ThinkOR - Think Operations Research

7.2% raise for 1,000 best paid Ontario public sector employees

2013-04-06T02:42:00.000+01:00

The top 1,000 employees with the highest package (salary + taxable benefits) in the Ontario Public Sector Salary Disclosure, the so-called “Sunshine List”, saw an average increase of almost $25,000 in 2012 compared to the previous year, an increase of 7.2%, much higher than the bottom half of the 80,000-strong list which saw an increase of only 2.2%.

Is this cause for alarm? Highly paid CEO's are fully in the public spotlight, and the many many school principals have their pay closely monitored, but what about the highly paid individuals near, but not at the top? The data shows that for them, 2012 was a good year.

Every year since 1996, the Ontario Ministry of Finance has released a list of all public sector employees who earned more than $100,000 in the previous year.

Oversight

We can all see that “Sunshine List” champion Thomas Mitchell, President & CEO of Ontario Power Generation took a pay cut this year, but with approaching 100,000 names on the list, more sophisticated, data-drive oversight is possible.

Government-friendly observes point out that the average salary on the list has decreased, just like last year, but that is a red herring. Anyone can add over 9,000 people earning just over $100k to a list with an average salary of $129k and bring down the average. As the list continues to grow from the bottom, we can expect the average salary to decline, without this being any indicator of public fiscal discipline.

Opposition partisans will lament the increasing growth of the list, 9,000 more this year and 7,500 the year before. This is again misleading. The pyramid shape of any organisation tells us that there are more people as you move down the salary brackets. With a perfectly reasonable average salary growth at just over 2.5%, 9,600 employees graduated to the “Sunshine List” this year after having earned around $98k last year. Probably more than 9,600 employees, currently earning around $98k will be new additions to the list next year, and more the year after. Inflation and economic growth will ensure that the list grows, and the pyramid shape will ensure that it grows faster.

Top 1,000

So who are these lucky 1,000 who on average made 7.2% more in 2012?

This year the top 1000 best packages on the list included:

583 individuals working in hospitals
- 176 Pathologists
- 50 Chief Executive Officers
- 66 Vice-Presidents (Senior, Executive, etc.)
- 79 Psychiatrists
86 employees in electricity
- 56 Vice-Presidents (Senior, Executive, etc.)
144 working at Universities
- 100 Professors

Big raises

Of the 1,000, 737 can be matched exactly by name and organisation type to last year. 92 of those fortunate souls saw an increase of over 25%! At the top of the pack was Mohamed Abelaziz Elbestawi, Vice-President Research/Professor at McMaster University who was reported as paid salary $266k in 2011 and $506k in 2012! Trung Kien Mai, a Pathologist at The Ottawa Hospital saw his paid salary move from $306k in 2011 to $515k in 2012!

Of those 92 with big raises:

83 work in hospitals
- 50 are Pathologists

More questions

At this point, this analysis raises more questions than it answers, but that is to be expected from an analysis of this salary disclosure data. The Public Salary Disclosure Act can help us find questions, not answers. What we do know is that:

Salaries near the top grew substantially
Those salaries grew much more, even on a % basis than those at the bottom
Growth was higher than expected given slow economic growth
Some individuals can be shown to have experienced extraordinary raises
Pathologists do well, and 2012 was a particularly good year for some

Source: http://www.fin.gov.on.ca/en/publications/salarydisclosure/pssd/

Timberland customer care & operations - I approve!

2013-04-06T02:28:00.000+01:00

Buying a brand is buying quality - that's especially true for outdoor equipment.

With this belief, I purchased a pair of Timberland hiking boots that said "Waterproof" on a piece of official-looking metal attached to them. I then ended up with wet feet during an 8-day trek in Patagonia where it often rains - that sucked.

With my toes literally swimming in water within the boots, after a soppy wet day of a 19km hike, I was not a happy camper. However, my perception of Timberland took an 180 degree turn for the better.

Having bought the boots in southern Chile in a Bata store, having used them extensively and been disappointed and upset by them, I ran into a Timberland brand store 2,500km away from where I bought them, still in Chile. I went and complained about my disappointment in these supposedly "waterproof" boots, and I was offered the chance to exchange them for a brand new pair that is indeed waterproof, paying only the small price difference between the two pairs.

This is operationally remarkable:

Different stores (Bata vs Timberland)

I bought them in Bata, which is a popular international brand that happens to carry the Timberland boots. However, I was able to exchange them in a Timberland own brand store. Given the receipts I got from the Timberland store says "Bata" on it, I suspect the two are operated by the same company. However, as a western audience, can you imagine buying something in Gap and then returning in Banana Republic (same mother company)?

Different cities and provinces

I don't know how it's like in the US, but in Canada, returns and exchanges wouldn't be possible cross provincial borders. Yet, in this case, it was not a problem.

After the 14-day exchange period without the paper receipt

It was at least 3 weeks after the original purchase date, while the receipt stated a 14-day exchange period. I also didn't keep the paper receipt (trying to be light while travelling), but I had a photo of it on my phone. This I was able to email to them to enable the processing. Again, can you imagine this to happen in a western country?

"Waterproof" ≠ "Gore-Tex"

Finally, for everyone's learning, apparently, if it only says "waterproof", it's not waterproof. Only if it says "Gore-Tex", then it's actually waterprof.

I went into the Timberland store only to vent my frustration. I was positively flabbergasted when they offered to exchange for a new pair. Not only is the customer care commendable, but operationally that this could happen is something I would never have expected. They basically went against all the rules I know that would make this infeasible in western countries. Yet, the teens that worked at the Timberland store were willing enough to find ways to help me, a foreigner with broken Spanish, so I would have this outstanding experience and be happy with the decently expensive pair of hiking boots. How they keep the books straight on this transaction is beyond me, 'cause surely they are running Bata and Timberland as two separate business entities.

The result: Timberland now has a new loyal customer. This is an outstanding example of great customer care made possible by some well-integrated and smooth operations.

Coursera and the analytics talent gap

2012-12-30T16:50:00.002Z

It's been a while, and ThinkOR is back to blogging about Operational Research and its related themes.

