Predictive Analytics

Predictive analytics is  hot.  Advances in hardware,statistics and business intelligence software  have made it usable and performant. Predictive analytics , as the name suggests helps gain business intelligence from data using various data mining, pattern recognition and probablistic algorithms. Consider the following example - Given the history of orders for a product , how likely is a customer belonging to a certain age group to buy a certain product ? The answer can be computed in several ways.

1.Find out clusters of customers who bought the product. Find out the distance of the cluster from this customer to indicate the likelihood of buying.
2.Use regression analysis.   Y = a1*X1 + a2*X2 .....
where Y  represents  revenue for a product and X1,X2,X3  could represent causal factors such as age,geography etc.a1,a2,a3 are the coefficients. If age is statistically signigicant, the coefficient will have a significant value. Once age is confirmed to be statistically significant, we could have multiple causal variables
for each age bracket and then find out which among those is the most significant.

Oracle offers predictive analytics at several layers. PL/SQL/JAVA  comes with an API for predictive analytics called  DBMS_PREDICTIVE_ANALYTICS . Oracle also has a product called RTD - Real time decision making that is bundled with OBIEE - Oracle business intelligence enterprise edition.
Other tools like crystall ball, excel add on for predictive analytics  and  Oracle data mining are some other tools in Oracle's arsenal . Oracle Demantra  provide predictive analytics related to forecasting and demand management. With IBM having acquired SPSS  , the industry's  landscape has become interesting.

Academic publications like "Competing on Analytics" by Harvard Press  and the recent survey published in

MIT's Sloan Management Review on BI trends have contributed to the hieghtened   interest and investment in this upcoming discipline. Companies like Netflix have grown to a billion dollar by predicting  consumer buying patterns based on  "clicks".

Correlation vs Causality

I was in Chennai a few years ago at a conference sitting atop the terrace of a restaurant with a few colleagues. The sales of cold drinks were at an all time high with everybody ordering coke ,beer etc.
At the same time, I noticed that there was a huge influx of patients at the hospital  near the restaurant .
So there must have been a positive correlation between the data for the sale of cold drinks and the data for the inflow of patients to the hospital , meaning as one went up or down the other went up or down too.
However does it mean that the  cold drinks caused people to get hospitalized ? Or vice versa - did people
drink because someone got hospitalized ?
None of the above was entirely correct in this situation. The reality was , the influx of people to the hospital and the sales of cold drinks were  caused by the sweltering heat of Chennai.. So if we were to forecast the sale of cold drinks ,  the causal factor would be  "temperature"  and not the "number of people admitted to the nearby hospital" . 
And this precisely  is one of the key  things to watch out for in analyzing the results of  regression analysis. While regression will give you correlation between 2 variables  , it may require an expert to confirm  if there is causality between the two.

The dangers of serial thinking

I bought a horse for 10$ and sold it to a friend for 20$. I bought the same horse from the same person for 30$ and sold it to the same person for 40$ . What was my profit ?

If you came up with 10$ as the answer  (20-10) + (20-30) + (40-30)  , you fell into the trap of serial thinking.

If you came up with 20$ as the answer , you are right because these two are separate transactions.

Transaction 1 - You bought and sold the horse. Profit = 20 - 10 = 10$
Transaction 2 - You bought and sold the horse. Profit = 40-30 = 10$

Total profit = 10 + 10 = 20$

If  you are financially savvy  , you would do

profit = total cash inflow - total cash outflow
         = (40 + 20) - ( 30 + 10)
        = 60 - 40
        = 20$

Beware  the dangers of serial thinking .
Start  thinking laterally.

The three best practices in software development

Michael Cusumano has documented the three most effective practices in software development  in his

book  "The Business of Software". These three practices have resulted in significant defect reductions in large companies.While  Six Sigma, Lean, CMMI and other methodologies can be applied to software and have been effective with varying degrees  , the following three practices have to be   implemented. All other methodologies and practices would be add-ons. So,  what are the three key practices ?.

1.Early prototyping -

Do a proof of concept by prototyping early and show it to the users.

