Difference between revisions of "Group 6"
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== Strategic management schools
== Strategic management schoolsmanagement schools: ]]
== Assembly ==
== Assembly ==
Revision as of 08:20, 7 June 2012
- 1 Introduction to the subject
- 2 Crisis
- 3 Prediction and monitoring
- 4 Examples, Lessons and Curiosities
- 5 Case Study
- 6 Group and Work Plan overview
- 7 Presentation
- 8 References
- 9 Literature
Introduction to the subject
How can enterprises use information to be prepared when a crisis comes?
Now-a-days enterprises are gifted with powerful technology, however, they are also damned with a fast-pace constant changing market. The whole market evolution and product cycle are faster, new technologies overcome old technologies almost in a monthly basis. It is very hard for a company to keep on the top of the edge, without falling into disaster.
Companies need then, to be able to understand the market, learn it and - most importantly - react to it. The extra mile on the path to organizational success might reside in the information we get from the market every moment of the day, the enterprise internal and external information, and in the analysis put on both of the information together.
If enterprises are able to capture the small changes that take place every day, record minimal evolutions in the market, and notice the competition moves, they could be always one step ahead of any crisis.
Our group is interested in analyzing how can enterprises be better prepared when a crisis comes, through the use information?
The Institute for Crisis Management defines crisis as:
- A significant business disruption that stimulates extensive news media coverage. The resulting public scrutiny will affect the organization’s normal operations and also could have a political, legal, financial and governmental impact on its business.
There are two types of Crisis. Sudden Crisis and Smoldering Crisis.
Sudden Crisis are circumstances that occur without warning and beyond an institution’s control, usually the company is not to blame for this.
This type of crisis is impossible to predict, the variables affecting it are out of scope, and modeling it is simply too complex.
Smoldering Crisis are “events that start out as small, internal problems within a firm, become public to stakeholders, and, over time, escalate to crisis status as a result of inattention by management." [James and Wooten, “  This type of crisis is possible to predict/anticipate, the variables affecting it are manageable although it is still very complicated.
As shown in the graphs with data from The Institute for Crisis Management , smoldering crisis are a huge threat for organizations. Normally, organizations have contingency plans, crisis management plans and/or other mechanisms to prevent organizational collapse during and after a crisis. Before a crisis starts to happen is almost impossible to predict it. However, it is possible to identify the crisis in a very early stage, being easy to mitigate the threat. This is the goal of our presentation.
Smoldering crisis classification 
|Level 1||Level 2||Level 3||Level 4|
|Description||An internal business problem or disruption that can be dealt with and resolved by management responsible for responding to this kind of situation.||An internal problem that can be managed by those who are responsible for this area of business, with support from other management or employees who may have to be brought in to assess the situation and help resolve it.||An internal problem that has the potential of going “public” via the news media and generating negative reactions from government officials, plaintiff’s attorneys, competitors, investors consumer activists, labor unions, etc. The crisis can still be contained but will require specialized assistance beyond the management capabilities in place to deal with normal business problems. This assistance may be from corporate headquarters, outside legal counsel, and/or consultants who specialize in resolving this kind of problem.||The situation is very serious and is likely to be disclosed publicly in the very near future. The public reaction will have a significant adverse impact on the business for a period of weeks or months and top management along with numerous employees and outside consultants will have be diverted from their normal activities to resolve this situation. The financial impact will be substantial and will have a direct and indirect effect on operating results.|
|Example||Employees of an organization are upset with bad work conditions. They talk with their boss to find a solution for this problem.||Employees decided to boycott their work temporarily until their problem is solved. Their boss and other higher management discuss the problem with them.||Employees decide to stand in demonstration to achieve what they asked for. They also decide to send a letter to their syndicate to find a resolution for their problems.||Employees are resisted and don’t have the chance to manifest dissatisfaction. However, they decided manifest. There’s high friction between them and the management, it draws the attention of media which makes the whole story public. Sales go down.|
Prediction and monitoring
Data Mining: A Definition
Data Mining is knowledge discovery in databases. It stands for the extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases.
Alternative names for data mining are: Knowledge discovery(mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, business intelligence, etc.
Purpose of Socio-Economic Data Mining
Many crises in techno-socio-economic-environmental systems are caused by random coincidences or overcritical perturbations, which trigger cascade failures (also known as domino or avalanche effects) in such a way that the impact of random local events or perturbations becomes systemic in size. The sensitivity of the system often results from the occurence of instabilities, which create fertile ground for so called regime shifts. Such regime shifts happen at so called critical points (“tipping points”). Interestingly, when a system gets close to a tipping point, it is often characterized by
- slow relaxation (recovery) from perturbations,
- increasing auto-correlations, and
- critical fluctuations (a large variability).
