Once merely a dream, digital networks are now a rapidly maturing reality. These digital networks can expose unexpected behavioral properties of the individual actors. Combined as a swarm, networked businesses are able to produce exceptional or "smart" results they were not previously capable of generating. Companies make different linkages, combine different capabilities from many different parties, are more agile, and move positions faster. What are these "smart" business networks and why are they important? What should chief information officers and IT professionals do to help their companies succeed in a networked world?
In less than 10 years, Amazon moved from electronic book retailing to become the world's leading "e-tailer." Without stores and with limited inventory, Amazon possibly has more information on retail goods and their buyers and sellers than many other businesses. Amazon offers a business platform for the traditional retailer to "make a market" [9]. Within the Amazon business networks, the retailer can:
Thousands of electronic retailers join Amazon every month for all or some of these functions. At the same time many leave, or are rebuked by the Amazon system. Amazon facilitates product representation, regulatory compliance, risk management, and conflict resolution; it has quickly established a reputation for trustworthy transactions. EBay, with over 222 million registered buyers and sellers, has done the same for auctions. At the end of 2006, Skype had attracted 171 million registered users. In less than four years, Skype has surpassed anything traditional telephone service providers have ever achieved.
These companies offer platforms on which users can freely move and interact as long as the platform provider allows them. These platforms show a strong network effect: the more users, the more useful the network becomes, the more difficult it becomes to switch, and the less likely the user will move to another network. Albert László Barabási [1] recognized this by analyzing Internet traffic. He demonstrated that the Net is not democratic, and the number of links per node follows a so-called power law distribution. A few nodes have many links while many more other nodes have very few links. The node with many links attracts nodes with fewer links faster than the lesser-connected nodes. As the big get bigger, what options do the smaller actors have?
Consider the example of Kenny's Bookshop and Art Gallery, a family-run small business in Galway, Ireland (www.kennys.ie). It sources valuable secondhand books and sells them to interested collectors [5]. Established in 1940, Kenny's began focusing on its bookshop customers and then, in the 1970s, by mailing paper catalogs to libraries overseas. In 2003, Kenny's linked its library management system to the Online Computer Library Center (OCLC), a not-for-profit organization that owns the largest database of bibliographic records in the world. Kenny's was the first business to have a commercial arrangement with OCLC, which allowed it to provide a full electronic catalog on virtually any secondhand book highly efficiently and faster than anyone else. Kenny's reaped instant financial rewards since a cataloged secondhand book is usually valued four times higher than an uncataloged item. Through Bookrouter.com, Kenny's published its rapidly growing stock of now-cataloged secondhand books on multiple Internet sites such as Amazon, Alibris, and Bibliodirect, using their, or other logistics providers, physical delivery capabilities. In 2006, Kenny's bookshop went completely online and their shop in Galway is now just hosting the art gallery.
Kenny's could have remained a traditional bookshop with othersAmazon and eBaycapturing their business. They did the reverse. They became smart in their business network by capturing a valuable position and leveraging that position across as many links as they could.
Capturing and leveraging a position in a business network does not mean one must own, or control, the platforms on which those networks run. For example, TheBigWord company (www.thebigword.com) is able to serve the diverse translation needs of large companies worldwide by sharing a "translation memory" across a network of its clients and thousands of local "mother tongue" linguists [7]. It responds almost instantly to translation needssuch as for publishing on Web sites in many different languagesby posting the work to targeted groups of qualified translators, dividing and allocating the work, and managing the process in a way that is fully transparent to the client. TheBigWord rewards the translators by paying by the number of words they translate and by providing their administration and payment services. TheBigWord example demonstrates these business networks can respond with much more agility together than acting as an individual company. But what are other characteristics and what are the reasons why these new forms are starting to be developed and implemented now?
Organizations are moving, or must move, from today's relatively stable and slow-moving business networks to an open digital platform where business is conducted across a rapidly formed network with anyone, anywhere, anytime despite different business processes and computer systems. Table 1 provides an overview of the characteristics of the traditional and new business network approaches. The disadvantages and associated costs of the more traditional approaches are caused by the inability to provide relatively complex, bundled, and quickly delivered products and services. The potential of the new business network approach is to create these types of products and services with the help of combining business network insights with telecommunication capabilities.
