Scaling up production of a new company that involves untested parts is a challenging and risky task. Unlike in the digital world, when working with physical processes production requires more upfront investments. When a company has to commit to significant investments in the early stages with limited experience and knowledge, there is an increased risk of those investments going wrong. How a company can mitigate this risk was discussed in my earlier blog post that you can find here. In the previous text, I suggested what you should do. In this one, I suggest what you should not do: Automation.
Early automation of a production process has many downsides. When production is carried out by human operators instead of machines, they can use all their senses to learn and improve the process. While running the process, the operators do not only learn about the process and get ideas for improvements; they also do quality control. Learning from where the failures are originating from first hand gives the company better position comparing to fully automated production line where the raw materials go in one end and the ready and faulty products out from the other. First-hand knowledge from the production line is essential for process development. In the early stages when the understanding of the production process is low, long hours of human labor are needed to learn what could be automated in the first place. Automating too early in the learning curve will hinder the learning for the presented reasons. Additionally, machines and other types of automation hardware are costly, acquiring them involves long lead-times, machines are not readily adaptable, and they have a high risk of becoming obsolete when build-measure-learn loops are completed.
Instead of automation a cheap and fast way to improve the output of production and to reduce the workload of human operators are Karakuri- methods. Karakuri is Japanese and literal translation of the word to English is “mechanism.” Small makeshift automation solutions in a production line are examples of Karakuri. Let’s say an operator needs to move a heavy machine to her workstation frequently, but it must be returned after using because it would be blocking the operator from executing the next process step. The machine moves on wheels a small distance back and forth, but the high frequency of moving makes it exhausting for the operator. Instead of building larger workstation (expensive!) or automating the movement of the machine by a motor (very expensive!) wire can be attached to the machine on one end and a bucket full of sand to the other. Also, a rail is placed on the floor for the wheels to move on. Now, the operator can pull the machine to her and lock it in place while in use. After using the weight of the heavy sand, the bucket will pull the machine pack automatically (and cheaply!). A more straightforward example of Karakuri- solutions are shelves that are tilted to a small angle making the products on the shelf to fall to the edge closer to the operator. In general, shelf-made Karakuri solutions are cheaper, easy to maintain and easy to improve compared to purchased automation solutions.
One of the only places where automation can be recommended early on is data collection. Data collection and analyzing tools such as data loggers and analyzing software are relatively cheap, but they are also a lot more reliable than human operators. Data is in the core of the learning process, unlike improving the speed or quality of the production where automation could be considered as well.