Therefore, these features can serve as advance warning signs. Since they can be determined from empirical data, massive data mining will be able to increase the level of awareness of upcoming crises and to trigger early preparations in order to avoid or mitigate them.
Data Mining can have the following purposes:
- It can reduce serious gaps in our knowledge and understanding of techno-social- economic-environmental systems.
- Crises Observatories (analyzing and mapping financial and economic stability, conflicts, the spreading of diseases...) could predict crises or identify systemic weaknesses, and help to avoid or mitigate impacts of crises.
- Real-time sensing and data collection (“reality mining” of weather data, environmental data, cooperativeness, compliance, trust, ...) could reduce mistakes and delays in decision making, which often cause an inaccurate or unstable system management.
Advanced Data Mining techniques have not been extensively applied to anticipate and fight systemic crises in the past. They certainly promise better solutions for the future, supporting crises containment and the detection of feedback loops and possible cascading effects, before they cause wide-spread damage. They should be an integrative part of new ICT concepts for an adaptive risk management, facilitating and supporting a better disaster preparedness and response management.
Socio-economic Data Mining is interesting for governmental, non-profit and commercial organization. It can help them to observe various internal as well as external variables. For example, it can facilitate the prediction of markets to estimate future economic developments, outcomes of elections, fashions, the spreading of diseases, and socio-economic trends. These areas are now becoming an own business branch, complementing classical consultancy, offering services like: real-time measurement of actual user activity, identification of trendsetters, opinion leaders, and innovators in social networks, trend prediction, trend tracking, etc.
source: Helbing, D., & Balietti, S. (2011). From social data mining to forecasting socio-economic crises. The European Physical Journal, 195, 3-68.
Sorts of Data and its Sources
Generally, one can distinguish between internal and external data; Internal data of a company can be all kind of data concerning transactions and communications with clients and customers (e.g. purchases, payments, bank/credit card transactions, website usage, emails, customer feedback) as well as data concerning past organizational behavior and performance (e.g. employee information, expenses/earnings, travel data, resource use, etc.). External data include all data concerning processes outside the organization, which, however, my be highly relevant to the wellbeing and further development of the organization. Below are some examples of different (mostly open) data sources for different categories/sorts of data available on the Internet:
- Internet Archive, Digital Encyclopedia and Public Libraries
- e.g. Internet Archive/Wayback Machine (www.archive.org); Wikipedia; The Knowledge Centers (searchengineshowdown.com); whenago.com; World Digital Library; Books Ngram Viewer (grams.googlelabs.com);
- Lexus Nexus; Factiva; Projekt Guternberg
- Social Networks and Blogs
- Twiiter; Facebook; Linked In
- Information Retrieval Engines
- freebase.com; wolframalpha.com
- Text Mining on the Web
- Google Trends; Google Flu Trends; theobservatorium.eu; wefeelfine.org; cyberemotions.eu
- Social Data Sharing
- linkeddata.org; Dataverse Network Project; thedata.org; data360.org
- Conflict Data
- CSCW Data on armed Conflicts; War Views; acleddata.com
- Data in Economics and Finance
- bloomberg.com; Unctad Statistics; EUROSTAT; World Input Output Database
- Scientific Collaboration Data
- e.g. ISI Web of Knowledge; Google Scholar; Scopus; World Value Survey; Gapminder Data
- Urban Data
- Global Urban Observatory Database; Urban Observatory; Urban Audit
- Traffic Data
- NGSIM; Tafficdata.info
- Open Maps
- Google Maps; Open Street Maps
- Health Data
- World Health Organization
- Climate and Environmental Data
- e.g. PSD Climate and Weather Data; Footprint Network; Buienradar
- International Energy Agency
From Data to Knowledge
- Internal (operational & transactional data, customer data) vs
- external (web, news media, social media, metadata, web archive, science databases, etc.)