ThinkOR authors are about to start on 3 Coursera courses over the next couple months:

I am not only learning about some new topics for my own benefit, but also interested in assessing how such easily accessible courses could help the so-called 'big data and analytics talent gap' in businesses. As a Business Analytics consultant, this is one of the biggest issues I see my clients facing in today's business world - one wouldn't think about it, if they don't know about it, and once they know about it, they don't know how to get more of it. Obviously, there would need to be some sort of a step progression, such as (just an example without much research at this point):

Statistics One
Data Analysis (with R)
some sort of programming course, check the computing course catalogue
Focus on one or several of the main OR techniques and their associated tools, such as Discrete Event Simulation, Monte Carlo Simulation, Optimisation, Forecasting, Machine Learning, and the good old Volumetric Modelling, as some examples (not sure Coursera has courses on these topics)
and if you are going to work with humongous data sets, Intro to Data Science sounds reasonable to become familiar with the various big data technology to apply data science (I suspect this often eludes traditional OR practitioners)

As ThinkOR goes along, we will be blogging about these courses and our learning experience. So far, there has only been very positive feedback. Let's get going!

Merry Christmas and Happy New Year!

Consistent Education Divide in Cities

2012-05-31T21:44:00.002+01:00

The Daily Viz brought this to my attention. It's a visual by the New York Times showing how the distribution of cities by proportion of adults with college degrees has changed over the last 40 years.

Nicely formatted and presented, though my ability to compare the distributions side-by-side is a little bit limited.

The key story that this visual is telling is that the average has moved from 12% to 32%, but that the number of cities more than 5% above or below the average has increased substantially. "College graduates are more unevenly distributed in the top 100 metropolitan areas now than they were four decades ago." But i'm not sure if it's as simple as that.

Suppose I was measuring trees. One species was 10 feet tall on average and species two was 100 feet tall. If the first tended to vary between 7 feet and 13 feet, but the latter tended to vary from 85 feet to 115 feet, I wouldn't remark at how much more variable these trees were. For species one, no tree was more than 3 feet from the average, but in species two, presumably many are. Is this a sign that species two is more unevenly distributed? Not really. Species one varies up and down by 30% where two does so by 15%.

So I asked myself, given that the average proportion of adults with college degrees has nearly tripled to 32%, has their variability increased proportionally? Now that these trees are 32 feet tall, it seems strange to still measure their "unevenness" by how many of them are between 27 and 37.

So I reached out to a statistic, the Coefficient of Variation. Using my eyes to collect the data from the charts (so not precisely the correct data), I calculate a coefficient of 0.25 in 1970 and 0.22 in 2010. The variation in the data as a proportion of the average has gone down in the last four decades.

Again, the NYT concludes that "College graduates are more unevenly distributed in the top 100 metropolitan areas now than they were four decades ago.", but I would argue that if anything they are slightly more evenly spread than before and not remarkably so.

Data Journalism

2012-04-28T00:46:00.000+01:00

I've recently started following the Guardian's Data Blog, but I was a little disappointed with their recent article on grammar schools inthe UK.

My understanding is that grammar schools are a subset of schools in the UK that supposedly offer entry on a meritocratic basis and deliver higher quality education. Depending on your political leanings you either believe that grammar schools re-enforce the class division in the UK by giving entry disproportionately to the already higher class and then giving them a better education or you believe that grammar schools enable class mobility by delivering a better education to bright lower class students who would not otherwise afford such a thing. As an outsider in the UK I’m not qualified to hold an opinion here, but I suspect that naturally each extreme fails to appreciate some nuanced details.

The article appears to have pulled off a classic journalist's ploy:

Present a statistical analysis of the data in a leading way without drawing conclusions
Quote somebody else's opinion on the topic

Essentially you can deliver opinion supported by the apparent full weight of objective statistical analysis without having to put your name to the conclusions which might not hold up to rigorous challenge.

Notice also that one of the opinions is much stronger than the other. Notice also that Rosemary Joyce's note has very complex implications which are not at all explored for the reader. Even I’m not sure if she has a point.

I could offer a very different view of the same data:

14 of 32 schools favoured those not privately educated, giving fewer than 6% of offers to the privately educated
Taking 24 of the 32 schools (3/4) with the lowest privately educated proportions, the average was 6%, the same as the overall population
Removing the two clear outliers in the data, "Tonbridge Grammar School", "The Judd School" overall the privately educated averaged to 8.9%

I feel the key fact I’m missing is: What % of students in Kent who scored well on the 11-plus exam were privately educated? How does this compare to the 10.89%? How does this compare to the 8.9% removing outliers? Is there a social bias in the offers?

I’m also missing any information about how these numbers have been changing with time. Simon Murphy complains that the government is not taking steps to improve the chances of poor children, and yet for all I know that 10.89% was maybe 12% last year.

What about this “local context” anyhow? How do these percentages compare at a lower level of granularity that county-wide? How do these percentages compare to applications?

Is this a story of a county-wide bias, or just the story of two bad apples and handful of not-so-good-ones? I think I know what The Guardian wants me to think. Data Journalism is still Journalism I suppose.

For my readers, I ask, why do you suppose the 10.89% number is the only one in the text of the article to two decimal places?

PW 1000 W (Picture is Worth of 1000 Words)

2012-04-14T19:36:00.012+01:00

Few inspirational pictures to stimulate your imagination.

1)

2)

3)

My Jealous Supermarket II

2012-03-29T21:11:00.003+01:00

Last week I wrote the Figure It Out article that was published to the Capgemini Consulting UK Operational Research team blog, My Jealous Supermarket.

I encourage to click through and read the article. To summarise, my supermarket is targeting me with discount coupons in order to maintain my loyalty which it mistakenly thinks it is losing because I am a travelling consultant and have shopped very little lately.

Anyhow, after returning from a week in the north of England followed by a weekend in Florence followed by another week in the north, it was immensely satisfying to see the "Spend £30, get £3 off" coupon roll off the receipt printer when I purchased ingredients for my meal this evening after making no purchases for two weeks. They DO care!

I wonder how this initiative is going for them? Are they successfully winning people back? Any proper initiative would have a benefits tracking element following implementation, but comparing before and after and asserting causality is always difficult. Consider myself. One day I will wrap up my project in the north and spend some time in London again. I will return to my supermarket and purchase lots of food. Success! After months of giving me coupons, they will have finally won back my favour and loyalty. Or not...

Numbers in 2011 - from More or Less podcast

2012-02-13T19:00:00.000Z

One of my favourite podcasts is BBC's More or Less. At the start of 2012, they did a series on Numbers in 2011. I know it's a little late in sharing this, but here we go - enjoy.

I'm sharing with you a selection of the numbers from the 30min podcast. They are somewhat UK centric, but still worthwhile sharing.

Listen to the whole podcast here.