The business is no longer a self-contained organization working together with closely coupled partners: it is a participant in a number of networks where it may lead or act together with others. The network includes additional layers of meaningfrom the ICT infrastructures to the interactions between businesses and individuals. Rather than viewing the business as a sequential chain of events (a value chain), actors in a smart business network seek linkages that are novel and different to create remarkable, "better than usual" results. "Smart" has a connotation with fashionable and distinguished but can also be somewhat short-lived. What is smart today will be considered common tomorrow. Smart is therefore a relative rather than an absolute term. Smartness means the network of cooperating businesses can create better results than other, less smart, business networks or other forms of business arrangement. To be smart in business is to be smarter than the competitors just as an athlete who is considered fast means faster than the other competitors.
Another way the new business network approach distinguishes itself is the way the network is orchestrated. In the sidebar "Multiasistencia: The Network Orchestrator," Busquets, Rodón, and Wareham introduce the Spanish Grupo Multiasistencia. They show how the smart business network approach with embedded business processes leads to substantial business advantages, demonstrating the importance of information sharing in the business network and the design and organizational dynamics of the infrastructure.
The pivotal question of smart business networks concerns the relationship between the strategy and structure of the business network on one hand and the underlying infrastructure on the other. As new technologies, such as RFID, allow networks of organizations almost complete insight into where their people, materials, suppliers and customers are at any point in time, the organizations are able to organize differently. But if all other players in the network space have that same insight, the result of the interactions may not be competitive. Therefore, a first critical step is to develop a profound understanding about the functioning of the business network.1
If two cars drive on the highway with enough distance between them they have no relationship other than to share the same roadway. If, however, these same cars get very close, they start behaving differently. If the first car brakes, the second car will brakebut with a delay. If the first car accelerates, the second car will also speed up; again, with a delay. The drivers of these two cars may not notice much more than the distance between them. But from a helicopter their behavior will appear as a wave moving along the flow of traffic. Each participant does not see the behavior of the network but responds to the local situation with his or her driving logic. The impact of the individual driver's actions in response to their specific situation and the road rules they follow creates a collective network behavior not seen nor understood by the individuals. Each driver acts on self-organizing driving logic according to the driving rules of the network.
The study of networked behavior beyond the familiar territories of business and ICT networksthose of social interactions, ant colonies, bees, and other biological systemsreveals attributes and characteristics that can be applied to the design of smart business networks. The behavior of the individual drivers, as described in the preceding example, demonstrates swarm intelligence: the emergence of seemingly intelligent or, perhaps, smart, behavior from many individuals [2]. Swarm intelligence studies collective behavior in self-organizing systems populated by simple individuals interacting locally with one another and with their environment without centralized control. However, in many cases, despite being unpredictable, such swarms are able to exhibit impressive capabilities for problem solving to, for example, seek food or respond to an unforeseen problem.
While these studies provide indicators for network dynamicsformation, change, decayand for the ways in which the individual intelligence of the network actors is combined, the research in social network analysis has made a significant contribution to a more profound understanding of network behavior. Social network researchers take into account the social relationships and ties of individuals and therefore the structure of the network. Building on social network analysis using complex systems theory Dan Braha and Yaneer Bar-Yam [3] examined the statistical properties of large-scale product development information networks for vehicle design. They find that such networks have properties (sparseness, small world, scaling regimes) like those of other biological, social, and technological networks. They demonstrate that the distribution of incoming communication links always has a cut-offtheir numbers are restrictedwhile the distribution of outgoing communication links is considered scale-free, meaning some nodes act as highly connected hubs. This would be consistent with Herbert Simon's bounded rationality argument that rational agents experience limits in formulating and solving complex problems and in processing (receiving, storing, retrieving, transmitting) information.
Braha and Bar-Yam found it seems easier to transmit information than to process information. Like individual human beings, a group of peopleor network of nodesis limited by an inability to digest an intense input of data. It seems that smartness could be related to the capability to organize the information flows within the business network as well as to the topological structure of the network.
Other researchers have shown the attractiveness and importance of certain positions in the network, that is, those nodes that are dominant and those that take subservient roles. For example, in 1992, Ronald Burt identified "bridging positions" where the network participants link through a focal actor who holds the bridge [4]. This structure brings information and control benefits (a central player) but also encourages the dependent actors to find alternative routes, for example, to disintermediate the bridger.