- Pattern, associations, relationships among data can provide information
- Information can be converted into knowledge about historical patterns and future trends
- Descriptive Analysis
- finding distinctive features in existing data
- Predictive Analysis
- deriving trends from data
- interest in temporal patterns, rules, dependencies, regularities
- Statistical Methods
- Neural Networks
- Machine Learning
A crisis is described by a number of key aspects. See also: From Crisis Prone to Crisis Prepared: A Framework for Crisis Management
- High magnitude
- Require immediate attention
- Element of surprise
- Need to take action
- Out of a company's direct control
The element of surprise is important in relation to prediction. If something is predictable it means that there is no element of surprise. The question leading from this: can you predict a crisis? It sounds contrary since a crisis is a situation that is unexpected. Black swans are even more unpredictable since they are sudden and unexpected. Greater element of surprise. What we possibly can predict is certain future changes in an environment that might if no action are taken might evolve into a crisis. The degree to which we can predict future events in an environment depends on the properties of an environment. The environment a company operates in dictates which management school principles might work. The more uncertain an environment the more the adaptive school will prevail, since the future is uncertain, the environment to complex and uncertain to understand. Adaptive school lends to a mode of monitoring and reacting effectively on important changes in the environment. An environment that is more stable, more linear with less unexpected events over time lends itself to prediction based on the understanding of variables in the environment. In these environments planning school could be useful to plan strategically decisions. Transformative and Control are relatively the same as Adaptive and Transformative although their theory underlines the importance a company has on the environment variables. Globalization, trends towards more efficiency and increased integration however lead to a more complex world, increasing variable dependency.
The level of prediction and the management mode are dependent on the environment a company operates in. Understanding and collecting important environment variables is important for any management mode whether used for prediction or for decisions. Adaptive approches are more likely to gain popularity because of the increase of complexity and non linearity in environments.
Extra information on prediction efforts from big datasets: http://www.springerlink.com/content/k073277jm476074t/
Examples, Lessons and Curiosities
Crisis related examples can be found in the page Crisis: Examples, Lessons and Curiosities.
The overview of our case study, the questionnaire and other info can be found at Case study Group 6.
Group and Work Plan overview
From the beginning of the Information Management and Organizational Change (IMOC) course we were informed that we have to do a group assignment related to the Adaptive Cycle and the general content of the course.
Our group is formed by the following students:
The two last mentioned members of our group had to be away for personal issues, therefore they were informed for the progress of the group’s work via emails and Skype communication and they were actively participating by distance.
To start with, several discussions took place from February, to find an appropriate topic for our group assignment. Most of the time, discussions were taking place at the library of the University of Amsterdam, located on Singel street.
Difficulties have arisen from day one, since all of the group members had their own idea about the general topic of the assignment. After 2-3 meetings with the group, we concluded that it would be good to investigate the general idea of “How a black swan can be predicted”.
Some members of the group expressed their opinion that a “Black Swan” cannot be predicted – and in some extent this is true- therefore we changed our direction to “How can companies predict when a crisis will take place in order for them to be prepared”.
In the context of this topic, the following fields had to be discussed to come up with conclusions:
Crisis (in general)
Different kinds of crisis can occur, therefore we have to know about them in order to understand them.
In order to analyse, we tried to answer to the most basic questions about crisis. Some of them:
- What patterns can be detected in the crisis process?
- Which variables are connected to the crisis?
- How can a crisis be identified looking to the data?
- Which types of crisis should an organization look for?
Crisis is a complex issue. It deals with an extremely vast set of variables making it very difficult, or impossible, to model. Therefore we considered we should have a look at the importance of chaos theory in the crisis context.
Organizations have at their disposal an huge amount of internal, and external data. Some of this data is actually being store everyday, but not analyzed. Usually, when a crisis starts to grow it creates variances that can identified looking at the data sets. This variables could be anything from news articles to the temperature in the engine room.
Extracting all of this data by it self it's not enough, it needs to be analyzed, processed, correlated and so on. The answer lies in Data Mining.
Data mining is the analysis of raw data to find patterns and get information out of them. By adding meaning to the information that you extract from data mining techniques, you get knowledge which is the main point of the data analysis!
In general, everything was a “team work”, since we did not divide the different parts of our research individually. Several articles were read for the above mentioned fields from all of us, and every time we had information to share related to the matter we were exchanging opinions via email and Skype. Furthermore, Google docs and Drop Box folders were created, to share the knowledge and the articles.
Last but not least, we had to do a case study in an industrial environment to see the practical applications of our how our topic.
Thanks to Hamid Nasiri, who works at IKEA, we arranged a meeting with the store manager to discuss crisis-related issues for IKEA.
A questionnaire for the case study on an industrial environment was prepared in advance.
Finally, we present our findings in class and in the presentation we included some examples of crisis in companies and some examples of how to a crisis can be predicted (such as the Google flu).
The handouts for the IMOC Group 6 presentation on "Predicting a Crisis" are available here:
- Institute for Crisis Management - Crisis Definition, http://www.crisisexperts.com/crisisdef_main.htm
- Leadership as (Un)usual (2007) [142–143]. Leadership Review, Kravis Leadership Institute, Claremont McKenna College, Vol. 7
- Institute for Crisis Management - Crisis Definition, http://www.crisisexperts.com/crisisdef_main.htm