Written by: Paul Hogendoorn, MEE Cluster Digital Transformation Consultant
In the first column in this “tips for digitization” series, the focus was on making sure the company leadership was on board with the digitization initiative, and that the company’s business objectives and vision for the future were aligned with the digitization initiative. The second column focused on how easy it is to establish a digital baseline by connecting to all your machines, even old and manually operated ones. The third column focused on creating KPIs that would make an immediate impact through something called “the Hawthorne Effect”. This column is on how to use your data, and KPIs, to fuel your Continuous Improvement efforts.
Continuous Improvement can be thought of in two ways: 1) as an ongoing general effort to continually improve the overall output of the organization, and 2) as a task, or series of specific tasks, aimed at improving identified areas or processes that are problems. The ongoing, general CI objective was dealt with in Blog #3, through creating and displaying real-time dashboards with meaningful KPIs. This blog is on the benefit of deploying digital technology in your specific CI projects.
Specific CI tasks were likely identified by utilizing Lean tools and Lean thinking. The disciplines taught in the Lean approach help your CI efforts in several critical ways: they help you triage to make sure you are focused on solving the right problems first; they help you narrow down and identify all the various factors that might be contributing to the cause of the problem; and, they make sure you include all the stakeholders in the process, gaining all the available insight from people most involved, as well as gaining their buy-in and participation in the solution.
In these situations, digitization is simply an effective tool that puts the whole process on steroids, increasing the level of success possible for the project, and the speed at which that improvement can be achieved.
There are 2 types of data that can be collected during a CI project: empirical, and narrative. Here is one example showing how each applies.
Assume that a semi-automated manufacturing process produces a part that is first drawn from a reel, punched, bent, laser etched, and then cut, and that the daily productivity has become inconsistent and is often a bottleneck. In this case, there is a cycle counter connected to the cutting shear, and each cut cycle is interpreted as a completed part. A KPI is already visible on a live dashboard and is frequently indicating that the actual output rate is below the required target rate, and a CI project has been initiated. The CI team identifies the possible causes and has additional sensors to be installed to keep track of the presence of material on the reel, and the punch press and bending press cycling. The team was then able to collect empirical information to determine when and where issues can occur. But, instead of having the operator record down time events by filling in a sheet of paper, they deployed an effective and available software tool that makes it easy for the operator to assign reasons for any period where there was an interruption in the process with a single click. The empirical data can indicate which of the processes involved (i.e. drawing, punching, bending or etching) is the cause, and the operator can quickly fill out the “narrative” (i.e. “jammed punch”, “no material”, “broken punch”, or even “waiting for inspection”, “forklift or crane”). At the end of two weeks, the data revealed that the number one issue was “no material”, followed by “operator waiting (for something)”, followed by tooling issues. The team concluded that the material present sensor that was added for the CI test should remain on the machine and be attached to an indicator to inform the operator and materials people to prepare to load another reel. A call button was added and attached to highly visible indictor to speed up responses to the operators’ requests for support. The tooling issues were then delegated specifically to purchasing and engineering for their analysis and recommendation.
The key takeaways from the above example for digitization are: 1) the output counter from the cutter was the only signal required to indicate if the process is healthy or not, 2) the sensors on the punch press and bending press did not have to be continuously monitored as they did not add any valuable insight when the process was healthy, so they were removed and available for other CI projects, 3) the material present signal has value in the work area, as well for the material handlers, and could be connected to a remote display to alert them, and 4) the operator call function used to track response times for that area could be of significant benefit elsewhere in the plant.
In short, the example CI with digitization project was a success on multiple fronts. It remedied an immediate problem area that was negatively affecting productivity and profitability, and it gave the company an improved live dashboard to drive their ongoing general continuous improvement efforts.
There’s a third way that companies can benefit by using digital tools in their CI projects, and that will be the focus of the next blog in the series!
(For more ideas or information on how digital technology can be used in conjunction with your CI efforts, contact Paul at firstname.lastname@example.org)