Management of R&D - Reaction Paper No. 1

1. The Sociology of Science and Research

A. “Research and Development”

(Summary) Advances in scientific and engineering disciplines (i.e. specilization by dicipline) gave birth to new technological paradigms (e.g. mechanical paradigm which would lead to the Industrial Revolutoin) that would in turn become the basis of the industrial R&D laboratory. Initially, the first R&D laboratories developed mundane (but cost-saving and thus, profitable) applications but it soon became apparent that  technological opportunities are applicable to a wide-range of product markets and the establishment of R&D laboratories would spread to more firms, large and small, across sectors and business R&D  (i.e. specialization by corporate function) would grow to become a significant contributor to technological change and progress. As business R&D direction is largely guided by the prospect of commercial return, research of a more fundamental nature (those with no immediate application) would need to be supported and stimulated by the public sector. In all advanced countries, this is exactly what happened. It is the government that has become the primary source of funding of research activities in universities and similar institutions. The research activities in these institutions (i.e. specilization by institution) would become an important source of knowledge and experts to corporate R&D laboratories. Historically, it has been widely observed that the above pattern of R&D growth and economic modernization would come hand in hand. Systematic business investments  in product engineering, quality control and design activities would precede R&D. These activities would initially produce minor improvements but later would become the basis for significant innovations. At this stage, business R&D would increase rapidly (but focused on applied research) accompanied by advances in the underlying disciplines brought about by academic research (largely government-funded).

(Reaction) That technological innovations have been and will continue to be key in the transformation of human society is without question. Thoughout history, we have seen technological innovations dismantle the old order and usher in the new. In general, these transformation have been for the better; human society have been advancing forward. The question then is, how do we sustain this advance? Can we produce new innovations systematically and consistently? Pavitt presents a convincing blue-print (taken from the pages of history). The capitalists drive of businesses for greater profits can be used to promote a virtuous cycle of innovation. It simply needs to be seeded (and watered) and it will begin to pursue innovation on its own. The seed it requires is the existing body of knowledge and supply of experts. This is where the government must step in. Initially, through focused investments in higher and specialized education and later on through funding basic research in the universities and similar institutions.

B. “The Once and Future Industrial Research”

(Summary) The first industrial labs were born out of a specific need (e.g. discover new colors; protect an existing business / product). Perhaps owing to this narrow initial directive, the sudden expansion of scope of their activities (fueled by their initial success) and a centralized funding model, the labs would become disconnected from the rest of the company (as they tried to do more “cutting-edge research in a vacuum” which did not contribute to the bottom line). The growth of this gap would accelerate after World War II, as the Cold War was heating up, when the federal government provided unprecedented funding to universities and labs (mostly for basic research)  (though, it did help the labs grow faster as well). It would reach a tipping point when big (U.S.) companies (like IBM) found itself hemorrhaging billions in research with nothing to show for it. The wall between research and development and the rest of the company had to be brought down. To do this, the old model of centralized funding and linear development (i.e. “ideas from lab to development to manufacturing to market”) had to make way to the new model of decentralized funding (i.e. funding from business divisions) and dynamic interaction amoung the different organizational units. These allowed the researchers to “think more about market needs and realities and to understand how business and the real world works”. It enabled the lab to realign itself with strategic corporate interests (i.e. by focusing more on research that matters to the customers). Today, the best labs maintain a small core of basic research while the rest (of the funding and effort) go to research directed towards customer solutions. This is not to say that basic research is unimportant. On the contrary, labs protect this core for many reasons among them are: 1. to create a climate of discovery that attracts people to the lab; 2. to gain a deeper understanding of the processes underlying their products and 3. to make fundamental discoveries. Moreover, a new trend is arising in research. As the world around us grow ever more connected and technologies begin to converge, Central labs, where people from different disciplines can interact, are coming back to the fore but this time in an even more grander scale as we are not just talking about mixing researchers from different fields but mixing researchers with the rest of the company and company interacting with other companies globally in the hope of  producing innovations at ever-faster rate.

(Reaction) The similarities between the evolution of industrial research and computing are striking. Just as the initial model of industrial research was centralized and multi-purpose so was the initial computing model (i.e. centralized processing in mainframes). Then, there was a move towards decentralization and specialization. Desktop/personal computers replaced the mainframe as the primary mode of computing. On top of that, a variety of specialized computers (i.e. embedded systems) permeated every facet of moden life. And today, we are witnessing the synthesis of the two approaches in cloud computing (appears "centralized" and has the characteristics of centralized computing but is highly decentralized / distributed ) which parellels a new trend in industrial research, namely that of the networked, cross-functional and highly collaborative (virtual) groups or organizational units. Perhaps, this similaries reveal something  fundamental about the nature of both reseach and computing (or  a discipline or paradigm underlying both).