Seeing is believing. When the users see something, they can spot obvious flaws and limitations.  

2.Reviews,reviews,reviews

We need reviews at each stage - High level design reviews ,detailed design review, code reviews, unit test cases reviews, system test reviews , project plan reviews etc. Review provides a negative feedback mechanism and hence stabilizes the output, to give an analogy of a closed look negative feedback control system.  In a control system with a negative feedback, the output Y = X * (G/1 + GH) .

X is the input, G is the gain/amplification/distortion  and H is the amount of feedback

In a  large project , G can be assumed to  be large and H close to 1 . Hence  GH  can be assumed to be much greater 

than 1. Thus Y = X * (G/GH) = X/H

Now if the review is 100% perfect , H =1 . Therefore,  Y =X .

Thus the output of the software stage equals the inputs which is the   specification to that stage.

In essence the output of the software development   will follow the specifications accurately if there are

rigorous reviews.

Linus Torvalds , the inventor of Linux said " Every bug appears shallow if a large number of eyes look at the code". How true ! No wonder why Linux is such a robust operating system despite being open source.

Millions of eyes look at each line of code each day  and suggest improvements.  So the next time you want to review code, call all developers in a room  and project their code . Let them all look at it and have fun dissecting it in a cordial  and friendly environment.

3.Daily regression tests and builds.

The code must be compiled and linked daily.

Automated regression tests must be run daily - the more the better and emails send to all concerned including

senior management when RT's fail. This ensures that as new features are added, existing features continue to work.

Bayes theorem

Bayes theorem is the foundation of Bayesian statistics and the new and emerging discipline of  predictive analytics. Reverend Bayes , a 17th century British mathematician wrote the Bayes theorem as a special case of probability theory. He did not disclose the theorem  fearing that it might not pass rigorous scientific scruitiny. The theorem was recovered after his death .
Here's the theorem and  a  few practical applications.

P(Ri/E) = p(E/Ri) p(Ri) / sum [p(E/Ri) * p(Ri)] i = 1 to n

p(Ri) is called anterior probability  of event Ri. It represents what we already know from past history about event Ri..E is the fresh new evidence that has arrived that would influence the event Ri.

p(Ri/E) is the probability of event Ri given the evidence E.
p(E/Ri) is the likelihood of the evidence itself.

As can be seen above as new evidence surfaces, the theorem lets us update our knowledge about the probability of occurrence of the event Ri. p(Ri) was our knowledge of the event based on its history,
while p(Ri/E) is our updated knowledge after taking the evidence E into consideration.

These probabilities can be deterministic or may represent a certain distribution.
Here are a few practical examples  where the theroem could be applied.
(An example similar to the first example below was  cited in a recent issue of Sloan Management Review).

1.I know the history of rainfall in my region for the past 10 years. I now have the evidence that this year
the temperatures were higher than normal. We know that higher temperatures correspond to higher rainfall
and follows a certain distribution. Using this evidence, we can predict the probability of rains this year  .
If I calculated probability based on history alone (which is what frequentists would do)  , I would be ignorning the key evidence that surfaced this year.

2. We know the history of deliveries of a certain supplier. A new evidence has arrived that the supplier's capacity is full due to a new contract they have signed with some customer. We know the relationship between the supplier's capacity and on time delivery . Given the evidence, we can find out the probability of on time delivery this month. If we had not used Bayes theorem, (and instead relied  on history alone as a frequentist would do)  the probability would have been based solely on the supplier's  historical performance and hence would have ignored the critical new evidence.

Bayesian theorem does have certain limitations/gotchas  in practical life as would be seen in future blogs.

System thinking

Most engineers , when given something to analyze  try to break it up into several parts and then  analyze each one of those parts  one by one. While there is merit to  analyzing a problem , it is important to first synthesize it. Synthesis is the opposite of analysis. It involves 3 simple steps.

1.What  system  is the  object under investigation a part of ?

2.What are the other  objects in that system ?

3.How do these other objects interact ?

Once these questions are answered , the engineer gets  the so called  holistic view of the problem under investigation. Thereafter the process of analysis of the object in question  may begin which will be described in  my subsequent posts.

