Friday, 30 December 2011
When comparing the dynamics of human resource capacity in supply chains with other resources in the supply chain you will see that human resources are different. The dynamics of other resources are mostly restricted to ordering, whereas human resources have a wide variety of characteristics which all influence the availability of human resources over time. To name a few; acquiring new skills, productivity increases due to learning, change of role/function and getting hired or fired. These characteristics will influence the available resource capacity and cause forecasting resource availability to be difficult. Operations Research offers all kinds of methods to incorporate these dynamics, improving the quality of forecasted availability. For example, stochastic loss network models or somewhat simpler the Markov approach as described in my The OR in HR blog entry. The parameters of these models, for example transition probabilities, need to be estimated based on the data in the HR systems and can best used together with subject matter experts to incorporate factors that are not present in the available data. These models than can be used stand alone or for optimisation purposes, like in deciding on the most cost effective capacity deployment.
(Parkinson’s Law and the Student Syndrome in action?). Also human resources can perform more than one skill at a time and doing so across multiple assignments. In the service industry, like in Operations Research consulting and IT services, the process of assigning people to tasks/roles is further complicated by simultaneous allocation of multiple resources and resource sharing over assignments. I know from own experience that making a long term capacity plan for my team is a hard. Assignments simply don’t start when you would want them to start leading to over-utilized or underutilized consultants. Also you want assignments to fit with the capabilities of the consultant and stimulate the development of new capabilities and knowledge. The right assignment for that is not always available. To complicate things even more, both demand and supply for human resources are uncertain. In long-term capacity planning the exact assignment of resources to projects or tasks is not required. What is needed is to verify if there is enough capacity to satisfy demand or master plan. One way to answer that question is by making a rough cut capacity plan, RCCP. Usually this requires formulating and solving a mixed integer linear program. The RCCP will indicate if the master plan can be satisfied (is it do-able?) and what are possible bottlenecks and mitigation actions when the master plan changes, for example due to changes in demand.
Operations Research will improve managing the balance between supply and demand for human resources significantly. It allows for the incorporation of the dynamics of human resources in the decision making, resulting in better quality and fact based decisions. It will support management in making a choice on the instruments for adjusting human resource supply on the longer term optimizing the human resource supply chain.
Sunday, 6 November 2011
Tuesday, 4 October 2011
Sunday, 25 September 2011
- What do I know (Information) about the business opportunity under consideration and the environment in which it resides?
- What are the options (Alternatives) open to me?
- What are my preferences (Values) in deciding between the alternatives?
Monday, 15 August 2011
A recent survey of KPMG among senior executives around the globe (Confronting Complexity, 2011) shows that the ability to manage today’s complex business issues is seen as one of the key factors for success. Complexity in business has increased over the past years because of changes in economic, regulatory, political and social environments. Also its causes change as companies move through the business cycle and as economies develop. Increased complexity leads to cost increases and the need for new skills within an organisation. Besides being an important challenge, the senior executives find that increased complexity also creates new opportunities, including gaining a competitive advantage and improving efficiencies. Interesting result from the survey is that technology is a critical issue, both as a cause of complexity and a key solution. New technology changes business models, enables process improvements and opens new markets, but also creates new challenges like how to incorporate it into every day business. Operations Research is one of those new technologies. It requires effort to incorporate it into the decision making DNA of your organisation, but when available it lets you defy complexity.
Operations Research has proved to be the best answer to handle complexity many times. A well known example is the way in which American Airlines used Operations Research to turn the effects of the Airline Deregulation act into an opportunity, changing the way the airline industry operated completely. Comparable to this, the Dutch based Sundio Group reinvented the online travel business with the application of Operations Research. Key for success at Sundio is to offer the best price for flight seats, hotel rooms and package trips. Finding the best price is complex, because Sundio must buy capacity at hotels, resorts and airlines before the can sell it to their customers. Because of the uncertainty in demand and the great amount of products in their portfolio, deciding on the best possible price mix is complex. A dedicated decision support system with optimisation algorithms was build to support Sundio in handling this complexity and turned it into a competitive edge.
