The seven stages of chemical technology from idea to industrialization (Issue 15/100) – Common pitfalls and how to avoid them

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This post was last edited by xiouxingzhe on 2026-6-23 15:24. Seven stages of chemical technology from idea to industrialization (Issue 15/100). The birth of innovation: common pitfalls and how to avoid them. Dear friends: Hello everyone! The previous issues covered the main aspects of the innovation incubation phase: scientific validation, business case development, risk assessment, and quadrant decision-making. Let’s not rush to move forward in this installment; let’s pause for a moment and talk about some pitfalls I’ve encountered or made at this stage. The innovation incubation stage is the one that requires the least investment in the seven-stage model, yet it has the greatest impact on decision-making. It is precisely because it costs little that many people don’t pay much attention to it, thinking that “it’s good enough.” But it was this attitude of “it’s fine as long as it’s more or less okay” that later turned into a situation where “even big shortcomings can’t be made up for.” Let me mention a few pitfalls that I think are quite common and worth being vigilant about. 1. Being reluctant to give up – This is what I consider the most common pitfall at this stage, as well as the one with the highest costs. Time, effort, and research costs have been invested in an idea, and affection has developed for it. At that point, it suddenly becomes clear that scientifically it doesn’t quite add up, or that there seems to be little room in the market for it; many people’s first reaction isn’t to stop and reassess, but rather to \"keep going and see – maybe things will work out.\" This mindset is known as the sunk cost fallacy – time and effort already spent should not influence future decisions. The principle is simple, but it’s not easy to put it into practice when it comes to oneself. I’ve seen projects where, already during the innovation incubation phase, clear warning signs were present: the thermodynamic calculations showed that it was not feasible, raw material costs were unmanageable, and there were already mature processes on the market that had a decisive advantage – yet the team still decided to \"go to the advanced laboratory and take a look.\" As a result, after years of research and development and millions spent, things ended up back where they started. That’s why in issue 11 I repeated the same sentence over and over: **It takes more courage to be willing to give up than to be willing to invest.** ** This isn’t some motivational quote; it’s something I’ve learned at the cost of real experience. Later, I set a rule for myself: during the stage of idea development, at each key decision point, I force myself to ask – “If this were the first time I heard about this approach, without any emotional biases, would I still choose it?” ”If the answer is no, then it should be stopped. II. Superficial argumentation: This is another pitfall into which one can easily fall. A scientific analysis was conducted formally—ΔG was calculated, and a simple material balance diagram was drawn; the conclusion was that it “should be feasible”. A commercial feasibility study was conducted on paper’s end—several industry reports were reviewed and market size data was obtained, with the conclusion being that “the market should be fine.” But in retrospect, these arguments were not deep enough. When conducting scientific calculations, thermodynamics takes into account the standard conditions but not the actual operating conditions. The material balance has been calculated, but an ideal conversion rate was used. When conducting the business case, “public data” was used, but the definition and source of that data were not thoroughly examined. The competitor analysis relied only on public information without conducting any on-the-ground verification. Several risk assessments were listed, but each one had “under control” or “low risk” written after it – there aren’t that many things with low risk; I just didn’t think about it carefully at the time. This kind of perfunctory reasoning can be even more dangerous than no reasoning at all. Because if you don’t provide a justification, you’ll feel guilty; while going through the formality, you think, \"It’s been justified, so there should be no problem,\" and then you proceed towards the pit with a clear conscience. I later developed a simple self-check method: after completing each argument, I ask myself, “If my competitors were also working in this area, what kind of argument would I hope they used?” ”The answer is usually, “I hope they go through the motions.” III. Ignoring policy risks: I have personal experience with this pitfall in the phenylacetic acid project. When conducting a business case analysis, people generally consider the market size, competitive landscape, and cost-benefit analysis. However, policy risks are sometimes given lower priority – they are mentioned, but the phrasing is often something like “policy risks are generally under control” or “close attention is being paid to policy developments”. Only when the policy actually changed did they realize that this was not something that could be “controlled” at all. The impact of the Law of the People’s Republic of China on the Protection of the Yangtze River on the site selection for phenylacetic acid projects is a vivid example of this. This policy signal was actually present at the time the project was initiated; it just didn’t receive enough attention. Later, it was forced to relocate at a huge cost. I will elaborate on this lesson in the subsequent case reviews. For the assessment of policy risks, I think there are two things that must be done. First, isolate the policy signals from the \"background noise\" and treat them with seriousness. Second, one should not only focus on current policies but also strive to predict the direction in which they will evolve. This is indeed not easy to