80%: developed world's debt to GDP ratio
1.37: cost of petro in GBP on 9 May 2011 (highest in 2011), due to duty, value added tax (20%) & exchange rate (weaker GBP against USD)
1%: BBALIBOR (interest to be paid in 3 months time) 10 Nov 2011 crossed 1%, doubling of the bank interest rate. BBALIBOR indicates the risk of money not being paid back in 3 months - a show of lower confidence/trust between banks.
2.64m: unemployment in UK by December 2011 (highest in 17 years). Note UK population is just over 62m.
900k: people today working beyond 65 years old in the UK
12,500: people celebrated their 100's birthday in 2011 in the UK; and will rise to 100,000 over the next 25 years
7bn: world population
2.5: average fertility of women on earth (babies per lifetime of earth, falling from 6 from 60 years ago), easing on the environment I suppose
3,000gbp: cost of sequencing the human genome; in 2003, the first sequencing of human genome cost 600m GBP - that's a 200,000 fold reduction in cost in 8 years
2 weeks: to sequence 5 human genomes in 2010; in 2003, it took 10 years for one

School uniforms in developing countries: An unnecessary evil? - High-level test

2012-01-03T23:07:00.005Z

Earlier I wrote a post about the requirement for school uniforms in developing countries and how I saw this as a potentially offensive injustice. I completed the first step by forming my hypothesis, "The unnecessary requirement for school uniforms in developing countries puts undue financial stress on families already struggling to afford basic necessities and/or tuition, and potentially even excludes some children from attendance." Now I am looking to test that hypothesis quickly at a high level. I want to do some research to gain reasonable assurance that the hypothesis is correct before I might move on to establish the magnitude of the problem.

Schools for Africa is a UK Registered Charity mainly focused on building schools, but who also say: "£40 will buy 10 sets of primary school uniforms". To put this into perspective:

They also say: "£235 will buy 50 text books for the children to share". That's £4.70 per textbook vs. £4.00 per uniform.
£4 is about the same as an average day's wages in Ghana

$2,500 USD GDP PPP per capita de-adjusted for PPP, converted to GBP and divided by 365 is £3.02

£4 is about the same as an average week's wages in Ethiopia

$1,000 USD GDP PPP per capita is about £4.23 per week calculated as above, sadly reasonably assuming no days off

I choose these countries as I visited them in 2011, but it is worth noting that Wikipedia reports school uniforms as required in Ghana

The folks at Project Ethiopia, an American 501(c)(3) have reportedly bought 1,695 school uniforms at $8 a piece. These uniforms are also said to last two years, so that's an annual cost of only $4. They make the relevant point that these uniforms are the only set of clothing for many, which would lower the additional burden of the uniform requirement on top of that for clothes. Note, however, that $8 is more than a week's wages as calculated above. Again for perspective:

They also claim to buy over library books for $3 a piece
They also claim to buy a years school supplies (5 exercise books, 1 pen, bar of soap) for $3

Gift Ethiopia, a UK Charity will provide an Ethiopian school uniform for £8, describing it as such:

Without a uniform, many children in Ethiopia are unable to attend school. Many families, especially larger ones, struggle to provide a uniform for all their children. These children are denied an education and the chance to socialise with children their own age. Your gift will provide a student with a brand new, full school uniform, ensuring they can take their place in the classroom with pride.

£8 for a school uniform is about the same as they say it will cost to provide a school dinner for over 10 weeks

The first program listed on the website for Common Threadz, a 501(c)(3) American non-profit, is "School Uniforms for Orphans & Vulnerable Children". They describe the problem:

For families facing the challenges of poverty in Africa, school clothes are not as crucial as the next meal. The direct costs of education, from a uniform and shoes to books and stationery, force millions of orphans and vulnerable children to miss out on school each year. For a child in need from a poor rural family who may only own one pair of old pants or a tattered dress, a school uniform is not just a requirement, but essential to build confidence and academic success.

World Vision UK runs MustHaveGifts, and sells a pretty smart looking Pakistani school uniform for £12.

The uniform is described thusly:

Pakistan: Children who can't afford a compulsory school uniform can be denied the right to an education, leaving them vulnerable to exploitation. With a school uniform, children can attend school for the very first time and get on the path to a brighter future.

At $2,500 USD per capita PPP GDP de-adjusted to remove PPP is $941 or £1.65 per day or almost £12 per week

Based on the above I think that we can conclude that there is reasonable evidence to suggest that in parts of the developing world school uniforms are comparatively expensive and a prerequisite to education.

The next step, though I may not endeavour to take it due to the scale of effort required, is to gather all of the available evidence together to establish a high-level estimate of the scale of the problem. What is the aggregate cost of school uniforms across the developing world? How many children are denied an education as a consequence of their family not being able to afford school uniforms? Ultimately building to the question, What if the requirement were abolished? Once we know the "size of the prize", and please do forgive me for that blatant consultant-ism, we can begin sizing up what can be done about it.

School uniforms in developing countries: An unnecessary evil? - Hypothesis

2012-01-03T22:12:00.008Z

There are charities helping families in developing countries to buy school uniforms for their children so that they can attend school. This is a good thing, right? Which part? The part about charities helping families in developing countries or the part where this is even a problem? If what I consider to be an arbitrary policy is preventing impoverished children from getting a primary education, this is a great injustice.

Testing this with a few friends, I have concluded that this quite possibly is the case, and I also received some stark warnings about the social, cultural, and psychological dimensions to school uniforms. These warnings are certainly valid, but many great in justices in this world have been toppled that were held up by social, cultural, and psychological factors. The question is, how big is the problem, how big are the barriers, and are our efforts best placed elsewhere?

It occurred to me that this is an opportunity to try out some strategic modelling and analysis, something that I do often in my current work. I have already completed the first step of forming a hypothesis and testing with a few peers. To pursue the problem further I would take the following steps:

Form a hypothesis:
Test hypothesis at a high level
Estimate the magnitude of the problem/scale of the potential benefits from taking action
Develop a portfolio of initiatives
Prioritize initiatives

Operational Research Consulting & Data Journalism

2011-12-30T10:00:00.000Z

As data becomes more and more accessible, together with visualisation tools becoming more available and user friendly, Data Journalism is heating up. I've been following the Guardian's Data Blog enthusiastically, it is full of interesting information relevant to current affairs, explained with much facts and data.

This article talks about the 10 point guide to data journalism. I particularly like point 5:

Data journalism is 80% perspiration, 10% great idea, 10% output

The Prezi under point 5 explains the process of how data is used to support news, the angles to consider when mashing datasets together, the technical challenges of working with data, iterative calculation and QA process, which finally get turned into the beautiful output with the various (mostly free) visualisation tools.