The system is always greater than the sum of its parts because of the complex interactions between those parts. A car transports people - however none of its parts, for example, the engine,gearbox or frame can perform that function. Hence theorists posit  that systems have  emergent properties which means functions evolve as various parts of the system interact.

This underscores the need for multiple rounds of system testing in the software development cycle because each round is essentially an attempt to discover emergent properties of the system , not anticipated in the unit testing of its parts. People, process and technology are the three basic parts of a software system or any system for that matter. They can interact in at least 4 ways - people and process, process and technology,

people and technology and people,process and technology  - therefore system test case design should consider these interactions. Compounding the equation is that these interactions are most of the time non linear  (i.e output is not proportional to input)  and higher order meaning one needs to differentiate them multiple times wrt the independent variable to remove the effect of the independent variable. These interactions could be static - time indepenent or dynamic - dependent on time. These interactions could be location dependent or independent - e.g. geography of where people,process or technology are located.

The interactions may have a causal loop meaning you do not know what is the  cause and what is the effect - e.g  Is the technology poor because processes are poor or proceeses are poor because technology is so. Modelling such an effect is very difficult because it is not clear what the dependent and independent variables are. That's where a system  consultant's expertise comes into play - to apply judgement in situations where  modelling is infeasible.  

3 idiots and Five Point Someone

I finally managed to find some time to watch Aamir Khan's Bollywood movie '3 idiots' and read Chetan Bhagat's "five point someone" , the novel on which Aamir's movie is based. I am very impressed with Chetan's portrayal of life at the IIT's and the story's unusual plot . The protagonist - "Ryan" in the novel and "Rancho" in the movie carry a theme that resonated with me the most - "Follow your passion and success will follow you sooner or later" . Victor Menezes, former Chairman of Citigroup had once quoted the following in his speech to the students at Sloan. "Do a job that you like and you would not have to work throughout your life".
Of course following ones passion is easier said than done. There are constraints in practical life, there are sacrifices ,risks and costs associated with following your passions and more often than not pragmatic considerations triumph the pursuit of passion. Chetan represents the voice of the Indian youth aptly but would do well to avoid profanity in order to take his literature to the next level.Aamir on the other hand has replaced some of the profanity in the novel with some profound thoughts, events and music making the movie truely entertaining yet full of substance.

The 5 stage hierarchy

Russell Ackoff describes the 5 stages of the human mind using the data-information-knowledge-understanding-wisdom hierarchy in
• Ackoff, R. L., "From Data to Wisdom", Journal of Applied Systems Analysis, Volume 16,
1989 p 3-9.
The framework is powerful in a number of ways.
1.It helps evaluate the current state of a company's IT systems.
It turns out that a lot of IT systems are still in the information stage or halfway through the
knowledge stage.

Here's an example -
1.Data - Cost of goods sold is X million $ and average inventory for the period is say Y million $.
2.Information - Inventory turns is X/Y , say 5 .
3.Knowledge - Inventory turns are low compared to the industry averages for this sector.
4.Understanding - We exactly understand what parts are contributing to the low inventory
turns and why. We know the characteristics of those parts , their historical consumption rates and future demand.
5.Wisdom - We still need to hold on to these parts because manufacturers have already obsoleted them and our valued customers are likely to order them in the future. This might mean carrying inventory of slow moving parts but the decision is in the interest of our most profitable customers. It is therefore a wise policy to keep stock of obsolete parts although it might impact the inventory turns metric.

Data,information and knowledge are representations of events that have already happened.
While data and information are tangible , knowledge is not, although knowledge can be tacit or explicit. Data and information are "real" objects that can be stored, accessed , retrieved,copied,transmitted,received,destroyed. Tacit knowledge is the stickiest one and lies in the expert's head while data and information are either already public or can be accessed at least privately. Wisdom provides the behavior necessary to react to future events and includes the ethical and moral elements . Nation's policies represent wisdom (or the lack thereof) while data , information and knowledge govern processes. Understanding lies between knowledge and wisdom and is about "internalizing" knowledge. I know does not mean I understand. Knowledge is often theoretical while understanding is experential. Hence the Chinese proverb , "I read, I know. I do, I understand". No wonder why MIT , my alma mater is ranked one of the best engineering institutes in the world. It is the institute's philosphy of "mens et manus" which literally means hands and mind to reinforce both knowledge and understanding. Finally though, it turns out that highly knowledgeable people with great understanding of the system may be unwise.