As the analysis is applied to larger and more complex networks, more advanced ways are required to analyze the structure of the network. In the sidebar "Network Horizon and Obtaining a Favorable Network Position," van Liere and Koppius use social network analysis and simulation techniques to explore the concept of the network horizon: the number of nodes an actor can "see" from a specific position in the network [12]. With a larger network horizon a company can take a more advantageous network position depending on the distribution of the network horizons across all actors and up to a certain saturation point. The results indicate the expansion of the network horizon will soon be a critical success factor for companies.
Most network scientists analyze the structure and dynamics of business networks independent of the technologies that enable the networks to perform. Instead, researchers tend to concentrate on what makes the network effective, the linked relationships between the actors, and how their intelligence is combined to reach the network's goals. Digital technologies play a fundamental role in today's networks. They have facilitated improvements and fundamental changes in the ways in which organizations and individuals interact and combine as well as revealing unexpected capabilities that create new markets and opportunities. One need only consider the rapid rise in digital social networks and massive online computer games such as Second Life. These are exhibiting capabilities that seem well beyond those of existing business networks. The next critical step is to develop a comprehensive understanding of the expected smartness of the business network.
The key characteristics of a smart business network are that it has the ability to "rapidly pick, plug, and play" business processes to configure rapidly to meet a specific objective, for example, to react to a customer order or an unexpected situation (such as dealing with emergencies) [11]. One might regard a smart business network as an expectant web of participants ready to jump into action (pick) and combine rapidly (plug) to meet the requirements of a specific situation (play). On completion, the participants are dispersed to "rest" while, perhaps, being active in other business networks or more traditional supply chains.
This combination of pick, plug, play, and disperse means the fundamental organizing capabilities for a smart business network are: the ability to quickly connect and disconnect with an actor; the selection and execution of business processes across the network; and establishing the decision rules and the embedded logic within the business network.
Quick connect requires that, as a result of an event the smart business network must seek and select those members who, together, can fulfill the required goal. This means the network logic acts on a menu of potential fulfillment partners to select those who can combine to produce the desired results. Once the appropriate participants are found and the connection has been established, the process of "play"performing the business transactioncan begin. Goldman, Nagel, and Preiss [6] described this in their discussion of virtual organizations. The capability of quickly connected plug-compatibility enables a superior response speed and greater component variety particularly for dealing with new requirements.
While the ability to quickly connect has received attention, the capability to quickly disconnect requires more. Members will join the business network and participate on the basis of risk and reward. While this can be clear while they are active, rules should be agreed upon for when the actors are disconnected (having completed a specific customer order or while they will no longer be a member of the network). Decision rules and logic with regard to connection and disconnection will be a crucial component for the success of the business network.
When selected, the network participants must be able to interoperate. They must be plugged together to enable the required network outcome. This means they must act with modularity: the decomposition of a system by grouping elements into a smaller number of subsystems with rules governing the architecture for mixing and matching these components.
The concept of modularity has a long history in product design and manufacturing enabling product construction of tailored products from standardized components: the combination of Lego-like modules that are combined in a specific way. Modularity brings the benefits of versatility (the diverse set of products that an organization can produce) and agility (the ability to respond quickly to fulfill an unpredictable customer order) while, at the same time, delivering within the boundaries of allowed value chain total costs and lead times. Martijn Hoogeweegen et al. developed a method to design modular business networks and to optimize the allocation of tasks in a business network based on modularity principles [8]. The network nodes will be considered black boxes providing the functionality required by the business network and are "played" according to the network rules. However, modular design requires much more coordination than non-modular: the greater the number of components the higher the organizing cost. A crucial decision is the degree of modularity or granularity of a system, or business network, and that is determined by the balance between coordination costs and the complexity of the network.
Each business network participant has specific capabilities captured in its business processes (own business logic) that it executes according to this logic. Traditionally, when such participants combine they create interfaces between capabilities: translating from one business logic to another and executing accordingly. This can be seen in the outsourcing phenomenon: carve out the total function of a particular business operation and hand it over to another party. As indicated earlier, traditional business network approaches lack the ability to rapidly pick, plug, and play to configure rapidly to meet a specific objective, for example, to react to a customer order or an unexpected event. Figure 1 presents part of a global business network. Its focus is on the actors and relationships from manufacturers via multi-modal transportation (road, train, seaship) to retailers. In most current business networks, companies are developing capabilities at the logistics layer and the transaction layer. As discussed in Table 1, these actors focus on their direct partners and are not able to have the end-to-end management of processes running across many different organizations in many different forms. The actor platforms are dominated by information silos residing either in different places within an organization as "islands," or in two or more different organizations. Individual actors are orchestrating processes in their part of the supply chain.
