Notes on IT and NPD

I read a paper-Information Technology and New Product Development by Muammer Ozer-and summarized it using the diagram below.

Some Notes on Knowledge Management

Definition
"Knowledge is a fluid mix of framed experience, values, contextual information, and expert insight that provides a framework for evaluating  and incorporating new  experiences  and information. It
originates and is applied in the minds of knowers. In organizations, it often becomes  embedded not only in documents or repositories but also in organizational routines, processes, practices,  and norms." [1]

Explicit and tacit

Explicit knowledge is often referred to those that can be codified and transmitted in a systematic and formal representation or language.

Tacit knowledge is often referred to personal, context specific knowledge that is difficult to formalize,
record, articulate, or encode. Tacit knowledge can be converted to explicit knowledge (externalization).

Both kinds of knowledge can be embedded in a company's products and processes.

Categories of KM strategies[2]

Codification and Personalization

Codification relies on computers. The value of codification lies in its ability to create economics of reuse. The goal of such an approach is that of connecting people with reusable codified knowledge.

Personalization relies more on social networks that allow knowledge workers to share tacit knowledge. Personalization creates value by connecting people with relevant knowledge. 

NPD[2]

New Product Development is a knowledge-intensive activity.

Characteristics:

(1) Short product & process life cycle

(2) Cross functional collaboration

(3) Cross institutional collaboration

(4) Transient existence of teams & high turn over (loss of knowledge)

[1] Davenport, T. H. and L. Prusak (2000). Working Knowledge: How Organizations Manage What They Know, Harvard Business Press.

[2] Ramesh, B. and A. Tiwana (1999). "Supporting collaborative process knowledge management in new product development teams." Decis. Support Syst. 27(1-2): 213-235.

Complexity research from Nam Suh

Nam P. Suh gave "Complexity in Engineering" in the CIRP 2005 Annual, which seems to be an abstract of his book. The book cover is a nice picture of fractals. 

I enjoy the introduction part greatly as it has some interesting points and says a lot of things I want to say but cannot say that clear. There is no unified definition of complexity, because researchers just use this word to satisfy their 'immediate needs'. So he intended to create a fundamental and scientific approach to complexity.

There are two interesting points about which  I would like to read more literature. Firstly, he gave different goals of engineering, natural science and social science in dealing with complexity, and stated that the underlying principles of complexity are the same in different disciplines. Secondly, he classified complexity methods into two camps- the physical domain and the functional domain.

His complexity theory is based on his former work-axiomatic design (AD). He defined four types of complexity- real, imaginary, combinatorial and periodic- as shown in the figure I created below.

It has also been mentioned that the introduction of functional periodicity can reduce system complexity. But there is not much detail about this. I would like to read his book and learn more. 

Some questions to think about:

1. Is this method applicable to PD process instead of design?

2. How do we implement this in real life?

乱想

今天看了同学的婚纱照，看着看着忽然想：我什么时候才能结婚啊...想完就被自己这个想法吓了一小跳

I should develop a better research plan/schedule

My reviewer, who is also a nice professor, recommended the book <DRM: a Design Research Methodology> in the PLM conference.

I am now reading the framework of the methodology. It would be a good idea if I can develop a research plan/schedule based on this.
Stage1: RC-Research Clarification
Stage2: DS I- Descriptive Study I
Stage3: PS -Prescriptive Study
Stage4: DS II -Descriptive Study II

Some questions to be considered:
How do you define the success of this research?
How do you evaluate the outcome?
What is the existing situation and desired situation respectively?

About the definition of complexity

The term 'complexity' does not have a unified definition yet. I used to think that a clear and widely accepted definition is necessary to do research on complexity, so the first objective of my research is to understand complexity and try to give it a general definition covering most fields. But after doing some literature review I found it not easy. This term has been used so widely, and even 'abused' [1],  in numerous fields. Complexity in one field can be defined totally different from it is in another. For example, in evolution, Kauffman viewed complexity as “the consequence of attempting to optimize systems with increasingly many conflicting constraints among the components” [2], whereas in computer science, complexity is often referred as the time or space used in computation [3]. The term 'complexity' itself is quite complex in the literature.

Why is this? It can be seen from the literature review that the definitions vary with the research target and scope. Each researcher lives in his/her own context, sees his/her own target, thinks about his/her own objectives. As a result, definitions are mostly limited to some specific problems, which cannot be applied to other areas. So others will then define their own complexity.

Do we need a unified definition? I would say no. Sometimes it is good we have various definitions for various fields and situations. Really general definitions will become those in dictionaries - 'the features of a problem or situation that are difficult to understand', which cannot provide much useful information to a specific problem or situation.

But we do need a definition for our own research scope in order to understand the problem and provide a basis for the research. So actually what we should do first is to clarify the research scope and target. 'Complexity in PD' is still big and need to be reduced.

1.Vicsek, T., Complexity: The bigger picture. Nature, 2002. 418(6894): p. 131-131.
2. Kauffman, S.A., The Origins of Order: Self Organization and Selection in Evolution1993: Oxford University Press.
3. Hartmanis, J. and A.M. Society, Computational Complexity Theory1989: American Mathematical Society.