Patent awarded

The United State Patent and Trademark Office recently awarded me and my co-inventors a patent on quantifying the output of credit research systems. The work was done when I worked at the Bank of America and all the co-inventors are my former colleagues/bosses at the Bank. Since all intellectual property filed on behalf of the Bank belongs to it (and rightfully so) , BoFA will hold all legal and financial rights to the patent. The invention would seem too late and too little given the magnitude of the global financial crisis that we witnessed , however I am glad that I could put probability and statistics to good use here.

Quantitative techniques for risk management

How can we quantify risks ? A deceptively simple question indeed.
The state of the art in risk management would lead one to the following approaches to
quantify risks.

1.The probabilistic approach
2.The statistical approach
3.The operations research approach
4.The aritifical intelligence approach

The probabilistic approach seeks to find the probability of each occurence and the payoff
associated with it. This leads us to a decision tree with the payoffs associated with each
outcome. The expected monetary value (EMV) is then found out for each outcome.
My experience in modelling real life problems is that it is very hard to find out the probability of each outcome. Nevertheless a probabilistic distribution can be inferred if there is
enough data. A more powerful technique is Bayesian modelling. A common scenario that
I have modelled is the following.
Given that a supplier has delivered the material 10 days late last month, what is the
probability that he will deliver on time this month ?
Bayesian statistics is a rapidly evolving branch and is the basis of forecasting engines such
as Demantra. At the heart of it though is the Bayes theorem. My current work
involves improving the accuracy and stability of forecasts by tuning the forecasting engine.
Quantification of credit risks for fixed income securities is another area where I have used
probabilistic techniques.

Statistical approaches rely on inferring based on data. Regression analysis is a common
technique. I have quantified marketing risks using JMP in the past.
MINITAB is another friendly tool for statistical analysis. It is not as heavy duty as SAS
but is powerful enough for many practical applications. Some of the scenarios where I have
used are finding the factors that lead to mutual fund redemptions . I know my cousin
used it to quantify the factors that lead to the risk of malnutrition - specifically fluorosis.

The OR based approaches typically formulate a linear/non linear model and try to solve it.
SOLVER is a powerful tool here. I have used it for the quantification of strategic supply chain
risks and what if scenarios. I have also used it to find out the amount of insurance required
for factories. The other powerful technique for risk quantification is Monte Carlo simulation.
Crystall Ball is a powerful tool here. I have used it to quantify project schedule risks,
budget risks, the probability of background processes getting delayed , compensation risks
and in post acquisition integrations.

Some of the AI approaches to risk quantification include neural networks, data mining and
fuzzy logic. Fuzzy logic , developed by Prof.Zadeh is used in consumer devices such as
washing machines. Fuzzy sets differ from regular sets in that they each member of a
fuzzy set has a degree of association with that set from 0 to 1 while regular sets either have
members (degree =1 ) or do not have them (degree = 0). I have read about fuzzy logic having been used to reduce the risk of overdoze of anesthesia administered to patients.
I tried fuzzy logic almost a decade ago but I think I should revisit that approach in the light of the business knowledge that I have gained over the years.
Neural networks are powerful but have been less prevalent in the industry.
The amount of data needed to train a neural network has been an issue .
Oracle's DARWIN is a powerful suite for data mining algorithms. There are tons of them. I have used them in the past to improve forecast accuracy where data was sparse.

