A bottleneck is a constraint where upstream work in batches or on a production line arrives faster than the overall production line can handle. The congestion is like the neck of a bottle, or a funnel, that creates inefficiency and drives up costs through increased handling and lower equipment utilization in downstream equipment.
Bottlenecks in production can occur at almost any point. They can be related to communication, processes, resources, or technology. Some examples of production bottlenecks include:
Communication between teams or departments is critical. When these communications are verbal or paper-based, they can cause a bottleneck. This type of bottleneck may be as simple as an operator going on a break, or a lost clipboard. This can be solved by deploying automated machine data collection solutions.
Manually collected production data is time consuming and error prone, leading to poor communication and insights.
Process bottlenecks are task-driven constraints where the number of requests for a production machine exceeds the equipment's throughput capacity. An example would be a drill press station that receives requests from several upstream machines, each requiring different hole sizes and depths. Time taken to change the tool and set the depth starves the downstream components.
Sometimes resources can cause bottlenecks. One example is labor hours available for specialized skill sets. If one technician is required to split their time between two or three specialized pieces of equipment, the flow of different parts can create a resource bottleneck where the labor hours available for each are less than what is available.
Technology bottlenecks often occur with fragmented software systems in siloed original equipment manufacturer (OEM) machinery. If these systems have no interoperability, the time required to program settings at each production step creates a bottleneck. If a programmable CNC machine is in the production stream before a manually set drill press, the technology is mismatched, prohibiting communication and interrupting the workflow.
Integrating the systems, machine, and people on a shop floor can reduce bottlenecks thats result from technology siloes.
For improving efficiency and overall equipment effectiveness (OEE), using a bottleneck analysis can help to remove or mitigate production holdups. Bottleneck analysis can be conducted on any constraint types above and help to identify the bottleneck location, category, root cause, and impact. From this analysis, managers can implement process improvements designed to eliminate future bottlenecks.
Bottleneck analysis has several key benefits. For one, it helps eliminate waste. Any constraint that slows or stops production will generate waste in the form of labor, material loss, or capacity loss. Practical bottleneck analysis will help eliminate this waste.
A second benefit is an increased knowledge among managers. By understanding the reasons for the bottleneck, managers can not only correct existing bottlenecks but also helps in the design of future production lines or production expansion.
Bottleneck analysis requires looking at the entire production process. While data and performance on each piece of equipment in the line are needed, the bottlenecks often occur during the transition, staging, communication, or setup phase.
In addition to looking at the overall production process, analysis needs to include people issues such as labor, training, and skillsets. It may also require a review of the supply chain to address any material constraints. And of course, measurable performance such as machine speed, equipment age and capability, and capacity analysis must be included.
Conducting a capacity analysis requires a tremendous amount of data collection to be effective. This means collecting production data, collating it, standardizing and analyzing it for trends, and spotting the areas where bottlenecks occur.
There are many well-established strategies available to identify and resolve bottlenecks on the shop floor. We document several of these below:
DMAIC stands for Define, Measure, Analyze, Improve, and Control. It is a Six Sigma process improvement tool that allows continuous feedback loops to refine a process once a bottleneck has been addressed.
Ideal for bottleneck analysis, the theory of constraints helps teams identify the biggest limiter for achieving the best results. Once the limiting factor is identified, it’s adjusted and improved until the constraint is eliminated.
Every constraint has a root cause. By digging deep to uncover a root cause, systemic improvement can be achieved, eliminating "band-aid" solutions. It focuses on the how and why aspects of the constraint.
A fishbone diagram approaches the cause and effect of a bottleneck. The problem is the "head" of the fish, and the causes feed into the spine. This is an effective tool that helps teams visualize the problems as they iterate solutions.
A fishbone diagram, also known as an Ishikawa Diagram, shows the factors contributing to a problem. [Image Source]
A continuous improvement tool, PDCA approaches improvement from a cyclical view. The cycle can be repeated endlessly to improve the system further.
By addressing the physical space, constraints can often be uncovered and addressed. 5S Framework – most have heard of the sort, set, shine, standardize, sustain components of 5S framework in the context of lean. It’s a hands-on physical and easily visualized tool when conducting bottleneck analysis.
Another lean tool, value stream mapping captures the information and materials for a process. It’s also highly visual for teams conducting bottleneck analysis and is meant to eliminate constraints by including only optimized steps that add value.
Any bottleneck analysis will include measurements of takt time, the time required to produce a product, and the materials, labor, and equipment available. An optimized takt time will act as a benchmark and will help identify capacity and labor constraints.
Avalign Technologies, a medical device manufacturer with facilities across the US, was experiencing difficulty in tracking OEE and machine downtime, leading to issues on the shop floor that included poor machine performance, unclear standard operating procedures, and production bottlenecks.
In this exclusive video case study hosted by AWS, OEE Director Matt Townsend discusses the impact of deploying MachineMetrics across four of Avalign's facilities.
Learn how instant visibility into shop floor performance resulted in a 25-30% increase in OEE, a more effectively leveraged workforce, millions of dollars in increased capacity (without additional equipment), and increased throughput via the reduction of bottlenecks.
MachineMetrics is helping Avalign pursue their goal of providing best-in-class medical equipment by giving them full control of machine and operational data, allowing them to build a strong, competitive advantage in the marketplace.
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