PLM and social network - some ideas from the 9th international conference on PLM

The 9th international conference on PLM was held in Montreal. From the presentations I attended, it seems that one trend for PLM is that it will become more and more 'social' in the future.

One professor from Germany asked an interesting question: can product have a facebook? He proposed to use facebook as a Product Avatar representation for intelligent product. He said a product can have a facebook profile instead of a fan page. The facebook functions were interpreted as PLM functions. For example, 'in a relationship to' means 'connected', 'married' means 'constantly connected' and 'it's complicated' means 'connection with problems'. It is pretty fun seeing a set-off shaft is' in a relationship' XD. It seems that a facebook profile of products help manufacturers and customers to manage the huge amount of variations and keep all information and records up-to-date.


The keynote speaker from Dassault System gave the speech on the topic 'From traditional PLM to social PLM'. They developed a '3D Experience' platform, including '3D SwYm' for online collaboration and social innovation.


A presenter from a German company proposed informal communication in PD because this would-as she put-'improve the teamwork inside companies'.


Social PLM offers the advantages of faster information sharing, more customer interaction, more opportunities of innovation, etc. But does this mitigate complexity in PD or the opposite? Firstly, more customization adds product variety thus management may need to be improved; secondly, more information, if not managed well, will make searching very difficult and time consuming, thus good mechanism is needed to manage all the knowledge. 

Notes about complexity - some theoretical background

We live in a boom period of both technology and communication, which provide us with more convenience and opportunities. However, what inherent in this boom is the inevitably increasing complexity in product development. More diverse demands from customers and advanced technologies drive enterprises to provide products with high variety. And the growing cooperation among organizations generates a more complex global network. Also, the environments, such as market, technology and policy, are changing more frequently because of the fast development. Great product variety, complex dependencies among organizations and unpredictable environment all contribute to continuously increasing complexity.
But are we prepared for this increasing complexity? A CEO study conducted by IBM in 2010 showed that Canadian CEOs anticipate much more complexity than they feel confident about handling. Seventy-eight percent expect the level of complexity to grow significantly over the next five years, but only 36 percent believe they know how to deal with it successfully. In this situation, it is really necessary to find some way to mitigate complexity.

It is pretty interesting to see complexity in the perspective of “pure science”. Until the early 20th century, classical mechanics, as first formulated by Newton and further developed by others, was seen as the foundation for science. Not only physics, but also other disciplines adopted the Newtonian worldview. So the traditional science is often referred to as “Newtonian science”.
One important principle in Newtonian science is reductionism, meaning that to understand any complex phenomenon, you need to take it apart, reduce it to its individual components. For example, people learnt about life by observing cells through microscope, learnt about some properties of water by analyzing the molecules. Despite the success, this approach is one-sided. We can ask questions like how these cells organize themselves into something that is alive, and how these atoms go together into a complex whole with new properties emerged. These cannot be answered by reductionism. An opposite idea “holism” was proposed in 1926, indicating that a whole is more than the sum of its parts. There are not only parts, but also interactions and relationships between parts in a system. And a holistic view should be adopted to understand complex systems.
Another implication of Newtonian science is determinism.  Because it was believed that the nature is governed by deterministic laws of cause and effect, so if you know the initial positions and velocities of the particles constituting a system together with the forces acting on those particles, then you can predict the further evolution of the system with complete certainty and accuracy. The famous quote of Einstein “I am convinced that God does not play dice” reflected the concept of determinism. However, at the beginning of 20th century, determinism was challenged by quantum mechanics, as it implied the unpredictable properties of particles. Also, chaos theory stated tiny differences in initial conditions   yield widely diverging outcomes for chaotic systems, making long-term prediction impossible. This is well known as the “butterfly effect”.
These developments such as holism and chaos theory are being integrated into complexity science. So we can see complexity science emphases wholeness, dependencies, uncertainty, etc. It shows a new way of thinking, a different perception of the laws of nature and changes the approaches required to understand the world.

The changes also happened in product development (PD). Research on PD has emphasized the dependencies between system parts, showing a holistic and systematic view; and much work has been done on handling the uncertainty, improving flexibility in PD, showing the realisation that the evolution of system is unpredictable. With this new way of thinking, many researchers have contributed to the field of PD to define, to measure complexity, and to propose strategies to manage it.
 As to the measurement of complexity, some considered complexity in multiple attributes such as structural, design, production and aggregate them to measure the total complexity. There are also researchers looking into functions. Some considered the number of functions and others also considered the level of functions in a functional hierarchy. Besides, many authors regarded uncertainty in information as equivalent to entropy in statistical mechanics, so they used entropy as a measure of complexity.
Many strategies have been introduced to mitigate complexity. One approach that has been successfully applied is scrum. Scrum is an agile software development method. It acknowledges that the development processes are incompletely defined. It uses more frequent inspection to perform a prompt adaption to the constantly changing environment. Some matrix methods were also used to manage complexity. These approaches provide insights into the relationships of components within or between complex systems, and thereby facilitate the reduction of uncertainty in PD. Matrices are graphs in another form, so graph theory here provides the mathematical basics for matrix method. Another way to reduce complexity is modularity. Modules  are  units  in  a  larger  system  that are structurally  independent  of one another,  but work together.  The system provides a framework that allows for both independence of structure and integration of function. Modularity reduces diversity in variants from design as well as from customers. Multi agent control was also applied to reduce complexity. A multiagent system (MAS) consists of a collection of individual agents. Each agent displays a certain amount of autonomy so multiagent systems were able to adapt to changing environments and more flexible.

