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A New Analytical Way of Managing Target Speeds

Product Target Speed

In the realm of production and manufacturing, accurate performance measurement is crucial for maintaining a consistent production pace, identifying any issues, and optimizing the final output. A reliable reference point is indispensable for any measurement system; otherwise, even data-driven decisions can be erroneous or subpar. To overcome this challenge, it is essential to have accurate and up-to-date master data, and continually maintain its accuracy. A critical piece of master data and a critical reference point for a production organization is the “Target Product Speeds”. Here is how SCW.AI Digital Factory enables users to manage Target Speeds.  

Actual Data vs. Master Data 

To ensure quality and productivity in manufacturing, we need to 

    • Gather production data, 
    • Measure the performance related parameters,
    • Compare them with our targets and find out the issues,
    • And decide which improvement action would make the most impact

    Easier said than done! Good news is, thanks to the digital transformation and the concept of IoT, we can now gather more data from the shop-floor. However, as it is written in the above steps, gathering data itself is not enough to ensure better performance. We need to compare the actual performance with our targets and define a reference point. Only by determining a correct reference, which is also called “Master Data”, can we discover the actual problems and focus on them. In the case of production performance, our reference is the Target Speed.

    Determine Target Speed Accurately

    Determining an accurate Target Speed allows us to find out if a drop in production is due to speed losses in the equipment or microstops in the processes. It mostly depends on the equipment, resources and processes used in production. Datasheet of the equipment will give us a theoretical speed, which in most cases cannot be achieved or cannot be sustained for a long time, so it is not a good reference point. Setting a reference based on past experience is also dependent on resources and condition changes which cannot be maintained properly. 

    That is why we worked on a new analytical way of determining and managing Target Speeds. It is based on the statistics of the actual production data. With this method, we can

      • Analyze all the past productions of a product on specific lines, i.e. assess the historical data
      • Check the actual production speeds and calculate the variance and standard deviation, 
      • Optimally combine the two variable sources of information with the Bayesian algorithm, (i.e. the historical data, master data) and calculate an optimal Bayesian estimation for that specific product line combination.


      Advanced filter options allow us to focus on specific dates, lines and products. Also, Product Speed calculation is flexible so that it can be done with different manufacturing quantities (initial production or good production) through different durations (Run Time or Down Time).

      Keep It Accurate

      A manufacturing system is a live system, where each day is a new day and each production segment is a new data to analyze. That is why a reference data set last month cannot necessarily be accurate for this month. Therefore, on top of finding the target speeds accurately, one needs to keep and maintain the target speeds, i.e. the master data, constantly and continuously. 

      SCW.AI Digital Factory’s Product Speed Analysis report precisely fills this gap. With the purpose of ease of maintenance, the user can keep all the data and calculations up to date. Its UX design is self explanatory and easy to use. Users can utilize this module to update their product speeds in specific intervals according to their needs. As can be seen below, the user can press the “Set” button to update the master data.


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