costs of equipment replacements. Ultimately, predictive maintenance reduces breakdowns by an average of 70% and maintenance costs by 25%.