Group 6 Work Plan
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, Group 6, 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.
Work Plan Evolution
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?
- What is the influence of the environment?
- 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 situations often are rooted in complex situations. Complex environments have to deal with an extremely vast set of intertwined variables making it very difficult, or impossible, to model and predict possible future happenings. In weather forecasting we are able to see when a storm is coming, however we cant predict where it will form. The chain of events that a single variable change might start cant be foreseen. Environments differ in the amount of complexity it holds. To investigate the relation between environment, complexity and crisis situation we decided we should take a look at the importance of chaos theory in the context of a crisis. Especially in relation to data, variables and complex environments chaos theory proved to be an important aspect to consider in our research.
- "...beyond two or three days the worlds best forecasts were speculative, and beyond six or seven days they were worthless. ...any prediction deteriorates rapidly. Errors and and uncertainties multiply, cascading upward through a chain of turbulent features, from dust devils and squalls up to continent-size eddies that only satellites can see."
Keeping in mind these extreme fluctuations in outcome based on small input fluctuations stresses the importance of the accuracy and timeliness of input data. The following example shows the fluctuations and uncertainties in the model even when every sixty miles apart variables are being recorded.
- "The modern weather models work with a grid of points on the order of sixty miles apart, and even so, some starting data has to be guessed"
To further explain the impact of small changes in complex environments consider the following example.
- "But suppose the earth could be covered with sensors spaced one foot apart... ...suppose every sensor gives perfectly accurate readings... ...Precisely at noon an infinitely powerful computer takes all the data and calculates what will happen at each point at 12:01, then 12:02, then 12:03... ...At noon the spaces between the sensors will hide fluctuations that the computer will not know about, tiny deviations from the average. By 12:01, those fluctuations will already have created small errors one foot away. Soon the errors will have multiplied to a ten foot scale, and so on up to the size of the globe"
Increased amount of data and processing power do tend to deliver more accurate forecasts. The increase of digital data available and out technological development will enable us to forecast more accurately in complex environments. The effect of chaos theory in complex systems on possible forecasting is something to keep in mind. Therefor it is highly important to our research.
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).