Similarly, the current entrepreneurial startup boom, definitely having its good sides, also undervalues the studying of existing theories and rather encourages just executing practical stuff and "failing fast". To bring up a different perspective, I share my experience in this blog post on how the mindset learned during a quality management course in university now inspires my daily work with continuous improvement at Stora Enso's corrugated board operations. To me it is really fascinating to think how big our company is; 26,000 people operate heavy machinery and processes to produce high quality products 24/7/362. Nothing happens by accident. Thus to make reality of our innovative ideas, everything must be carefully planned. To guide such improvement efforts, researcher Howard S. Gitlow presented principles for viewing life based on statistical process control. Principle 1: Life is variation Every day at the mill or office is different, every production run is different, and every end product is different. No matter how carefully we plan our actions, variation is always present, because it is natural. As an everyday example, think about your own body weight; there is probably one ideal weight for you set by yourself, but in reality the weight varies constantly around the ideal (or below/over it). However, as quality stands for "the end result is how it was planned to be", variation should be reduced to make the process in focus more manageable. To reduce variation, we need to understand what causes it in the first place. Principle 2: Life has two causes of variation Sometimes variation has clear, specific and external reasons, which we cannot control; most of us have an experience of being late from a meeting, but having a solid excuse for that, e.g. car broke down; flight was etc. These special causes of variation should be eliminated to be able to predict the process; e.g. fixing the car. When special causes are eliminated, we can say that the process is under statistical control. However, in fact majority of the variation derives from common causes, which are due to the process itself and how the process is designed. Regarding the meeting example above, we almost always have an external reason for being late, but in reality the problem might be that we didn't do proper maintenance for the car or booked too tight a schedule for flights. Processes with common causes of variation are statistically predictable, but improving them requires rethinking the whole process of how we operate. Principle 3: Planning life and continuous improvement requires stability When preparing for a project, personal or work related, we make plans on how to achieve the target. Plans are always built on assumptions, which are essentially predictions of future performance required by the plan. If a process is stable (including only common causes of variation) the predictions are more likely to be realized and this essentially increases the likelihood of the plans to succeed. Having stability is a good foundation for planning, but it is always economical to continuously improve by reducing variation. Why is that? Think about delivering our corrugated board packages to a customer. If we deliver the packages to the customer earlier than needed, products need to be stored somewhere, causing cost either to us or to the customer. However, if we deliver them late, it could cause a serious disruption in our customer's operations. This is why "Just in Time" delivery is important, but it requires relentless data collection, analysis and rethinking of the whole delivery process in order to make precise deliveries a standard, not a positive exception. Principle 4: Some things in life cannot be quantified Even in the age of Big Data and regardless of the fact that we at Stora Enso have enormous amounts of databases, information systems and resources, we all occasionally run into situations, where necessary figures are just not available to support decision-making. For example, when evaluating whether we should hire a new young talent to our team, there is no data to tell you whether to hire and when to hire –we may have comparison figures and experience from similar decisions beforehand, but they are hardly 100 % applicable now. Thus the decisions must also be made based on theory of what is going to happen. Expanding knowledge always requires underlying theory to be compared with reality. Conclusion In sum, continuous improvement is not repairing faults when they occur, but systematic theoretical planning, executing the plan, checking if it works and acting based on that. So next time you encounter a distinctive result, focus also on the past performance, variation and its causes: was it a result of a stable, but varying process, or are there some special causes present? In short, sit there and think for a while instead of rushing into executing the first thing that comes to mind. For me, these past 9 months in the Stora Enso GROW Global Trainee Programme have showed that our company is in a good position to fully exploit the rethinking mindset of the presented Gitlow's four principles – but it requires hard brain work and time! Luckily we are investing in our most important asset, people, more and more all the time. Thanks to this, I am confident that our transformation journey will be a success!