To summarise, globalisation and fast-paced development of technology have caused a steady increase in complexity in products and thus in product development, which calls for measures to be taken to manage this complexity. In contrast to classical science, complexity science holds a different perception of the world; it emphases relationships and wholeness, and admits that there is uncertainty in a system. Complexity research in product development also adopts this new perspective, so much effort has been done to measure and manage complexity based on dependencies, network, uncertainty, etc.

However, I find many metrics and management method are mostly based on experience or intuition, without a rigorous validation.Some questions:
How to define complexity?
How to validate a management method?
How to measure the effectiveness of a method?

P.S.
The Mandelbrot fractal is so amazing. Mathematics is a beautiful language as well as delicate art.

A meeting

Today my supervisor talked with me for about 1.5-2 hrs. I feel dizzy. A lot of things to do in such limited time...

Some thoughts on science and Chinese traditional culture

It is fun and exiting to find some similarities between the precise science world and mysterious Chinese traditional culture.

Newtonian science believed that “God does not play dice with the universe”, meaning that nature is a deterministic system which can be predicted and traced back if the initial state is well known. It is like those problems in our high school examination papers-calculate the situation of a particle at some point of time, given its initial position, velocity together with the forces acting on it. Since we can break a system into particles (reductionism), we can forecast its further evolution  or reconstruct its earlier state. Thus Newtonian science believes everything is "planned", everything has its cause and will have its effect. It is called causality, the relationship between cause and effect.

This reminds me of the causality, or karma, in Buddhism. Chinese Buddhists believe in the relationship between cause and effect. Everything at present is the cause of the future, as well as the effect of the past.  All the creatures struggle painfully and endlessly in the karmic cycle of transmigration. They also believe that everything is determined because of the karma.

The development of quantum physics, chaos theory, system science and complexity science casts doubt on Newtonian science. There are uncertainty, like the state of a particle in quantum physics. There are relationships, meaning the whole is more than the sum of parts. Thus scientists realized that they should treat system as a whole instead of breaking it into increasingly small particles. Particles are homogeneous, but when being together in different ways, they could show various phenomena, that's why we have such beautiful world. And just by learning the particles we cannot explain the diversity at all.
This reminds me of the difference between the western medicine and Chinese medicine. In western medicine, doctors treat a patient's feet if he feels pain in feet, and treat his face if he has pimples on the face. Western medicine is really focused on "parts". In contrast, a doctor learning Chinese medicine will condition the entire body of the patient. So after some time, the patient not only recovers from the disease but also finds himself aglow with health. Not just the body, Chinese medicine even takes into consideration of the season, the time and the location. So we can see Chinese medicine actually regards the patient as a part of the entire system of the world, and adopts a systematic view when curing diseases. So can we say Chinese medicine also belongs to system science? :-D

Learning and thinking is so much fun! :)

The phD defense

I went to Keyvan's phD defense today. His research is on computerized interface control documents.

There were 3 internal examiners (1 supervisor included) all from our department, 1 external examiner coming from the department of architecture (there is another one who did not show up), the Dean of our department and a facilitator coming from the department of music. Some students were also there.

The defense started with a 20-minute presentation. He did well, but the presentation could have been more fun and appealing if he had added some pictures or colors instead of a lot of text. If it was me, I would just use a few words in large size to show the outline, and try to illustrate my work using graphs and charts.

Questions from committee followed. There were at least two rounds of questions (coz I left at the end of the second round). The order is External-> Internal (Peter)-> Dean-> Internal (my dynamics teacher) ->supervisor (with questions from the absent external).
Peter is very concerned about the application of the research in other areas. He asked whether this can be used in systems like engineering education, manufacturing system,  and mechanical components instead of electronic components. I remembered he, when reviewing my master thesis, also asked whether my research on value optimization can be used in other fields other than aerospace.
My dynamics teacher acted kind of aggressive as I see. His questions are more about concepts coz he did not have the same understanding of some words like "model", "interface" with the presenter. And he interrupted the presenter's explanation, like "Yeah, yeah, yeah I know that,...", to further ask his question. Keyvan told me it's because the professor did not read his thesis. He also told me that is fine and normal, the teacher did that to everyone and he's just asking questions. (The professor is probably very strict coz I remember I got a B from him. I always get A and I did well on dynamics..). One lesson should be learnt: Give clear definitions of basic concept, reach a consensus with audience on concepts.
When the presenter explained, the committee members often asked him to give examples. Thus another lesson is that you should always prepare and look for good examples to illustrate your theory.


It is a little bit sad that Keyvan is leaving the lab and research team. He is a nice gentleman and gave me suggestions on my thesis. Good wishes for him.


I am starting my phD soon. It will be a challenging and fun adventure.