Tricky product problems are often sticky and chewy like chewing gum: Whatever you do, you can not get rid of them. In practice, they survive because conventional problem solving has repeatedly failed, both in product engineering and in production. The good, highly significant news: Using Statistical Engineering, tricky product problems can be solved quickly and sustainably. more ….
Technology leaps require more one-time costs. As a first reaction, R&D demands more budget, but does not get it that way and yet retains much of its previous development tasks. So it gradually taps into the R&D cost trap. Its second reaction is an increase in efficiency. This often leads to loss of quality and the cancellation of R&D projects. If day-to-day business dominates, technology leaps will be the first to fall by the wayside. At least now, the CEO has to restructure the R&D to prevent a failure of the company. more ….
Stretching of product development increases costs. Accelerating lowers costs, right? Wrong! more ….
The innovation timing determines the innovation success. Innovation projects fail when the environment goes unnoticed, when TTM takes first place instead of innovation quality, when planning ignores technical risks, when the right milestones are missing in early innovation phases, or when innovation teams fail to precisely meet quality milestones. more ….
The human-robot collaboration raises quality, increases sustainability and lowers unit costs. However, it is being introduced slowly, as the fears about jobs are slowing the process down, as the one-time costs of the first projects are too high, and as the responsibility has been delegated far too low. An enterprise-wide transformation through an innovation project overcomes these barriers. Therefore, human-robot collaboration is an entrepreneurial question for the CEO and CFO. more ….
Tricky process problems are often like diamonds: They are very sturdy and very expensive. In practice, they survive because conventional problem solving has failed both in product engineering and in production. The good, highly significant news: Using Statistical Engineering, tricky process problems can be solved quickly and sustainably. more ….
Does the transformation to an agile innovation system increase the company’s earnings? Can it adapt and survive in the face of external change? Or is agility essentially reserved for the start-ups of e-business? These are current entrepreneurial questions for managing directors, innovation managers and development managers. more ….
How is quality to be managed in the long term? Are there any unused sales and cost potentials in it? Can a realignment substantially increase the company’s earnings? These entrepreneurial questions can be posed by managing directors or quality managers in good times. more ….
A successful innovation project begins with an idea and ends in a resilient oligopoly. In between are uncertainties, inventions, complexity dynamics, financing dynamics, competitive dynamics and transition to the operative business. The innovation is similar to a pudding, which the innovation manager should nail to the wall. This is achieved if the four success factors for the constant innovation success are implemented in the innovation project. more ….
Competitive innovations must solve the dilemma of innovation risk and offer price. The risk of innovation is often uncertain or not calculated with probabilities. The offer price is more based on wishful thinking than on probabilities to win the competition. Both together cause the later overrun of the innovation budget. An innovation risk management solves the dilemma. more….