Finally we should not disregard the quasi quantiative techniques such as FMEA-
failure mode and effects analysis. FMEA does not require one to be a mathematician.
On the other hand it takes into account the inputs from all stakeholders to quantify
risks. The outcome , therefore, is generally acceptable to all , although FMEA might
involve conducting several sessions (sometimes heated) with the stakeholders to ensure
convergence of opinions.
I have used FMEA in scenarios such as assessing project risks, evaluating design options,
enforcing KYC regulations in the banking sector etc. FMEA is a key component of the six sigma
methodology and is a technique developed during the second world war.

Characterizing terrorism
Vinay Deshmukh

Terrorism has emerged as one of the most dangerous hazards in recent times. The risk of terrorism carries with it the potential to destroy civilian life , disrupt businesses and the global economy and cause untold suffering to innocent citizens. This article attempts to characterize terrorism by taking a “systems” approach.

Adaptivity
First and foremost, the threat of terrorism is adaptive which means if you plug one part of the system , the other part is rendered vulnerable. Terrorists diverted their attention to the seas and the railways once they realized that airports and aircrafts became virtually impgrenable. With railway security having been tightened , it is likely that they may focus their attention on the other means of transport. The adaptive nature of terrorism implies that governments have to identify the soft spots in the system apriori and deploy adequate resources to mitigate the risk. Techniques like failure modes and effects analysis (FMEA) developed during the second world war could be utilized to identify such soft spots. FMEA attempts to enumerate various risks and prioritize them using what is called a risk priority number (RPN).

RPN = probability of occurrence * severity of impact * detectability

The probability, likelihood and detectability of a risk is assigned a value from say 1 to 5 ,
1 being least likely and 5 being most likely for probability. A value of 1 would imply negligible impact and 5 would imply total devastation. Similarly a value of 1 would imply low detectability and 5, a high detectability. If an event can be detected apriori, it is detectable. For example, a full scale attack on the country is often a detectable event (detectability=5)
while an guerilla attack is often undetectable (detectability). In the case of 11/26 , one would have assigned a probability of 5 (strictly speaking , probability can only take a value from 0 to 1.) , a severity of 5 and a detectability of 3 ; thus giving an RPN = 75. Detectability is directly related to the efficiency of the country’s intelligence.
A highly efficient intelligence ensures high detectability and thus helps the internal and external security agencies take this risk on the highest priority.
Also the probability of occurrence is directly related to the branding of the establishment being targeted. Well known Indian brands like TATA, RELIANCE, BIRLA or well known tourist brands such as The GATEWAY OF INDIA carry a greater risk of being attacked compared to little known brands. After all , terrorists perform a cost-destructivity analysis of their missions and only undertake those that are likely to provide maximum destruction.

Power Law
Power law is often used to characterize earthquakes. For every 1000 earthquakes of magnitude 4 on the Richter scale, there are a 100 of magnitude 5 and 10 of magnitude 6 and 1 of magnitude 7 on the same scale. If we observe the pattern of terrorist attacks in the last few years , power law becomes evident. The bombings carried out in multiple cities in India were analogous to 10 earthquakes of magnitude 6 and the attack of 11/26 was analogous to an earthquake of magnitude 7. A corollary to this law is often expressed as follows.
“We have not had a major attack for a while now. Therefore something must be on the anvil.”

Asymmetry
To be successful , security forces must win 100% of the time. However to be successful , terrorists need win only 1% of the time. Terrorists want to die , citizens want to live – that’s the fundamental asymmetry. Citizens create, terrorists destroy. The asymmetrical nature of the war against terrorism puts enormous strain on limited government and private resources. The keys to mitigating this asymmetry are prioritization and critical parameter management. While we discussed prioritization using RPN above , critical parameter management is another effective technique. All systems are overloaded today – be it the government, business, finance or technology. The key to managing them is identifying the critical parameters governing security in each of these systems and managing them. For example, the issue of cell phones , the sale or lease of property, the issue of credit cards are processes that need to be tightly controlled. The source, usage and purpose (SUP) of financial instruments such as credit cards need to be assessed rigorously. Financial institutions that fail to do so should be held responsible for not having adequate anti money laundering safeguards. One major obstacle to critical parameter management in India is the lack of a national identification number along the lines of a social security number in the US. In lieu of a national identification number, governmental/private agencies need to verify the individual’s criminal, credit and rental histories using every source of information.