This is practically the same process that an Operational Research consulting project takes - or any application of OR or Science in general:

Understand what the problem/question is
Create a hypothesis to be proven or disproved
Define what data is needed for the quest
Get the data
Clean it, and manipulate/wrangle with it so it's usable for analysis
Analyse/calculate to come to some conclusion - hence proving or disproving the hypothesis
Compare it to subject matter experts' view on what the likely answer should be (sanity check)
Refine the analysis until satisfied
Shape the output message so it can be easily understood by the audience
Communicate the findings
All throughout the process, keep communicating to the audience to make sure they are engaged and understand (principle-wise) what you're trying to do, so that they are not unpleasantly surprised when the final answer is presented
Best yet, to ensure smooth change management if your solution is to be implemented, work closely with the end users from the start of designing the solution, and then implement and test, so that they believe in the solution because they were part of the creation process.

As the Flowing Data blog points out, this is what statisticians do. I will add that this is what Science does in general. I will also say that in practice, the first step, "understanding what the problem/question is", often takes 70-80% of the time. The technical 'doing' to follow, in practice, is relatively easy compared to what our academic institutions thoroughly prepare us for (which is needed).

For those interested in the how of data journalism, read this about the work that went into reporting on the 2011 London Riots. Fascinating social media analytics at work. Not easy. Impressive and very interdisciplinary.

P.S. Most of this post has been sitting as draft since the summer, hence referencing 'old' news. It's still relevant, so why not.

Operational Research considered 1 of 6 dsciplines in Social Sciences

2011-08-14T20:12:00.002+01:00

Okay, so OR is grouped with Statistics as one of the six disciplines of social sciences, but still, I'm pleasantly surprised that OR is mentioned!

According to QS World University Rankings, the six disciplines considered as part of social sciences are:

Finance
Economics and Econometrics
Law
Politics and International Relations
Sociology
Statistics and Operational Research

Here you can download the full table (yeah, Google Doc!), and see the top 10 universities at a glance for each of the above subjects. For Stats and OR, here are your top 10:

Rank	Institution	Country
SOURCE: QS Quacquarelli Symonds 2011 - www.topuniversities.com
1	Stanford University	United States
2	Harvard University	United States
3	University of California, Berkeley (UCB)	United States
4	University of Cambridge	United Kingdom
5	Massachusetts Institute of Technology (MIT)	United States
6	University of Oxford	United Kingdom
7	National University of Singapore (NUS)	Singapore
8	University of Toronto	Canada
9	Imperial College London	United Kingdom
10	Princeton University	United States

P.S. If you haven't discovered it already, the Guardian's Data Blog is great!

An Alternative Way to Fly (as long as expectations are managed)

2011-07-31T20:01:00.005+01:00

The purpose of this post is to share the discovery of an alternative way of operating an airline (flight schedule and route wise).

No matter how airlines degrade their service standards these days in the West, I think it's fair to say that most of us still believe that most airlines *intend* to:

Take off on-time
Land on-time
Fly us from A to B as the ticket says, without surprise stops
(Oh, and have toilets, of course)

On a recent trip to Ethiopia, we have been shown a rather different way of operating an airline. It contradicts with all of the above, but it works. We took 4 internal flights.

Here is how we experienced them first hand:

1 left on time as per the ticket, and even got us there early (bonus!), because...
None of the 4 flights flew the original path it said it would: stopovers were skipped to go direct instead, or the direct flights got stopovers added onto it last minute
None of them arrived late, because...
Some of them took off earlier than stated
Additionally, the air stewardesses were lovely, and they gave passengers snacks and drinks (*gasp* what novelty!)
To their credit, they did try to inform passengers of the changes a couple of days ahead of the flight (in our case by email, which we only read after we got back to London).
They also tell passengers to double check the flight times a couple of days before, to be aware of any late changes.

(For your curiosity: the international flights from London to Addis Ababa was quite standard. The only oddity was that they weighed everyone's carry-on luggage at the gate, because it's apparently a popular flight to take lots of stuff with you!)

IMHO, an airline would play this game, because: (we suspect - unconfirmed)

It wants to minimise costs - mainly fuel in this case.
It has 1-2 planes that fly in circles to cover off a handful of popular destinations.
As the airline gets more and more requests for seats through the form of purchased tickets, it is faced with an optimisation problem to fly all its customers to their expressed destinations with minimum cost. The best way to do this is probably through re-shuffling the schedule. For instance, if a plane is hopping from A to B to C in sequence, where B is closer to A than C is, and if we discover 2 days before the flight that the plane is filled with 2/3 passengers going to C, and 1/3 going to B, then flying A->C->B is cheaper than A->B->C. What if there are customers wishing to go from B to C? We hear that the airline is known for cancelling flights as well. Luckily, we didn't experience this.

This way of operating an airline is possible, because:

It is a monopoly.
The number of flights are few, so it's easy to manage change.
Customers expect it and adjust flying behaviour accordingly (i.e. always check the flight times before the day of flight, and always leave wiggle room before and after the flight).
For foreigners who are used to the typical western airline service (i.e. expect it to take-off and land on-time and fly the route it says it would), the price justifies it and shuts people up from complaining, and instead people will have a laugh (or write a blog post!) about it.
It doesn't call itself "Precision Airline" (the Tanzanian airline), and can afford to deviate a little. 8-)

P.S. If you are planning to visit Ethiopia, and intend to fly within the country, you may want to consider buying the tickets within the country rather than online. It is significantly cheaper due to price control. This is true as of spring 2011, so double check this before you travel.

YoungOR Conference 2011 - Talks in the Consultancy Stream

2011-03-28T23:18:00.001+01:00

It's only 1 week away from the YoungOR conference in Nottingham, UK. I am looking forward to chairing the consultancy stream, so I finally get to meet the speakers I worked hard at recruiting.

It will be the busiest the consultancy stream has seen it! We have 2 keynote talks plus 5 titles lined up for the stream over the first 2 days of the conference. The conference schedule is packed, with 5-6 talks to choose from at any time (except for the plenary slots, of course). If you are young to OR, that is 10 years or less in Operational Research, come and check it out.

The Consultancy talks are as follows in chronological order:

Keynote: OR Joining Analytics, by Russell Hodge, Capgemini Consulting
Revenue Management At British Airways, by Peter Wilson, British Airways
Pharmacy Service Cost Inquiry, by Nicholas Jones, PriceWaterhouseCoopers
Roundtable Panel Discussion Consultancy on "OR and Enterprise 2.0", "can OR people be leaders or are we destined to be the brains in the back room", and "Who is the boardroom champion for OR". Serving on the panel is a host of talent from various OR consultancies plus an independent, who is also giving a plenary talk at the conference
Keynote: An OR Professional On ‘The Apprentice'?, by Dave Buxton, dseConsulting
OR Consultancy For The Emergency, by Guy Bickerton and Graham Holland, OR in Health (ORH)
Scottish Rugby: Tackling Meaningful Statistics, by Ursula Mulholland, Capgemini Consulting
Day In The Life Of An OR Consultant, by James Lally

Also, if you're an experienced conference chair, care to share some of your tips on what to do / not to do, etc.?

