The average production lead time in the manufacturing industry can be anywhere from 10 days to 10 weeks. In order to stay competitive, manufacturers are always looking for ways to shorten their lead times. The use of analytics can help shorten production lead times by identifying and addressing issues in the manufacturing process. Manufacturing analytics is the process of analyzing data to improve manufacturing. This can include anything from tracking production times and defects to monitoring energy usage and machine performance. For example, if analysts notice that a machine is consistently running slowly, they can investigate the cause and make necessary adjustments. This may involve tweaking the machine’s settings or changing the way it is used in order to speed up production. Overall, data collection can help shorten lead times by identifying problems and potential improvements. Now that you’re aware of the definition of manufacturing analytics, you can keep reading to learn more about its impact on shortening production lead times.
The Types of Manufacturing Analytics
There are several different types of manufacturing analytics that can be used to reduce downtime. Process analysis involves studying the individual steps in a manufacturing process and identifying ways to improve efficiency and throughput. Techniques such as value stream mapping can be used to identify waste in the production process and brainstorm ways to eliminate it. Statistical analysis uses historical data to identify patterns in machine downtime, product defects, and other factors that impact productivity. This information can be used to develop models that predict how various changes will impact production output. Predictive modeling uses historical data to build models that predict future outcomes. These models can be used to forecast demand for products, schedule production runs, and allocate resources accordingly. Simulation modeling uses mathematical models to simulate the effects of different scenarios on production output. This allows manufacturers to test out different options before making changes in their operations. Lastly, optimization modeling uses mathematical algorithms to find the best possible solution for a given problem. This can include finding the optimal sequence for completing tasks or allocating resources so that they are used most efficiently.
What to Look for in an Analytics Solution Provider
Consider the impact the provider will have on shortening production lead times. The provider should have a robust and sophisticated technology platform that can handle large amounts of data quickly and accurately. This will allow them to analyze data in real-time and make recommendations that will help reduce lead times. They should have a deep understanding of how manufacturing works and be able to apply their analytics solutions in a way that improves production processes. They should also be able to work with your team to implement these changes seamlessly so there is minimal disruption to your operations. Finally, it is important to consider the cost of the solution. The provider should offer a pricing model that fits your budget and meets your needs. They should also provide support and maintenance so you can continue to see results over time. When considering all of these factors, it is clear that choosing the right analytics solution provider can have a significant impact on reducing production lead times.
How to Implement Manufacturing Analytics for Shorter Lead Times
If you are interested in implementing data collection, you’ll need data from a variety of sources, including production data, quality data, and process data. Production data will involve machine usage, cycle times, downtime, etc. Quality data will involve defective parts or products, rework required, etc. Process data will be on material usage, waste generation rates, energy consumption rates, etc. The collected data is then analyzed to identify trends and patterns. For example, if the quality department consistently identifies more defects in products manufactured on Monday than any other day of the week, this trend could be investigated to determine the root cause. Once the root cause is identified, steps can be taken to address it and prevent it from happening again. This process of improving manufacturing processes through the use of analytics is known as “continuous improvement.” There are a number of ways to implement manufacturing analytics. The most common methods include visual analytics, process mining, time series analysis, statistical analysis, and data mining.
By tracking and analyzing production data, manufacturers are able to identify and correct inefficiencies in their production processes, which leads to shorter lead times and improved throughput. In addition, the use of manufacturing analytics can help to identify and correct quality issues early in the production process, which can help to avoid costly rework and warranty claims.