Recently Midwest ISO, together with Paragon decision technology, won the Edelman award for their achievements in handling the complexity of the production and transportation of electric power. Midwest is responsible for the delivery of electric power across 14 US states and the Canadian province of Manitoba. In doing so it has to manage over a 1.000 power plants and nearly 60.000 miles of high voltage transportation lines to deliver electric power to 40 million end user customers. According to Midwest, the network is the most complex machine ever created by man. Due to changes in regulation, requiring open access to network transmission lines, Midwest transformed the electric utility industry in the Midwestern United States through the development and implementation of energy and ancillary services markets. Since electric power can’t be stored, Midwest needs to carefully balance supply and demand of electric power each moment in time. Changes in demand must be dealt with immediately by adapting the supply of electric power within the technical capabilities of the whole system. In order to do so Midwest has to solve a dazzling puzzle with millions of decision variables each 5 minutes. With the decision power of Operations Research, made available to decision makers across the Midwest network via the use of AIMMS, Midwest was able to achieve that. As a result Midwest ISO adds significant value to the region through improved reliability and increased efficiencies of the region’s power plants and transmission assets. It has been estimated that Midwest realized a cumulative saving from 2007 through 2010 of at least $2.1 billion.
So in confronting complexity, senior management need not despair. As the KPMG survey concludes, technology is a hot-spot. With the decision power of Operations Research available, senior management has the possibility and capability to improve or even change business models, open up new markets and electrify business performance, defying complexity.
Thursday, 30 June 2011
In his third law Arthur C. Clark states that any sufficiently advanced technology is indistinguishable from magic. So, in some cases at least, magic doesn’t derive from an actual mystical or spiritual source; rather, it is technology in disguise. When we would be able to send a car a couple of ages back in time, driving our horseless carriages probably would have created a witch hunt. Not to mention flying an aircraft. Time travelling over shorter distances; our computer networks and phones that can do nearly everything must seem magic for someone living in the late 1960’s. So Arthur C. Clark is probably right. What does it imply for Operations Research? Is it sufficiently advanced technology to be indistinguishable from magic? Initiated only a few decades ago in World War II, Operations Research has enabled us to achieve some remarkable things that to the non-initiated seem to be magic. It has enabled us to solve the unsolvable and step by step it is entering our daily lives becoming an indispensable piece of technology, doing its magic every day.
The simple task of assigning activities to people can become very complex and unsolvable for a human. For small instances it’s a simple puzzle. In case of 2 activities and 2 people there are only 2 possible assignments. Deciding which one is best is therefore easy. But when the size of the instance increases, the number of possible assignments explodes which makes it impossible to find the best possible assignment. In optimising the assignment of 70 activities to 70 people, there are 70! different assignments to be evaluated. Constructing all of the 1.19785717 × 10100 assignment possibilities would take forever. How to find the best possible one? Here is where Operations Research does its magic. One of the first optimisation techniques developed within Operations Research is the simplex method. It was discovered by George Dantzig in the late 1940’s and it still plays a very central role in Operations Research. With the simplex method the best possible assignment of 70 jobs to 70 people can be found within a few minutes or even seconds. Compared to what a human can do, this for sure is magic.
When we fast forward to our current era, Operations Research applications arise everywhere; in Finance & Accounting, Marketing, Procurement, Production management, Logistics, Personnel management, Government, Sports, etc, etc. It has become a deciding factor for companies to survive or become top players in their market. One of the areas where Operations Research has become a deciding factor is in the airline industry. In 1978 the Airline Deregulation act was signed, its purpose was to remove government control over commercial aviation. As a consequence competition increased with low-cost carriers seizing their opportunity to get a share of the commercial aviation market. The exposure to competition led to heavy losses for a number of carriers, some of them even went bankrupt. To counter low-cost airlines like People Express, several strategies were developed by the main carriers. American Airlines (AA) was the most successful one; using Operations Research they developed a pricing strategy now known as Revenue Management or Yield Management. According to AA it is “the single most important technical development in transportation management”. The essence of Yield Management is to use price to influence customer demand. As a consequence the price for a passenger seat may vary over time. This explains why the price for the same flight may vary between two visits to a booking site, leaving you clueless on the reason why. As if the airline uses a spell to maximise revenue. But because of this magic, we can fly cheap.
Operations Research is entering our daily lives more and more. It is responsible for the fact that we:
- have highly reliable electricity and gas supply networks,
- can use the internet,
- are able to develop reliable public transport schedules,
- have attractive soccer match schedules
- are able to operate global supply chains,
- have satellite navigation,
- can manage the risks of pension funds,
- have effective cancer treatments
It’s all possible because of Operations Research. It doesn’t matter if you understand why it is possible, just use it. Sit back and enjoy, let it do its magic!
