Angelo Hulshout has the ambition to bring the benefits of production agility to the market and set up a new business around that. A practical example from an imaginary paint factory.
Imagine we’re in a paint factory. There’s an automated production line and two warehouses, one containing ingredients for the paint: thinner, binder, colorant and additives, and one holding the cans of paint produced on the production line. The ingredients come in from two other plants, owned by the same company that runs the paint factory. The paint is made to order as well as in bulk – our factory supplies both specialist painting companies and wholesalers.
The bulk production runs 6 days per week, for 6 different kinds of paint. It’s interrupted whenever an order for a specialist company needs to be processed. Over time, management notices a number of issues starting to occur. First, special orders frequently have to be delayed because ingredients are out of stock. Second, production levels don’t match up with the calculated capacity of the production line. Also, at certain times, there appears to be a shortage of some of the additives.
Factory management wants to do something about this and decides that it wants to collect data from various sources to analyze what’s going on. These sources can be ERP and MES systems but also sensors, as well as PLCs and other control systems. When we decide to collect data from there, it takes time to get everything in place, but let’s assume that in this factory, we can do it instantaneously.
Establishing the information need
The first step is establishing what information we want to have available in the end. In our paint factory, for example, we want to understand why material is in short supply for special orders. This requires us to analyze the size and frequency of these orders against the production and logistics process for getting the materials to our factory, meaning we have to collect order sizes and frequencies and compare them to the production and logistics planning (and actual execution) for the ingredients.
Device lifecycle management for fleets of IoT devices
Microchip gives insight on device management, what exactly is it, how to implement it and how to roll over the device management during the roll out phase when the products are in the field. Read more. .
We also want to investigate why the additives are running short. For this, we need to check the production plans and the logistics for additives against the production plans and actual usage of the additives in the production of our paints.
Finally, we want to analyze why the production line fails to reach its capacity. For this, we mainly need to look at the running and idle times of the different machines and the causes for the various idle times.
Determining the data sources
To get the right data for these analyses, we need to tap into multiple sources. For material availability, we need to get order information from ERP (sizes and frequencies), the MES (bill of materials for each order) and the MES and logistics systems of the ingredient factory. With this information, we can determine how much material is needed, when it’s needed and when it’s produced and delivered to the main factory. This includes planning as well as execution data: we need to know when ingredients are planned for delivery, as well as when they actually arrive.
Similarly, for analyzing the shortage of additives, we need MES data about which amounts were required, available and missing during order execution. Since the additives are partly handled manually, we also need data about actual usage and spillage as well as measurement data from the equipment used to measure the doses of additives. This comes from the PLCs controlling the equipment and possibly additional sensors installed there.
For the production capacity analysis, we need to get from the MES the expected capacity of each production machine. Meanwhile, feedback data from PLCs and sensors can tell us exactly (from when to) when the machines were working, idle, blocked, and so on.
Collecting and using the data
Collecting the data from all these sources is handled by a data gateway. Typically, this is an IIoT gateway that allows all systems and devices to connect to it using standard internet protocols (TCP/IP). On top of this, we can run messaging protocols like OPC-UA and MQTT.
From the gateway, the data goes to a data store, either on-premises or in the cloud. We run our analysis software and dashboards on top of that.
The analyses can be done in a cockpit-like dashboard (on a tablet or a normal screen) but also in Excel sheets, if needed, or by feeding the data to machine learning models. The results can be used to improve the way of working in the factory.
Interpreting the results
In our imaginary paint factory, the analyses led to some interesting conclusions. First of all, it turned out that the ingredient factory was given a production and logistics schedule based on bulk orders only. As a result, some ingredients, mainly additives, weren’t produced in the quantities needed for the special orders. This was solved by modifying the production strategy to include an estimation of what’s needed for special orders. The initial estimate is based on data from the past year; in the next improvement step, we’re going to add the option to also take into account the current orders. We haven’t done this already because it requires a special coupling between the ERP system and the MES system for the ingredient factory.
The incomplete production and logistics schedule only partly explains the shortage of additives. We also found that during the manual handling of certain powders, a significant amount of material was lost. This could eventually be traced back to some of the production containers not being completely dry after their bi-weekly cleaning cycle, leading to material clogging to the container, making it useless. We detected this in two steps: the data analysis showed that a lot of material was reported as ‘lost’ and physical inspection at the work station showed why.
The production capacity problem turned out to have two causes. One was the absence of material for the special orders. The operators would reconfigure the systems for a special order without checking first if all the ingredients were available. That led to machines being doubly set up when not all the material was there: change to special order, then switch back to the previous product. The other reason was that the bulk production planning didn’t take into account setup changes related to the bill of materials. For some paints, the color mixer needs to be cleaned before switching to a different colorant. The schedule included extra cleaning cycles (as part of the setup) that could be avoided by changing the colorant order, thereby eliminating 20 percent of the setup time.
Digital or not?
All of these issues can be investigated and found without digitalization but they’re not likely to be uncovered quickly that way. This is because collecting the required data isn’t always easy by hand. It’s also a matter of mindset. If data and optimization aren’t first-class citizens in a factory, people will mainly focus on ‘getting the work done.’