Creativity (destructivity)
It sounds macabre but there is no denying that terrorists have a creative brain.
They have a battery of thinkers and an equally strong battery of doers. The attack of 11/26 was planned meticulously and executed precisely. They are adaptive because they are highly creative. Governments will have to employ professional risk consultants to forecast risk scenarios. The job of a risk consultant is to assess risks, prioritize and suggest ways to mitigate them. Risk consultants are equipped with techniques such as “what if simulation” , “mind mapping” , state of the art risk and process modeling techniques and above all some well known and time tested “heuristics”. Since terrorists often think out of the box , it is likely that the next attack may not be conventional but could be chemical, biological or nuclear. It is also likely that they might attempt to bleed India and other countries using what is called “death by a thousand wounds”. Such attempts could include attacks on the country’s IT networks or subtle cultural invasions. The other element of terrorist creativity is their impeccable sense of time and space. They choose their “time” and “space” such that people are caught by surprise while ensuring that their missions attract the most international attention. Hardly would anybody have expected an attack at 10 p.m on a working day on one of the busiest railway stations or one of the best hotels in the country. However since it was night time , they could sneak into lanes and alleys quite easily and ensure maximum devastation .

Support networks
Terrorists have a global support network which ensures that there is not a single point of failure. Indeed terrorists think and act like global multinationals . They think globally and act locally through their local henchmen . Their global network ensures continuous replenishment of their resources such as men, material, money, motivation and machinery.

In the ultimate analysis , it is important to take a holistic view of all systems operating within the country , identify the soft spots and mitigate them to deter future attacks. Professional risk consultants could be employed to assess and prioritize these risks and suggest actions that would mitigate them in close coordination with various agencies as well as the public.



The author , an alumnus of the MIT Sloan School of Management and the MIT School of Engineering and the Indian Institute of Technology,Roorkee has served several multinational companies at senior management levels.

Wrapping up

I am done with my studies at MIT.
The last two semesters have been hectic with dozens of courses,projects,thesis,assitantships
and internship.

I will be visiting Japan this month along with two fellow Sloanies and two professors to help solve strategic problems for an international airline. The trip is fully sponsored by the airline and we look forward to it.

Life will never be the same again without MIT and Sloan. It has been a wonderful learning experience from both theoretical and practical perspectives.

The democratization of information

Prof.Carl Malone of Sloan spoke on the democratization of information at the manufacturing conference at MIT-Sloan last month. With the internet reaching the nook and corner of the
world , information is now accessible to the common person across the globe at a reasonable cost.The implications of this phenomenon are profound and are given below.They are by no
means exhaustive .

1.Information asymetries are slowly going away.Simply put the "special" advantage that
certain sections of the society enjoyed by virtue of their access to information is fading.
Lewitt and Dubner explain this in their book 'Freakonomics' by citing the example of a real
estate agent whose privilege of special information is slowly declining.

2.The gulf between the haves and the have nots will slowly decline with the unfettered access to
information to the later sections of the society.

3.Teir 2 and tier 3 schools are improving their academic standards. In India , the IIX's
(IIT's,IIM's,IIFT etc.) have historically enjoyed special status - a lot of it had to do with the tight fisting of information (while fully acknowleding their superior academic standards). With students from tier2 and tier3 schools getting free access to the world's information , the information assymetries are going away.

4.Success in the corporate and academic worlds would lie in sharing information, not
suppressing it.As a corollary,power can be wielded by distributing information not by suppressing it.

5.The Indian government seems to have realized this phenomenon and has enacted a legislation called the right to information.Citizens just need to petition the district's information officer to solicit any kind of information.The idea might have been that if the government does not provide information,citizens would get it anyway through the internet and other modern media.

6.Both service providers and service beneficiaries will be better informed. A simple keyword search on google provides critical information.