Sunday, 26 June 2011
Major part of the air pollution in inner-cities comes from the vehicles doing last mile distribution. Think of the replenishment of shops and stores but also of bars and restaurants. Many of them use a Just-in-time (JIT) based replenishment strategy, increasing the number of vehicle visits while reducing the drop size. JIT minimises the inventory levels in the shop and hence reduces costs. Counter side of it is that the inner cities become congested with vehicles and the emission levels rise. So, reducing the number of vehicles and/or the total distance travelled by those vehicles will lower emission levels and therefore improve air quality. Easy, thing is how to set a reasonable enough level of emission reduction to improve living conditions in the inner-city without harming economic activity and second decide on the necessary measures to achieve it.
It has been the subject of a project that I have been working on in the past months. Objective of the project was to provide decision support to the municipality of cities in deciding on logistical measures to better regulate inner-city logistics and reduce emission levels. One of the objectives of the project was to supply insights on the current sustainability of a city and assist in setting reasonable emission reduction goals. The project resulted in a mathematical model that supports these decisions, fact based. Also the model is capable of evaluating different measures (like a curfew, consolidated transport from city depot, electric vehicles, etc) on congestion and emission reduction effects. That way the most effective measure can be selected before putting it in practice.
Setting a realistic goal is difficult. Cities have different infrastructures and different distribution of shops, bars and restaurants. Taking all this into account would take a lot of modelling effort and detailed information on the route the vehicles take. Information that is not available (yes, even in these data rich times, we sometimes lack the data!). Therefore we decided to use a technique that doesn’t require a lot of detail on the “production” of emissions but would give a good estimate on the “environmental performance”. To achieve this we used Data Envelopment Analysis (DEA). DEA is a technique that is used to calculate and compare the efficiency of decision making units (DMU’s), for example factories, that makes no assumption on the underlying form of the production function. It focuses on the efficiency of the transition of inputs to outputs only. Differences in efficiency tell you how improvements can be achieved. In case of the environmental performance we looked at how vehicle movements and emissions were “produced” by number of deliveries and number of drop-off locations (shops, bars, etc) in the inner-city. The resulting relative environmental performance of the cities in the benchmark tells them how well they are doing compared to other cities and how much the emission levels should be lowered to be as “green” as the most efficient city in the benchmark.
When comparing Rotterdam with Amsterdam, Utrecht, The Hague, Enschede and Tilburg I found that Tilburg is the most environmental efficient city. This is no surprise, Tilburg has been working hard to improve inner-city environment for some years already, with all kinds of initiatives. So in reviewing and perhaps setting new goals for 2015, the city counsel of Rotterdam should have a talk with Tilburg, discussing the measures Tilburg has in place to improve the environmental conditions of the inner-city. Knowing what to aim for, the Mayor of Rotterdam can than make a fact-based trade of between the countermeasures to take to improve the inner-city environment and the economic impact of them. And Mayor Aboutaleb, …..010 is doing better than 020!
Sunday, 22 May 2011
If have been working in O.R. consulting for over 20 years now and have learned something that Plato already knew over 2300 year ago, a good decision is based on knowledge not on numbers. It isn’t the analyses of data (=Analytics?) or building and solving a math models (=O.R.?) that leads to better decisions, it’s the knowledge gained in the process. It starts with understanding the problem and framing it right. This can best be achieved by gathering and analysing relevant data, measuring performance and identifying the applicable business rules. Analytics if you will. This analysis will increase the knowledge about the problem at hand and the environment in which it needs to be solved. Based on the data analysis and the identified business rules, directions for improvement (scenarios) can be identified. By analyzing the scenarios, the impact (consequences) of each of these can be identified, again increasing the knowledge about the problem, but also on how to solve it. The “do’s and dont’s” have come forward at this point. Next step is to use the knowledge about the challenge, the data and the business rules to build and use/solve a math model to find the best possible and achievable solution to the challenge (note: optimality in practice is something different compared to the textbook concept of optimality). With the knowledge gained during each of the above steps, implementing the solution is straight forward, apart from the “normal” potential change management issues. Result of it all is a solution to a practical challenge, and hopefully a satisfied customer.
