Experts say that innovation is “the effective implementation of new ideas for simple solutions to address relevant customer needs in order to generate value“. Innovation and Lean are different things, but they like working together. Innovative thinking is a common building block of Lean work. Additionally, lean tools can be used or adapted for creative thinking and innovation. In fact, there are very interesting initiatives going on working in the relationship between Lean and new product design (like Lean Startup by Eric Ries, http://theleanstartup.com/) or generating new business models (like Strategyzer by Alex Osterwalder, Tecnhttps://strategyzer.com/). Some might think that “innovation just happens”: new ideas show up suddenly generated by that limited group of people who are good at inventing new things. Well, there is some truth here (creative thinking is not 100% controllable, some people are more skilled than others) but there are many factors to consider that affect innovation:
- The S-Curve of innovation
- Innovation is not linear
- It is key to understand customer needs
- The importance of the environment
- The importance of execution
- It is all about the process
Innovation means “jumping the S-curve”. In other words, innovation is revolutionary, not evolutionary. New ideas and technology follow a performance/effort curve with an “S” shape. The first step is “experimentation” where a new success pattern emerges (“make it work”). After this, the “learning” phase (“increase efficiency”) shows a very rapid growth in performance and profit, apparently infinite. Suddenly, we reach the “maturity” phase (“minimize cost”) and performance stays constant or even declines . This is the moment to jump. True high-performance companies are able to jump repeatedly the S-curve, because before they reach the top of one “S-Curve” they are already getting ready for the next “S-Curve”. Research shows that 60% of the companies fail doing so and disappear after the first technology revolution.
Innovation follows a development pattern similar to PDCA. It starts understanding your customers needs, feeling their life with tools like interviews, focus groups, or the empathy map. We must always go to the gemba and see customers interact with the product or service we are studying. The process keeps going with the generation of innovative ideas (e.g. using SIT): create prototypes (something physical or not) and experiment with them to learn. These prototypes are often called Minimum Viable Product (MVP), defined by Eric Ries as “that version of a new product which allows a team to collect the maximum amount of validated learning about customers with the least effort“. Finally, connect with stakeholders to develop a business model (e.g. with a business model canvas) and validate assumptions (e.g. with a validation board). Now that you know more about your ideas, what things work and what things don’t (you will have many of these because approx. 99% of innovative ideas fail) it’s time to refine the prototype and start again if needed.
- The only important thing is the learning process, not the tools. “Learning” means understanding what the customer needs/likes/wants. The tools mentioned before are just suggestions: always use the tools and methods that work best for you. Experiment and learn.
- The process is not linear, so it is perfectly ok to move back and forth as many times as needed as long as you are learning.
- Your customer is the key part here. Understanding the customer and validating the prototype (MVP) is critical. Starting the idea generation phase without having understood the customer needs is a great and common error. Investing money in anything that has not been validated with the customer might be fatal.
It is important to keep in mind certain innovation killers that can make the difference between success and failure
- The environment: it is critical for innovation. Everybody fails the first time. We all need time, support and patience for some trial-and-error until we reach something that works. A place that encourages thinking differently and provides a safe place for testing-failing-learning has the highest probability of success.
- Implementing: Understanding the customer and generating ideas are important, but they are useless without robust implementation. The world is full of great ideas that fail because they are implemented wrong. Pay attention to business models and execute seriously.
- Talent and process: Innovating needs creativity AND a process. It is wrong to think that pure and simple talent is enough to make it work. Yes, talent is critical, but talented people helped by a great process will be unstoppable.
This is a summary of the post, enjoy!
- the basic time blocks that impact OEE
- typical problems that affect the time blocks
- how to divide time to calculate typical OEE components
- A high “loading” component indicates low demand. The work center is idle most of the time. Unfortunately, there is not much to do from an engineering point of view to improve this number.
- A high “planned downtime” component reveals:
- the need of many scheduled corrective and preventive actions. This is a potential issue if problems are recurrent.
- a high number of projects and updates. This is not necessarily a problem if projects are originated by new products or technologies.
- A high “availability loss” component shows breakdowns, large adjustments, or lack of personnel, materials or systems when they are needed. The equipment definitely needs reliability improvement work, standardization and / or better scheduling to avoid starving the machines.
- A high “performance loss” component proves low speed. Standardization and training are typical good next steps.
- A high “quality loss” component obviously indicates that improvement must focus in getting first time quality, reducing scrap and reprocessing.
This analysis is the most important part of applying OEE to our processes. OEE is a means, not a goal. It must drive action and improve the effectiveness of our operations.