However as with every phenomenon the democratization of information has limitations.
1.While information is available for free,wisdom comes through experience. Experiential knowledge is still not free especially to the weaker sections of the society.
2.The masses in general have profound wisdom and a robust common sense. It is likely that all the reserach available on the internet will not add much to the general wisdom of the masses.
3.Human expertise is indispensable.You still consult a doctor no matter how much information is out there on the internet.

Steve Balmer at the Harvard business school

Microsoft CEO,Steve Balmer spoke at HBS recently.He is an HBS undergrad and a Stanford
MBA dropout. The following are the highlights of his highly energetic and engaging presentation.

1.Microsoft's multi core strategy , the four cores being
A.Personal productivity software
B.Enterprise productivity software
C.Online world
D.Entertainment world

2.To be a successful leader in the hi tec world you need
A.Broad curiosity
B.Knowledge of technology and business
C.Empathy

3.Target costing for developing countries based on Mukesh Ambani's principle.
Mukesh happens to be his classmate at Stanford and also a dropout.
A.Figure out the selling price based on the market's willingness to pay.
B.Subtract from A the profit margins.
C.Arrive at target cost.
D.Ask your team to come up with products and services honoring the target cost.

4.Market segmentation for developing countries.
A.Top echelons of the society - Serve them the products that you would sell to all rich people in the world.The needs of the rich are the same globally.
B.Middle class - Localize the products.Give teams in developing countries the autonomy to
define and develop such products and features.
C.Lower class - Pay for use model. Similar to Unilever's pay for a drop model for shampoos. The idea here is to pay for each drop of information.

mailto:5.Hissteveb@microsoft.com

6.Microsoft's strategy is to not let anybody ride free. He admits zoon is not making money but is competing nevertheless with ipod.

Hofstede's cultural dimensions

I came across Prof.Geert Hofstede's cultural dimensions while reading a book on international management . He studied IBM employees from more than 70 countries between 1967 and 1973 . His web site http://www.geert-hofstede.com provides insights into various cultures across the world .

He used dimensions such as power distance , masculinity vs feminity , collectivism vs individualism , short term vs long term and finally uncertainty avoidance to measure various cultures . India and China are alike in their high power distance (defined as the distance between the highest and the lowest ranked individual in any institution) . The managerial implication of high power distance is that most employees want to become managers (also called manager mania) and that the technical ladders are not considered as lucrative .

Critics of Prof.Hofstede's work cite the following weaknesses though .

1.The survey was done long ago . Cultures have changed a lot since then due to rapid globalization.

2.A more accurate was of describing behaviours is through probabilistic distribution , mean and
standard deviation of the mean instead of an absolute number .

Despite the criticism , the web site should be quite useful to those who have the responsibility of managing individuals from various different cultural backgrounds .

Technology strategy

Lively class by Prof.Mary Tripsas , I would say . A lot of participation from the students .
Here is the summary of tools learnt in this class . The best part I believe is using these tools one can get a very good understanding of technolgies that one has no prior experience or knowledge about .
One often finds oneself confronted by new technologies , thrown at you with the expectation that you would master them quickly . These tools , I believe would help managers and architects deal with such situations .

Tech strategy – Summary of tools

1.Cash inflow analysis
2.Decision trees
3.Appropriability vs complementary assets
4.S curve- technology and diffusion .
5.Business models
6.Value chain
7.Network effects
8.Focus/broad vs low/plain
9. Role of IP
10.Modular vs vertically integrated product/enterprise .
11.Pie concept - Expanding the pie , expanding your share of the pie .
12.Porter’s 5 force analysis coupled with TEE’s analysis .
13.Price elasticity of demand .
14.Fixed vs variable cost and its impact to the business model .
15.Switching cost .
16.Economic vs industrial vs firm average
17.Standards