My clients have never asked me what techniques I use to help solve the challenge they face and I also never tell them. In the past 20 years (See: Does O.R. Sell?) I have never come across a client that hired me because I could analyse data, build a forecast model, build/solve a linear programme or was able to build a simulation model. There is a simple reason for that, they don’t know the difference and they don't need to. Introducing yourself with that you are really good at building a math model, have been in Monte Carlo simulation or Markov chains for years, doesn’t help build your credibility. Talking about O.R. or Analytics doesn’t either. What counts is that you understand or show that you’re able to understand the business of your client, his organisation and the challenge he faces. So discussing whether Analytics and O.R. are the same, part of each other or complementary doesn’t really matter from my point of view. I’ll use the technique that is required to solve my clients challenge, no matter if it’s descriptive, predictive or prescriptive. Whoever thought of the term “Prescriptive Analytics” by the way? It makes O.R. to something that can only be applied when a specialist tells you how and when to use it. “Solve this LP model 3 times a day and your problem is solved?”
I once used just a blank sheet of paper to solve a business challenge, a real back of the napkin situation. One drawing was enough to identify and solve the business issue. The drawing was a simple graph. The shortest path in the graph was the solution to the client’s challenge. Calculations where not required, even my client could see the solution immediately. This shows that O.R. can be down to earth and within reach of everybody. That is also how we should go about in the Analytics vs Operations Research discussion, down to earth and for everybody including clients. I would suggest a small twist and add some special focus on the practical side of it all.
Monday, 16 May 2011
There are many more examples like the silicon wafer example, which can cause shareholder value to degrade when practicing a JIT philosophy. An empirical analysis of share price performance shows that firms facing a disruption in the supply chain experienced share price returns that were 30-40% lower than the industry and general market benchmarks. Showing that being prepared and therefore tolerating some redundancies is a better strategy that will at least preserve shareholder value. But which supplies of raw material to keep in stock and at what level? Not a question that can be solved easy.
First step in making that decision is to understand the dynamics of the supply chain. Since disruptions may have impact not only locally but globally a holistic and system wide approach is required to analyse the risks involved. A structured approach for describing and analysing supply chains therefore is needed to help unravel the supply chain complexity. From personal experience, I find that combining the SCOR method of the Supply Chain Council (SCC) with Operations Research is a good way to achieve that. SCOR is a framework and a methodology that allows companies to create high-level descriptions for supply chain systems. Combined with the power of the mathematical models and optimisation techniques from Operations Research you end up with an understandable and manageable models that can be used to create insights and put fact to beliefs. Second step is to identify the appropriate metrics to measure the supply chain reliability, responsiveness or agility, which allows you to measure supply chain risks and asses and mitigate the risks by evaluating different policies, like which stock levels to keep and where to keep them. In a third step, using techniques like Monte Carlo simulation, stock keeping policies can be evaluated. Last but not least, the model can be used to optimise (not minimise!) the stock levels, while minimising supply chain risks. As a result the best stock keeping locations and levels can be identified.
Friday, 29 April 2011
To illustrate the quality of the Dutch healthcare system, let’s have a look at the accessibility of a hospital bed. A study from 2006 shows that for the European Union, 48% of the inhabitants can reach a hospital within 20 minutes. For the Netherlands this is even 70%, which is far above that EU figure. The travel distance to the closest hospital for a person living in the Netherlands is at most 12 KM for 80% of the population. Average distance travelled is 9.7 kilometres with the maximum distance to a hospital bed close to 50km for people living on the West Frisian Islands. The total number of hospital beds in the Netherlands is 52.714 which results in a hospital bed for every 314 inhabitants, given a total number of inhabitants of 16.5 million (all 2009 figures taken from http://www.dutchhospitaldata.nl/ ). When looking at the distribution of people per hospital bed on hospital level, something interesting comes forward. The number of people per hospital bed varies per hospital from 150 to 1230 with an average of 411! This smells like under and over utilisation of valuable assets, probably due to poorly located hospitals. Room for improvement!
