In the last months, there has been an unprecedented power struggle between Volkswagen and its suppliers. Two of the suppliers stopped delivering, leading to a full stop of multiple production lines at six Volkswagen plants, including its main plant Wolfsburg. This whole mess comes on top of the separate problems Volkswagen has had with its Dieselgate. In this post I would like to look in more detail at what happened.
A few weeks ago I wrote an article on the Cuban economy, focusing on commerce (See How a Planned Economy Can Screw Up an Entire Country – Analogy between Cuba’s Communist Economy and Push Systems). On the same visit I not only saw supermarkets, but I also had a look at industry. Unfortunately there are no visitors allowed in their government factories. Nevertheless, I was able to catch some glimpses of different industries.
Modern manufacturing works with a lot of performance measures, often called key performance indicators (KPIs). Unfortunately, they are rarely accurate, and often even intentionally misleading. In my previous post I described some examples of commonly manipulated KPIs. In this post I would like to explain the ugly consequences of incorrect or manipulated KPIs. In a final post I will also show some ways that you can reduce this negative effect. But first, how do bad KPIs (and hence most KPIs) hurt your company?
Statistical measurements, usually called key performance indicators (KPIs) are found on pretty much every shop floor and in every company. Many management decisions are made based on KPI. Unfortunately, these numbers often are not reliable at all.
Mark Twain popularized the phrase “Lies, damned lies, and statistics.” Winston Churchill famously said, “I only believe in statistics that I doctored myself.” Hence, both men were wary of trusting numbers. You should be too!
Over Christmas I escaped the cold weather in Germany and relaxed on the warm beaches in Cuba. Of course, being a lean expert, I was also interested in the Cuban economy. As a communist economy (or more precisely, a socialist economy), it is based on centralized planning. In comparison, the capitalist system of the US (and most of the rest of the world) leaves most business decisions to individual entrepreneurs. This is somewhat similar to push and pull in manufacturing. Push systems also rely on centralized planning, while pull systems have their signal from inside the system to match the customer demand. As capitalism outperforms communism, pull usually outperforms push. Hence, in this post I would like to show you the shenanigans that happen in Cuba due to the effects of centralized planning. Warning: Lots of images ahead!
I occasionally watch the reality show Undercover Boss, where top executives work undercover in their own companies. Over and over again I see these managers making the same mistake: They have no understanding whatsoever of what is really happening on the front lines. It is a typical case of not going to the shop floor often enough, or in lean speak, no genchi genbutsu (Japanese for “go and see”). So, <dramatic voice> Why do bosses all make the same mistake? Will they ever learn? Will you enjoy this post? See for yourself in the post below! </dramatic voice>.
To improve your system capacity, it is a must to find and improve your bottleneck. However, finding the bottleneck is difficult. Most methods used in industry fail at finding the bottleneck. As discussed in my previous post on Shifting Bottlenecks, this is mostly due to bottlenecks being dynamic and frequently shifting from one process to the next. In this post we will look at common bottleneck detection methods used in industry. More importantly, we will find out more about failures of bottleneck detection methods commonly used in industry. Subsequent posts look at bottleneck detection methods that actually DO work!