- By Dr. Pini Yehezkeally and Eyal Steiner
- 1 Comments
The management world is facing a revolution in the next decade.
Despite major changes in the complexity of our lives and its dynamics, the basic principles of Management Theory – the way we understand and teach how organizations operate – did not undergo significant changes over the last century. The Fortune magazine clamed, in 2003, that Peter Drucker, who was born in the early twentieth century, is still the most influential thinker in the field of management (Tanz, 2003). However, one hundred years after the field of management was formulated as a profession and as a science research, it faces a real change. This change was imposed on him by other academic fields such as Mathematics and Engineering. We can describe it in one word: Networks!
The pioneers in “translating” organizations to networks were military and police intelligence agencies: minimal information is enough to get a complete picture of a rival organization. Subsequently, the method was eventually transferred and adopted in the business world.
Varied networks’ measurement capabilities have been developed only recently. The recent developments stem from two main reasons: First, technological improvements such as new generation of computers and systems that can handle the challenge of complex calculations. The second, while managers can make things look any way they want with an Excel spreadsheet and PowerPoint presentation, growing pressure from shareholders and high-end managers requires everyone to show real results.
Network is a visual way to describe “complex systems” – such as organizations or any other social structure. It consists of two elements: entities and the relationships that connect them. One of the important features of networks is that each of the entities has what we call “degrees of freedom”: it won’t necessarily behave as we expect. Therefore, networks can explain complexity and simplify it. It is easier to understand complexity when we see it in our own eyes (Baraba’si, 2002; Razi & Yehezkeally, 2012). The Internet era has given a huge boost for engaging in social networks and their research.
Networks’ research is called by mathematicians – Graph Theory (Rosen, 2012), and by the members of other disciplines – Network Theory (Baraba’si, 2002; Razi & Yehezkeally, 2012). “Translation” of a complex system to network makes many systems’ characteristics measurable. Not only approximate quantification of the outcome, but countable criteria: we can objectively calculate and give numerical indices to “soft” criteria, which thus far were immeasurable, like: power, influence and cooperation of individuals and departments, “bottlenecks”, etc. We can quantify the matching of the organization’s structure, culture and management to the challenges facing the organization. The indices are so precise, that they can distinguish between different levels of joint activities in organizations – for example, “cooperation” and “coordination” (van der Aalst & Song, 2004; Razi & Yehezkeally, 2012). The numerical scores give us the ability to establish measurable indicators to our employees, organizational units and the whole organization.
Business firms offer network mapping and analysis in various techniques. Two main methods are currently available: one – through direct questionnaires/surveys to employees; and the other – comes from military and police intelligence organizations – through signal intelligence, making use of e-mail, social network and telephones in order to map corporate networking. The latter method is more convenient to organizations’ managements, but seems to have strong ethical problems. While in practice it does not affect the privacy of employees, it generally discourages them from communicating freely and creates a sense of a “big brother” that monitors and examines everything they do. Regulatory restrictions in developed democratic countries also tend to limit the ability of managements to create information using the employees’ electronic communication. In both methods, the results are visible and different calculation algorithms can be applied to them.
The Networks revolution focuses on three elements, each worthy of its own academic research:
• Visualization: the eye can see organizational problems and dilemmas, some of which are difficult to locate from “dry” data.
• Measurement: we can measure “soft” elements in the organization, such as: power, influence, concentration, coherence, popularity, disconnection, collaboration, mediation and the like.
• Reflection: the network reflects the organization’s properties: if we correctly translate a complex system into a network, we will gain a lot of knowledge about it.
With those three conditions, the networks’ revolution is at our doorstep: the organizational network we receive and the numerical indices that measure the relationships within, gives us a sort of an X-Ray of the organization – an “objective” look at it. This X-Ray differs from the ordered hierarchical tree known to us from the classic organizational “tree diagram”. It’s a combination of all hierarchy and working relations, as well as the missing links in the organization. To map it, we must turn to all the organization employees and check their real relations with one another.
The best way to understand the potential of a network is to feel it. Allow us to illustrate this with three pictures depicting an Israeli bank network (before the organizational change called “Dynamic Banking” that took place in 2013).