2. Evolution of R&D Management

A. "The Challenge of Fifth Generation R&D"

(Summary) To stay competitive,  an enterprise today needs to understand and adopt to five major forces transforming the global economy: 1. the shift from information to knowledge; 2. the shift from bureaucracies to networks; 3. the shift from training to learning; 4. shift from national to transnational and 5. the shift from competitive to collaborative strategies. Underlying these changes is the convergence of three interrelated domains: economic, behavioral and technological. As the scope of one broadens to include  core principles from the others, it becomes necessary for professionals to know and understand principles from the other domains to effectively perform their functions (i.e. acquire knowledge as opposed to just information; knowledge is information with meaning). To enable and facilitate this kind of knowledge flow and to be able to adopt to the new ever-changing reality, an enterprise therefore needs to move away from traditional monolithic hierarchical designs to  more flexible and agile organizational forms (i.e. from bureaucracy to networks). On top and complimentary to that, it must  foster a culture of continuous, real-time learning. Classroom-type training would no longer suffice. The continuous inflow of new knowledge no longer allows a passive orientation where the trainer drives the learning process. Rather, it requires an active perspective where the learner is at the heart of the process. Furthermore, enterprises can no longer rely on purely national approaches. To maximize returns, it becomes necessary for an enterprise to globalize (i.e. operate and formulate strategies in the international context) and collaborate (form strategic and symbiotic partnerships with other enterprises from the industry and the academe). The key to all these transformations is knowledge innovation. The challenge for technology leaders is then to “develop innovation systems that optimize knowledge flows” through networked,  collaborative organizational units. Business performance has to be measured not only in terms of financial but also intellectual assets and the “ability to create and apply new ideas in the market place”. Management must therefore monitor “knowledge flow” with as much rigor as they do with flow of capital, products and services and corporate business strategy must be integrated “in systematic ways that balance the economic, behavioral and technological factors of the enterprise”.

(Reaction) The development of the Internet paved the way to the Knowledge Age. It made all sorts of information accessible to people from various backgrounds facilitating interdisciplinary inquiries and blurring boundaries between disciplines leading to the exponential growth of knowledge. Dynamic, networked and collaborative are characteristics of the internet that enabled this knowledge-explosion. They must now be possesed by the knowledge-worker if he is to keep pace.

B.  "Money Isn’t Everything"

(Summary) Non-monetary factors may be the most important driver of a company's return on innovation investment. Spending more on R&D does not guarantee company success, growth or profitability (though spending too little hurts). Rather, it seems that the size of an organization, the quality of its innovation process and the degree of cross-functional collaboration are key. However, the belief that higher R&D spending translate to competiive advantage is widely held among corporations and nations alike. This may be a product of by-gone era when products and process are simpler, competition less and the different organizational units (R&D, manufacturing, marketing and sales) can work independently. But that era is in the past. Customers today are more demanding and the competition more fierce. Thus, there is a need for more effective innovation engines and a creative, analytical and disciplined management. A study, conducted on the world's top 1000 corparate R&D spenders, could not find a correlation between spending and a number of measures of financial returns (e.g. sales growth,  gross profit, etc.). It suggests that the "black box" approach to innovation, which assumes that there is no need to fully understand how R&D transform spending to results, is more likely to fail to deliver the expected performance. It also found that countrary to popular belief, it is the large incumbent and not the nimbler new comer that has the advantage. In short, size matters because of scale. Bigger companies can leverage R&D results better. Furthermore, the study found that financial returns depend on innovation effectiveness: how well a company generates, selects, develops and commercializes ideas. It is thus imperative to identify and  focus on priority areas where process improvement will have the greatest impact. Moreover, "successful innovation requires an exceptional cross-functional cooperation among R&D, marketing, sales, service and manufacturing." "The product or service can succeed only if all functions are integrated as a team and each function is doing its part to support the value proposition" It is therefore recomended for companies to: 1. align their innovation strategy with corporate strategy.; 2. focus on the right projects to maximize returns; 3. manage and support innovation effort; 4. establish an organizational structure and culture that promotes innovation.

(Reaction) It is not surprising that R&D spending does not guarantee financial success. After all, in any system, it is not only the input that determines the intended output. First, not all input go towards producing the intended output. In the process, some of the input gets wasted or becomes the unintended output. Second, a system can have several types of inputs. Some of which are more effeciency converted / used by the system than others (in producing the intented ouput). It is therefore neccesary to understand the nature of the system in order to know which input or combination of inputs would yield maximum returns (the optimal result).