Improving production performance from day 1 with data-driven trainings for your team.
Operational Excellence KPIs for Additive Manufacturing
When discussing with a client their production needs, we had a very lively review of how operational excellence metrics in Uptimo can benefit them in the medium and long term: We were clearly on the same page when it comes to solving their problems, but of course, the challenge in manufacturing is how do we solve the problems of yesterday? The answer back was that “the team doesn’t have enough capacity and so these topics are on the wish list until we get a grasp of the metal additive technology, put the correct manufacturing procedures in place and train our team.” This set us on a very interesting discussion, because unlike other operational excellence tools, Uptimo was created with the intent of getting a company into proper running order faster!
There is the inherent need in manufacturing to stay lean and only make investments that increase capacity or reduce costs. Both of these targets can be tackled in a number of different ways, but one thing that is quite challenging is to improve operations to increase output not only of the systems, but of the team that is utilizing the equipment itself. This is where metal AM and, specifically, the various combinations of machines, powder and materials make it challenging for the team to be performing at their very best on every project.
In order to begin addressing this problem, we first need to look into what an ‘ideal’ production is? This means maintaining the highest uptime, in which the machine is performing at the fastest production rate and all of the parts coming out of the machine fulfill quality requirements. Uptimo does this with three metrics, aptly named Planning, Parameters, and Parts. In addition to this, Uptimo accommodates the complexity of metal AM by providing users with feedback on specific wastes that are incurred in all of these steps. Let’s break down these different metrics first and then go into how Uptimo is used to train the team:
In the planning metric, a traditional manufacturing environment will estimate the uptime of systems to be the total time available in a year minus planned downtime, also called the Planning factor (Pf), as well as weekends and holidays. With additive technologies, this is very different because the machine can run 24/7 without interruptions as well as during weekends and holidays. This, of course, then makes the team be the weakest link in production. More on this topic later.
A second factor that contributes to a decrease of the planning metric is machine setup, powder changes, and specific calibration checks that need to be performed. In other words, every aspect of production with metal AM that is different for every project. Some of these differences are industry specific. For example, in aerospace and other highly regulated industries, we require that the laser power is checked on the systems between every build. In prototyping, on the other hand, this can be done once every few weeks or months. So how do we resolve these variations?
Since every project in metal additive is different and relatively expensive (in comparison to traditional manufacturing), care must be taken for each to succeed in production. Therefore, there is no silver bullet to productivity on all of these different configurations. In the case of Uptimo, that is not a problem; if a project requires more time to setup the machine, the project manager will select the corresponding required steps in Uptimo and that will be the new allotted time for machine preparation and calibration checks. For example, if the machine operator had two hours to prepare a standard machine and a specific project calls for additional calibration checks like laser power, then the new allotted time will be 2.5 hours and only if the machine requires more than 2.5 hours to prepare will a waste in the machine preparation show. Additionally, all of these steps are customizable by our customers to meet their internal requirements and company production goals.
When it comes to the machine parameters, metal additive systems are very difficult to gauge. Let’s say that you have two production batches, both of which have parts with the same volume. In metal AM, these two production runs can have wildly different build rates due to the fact that standard machine parameters simply cannot be ideal for both of these geometries. Of course, in serial production a project manager can spend hours tweaking the parameters, simulating the difference and seeing how much time has been shaved off of the production. This, however, is subjective, does not transparently demonstrate to the organization how much the job can still be optimized, and is extremely time consuming. With the predictive performance algorithms in Uptimo, this is done rapidly and shows the user how much time there is to save on every individual build, before the project manager ever begins to attempt parameter optimizations.
When the build is finally complete, it is important to assess how much of the production batch resulted in successful parts. After all, if we maintain high uptimes, shave off all the time from the parameters that are used, but then have 50% of the parts be rejected, we might as well have had more time to prepare the machine and run the parameters less efficiently but ensure that all of the product coming out meets customer requirements. These failures can be classified according to delaminated supports, part cracking, fault lines, missing powder, or any other customized metric that the customer deems necessary.
When quantifying wastes, Uptimo assesses the Planning, Parameters and Parts metrics and analyzes this data according to the nominal values of how long it should take to prepare the machine, how efficiently can the parameters run and what percentage of parts (or volume of parts) came out successful. This is then prioritized according to the actual hours lost in each step over the course of a project, a week, a month or any other timeframe.
What is unique about metal AM as well as Uptimo is that these wastes can then be categorized according to different classes of materials, machines, project types, types of parts, etc. This, is where the true power of utilizing Uptimo with your training needs occurs; if, for example, all of the Titanium projects in production constantly have significant breakdown wastes, then it is an indicator that the corresponding project manager is missing the right tools when it comes to how to utilize Titanium, how to design for the material, or the corresponding parameters that need to be utilized. This can be easily solved by dedicating time to enable him with new knowledge and trainings to broaden his material-process understanding in that area.
If, on the other hand, the entire project management team is continuously using parameters that result in a parameter efficiency of 60%, then they require more hands-on and theoretical support on how to exploit the parameter editor features of metal AM.
Alternatively, suppose that Bob and John are both using Inconel for production, and Bob is constantly outperforming John when it comes to the efficiency of his builds and the success of the parts coming out? Once the production manager looks into Uptimo and notices these differences, he should have these two team members support each other and ensure that good knowledge transfer is happening between them on a regular basis. Their progress can then be tracked over time in their individual KPIs.
All of this data is available in Uptimo and it is very easy to analyze and customize. The software is free to try and, as we discussed with the customer who mentioned that they don’t have the capacity or money to go ahead with this until they set up their operations, our question back to them was simple: “can you afford not to do this right now?”