In healthcare, as in any other industry, the implications of poor location decisions or too many or too few locations will result in increased expenses or poor service. If too many locations are deployed, capital costs, staffing costs and inventory carrying cost will be high. If too few locations are used service will degrade. Even if the number of hospitals is optimal, poorly chosen locations will impact service. Poor location decisions in healthcare go beyond cost. If too few hospitals are utilized or if they are poorly located, it will increase mortality and morbidity. So, great care must be taken in making location decisions in healthcare, assuring accessibility. Fortunately Operations Research offers all kinds of models that can assists in making that decision fact based.
To improve the utilisation of the Dutch hospital beds I constructed a math model to look for hospitals that could be closed without degrading the accessibility of hospital beds. So, in the optimised situation still at least 80% of the Dutch travel at most 12 kilometres to a hospital bed. Closing a hospital will reduce the number of available beds and therefore increase utilisation of others. This probably will also increase utilisation of expensive medical equipment like MRI, operating theatres and hospital staff. To make sure that hospitals don’t get overcrowded the model makes sure that utilisation of hospital beds in the optimised situation cannot rise above the maximum utilisation of the current situation. Besides increasing utilisation and productivity, closing hospitals will reduce capital costs and inventory carrying cost. These all together will make healthcare cheaper.
With the model I was able to identify 9 hospitals, out of 93, that could be closed without degrading accessibility. Most of the hospitals that can be closed lie in the west part of the Netherlands, which is not a coincidence. In that region there are many hospitals available which reduces the utilisation of the available beds in that region. Closing them won’t harm the accessibility to a hospital bed because of other hospitals in the vicinity. In the improved setting, still at least 80% of the Dutch need to travel at most 12 kilometres to reach a hospital bed. The spread in utilisation of beds decreases, it runs from 154 to 1181 with an average of 433, which is a 5% improvement. The average distance travelled to reach a hospital bed increases with only 3% to 10.0 kilometres.
So even when maintaining the very high level of accessibility to hospital care there is room for improvement. With the use of Operations Research the debate on healthcare costs can become fact based. Reviewing the current situation based on facts helps getting a clear view on current performance and directs the search for improvements. The optimisation techniques from Operations Research will help find the improvements that reduce cost without degrading our high level of accessibility in Healthcare. Above all they will help improve care in Third World countries.
Thursday, 31 March 2011
Saturday, 26 March 2011
Since the mid-1990’s the focus of many companies has been to lower operations cost, focussing on off-shoring and consolidation of production capacity. As a result many of them set up large plants in countries like China and India because of the low cost of labour and low cost of transporting the finished goods to Europe and the US. Also just-in-time inventory and continuous replenishment strategies emerged, especially in retail (causing inner-city areas to get congested). This was all possible due to low oil prices and therefore low transportation cost. With oil prices rising, things become different. A straightforward analysis of changes in Brent oil price versus changes in diesel price shows that a 10% increase in crude oil price will result in an increase in diesel price of 8.7%. The increase of the past year therefore resulted in 36% diesel price increase, or a € 0.12/km cost increase (assuming 3 km to 1 litre fuel consumption, current diesel price €1.329/litre). Although labour cost is still the highest cost component in transportation, the relative part of cost of fuel has risen drastically.
This increase in transportation cost is significant enough to rethink supply chain strategies especially for makers of products with low profit margins and long product life cycles. Think of consumer packaged goods and chemicals. Higher transportation costs will reduce their profits significantly. So what can they do? Without changing supply chain infrastructure, transportation cost will go down when shipping larger quantities and therefore achieving more economies of scale, but inventory costs will go up. Transportation costs will also decrease when using slower modes of transport; from air to road and from road to rail. This will however increase lead time and inventory. Math modelling can make the trade-off clear and lead to the optimal choice. Using 3rd party logistics providers will potentially reduce cost, because they have better consolidation possibilities. Last but not least better utilisation of truck capacity using efficient packaging, load and pallet building capabilities will decrease cost. A nice example is the improvement E-Logistics Control (part of Ewals group) was able to achieve. They managed to increase the truck utilisation by 10%.(Dutch) This was not easy, remember playing 3D-Tetris? Special optimisation models and software, like LoadDesigner, is required to get the best possible truck utilisation. It is not only stacking the goods as efficient as possible on the truck, you also have to think about the order in which the goods will be delivered. Otherwise you have to completely rearrange the truck at each stop. It is a combined routing, packing and stacking challenge.