Figure 1 depicts the hierarchy relations in the bank. Each employee is represented by a green rectangle, and hierarchy relations connecting them with orange lines (A strong relation – one that both sides mentioned and verified, was painted dark orange, while a weak relation was painted orange hollow, and indicates that this relation was only stated by one side). We can see that the division of authority and responsibility is perfect. This diagram is similar to the classic “tree diagram”, because there are no gaps in authority and responsibility (this is not the case in most other organizations). Such an organization is inherently a dictatorship restricted by strict bank regulations and its culture is most often characterized by tendency to closeness (i.e. lack of information flow between employees).
Figure 1: A Bank Hierarchy Interconnections
Figure 2 describes the state of work relations in the organization (painted blue) and demonstrates us the steep price of dictatorship. Rigid hierarchy suppresses the work relations, limiting them inside the departments. This is a problematic situation for the organization, because work relations are the organization’s immune system and its way to create lasting knowledge. They express its ability to be Agile (i.e., the level of its strategic flexibility to cope with rapid changes in the external environment), to understand the situation correctly in the beginning of crises and to find quick and effective solutions to any problem that rises.
Figure 2: A Bank Labor Interconnections
Figure 3 illustrates why this situation is problematic. It reflects the work relations that do not exist (so they are marked by dotted purple line), but the workers believe they are needed in order to be more effective. Dark purple colored ties are significant relationships: both sides sought to link them. Hollow purple dots were only requested by one party. It is easy to notice there are more missing work relations than existing relations! Unlike current relations, they cross departments.
Figure 3: A Bank Missing Interconnections
This illustration demonstrates the importance of one of the networks’ key features: the ability to produce knowledge (not “knowledge creativity” that are the result of special conditions) automatically by machines, in a way that the input is smaller and limited and the output is rich and meaningful.
Periodic measurement of organizations will show us that networks are dynamic and constantly evolving. Comparing the organization to itself in different periods will allow us to produce knowledge we cannot get through a single sample. Tracking the network can point out if we were successful in repairing the problems that we found in the network in the previous check and to track growing problems and dangers while they are still minor.
Identifying regular patterns in the network, can teach us the evolving trends that emerge in the organization, such as the formation of aristocracy, bottlenecks, and the like to the growth of talents and agents of change. In the future, it is safe to assume these patterns will enable us the ability to predict, with some certainty, the organization’s future behavior.
For example, police agencies worldwide have been successful in making use of networks for predicting the places and times where crime will occur in the future, particularly in the areas of outbreaks to businesses, homes and vehicles. It began in pointing the events on the maps to locate “hot spots” in which they can concentrate on. Then, they noticed the existence of crime patterns, which were the key to predict the future crime. According to the Economist (2013), the crime rate was declined in Los Angeles, which was using such methods to predict crimes, by 12% between 2010 and 2011, while in other districts that did not use the system, crime increased by 0.5%.
The future of management science lies within network research. The DNA-7 software allows us a glance into the future – easily viewing and measuring organizations, thereby giving managers, advisors and auditors a simple, quick and easy tool to find out strengths, weaknesses and different risks in the organization, enabling them to deal with the root of problems, stemming from lacking or redundant relations, rather than the symptoms.
- Baraba’si, A.-L. (2002). The New Science of Networks. USA: Perseus Books Group, ISBN 0-525-95160-1.
- Christensen, C. (2003). The Innovator’s Dilemma. New York: Harper Paperbacks.
- Razi, E., & Yehezkeally, P. (2012). Real Life Isn’t Linear. Glilot: Israel National Defense College Research Center, IDF.
- Rosen, K. H. (2012). Discrete mathematics and its applications. New York: McGraw-Hill, ISBN 978-0-07-338309-5. Retrieved from http://web.karabuk.edu.tr/hakankutucu/Discrete_Mathematics_and_Its_Applications_7th_Edition_Rosen.pdf
- Tanz, J. (2003, October 1). A Brief History of Management. Retrieved November 25, 2012, from CNNMoney: http://money.cnn.com/magazines/fsb/fsb_archive/2003/10/01/353427/index.htm,
- The Economist. (2013, July 20). Predictive policing – Don’t even think about it. Retrieved August 8, 2013, from The Economist: http://www.economist.com/news/briefing/21582042-it-getting-easier-foresee-wrongdoing-and-spot-likely-wrongdoers-dont-even-think-about-it
- van der Aalst, W. M., & Song, M. (2004). Mining Social Networks: Uncovering interaction. patterns in business processes. Retrieved November 25, 2012, from http://wwwis.win.tue.nl/~wvdaalst/publications/p233.pdf