85% of Statistics Are Made Up On The Spot

2011-02-28T19:41:00.003Z

I had a good chuckle the other day when I was caught by an example of numerical illiteracy on the part of at least two people: an author and an editor. I had to share.

I was flying with Air Asia from Banda Aceh, Indonesia to Kuala Lumpur, Malaysia. The in flight magazine isn't exactly high production value, as the airline is all about saving. Consider Air Asia to be the Ryan Air of the East. Anyways, here's the tasty treat now:

I can take no issue with the first section on young billionaires as it was actually quite interesting. In the second section, I am entertained by the translation of $122.1k GDP per capita to an average income of about $120,000 per year. Taking the crown though, was the gem at the bottom.

"72% of the 14.5 million population in Mali, Western Africa, earn about $0.003 a day with the average worker's salary of only US$1,500 per year!" Now what is that supposed to mean?

Before you reach for your calculator I can tell you that $0.003/day = $1.10/year.
Also I can tell you that 72% of 14.5 million = 10.44 million.
And that (10.44 million people * $1.10 per person per year ) / $1,500 per worker per year = 7656 workers.
And that 7656/10.44 million = 0.07% employment.

Curiously I can't quite determine what I think they were going for. Anything I try to explain the numbers I see gets destroyed anyway by the strange "72% of 14.5 million". According to Wikipedia, only 43.51 million out of the 81.76 million people in Germany are employed. I suppose I could say that 53% of Germans earn about $0 per day. By adding a dash of real workers I could make that figure $0.003.

Please comment and speculate.

Faking It On Your Wedding Day

2011-02-27T21:42:00.008Z

Earlier this month we wrote about our love of podcasts and just last week I was listening to Japan: A Friend In Need from the BBC Documentaries Archive. Here I was in the month of love, listening to a podcast on the subject and I found math in an unexpected place.

The documentary is about an agency in Japan that supplies fake people, or actors I suppose. In particular, this agency will supply people to fill out your side of a wedding. In the given example, we met a young man whose parents were deceased and his siblings were astranged, such that he only had two friends to attend his wedding. So as to keep up appearances, unbeknownest to the bride, he hired parents, friends and relatives. All told, 30 people at his wedding were fake, costing him something like £3,000, equal to his recent redundancy compensation.

The agency claims never to have been caught, and they say that they "research their assignments assiduously", but it got me wondering just how long you could operate such a service without getting caught. How many weddings could you do before a repeat guest noticed that they had seen one of your actors at a wedding before?

The first wedding is simple, and guaranteed to go off without a hitch, but what about the second? Suppose every wedding has on average 30 guests from each family. In the second wedding we need all 30 people to not be from the 30 in the previous wedding. Still pretty easy in a country of 127 million. But what about the 30th wedding when there are 900 previous guests out there in the population? Things are still looking pretty good, but the probabilities are starting to pile up in a similar way to the phenomenon that means that in a group of 23 people there's a 50% chance that two will have the same birthday.

So given a constant wedding size of 60, 30 real and 30 fake, what is the probability that this is the wedding that breaks us? This is the same as the probability that one or more of today's guests attended a previous wedding. This is the same as one minus the probability that none of today's guests attended a previous wedding. For wedding n and a population p:
Assuming 127 million people in Japan...

For wedding 1, it's a sure bet as nobody has attended a previous wedding.
For wedding 2, we face only a 0.0011% chance of getting caught.
Even for wedding 100 our risk is only a 0.11% chance. No problem!

But wait, the above probabilities are conditional probabilites. Our chance of getting caught at wedding 100 given that we got to wedding 99 is 0.11%. What is our chance of getting to wedding 99? This is the the probability that we didn't get caught in one or more of the previous weddings, the probability of a perfect record. Mathematically our chance of getting to and past wedding n is:

For wedding 1, it's a sure bet.
For wedding 2, it's 99.99%
For wedding 100, it's 94.58%.
For wedding 500, it's 24.57%.

Even though by the time we get to wedding 500, ony 15,000 people in Japan have been to weddings with our staff, we would be lucky to have made it that far.

If we started this agency today, on average how long can we expect to go before we get caught? Now I'm not going to bother expressing that mathematically, but hacking at it with Excel numerically, I can tell you that it comes to roughly 374. If we were to start such an agency today under such conditions and such assumptions, we would on average expect to do 374 weddings before getting caught.

So I think the moral of the story is, if you're looking to hire fake people for your wedding, you're doing alright, but if you're looking to run a business doing it, you might want to reconsider. Then again, if we're looking for morals in this story, honesty might come first.

I heart smartphones and podcast favourites

2011-02-07T22:32:00.001Z

I heart smartphones. It is the symbol of the new world, where the world is at your finger tips, and, in your pocket! There is so much information out there, digesting it is a big quest. I'd love to have the time to sit down and browse the net for a couple hours every day to catch up on all the news and events, but now I can do all this while on the move.

I am an owner of an HTC Hero on Android. It is the only digital device I carry in my hand bag (other than my obligatory work phone). Living in a busy city like London means I spend a fair amount of time in transit. If you are a Google fan like me, then Google Reader and Google Listen would be your good friends. My favourite activity during transit when I'm not walking about, is to catch up on the news and my favourite blogs through the RSS reader. My favourite activity during transit when I am walking about, is to plug into one of the following podcasts, which keeps me informed and entertained. If this is not optimising your time, then I don't know what would. I guess the next step is to jog to work while listening to podcasts: information downloading and calorie offloading all at once!

LSE lecture and events: London School of Economist half hour to hour long lectures or guest speakers plus Q&A session (frequent publishing of events)

The Economist: I like the magazine, but there is so much content to digest. The podcasts do a great job summarising the highlights (weekly publishing or more frequent ones available too)

NPR News: short bursts of news that keeps me informed of the North American highlights (hourly publishing)

Science of Better: Operations Research podcasts/interviews by INFORMS (monthly publishing)

More or Less: BBC radio programme making sense or debunking the numbers behind the news

Freakonomics: spin off by the authors of the ever so popular Freakonomics book/movie/blog/etc.

What are some of your favourite podcasts?