Figure 2 presents an example situation illustrating the use of the new business network approach. The central idea is that linking partners is on the basis of linking processes but allowing individual execution according to those processes: they act individually according to the joint rules of the network.
As shown in Figure 2, each of the smart business network participants becomes a "smart insect" in a goal-seeking swarm. The network separates process from execution. It shares the processes required to achieve its goals (the shared business logic) allowing each participant to execute in its own way according to this logic. This means that, to be a member of the network, an organization must be able to absorb the shared logic and execute accordingly. This is the "own business logic" of the network that can be enabled by a Networked Business Operating System (BOS). Based on the service-oriented architecture it resolves the problem of information silos by loose coupling of underlying systems, which are connected together in a business operating layer. This layer allows process execution and management "from a distance" from the underlying application systems. The enabling interorganizational technology architecture must reflect this loose coupling. Loose coupling is not synonymous with decentralized processes. It is quite the opposite, where the processes are more tightly coordinated because the rigidity of the IT architecture is no longer a constraint [10]. The business operating layer can become rather complex due to the fact that business logic is developed related to such issues as:
The concept of a BOS is analogous to that of the computer operating system. The invention of an operating system for the PC in the 1970s allowed application software to be run on different computers as long as they conform to the rules of the operating system. Implementation of a BOS enables the portability of business processes and facilitates the end-to-end management of processes running across many different organizations in many different forms. It coordinates the processes among the networked businesses and its logic is embedded in the systems used by these businesses.
For those recognizing the promise, or necessity, of capturing the capabilities of smart business networks to be proactive and effective in this largely unmapped territory of "being networked" the fundamental questions include:
Concerning the last question, we have defined a set of important trade-offs and related specific questions that must be answered with regard to the business network outcomes, execution, governance, design, and enabling technologies. Table 2 provides the trade-offs and critical questions.
We may believe that we are familiar with the networked world. However, in recent years our understanding has been disturbed by the rapid emergence of digital social networksa seeming swarm of consumers who are interacting in ways beyond that of most organizations. The pressures of global competition and the need for effectiveness and agility demand new ways to organize. Companies must develop and act "smart" in rapidly changing and expanding business networks enabled by today's pervasive communications technologies. Chief information officers and professionals must span the boundaries between their own organization and the growing networks in which their organizations operate. They must understand new vocabularies that are not necessarily technically or business oriented. Decision making in very large networks is fundamentally different from what we are used to. Understanding how federated activities emerge and operate is not a monolithic discipline and requires the CIO to adapt and adjust to changing conditions.
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4. Burt, R.S. Structural HolesThe Social Structure of Competition. Harvard University Press, Cambridge, MA, 1992.
5. Golden, W., Hughes, M., and Burke, H. Node to network: Partnerships in the secondhand book trade. In P. Vervest et al., Smart Business Networks, Springer, Heidelberg-New York, 2005.
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12. van Liere, D.W. Dynamics of Network Positions. RSM Erasmus University, ERIM Ph.D series, May 2007.
1See "Decision Making in Very Large Networks," by Peter J. Denning and Rick Hayes-Roth in the November 2006 issue of Communications for more detailed information.
This article is based on our work as discussed in the Smart Business Network Initiative (www.sbniweb.org).
Table 1. Characteristics of traditional and new business network approaches.
Table 2. Critical trade-offs and questions regarding smart business networks.
1. Busquets, J. Multiasistencia on the Internet (A), (B), and (C). Teaching Cases, DVD Multimedia and Teaching Note, European Case Clearing House, 2006; www.ecch.com.
2. Nohria, N. and Ghoshal, S. The Differentiated Network. Jossey-Bass, 1997.
3. Von Hippel, E. Democratizing Innovation. MIT Press, Cambridge, MA, 2005.
Figure. Multiasistencia business network actors.
1. Burt, R.S. Structural Holes: The Social Structure of Competition. Harvard University Press, Cambridge, MA, 1992.
2. Hoogeweegen, M.R., van Liere, D.W., Vervest, P.H.M., Hagdorn van der Meijden, L., and de Lepper, I. Strategizing for mass customization by playing the business networking game. Decision Support Systems 42, 3 (Mar. 2006), 14021412.
Figure. Bridging position and network horizon.
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