Organizing for innovative product development - Prof.Tom Allen

Very interesting class .
The approach is very different from what the run of the mill management consultants in the market take.
The professors approach stands out because of his highly scientific and analytical treatment of
an intrinsically subjective topic .
Some of the principles learnt are summarized below.
Principles-
1.Communication takes place for 3 reasons -coordination,information and inspiration .
2.Innovation brings together the market and thetechnology .
3.Time and coordination can be interchanged. Shorter the time (needed to complete a project) greater the coordination.
4.Remove specialists from their departments and assign to project teams for better innovation .
5.department vs. projects - departments are closer to technology , projects are closer to the customers. Power and glory may tend to shift to the departments since they are closer to the customers.
6.Rate of innovation vs. interdependencies in product structure determine how R&D should be organized. A third factor is project duration.
7.physical layout of the building should facilitate concentration and communication BOTH.
8.sources of ideas - internal - low quantity,high quality ideas. external - high quantity , low quality ideas. Still people consult external sources more often . Negative impedence at the boundary of the external source. High internal impedence.
9. Most information comes through personal contact.
10.Gatekeepers-Both high technical performers and high communicators.Beware -Visiting foreign engineers and scientists may not have local rapport and hence may not be goodgatekeepers in spite of their good foreign contacts . Instead a better method is to take a personal who has good local contactsand send him/her abroad for a while to develop foreign contacts.Other characteristics of gatekeepers are visibility andapproachability.
11.Dual ladder system - Do not make the technical ladder a dumping ground for failed managers.
12.Performance of project teams drops after 3 years .Keep the team young by rotating people.When managing an old team , be hard nosed .Give stiff targets to senior persons in the team.
13.Probability of email/phone communication is high if probability of face to face communication is high.We only email/phone those whom we talk to .
14.Beware of bandwidth limitations of emails and phones . This restricts the ability to convey complex information.
15.Vertical separation is disastrous for communication .
16.Visual contact between floors.
17.Science , technology and the customer linkage.

Product design and development - Prof Dan Whitney and Prof.Pat Hale

-We had the grande finale of the PDD class today . I thought all the teams did very well both in terms of content and presentation . Product design and development is about technique and temperament , BOTH.
Although I have been doing this for over a decade now , there is always something new to learn.
The best part I like about the class is a spirit of cooperation among competing teams creating a beautiful harmony all around . The concept of 'coopetition' , as taught by a management expert who conducted a workshop in the iap session this year , is a mix of 'cooperation' and 'competition' . The equivalent concept in Indian classical music is 'Jugalbandhi' . The way 'Jugalbandhi' works is as follows.
There are (at least) two artists on the stage . Each artist plays a note spontaneously and the other one is expected to repeat it using her own instruments or vocally . The notes get progressively difficult as the show progresses . There is thus competition among them but all of them are contributing to the harmony of the system and enjoying their own part .
Hear for example the maestros Balamurali Krishnan and Kishori Amonkar at
http://www.musicindiaonline.com/l/6/s/artist.2227/


The day ended on a positive note for our team because our product happened to generate
a lot of interest among the audience. Of course rave reviews do not necessarily imply revenues,
I thought it was certainly a morale booster for the team .

Let's see how far the product goes.

Some of the lessons learnt are summarized below .

1.Pugh's concept selection technique taught by Prof.Pat Hale is very useful in software .
We , software guys just don't seem to do it enough . It can be applied to user interface design,
data model design , backend design . Also as part of the concept selection we need to consider
out of the box alternatives like , can this be done manually or using an spreadsheet ? Can this be done mechanically , electrically , biologically or electronically instead of doing via software ?

2.Prof.Whitney was brilliant in his treatment of design for manufacturing and his outsourcing model . I actually applied DFM to the HP's case study by Prof.Mary Tripsas and was among the
three guys in the class who got the org structure right .
DFM is an 80's concept but continues to pervade the world of manufacturing and supply chain.

Also Prof.Whitney's knack of hitting the nail on its head instantly is amazing .
Some examples from the class are -

1.He asked us why we did not use a linear program in our software . Exactly the approach that
the big vendors take.
2.He remarked that if one of the teams had used finite element analysis in the weight of the product would have reduced by at least a third .

3.Another lesson is , to get your product to be successful you need to ensure that the following
elements are successful .
3.A Politics (internal - Buy in of colleagues and superiors and external - customer and government)
3.B Company's financials
3.C Company's marketing strategy
3.D Engineering -



Vinay