Aside from being my RSS reader and podcast player, my smartphone is also my:
- phone (first and foremost)
- email
- calendar
- access to the internet
- Skype to call anyone around the world
- instant messaging to keep in touch with friends
- handy document storage
- camera / video cam
- GPS and compass
- maps (offline maps too)
- ebook reader
- notebook (takes my hand scribbling too)
- news reader
- scanner
- games when I'm bored waiting in a queue somewhere
- MP3 player
- all the other things that come with a phone (alarm clock, calculator, voice recorder, etc.)
- and thousands of other applications available for download (often for free) that keep my life organised and what not

Excellent Data Visualisation - Mortality Statistics Meets Modern Video Technology

2010-12-11T10:28:00.002Z

Exciting statistics on visual display on BBC4. It indeed is an exciting, visually pleasing and modern video. The presenter, Hans Rosling, a statistician and a guru of data animation, makes numbers look matrix-cool! I thought I was watching a 4-minute magic show. Savour in the power of great data visualisation. Watch the life expectancy and wealth progression of 200 countries in 200 years in 4 minutes.

Smart Systems and Competent Systems

2010-11-08T18:49:00.005Z

It amazes me how companies won't do the most basic things with their data. About once a quarter the company that rents us our flat solicits us by mail to sell the place. I just recycled a letter from our current broadband provider encouraging us to switch to them as they have better reliability and lower rates than the competition.

Surely there should be a database out there where a simple join between a residential addresses table and a current customers table would result in a mailing list that does not include me. I'm not sure what offends me more, the excess waste this represents not just in felled trees, but in the entire supply chain that delivers me this mail, or the simple incompetence that it represents.

The Economist has an interesting special report this week on Smart Systems. This report portrays a future where the rapidly progressing sensor, wireless communication and power/battery technologies converge to deliver endless data enabling us to analyse and optimise everything. Power grids, water works, and even cows are candidates for this new age of analytics. They could be exciting times for Operations Researcher practitioners. Early benefits will probably come from simple applications and may resemble the traditional benefits from IT and access to information. As a second level, Operations Research will be able to do more sophisticated things with the data, but when I see the examples I mentioned above, it can be possible to lose faith.

Young OR Conference April 2011 - Consultancy Stream

2010-10-30T19:14:00.002+01:00

The OR Society is hosting the biennial Young OR conference in the University of Nottingham, United Kingdom, on 5-7 April 2011. I am organising the Consultancy stream, and I am looking for speakers, presenters and of course audience. If you are disregarding the conference because of the word 'young', think again, because the definition of 'young' in this context is <= 10 years in the field of OR. You can find more information for presenters here. Essentially, this is what you need to do:

a 200 word abstract for the conference programme
a presentation of max 20 minutes

I described the stream as follows:

The consultancy stream aims to attract speakers and audience interested in sharing their experiences in the practical application of Operational Research in a client-consultant setting. The consultant can be internal or external to an organisation. The problem at hand can be simple or complex, technically or organisationally.

The challenges we face as OR consultants are very similar no matter the industry, the organisation or the problem at hand. There are definite gaps between practical application and academic research in OR, but it is still one of the most rewarding jobs. The recommended format would be a case study presentation covering the entire cycle of the project where possible, but presentation creativity is absolutely encouraged.

How did the problem find you or how did you find the problem? i.e. How was it sold?
Steps taken to establish your course of action
OR and non-OR techniques and methodologies used to structure and solve the problem
How were your findings and recommendations communicated to the stakeholders and decision makers in an effective way?
How did the client take your recommendations? Did they implement?
Finally, what do you enjoy most about your job?

Most of all, have fun and meet some fellow Operational Research practitioners.

Please pass on the message. Better yet, please drop me a line to present! As you can see, the stream description is very wide, encompassing all real life applications of OR. You don't have to have 'consultant' in your title, neither does your company or organisation. Come and share your experience and the fun (or pain?) of applying Operational Research in anything from ordinary day to day life to extraordinary situations of, for instance, life and death and taxes. How have you helped with better and more informed decision making?

Oyster Card Optimisation

2010-10-13T22:24:00.007+01:00

Transportation is an industry where a lot of Operations Research is practiced. In the following article I would like to share an example of optimisation that I have noticed in the fare pricing system on the London Underground.

Public transportation in London, England has a convenient and efficient means of collecting fares from travellers. Introduced back in 2003, the Oyster Card is the size of a credit card and is pre-loaded with money by the traveller. On each trip they take, the traveller touches the oyster card to a reader, registering their journey with the system which deducts payment from their balance. Each single journey is charged at a different rate depending on the origin zone, destination zone, and time of day.

A daily capping system is in place such that you will never pay, in a day, more than the price of a day-pass covering all of your journeys for the day. For example, in a day where you only travel in zone 1 off-peak your journeys will cost £1.80, £1.80, £1.80, £0.20, £0, £0 and each journey after that is free, as you essentially now have a day-pass on your card when your daily cap has reach at £1.80*3 + £0.20 = £5.60.

A Canadian friend of mine, currently residing in Australia, visited me here in London the other weekend. Knowing the ease, convenience, and price-capping guarantee, I recommended that he get an Oyster Card. He loaded it up with £10 at Heathrow and came into town to drop his bags at my place. After a short jet-lag nap he headed out into the core to see the tourist sights, travelling frequently on the underground. At the end of the day he reported that his Oyster Card credit had run out and that he had needed to top up the balance. This surprised me, so we worked out his journeys and payments:

Zone 6 (Heathrow) to Zone 1 at Peak - £4.20
6 x Zone 1 Off-Peak - £1.80 each

Because he travelled from Zone 6 to Zone 1 at peak, his cap for the day was £14.80 even though had he bought a Zone 1 day-pass at Heathrow he would have only paid £5.60 + £4.20 = £9.80. So the Oyster Card is convenient and comes with a price capping system, but there are holes in that system. In this case it cost him £5.00 which is about an hours work at minimum wage in the UK, so not trivial.

Any individual travelling on a public transportation network wants to perform an optimisation. In this case, they want to minimize their total cost by selecting the most efficient combination of fares to cover all of their journeys. This problem presents itself as a classic optimization problem; Subject to constraints, like the requirement to purchase tickets to cover all journeys, the goal is to minimize total cost, a function of the decisions to buy tickets. An optimisation problem like this can be formulated mathematically and solved by computers using a discipline called integer programming, one of the tools in the Operations Research practitioner's toolbox.

If this problem can be solved by computers, why doesn't the Oyster Card system provide a lowest price guarantee rather than the evidently imperfect price-capping system? Consider for a moment the requirements of the system:

Daily ridership of around 3 million
At the end of their journey, users must be told almost instantaneously what the cost was and what their remaining balance is

Optimisation problems of this nature are not always fast, easy, or even possible to solve optimally. The computers of today are fast, but there's plenty still beyond them. The tube system isn't even using the latest technology. I've been told that some Underground components still use punch cards! Every time a customer makes a journey this optimisation must be calculated and that must be done 3 million times a day and that is unfortunately too much.

