Von Rolf Aang
Dass Software für Unternehmen eine erfolgskritische Bedeutung hat, gehört mittlerweile zum Allgemeingut. Vor zwanzig Jahren war dieser Gedanke – gerade im Hardware-Land Deutschland – neu. Er ist es eigentlich bis heute. Die – gemessen an der Marktkapitalisierung und z.T. an Umsätzen und Gewinnen – erfolgreichsten Unternehmen verdanken ihre Stellung dem Einsatz von Software über alle Unternehmensbereiche hinweg. Software ist integraler Bestandteil des Geschäftsmodell und der Strategie.
In ihrem Buch Innovation Explosion. Using Intellect and Software to Revolutionize Growth Strategies formulierten James Brian Quinn u.a. ihre zentralen Thesen:
We argue that … software (1) is becoming the critical component in the innovation process, (2) enables innovations that would otherwise not be possible, and (3) is often itself the central element of discovery and innovation. In the past, software has generally been regarded as an adjunct to research, development, engineering, and product and process design. The management of software has now become the crucial element in innovation management. And venture capitalists investments in software are growing apace; many of the most profitable recent initial public offerings have been software companies.
Zu dem Zeitpunkt war Amazon gerade ein Jahr alt, Google, facebook, Uber, Tesla, Airbnb oder Nvidia waren noch nicht gegründet.
Weitere Gründe für die erfolgskritische Bedeutung von Software:
Virtually any product- form insurance policies to yachts – can already be interactively custom-designed to meet the specific and varying needs of niched markets or individuals throughout the world. Software thus become the critical element in continual innovation for most enterprises today. For example:
In manufacturing, clothing designers no longer need to design their line in advance on a make-or-break basis. Instead, they can offer a series of suggested samples that salespeople show to potential buyers physically and electronically. Then, working with the buyer on an electronic pallet, the salesperson and the buyer jointly sketch out precisely what modifications the buyer wants. The pallet can be connected directly to the design unit at the clothing manufacturer’s plant, where professionals interact electronically with the retail buyer to detail and price the buyer’s exact desires. …
Through software, each business is becoming a connector and converter of worldwide knowledge sources to serve the needs of specific customer groups.
Aus dem Kapitel Tacit versus Specialist Knowledge:
In collaborative innovation, the greatest technical expert may not be as valuable as a very good specialist who is interested in a broad range of different phenomena and is able to relate to other people. Increasingly, human resources people are trying to find and nurture people with T-shaped skills: those deeply knowledgeable in a few disciplines (the vertical part of the T), and with broad interests and psychological capabilities that allow them to connect with other specialties (the horizontal part of the T). A key element in managing independent collaboration is nurtuing both aspects of the T. The two branches unfortunately have conflicting organizational imperatives. Deep knowledge requires concentration on specialized topics within peer groups. Connectedness requires wide-ranging interests and a profuse network of interactions with others. This takes time, conscious interacting with different people and skills, and a culture that rewards lateral participation.
Aus dem Kapitel Software as Inventor:
In large-scale systems, “genetic” and related learning algorithms and software can often identify patterns, optimize research protocols, and define potential solutions by trial and error much more efficiently that can direct physical experimentation or a preplanned sequence of hypothesis tests. Such programs can economically attack problems that were of unthinkable complexity a decade ago. In business applications, self-learning programs can identify developing problems or opportunities in the competitive environment, suggest the most likely causes and alternative solutions available, eliminate those of least promise, and pretest or implement promising new options – as key commonly do in telecommunications, switching, power distribution, vehicle routing, and ad campaign targeting and design.
Das war lange vor “Big Data”.
Aus dem Kapitel Sorting and Visualizing Data:
Combining objects or pictures with other symbol-based information intensifies and shortens learning cycles enormously. Use of visual software changes the entire collaboration experience. Interactions are no longer filtered through different perceptions. Any idea can be tested immediatley, directly, and rigorously. Agent software can bring considerations to bear that participants otherwise would not contemplate. Virtual laboratories can extend personal contacts and make equipment equally available to all, thus decreasing turf battles and misunderstandings. Interaction with images and data through simulations – as in virtual experimental, operations, emergency, product use, construction, or competitive environments – can integrate users into the design team more directly and intensively that any formal organizational diagram ever could.
Auch das einige Zeit vor der Verbreitung der Augmented Reality oder Digitaler Zwillinge.