achieve, but it must be done at least—when policy signals are already clear, one should not selectively ignore them. IV. Only scientific justification, no commercial justification: Some technical teams tend to focus only on providing scientific justification, neglecting commercial justification, or conducting commercial analysis in a very careless manner. “With such excellent technology, how is it possible that there’s no market for it? ”——This kind of mindset seems quite common in technical teams. The technical specifications are indeed excellent: high yield, good selectivity, and low energy consumption. But is there anyone in the market willing to pay for it? Are customers willing to pay a premium for your technology? If the investment required to build a new set of equipment is much higher than that for existing equipment, customers will hesitate even if the operating costs are low – who will bear the additional investment? There are also some technologies suitable for producing high-value, niche products, but the team insisted on targeting the bulk market; as a result, the volume was insufficient, costs could not be controlled, and customers did not require such high quality. This is a typical case of “good skills but the wrong direction.” On the other hand, there are also those who only conduct business arguments and not scientific ones. Seeing the market’s enthusiasm and high profits, they think, “Just rush in first.” A few years ago, when a new material concept emerged, many companies flocked to get involved. So what was the result? If technical bottlenecks aren’t overcome, product quality can’t be improved, and the devices can’t be made to function; no matter how large the market is, it has nothing to do with you. Science and business – both are essential; neither can be missing. This is something I have emphasized repeatedly. V. Mistaking “it might work” for “it will definitely work.” This mistake is quite subtle, but I believe it is the most common cognitive bias during the innovation incubation phase. The biggest characteristic of the innovation incubation stage is that both data and information are incomplete. Many judgments are based on assumptions and inferences. The conversion rate obtained through desktop simulations represents theoretical values under ideal conditions. The estimation of market potential is based on third-party forecast data along with one’s own judgment. The assessment of competitors’ capabilities is based on public information along with reasonable assumptions. None of these are mistakes. At this stage, it is normal to make judgments based on incomplete information. What is wrong? One gradually forgets that “these judgments are merely assumptions” and treats them as established facts. “The theoretical yield can reach 85%” – this is likely the result under optimal conditions; in actual operating conditions, it may be only 70%. “The market size is roughly 5 billion” – this figure is derived from various assumptions; if any of those assumptions change, this number could be reduced by half. “The barrier to entry lies mainly in costs” — but there might be an unpublicized patent, or a new technology that has not yet been noticed, which could completely change the landscape. How to deal with it? The habit I developed later on was to clearly list the assumptions behind each judgment. Instead of just stating the conclusion – “The yield can reach 85%” – it should be phrased as – “If the catalyst selectivity reaches 92%, the separation efficiency meets the designed value, and the quality of the raw materials remains at the current level”, “then the yield can reach 85%”. If this “if” does not hold true, then the conclusion changes as well. Doing this is not to make oneself hesitant or uncertain—on the contrary, it helps one move forward more confidently, because one knows what foundation one is standing on. VI. Phase Summary: The five pitfalls discussed in this session—hesitation to give up, perfunctory justification, ignoring policy risks, overemphasis on a single form of justification, and treating possibilities as certainties—are issues that I believe are common during the innovation incubation phase and warrant caution. But to be honest, knowing where the trap is doesn’t mean you won’t fall into it. Many pitfalls can only be truly understood for how deep they are after falling into them once. I went through it the same way. So I’m writing this not to say that “you should be smarter than me and avoid it all from the start,” but rather to say that “if you happen to fall into it, at least know that others have fallen into this pit before you; you’re not the first, so don’t be too hard on yourself.” What’s important is to be able to reflect on what happened after getting out of the pit, to remember it, and to avoid it next time. The core value of the innovation incubation phase has been repeated time and again: eliminate unviable directions at the lowest possible cost. To achieve this, two things are needed—one is a rigorous method, and the other is the courage to give up. Methods have been discussed in many previous issues; courage cannot be taught. All one can say is that giving up is not failure – it is about directing limited resources toward more promising avenues. Preview for the next issue: Issue 16 – Review of Phase 2 cases: The “double-high” decision-making in the PPS project. After discussing the theories and methods, the next issue will conclude Phase 2 with a real-world case study. The decision-making process for the PPS project at that time, from a pilot scale of several thousand tons to an expansion to tens of thousands of tons, is a typical example of a successful implementation of the “double-high” approach. Both the scientific and commercial arguments have been approved, but there are still many aspects worth discussing throughout the process. We’ll discuss it in the next issue. Table of Contents link: Seven-Stage Model of Chemical Technology from Idea to Industrialization (Issue 100) – Table of Contents (updated in real time)