When an optimisation problem is too big or too complex to solve directly and perfectly, analysts use something called heuristics to come up with near-optimal solutions. There are commonly used methods, but depending on the problem, customised heuristics can be developed, using the unique structure of the problem in question to produce a near-optimal result. That is exactly what the price capping system is; It is a heuristic used to make a good approximation of the lowest price.

There are effectively only two types of tickets in the system: single tickets and day passes. Day passes are the only way to save money. It is rarely worthwhile buying two separate day passes. It follows naturally that a simple rule of thumb for cost optimisation is to compare your daily total of single trips to the price of a day pass covering all those journeys and choose the lower option. The conditions that I list at the start of this paragraph are essential consequences of the structure of the problem, and we can exploit them to arrive at our simple heuristic, the same one that the oyster cards use.

In a future article I hope to look into formulating the optimisation problem of the London Underground and consider alternative heuristics.

Expedia Revenue Management at Check-out or Rule Compliance

2010-10-09T11:05:00.010+01:00

We have all been shopping online for something only to be told after making the purchase decision that it is no longer available or no longer available at that price. This often happens when buying flights, as prices can change minute-to-minute and you can be left with a much higher ticket price which makes you abandon your purchase. Disappointment all around.

However, the opposite happens from time to time as well! The price of a London to Seattle flight, when I found it was £649.07 (including all fees). I clicked to start jumping through all the purchase hoops, but after a couple steps into the check-out process, it flagged up, rather alarmingly, as £616.07. That's a 5% decrease in price. (See, I'm not making it up!)

I was pleasantly surprised, of course. But why would they do that?

I've got 2 suspicions.

1. Revenue Management / Yield Management / Consumer Psychology

In the weeks prior to this screen capture, I've been to the site a few times already looking for the exact same flight. Even though I'm not logged in, I'd venture to guess that the site has looked up my cookies and knew that I've been looking for these flights. Therefore, it should know that I was a likely buyer, rather than a window shopper (pc pun intended). I've been at the check-out stage before, but have abandoned the shopping cart eventually. It would be quite logical for the site to entice me with a lower price as a 'pleasant surprise' to finally get me to spill my moola. Not to mention the positive impression it's left with the shopper (look what I'm doing now - free advertising!).

However, is it worth the 5% price drop? How does Expedia decide 5% was the right balance of customer incentive and revenue loss? I was already a willing customer, ready to bite. Isn't it just giving the 5% away for free? In my case, it's difficult to say whether the move has gained my loyalty to Expedia, because I was already a frequent visitor and buyer there. It may have re-enforced my loyalty though. It would be very interesting to analyse a few year's purchase and cart abandonment data of customers where this has happened to, versus a control group. Would we observe a lower purchase completion rate, which would drive a higher lifetime revenue per customer?

2. Airline price adjustment rule compliance

There could exist such a regulatory rule in the online airline pricing world to protect consumers, such that the vendor must notify the buyer of last minute price changes before the final purchase is completed. Now, I don't know if such a rule exists, but it is possible. However, it sounds extremely difficult for the regulators to enforce and monitor compliance.

I personally think it's more the former than the latter. One way to test the real reason behind the price drop could be to see if it's always a 5% decrease. Time to do some more flights window shopping!

P.S. In a previous article where we observed operational inefficiencies at London's Gatwick Airport, we erroneously stated that the airport operator was BAA (British Airports Authority). In fact, BAA was forced to sell Gatwick to please regulators seeking to break a monopoly on UK's airports. Our apologies to BAA. The current owners are Global Infrastructure Partners, who also owns 75% of the London City Airport.

Update:

Responding to two unconstructive comments, one of which was downright rude and was deleted, we thought we would add to this article.

The commenters suggest that Expedia is not a price setter, but just a re-seller making possibility one above unlikely. That said, the question still stands, "What's going on here?". If the prices that Expedia gives you when you search are cached and not live, that seems to be to be a surprising shortcoming. If they are, why offer a lower price to someone who appears to have already made the decision to purchase?

There are probably a number of factors at play that someone from the online travel community could answer.

If I were reselling through Expedia, I would want my price-updating algorithm to give the higher of the two prices at the point of payment, i.e. more profit. Both Expedia and the vendor are motivated to collect a higher price and therefore a higher commission as a percentage of the selling price.

The commenters may be very correct in saying that Expedia doesn't set the price, but merely re-sells at whatever the price the vendor names. That's why we said there were two possibilities, the second being not revenue management. However, if Expedia is not practicing revenue management in this way, they probably should at least experiment with it. Their commission represents a headroom within which they can optimize and the goal, after all, is not to make the greatest profit on each sale, but instead the greatest profit across all possible sales.

Restaurant Systems Dynamics - Influence Diagrams

2010-09-15T13:44:00.005+01:00

Systems Dynamics is a discipline that floats about in the management science/management consulting ecosystem. It is genetically related to Systems Thinking, though Systems Thinking contains much more, but no aspect of simulation. The two most important aspects of Systems Dynamics are influence/causal diagrams and continuous simulation. Today I would like to outline an example of the use of influence diagrams to study a simple system, gain strategic insight, and form the basis of a stock and flow continuous simulation.

I was in Paris the other weekend, looking for a restaurant for Sunday lunch. Finding a good restaurant as a tourist is always difficult because tourist restaurants just aren't very good. The restaurants in my neighbourhood in London rely a lot on repeat business and referrals from friends and engage in a repeated interaction with their customers. The restaurants in touristy areas on the other hand get the majority of their business based on location. My local restaurant wants to delivery value for money so that I or my friends will come again. The restaurant in Venice never expects to see me again and is motivated to give me the lowest value for money to maximize profit. We have an example here of repeated and non-repeated games, but this is not an article about game theory.

As regular travellers, we have a strategy for finding the right place. There are a number of aspects to that strategy, but the one I want to highlight today is: Find busy restaurants. We are by no means the only people employing this strategy, as it is clear that busyness should be an indication of quality.

