Architecting Reason Code Trees in DPM Tori Firewind, IoT EDC What are Machine Codes? Factory hardware devices communicate status changes to their human operators and other machines (IoT) via machine codes. The manufacturers often determine the machine codes for different types of factory hardware, so those are often pre-determined. However, how the reason trees map these machine codes to corresponding business logic in ThingWorx is entirely customizable. Knowing the best way to design your reason trees for this purpose can be challenging, so this guide is here to help with your conceptual knowledge. Using the UI to create, edit, and configure reason codes in technical detail can be found in the Help Center. The Tree Trunk At the highest level of the reason tree, the trunk, there are really 3 categories: Availability (A), Performance or Productivity (P), and Quality (Q). These should look familiar; they are the three dimensions of OEE (Overall Equipment Effectiveness). Fg 1. Calculation of OEE Availability refers to long stops, events that stop planned production long enough that it makes sense to track a reason for being down (typically several minutes, but the threshold between a long stop and a short stop can vary depending on the ideal rate of production of materials). Availability = Run Time / Planned Production Time Productivity/Performance really refers to short stops, things that cause the machine to run at less-than- optimal speeds. This can include stops caused by running out of materials for production, doing minor maintenance like switching out a single, easily-changed part, or even frequent breaks due to ill health of an operator. User error can be a cause as well, say if the machine needs a certain heat to produce parts, and the heat keeps fluctuating (requiring the machine to take the time to calibrate for this before starting on production) because operators are smoking out a back door or adjusting thermostat temperatures. Fg 2. Levels of Runnable Time Operator influence often is a factor when it comes to the conditions that permit optimal performance from machinery, and every factory may face different challenges. Stops like these are not really outages; the amount of downtime isn’t enough to consider the production block entirely unproductive. Production was continuing and ongoing throughout most of the block despite the issues; the rate was just slower than ideal. Performance/Productivity = (Total Count / Run Time) / Ideal Run Rate Quality refers mostly to the number of items that are considered scrap or rework, and it can be split into two categories: start up scrap (that which is expected because the machine is in the process of warming up or being fine-tuned by the operator) and production scrap (things which come out wrong and must be tossed or reworked because the conditions under which they were produced weren’t ideal; this is called first-pass yield only, meaning it's only a "good" product if it passes inspection the first time). Quality = Good Count / Total Count The Branches and the Leaves of the Tree The “leaves” are the reason codes which directly map to machine codes , and the “branches” are the method of categorization that connects them to the trunk. Both the leaves and the trees, the children and the parent nodes of the tree, are split into two states: planned versus unplanned downtime. Changeovers, maintenance, and even scrap, can be broken down into this dichotomy. For scrap, there are startup rejects (planned, because the machines have ramp up periods) and production rejects (unplanned, because the conditions weren’t ideal). For maintenance there is planned and unplanned, small changes that occur on the fly that result in productivity loss, and maybe also reduce availability in the long run. Small, unplanned changes can occasionally shift into the availability loss category if a simple, quick repair winds up being complex and time-consuming. A good reason tree can differentiate easily between short and longer stops in order to respond to each in a deliberate way. To start off in the process of architecting your reason tree, try writing the three categories on a board in a common room in an average factory (or several as a survey). Ask operators to stop in over the course of a few days and write various machine codes that they see often and find useful under one of these categories, or more than one if the machine code pops up under different circumstances and can mean different things. Have them write a 10 word justification, if the association isn’t obvious. Gather all of the “leaves” in this way, and then begin to associate them with the “trunk”, forming the “branches”. An example tree can be seen in figure 3 here, with leaves like “Changeover” and “Maintenance” being semi-ambiguous; they could just as easily be seen as unplanned stops. Therefore, there may be multiple reason codes mapping up to the top of the tree in more than one branch, and these can have different categories, which controls how the business logic responds to the different codes. The Help Center has more details about how the events are mapped to types, and each type contains multiple categories, as configured by you when you set-up the DPM model. Fg 3: Different types of changeovers may have different codes, and can map up as either planned or unplanned, but all planned and unplanned stops (long stops) are under the Availability category of the trunk. Similarly, small stops can involve idling, like if there are not enough materials, reduced speed if the conditions are not ideal, or other small stops, usually caused by human error or unforeseeable circumstances. Quality loss then refers to the products which fail quality checks, either because the machine still has the wrong paint in the applicator and needs a few rounds to be ready for the next production item, or because the conditions are again, not ideal, and items wind up scrapped. Example Reason Tree Fg 5 example tree with more specific tags (there may be dozens or hundreds in a full reason tree, though the fewer are needed to capture the events we care about, the better). Theory of Constraints Fg 6 theory of constraint wheel: an industry process for gradual OEE improvement in factories that has been adapted into the PTC methodology as well. While architecting your reason tree, always remember the key purpose: gathering only as much data as necessary to analyze the efficiency of a factory and to identify the bottleneck, or the most limiting factor. The important point is to identify not just the bottleneck that seems the most troublesome, but the one that actually results in the greatest impact to OEE across the entire factory. Without software like DPM, and a properly designed reason code tree, the process of improving a factory can be very challenging, involving a lot of guesswork, and sometimes solving one problem at the cost of another. The issue is that these machines produce a LOT of raw data, and humans are not the best tool available to gather and aggregate this data in a consumable way. A good reason tree ensures a smart application that can quickly prioritize the machine (bottleneck) that most impacts production, and not just the machine that functions in the least optimal way. So, the theory of constraints is really a process for identifying small, incremental changes, which together can make a big difference, and fast, in factory OEE. The rate at which this cycle can be completed varies, however. The slower the process of identifying constraints and the less information that is gathered, the slower and less precise the first two steps of this process. Alternatively, in a traditional constraint identification process, too much information can be a problem as well, due to human limitation, as discussed above. So, DPM is a great benefit in this regard, because it aggregates the data into a consumable, comparable way every 5 minutes, freeing up your human analytics for problem solving and prioritization, and not data gathering and sorting. Other Key Tips Also remember that a good tree treats the trunk like a whole unit, with each category occupying a percentage of the overall OEE. Afterall, look back up at the 3 dimensions of OEE in the equation above. For example, the more you see issues with availability, the less you will see issues with scrap, for the machine simply doesn’t have as much time to produce scrap if it is constantly down. The more you see issues with quality loss, the less you should see of productivity loss, because these are simply inversely proportional modes; to say it differently, if a machine is running quickly and seeing few minor maintenance stops, then it is likely to produce more scrap (as well as more good product as well). Another thing to remember is that even DPM is limited in its capacity to interpret raw data. Even while many magnitudes more efficient than any human gathering and analysis could ever be, there is an upper limit to how much raw data DPM can ingest and analyze before the system gets very expensive. For this reason, you want to ensure your reason trees use only as many reason codes as are required to capture the OEE of a factory site. This will mean using different codes for different types of things, most likely, which is easy to do maintainably across many sites using thing shapes. Keeping things tightly defined and organized is the easiest way to ensure a clean, efficient system for gathering and storing data. Also remember that data will not need to persist very long once DPM is fully operational and adopted by your factories. DPM ensures that the changes made to the production line to improve efficiency are the highest impact, and the least difficult to implement, meaning that there will be a very rapid return on investment, and a process to ensure future issues are identified and resolved quickly. Data from past issues in the factory won’t be as relevant, and historical data stores can be kept smaller than one might think. It is the power of ingesting data directly into the processing and aggregation process, the automatic reduction of data down into presentable, consumable webpages, that makes DPM and ThingWorx such a great factory solution for optimizing OEE.
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