Where is this all going? I'm telling this story because I want to use an influence diagram to study restaurants in general, study touristy restaurants in particular and gain strategic insight from that. Influence diagrams are used to study the interactions in a system, particularly the between key strategic resources. In the case of our restaurants these will be:

Customers occupying tables
Customers queuing for tables
Perceived restaurant quality
Available customers

Figure 1. Simple Tourist Restaurant Influence Diagram

The make-up of an influence diagram is relatively simple:

Strategic resources, flows or other system variables
Arrows indicating one influencing another
An indication of a positive influence or negative influence
Optionally indications of re-enforcing and balancing loops

Consider Figure 1 above, the influences shown are as follows:

As the number of "New Customers Arriving" increases, the number of "Customers Occupying Tables" increases
As the number of "Customers Occupying Tables" increases, the "Perceived Restaurant Quality" increases
As the "Perceived Restaurant Quality" increases, the "New Customers Arriving" increases
As the number of "Customers Occupying Tables" increases, the "Length of Queue for Seating" increases
As the "Length of Queue for Seating" increases people will be discouraged and it will reduce the number of "New Customers Arriving"
As the number of "New Customers Arriving" increases, the number of "Available Customers" decreases
As the number of "Available Customers" decreases, the number of "New Customers Arriving" decreases

Re-enforcing loops can be exploited to achieve exponential growth and profit, but can also cause exponential collapse and bankruptcy. Balancing loops are often related to limited resources which limit what we can achieve, but also serve to mitigate damage.

Loop B1 is a balancing loop: As more customers choose to enter our restaurant, the total number of potential customers is diminished, thus reducing the flow of new customers. This puts a natural limit on our business, the number of potential customers.

Loop B2 is a balancing loop: As more customers arrive, our tables experience a higher and higher occupancy and customers must wait in a queue either for other customers to leave or for dirty tables to be turned over. Here is another resource constraint on our system: capacity.

Loop R1 is a re-enforcing loop: More customers leads to an increased perception of quality which then leads to more customers. This is they key re-enforcing loop that we should study further.

The key strategic conclusion that can be drawn form studying this influence diagram comes out of loop R1, the re-enforcing loop. The consequence of this loop is that full restaurants tend to stay full and empty restaurants tend to stay empty. Given that each restaurant starts empty each day, the key challenge appears to be in first becoming not empty. Easier said than done.

Restaurants and bars have a number of ways of achieving this. The first, but least interesting, is simply good quality. A regular customer base or recommendations in guide books will provide the seed customers from which a full house can grow. Alternatively, we need some other means of getting people in the door. This makes me think of my time in Turkey on the Mediterranean coast. Walking along the waterfront in a tourist town, a restaurant owner offered me a half-priced beer as long as I would sit along the front edge of his balcony. If this makes you think of happy hour there's probably a good reason.

I will admit that the "strategic insights" discussed above with respect to the restaurant industry are not earth moving, profound, or even unexpected. However, this article provides a simple real-world example of a dynamic system, and demonstrates the concept nicely. Had we not already known that full restaurants stay full and empty restaurants stay empty, going through this exercise could have revealed that to us.

The next step would be to design a simulation based on the influence diagram, something that I will endeavour to do in a future article.

What motivates us the most

2010-09-01T20:58:00.012+01:00

First let me make clear that I am talking about the motivation in workplace. In personal life it's easy - in first half of our life it's the Sex, in second half it's the Comfort. (So to speak with tongue in cheek)

But the workplace motivation is more intriguing. And that is the area that every OR specialist should always keep in the forefront of their mind - the questions and aspects of human motivation. Here's an excellent animated video derived from the talk of one Dan Pink at RSA. Seems that Mr. Pink also excels in self-motivation, since this lecture is a small masterpiece.
True, these research findings are popping up here and there for the last two decades, at least, and lots of companies are adopting some of those principles, however this short video sums it up in excellent concise way. Enjoy!

However, I personally think that all these findings are missing some essential qualifications. I thinks that it reflects the motivation of people in developed countries, where there is no hunger and war is something nobody really remembers.
To echo the words of Mika Waltari in his book Egyptian Sinuhe, where he describes one lucky country he travels through, "...and the people who knew neither hunger no war, were already in middle age...".
I wonder, how the same research would turned out in war torn Angola, or Iraq.
I suspect that this type of "Make the world a better place" altruism grows best in economically nutritious Petri dish - relatively wealthy society. But what do I know about the poor countries. Maybe they would surprise us the most. The world is changing after all. It's the Internet age now.

One observation I made about the phenomenon of people working in their free time for free. (Linux developers, etc.) First I would liken it to simple hobby-ism. And I think that it indeed has the roots in hobbies. Everybody at some time in their life likes to build some "model airplane" and see it fly. But, and here comes my observation, they would like more to see it soar, than just fly. In other words, people don't mind to work for free on somebody's else project (i.e. Linux), but they prefer to jump on winning bandwagon. The likelihood of overall impact (let's even say world wide impact) is a specific motivation on its own.

It's the Internet age now.

Want to be creative? Don't brainstorm.

2010-08-01T20:44:00.003+01:00

I'm sure many of us have had the thought before, "Oh, I wish I were more creative". I have. I'm also sure many, many of us have led or participated in a brainstorming session before. I also have. Apparently, these are both counter productive to being more creative, according to this article. To top it off, apparently since 1958 it has been proven that brainstorming doesn't work. I never knew about this. Did you?

In businesses, one of the common outcomes of operational research work is improving a particular process. We often start with understanding the problem, to mapping the process, and to building a model that reflects the current process. Eventually, to add value to the bottom-line, the model hopefully reveals some insights, and is the tool to test out certain ideas to support any process changes. Personally, it is often a pleasure to be involved from the beginning of problem understanding to the end, managing the recommended process changes, because as an OR consultant, you get to see your work to fruition.

Of course, to succeed in change management, the ideas should come from the stakeholders who live and breathe the process in question, and eventually own the solutions to be implemented. To get ideas from stakeholders for process improvement, the common technique is to gather stakeholders in a room and brainstorm on possible solutions.

Given the information presented in the article, to prepare for the brainstorming session, I take away that we should consider to:

present the problem to the group before the brainstorming session,
ask them to prepare and think about possible solutions that their colleagues or friends wouldn't have thought of for resolving the issues,
get them back in a room to discuss each other's ideas and prioritise on the ones to investigate feasibility and impact,
but before they start discussions, get them do some aerobics for 30 minutes if they are somewhat fit (half serious, but wouldn't that be fun?),
culture them with a youtube video about the weird and cool stuff in other countries (half serious, but wouldn't that lighten up the mood?),
facilitate the session with careful language to not instruct people to be creative
facilitate the session so the group moves back and forth between a couple topics to be able to take a break from focussing on just one solution
and perhaps not name it a brainstorming session, because it may be the forum that people associate with "get creative...now", which is counter productive, as per the article

Read it if you've got 3 minutes. Let me know what you take away from it that I've missed. (See the instant application?) The main points in the article to help someone to be more creative are:

Don't tell them to be creative
Get moving
Take a break
Reduce screen time
Explore other cultures
Follow a passion
Ditch the suggestion box