As transportation costs continue to rise optimisation of the supply chain infrastructure might be interesting. Reducing the length of the final leg in the supply chain and consolidation of shipments will reduce transportation cost but will require additional and larger warehouses, which implies more stock, hence higher inventory levels and costs. Deciding on the number of locations to add, requires finding a balance between transportation costs, inventory cost, handling cost and warehouse costs. The best supply chain design can only be found with the use of a supply chain infrastructure optimisation models. Using these models different supply chain designs can be modelled, evaluated and optimised, taking into account not only the costs involved but the impact on lead times and inventory levels as well. So oil price increases are fuel for thought. Supply chain managers have all kinds of options to deal with oil price induced cost increases. Operations Research can assist them, whether a complete supply chain redesign is considered or just better using the available assets.
Sunday, 20 February 2011
The #LoveSafely hashtag was introduced on Valentine’s Day by the joined UN programme on HIV/AIDS, UNAIDS, to utilise social media to raise awareness about HIV and AIDS. It was used by over one million tweeters! Mission accomplished? No! Much more needs to be done in the fight against HIV/AIDS. One of the major problems encountered in delivering relief food in Africa is a lack of truck drivers. Number one reason for that is AIDS. The truck drivers represent a whole industry that is fighting to survive and, without them, many businesses within Africa cannot be sustained. Ending the spread of HIV and therefore securing the transport sector in Africa is about education, access to health care, and making sure that we all #LoveSafely. That is what the North Star Alliance (NSA) is aiming at by providing health care services in a number of Roadside Wellness Centres, which are strategically set up across the African continent.
As part of our Optimising the World programme, ORTEC partners with the NSA. Last week I had a talk with Luke Disney, the executive director of NSA. We discussed the challenges NSA has, many of which I belief can be solved with the use of Operations Research. The past years of AIDS prevention have been on scaling up. Primary focus was on getting antiretroviral drugs (ARV) out there, training people and setting up healthcare centres no matter what the cost. This way of providing care will eventually hit a wall, donors and governments will not be able to supply the required funds forever. The recent financial crisis already had its impact on the funds available in support of the fight against AIDS in Africa. As Luke indicated in our talk there is still a lot of ground to cover to reach out to everybody in need of care, but there is only a limited number of people and resources. OR can help to start using the available resources as efficient as possible.
A news item from the Uganda newspaper the Monitor of 2007 shows that the challenges in HIV/AIDS prevention/care are not much different from the supply chain challenges we as OR professionals solve for our customers.
Entebbe — AS thousands of Ugandans die everyday of HIV/Aids and malaria, drugs worth about Shs4 billion are rotting in the National Medical Stores Entebbe. While on their fact finding tour of NMS in Entebbe yesterday, MPs on the Social Services Committee led by James Kubeketerya (Bunya East) were shocked to find eight containers of 2- feet, full of expired drugs yet Ugandans are perishing in hospitals without treatment
Straight forward Inventory management and forecasting the need for ARV drugs could have prevented the available drugs to be wasted and shows that the OR community needs to get involved. The Center for Strategic HIV Operations Research (CSHOR) is one of them. But more is needed, focussing on tactical and operational issues as well.
Together with Luke we decided to start a project to help NSA develop a strategy on how to best extend the wellness centre network in Africa. It is not as straightforward as a normal location problem as you might think, but than again which practical OR challenge is? The wellness centres focus on truck drivers, sex workers and local communities at truck stops and border crossings. At the wellness centres basic healthcare can be offered. For more advanced care people have to be redirected to a nearby hospital. In deciding were to locate the next wellness centre this needs to be taken into account. Also NSA is not the only NGO in Africa. It doesn’t make sense to open a new wellness centre in the vicinity of another centre that offers similar care. Last but not least, since the wellness centre offers care to the local communities, the accessibility of the wellness centre is important as well. Many people travel by foot so it should be close enough for the people to be able to reach it. These are just a few conditions that we know that need to be satisfied; in the project many more will surface.
First step we will take is gathering the relevant data and do a survey to better understand the current situation and develop some basic ideas to develop the network expansion strategy. NSA aims to cover 85% of cross border traffic in sub-Saharan Africa by 2013. I committed myself to support Luke and his team to make that happen. I’ll keep you informed on our progress in a next blog entry. With the support of OR, NSA will be able to offer the required education and safeguard access to healthcare to mobile workers and make sure that we all #LoveSafely.
Sunday, 30 January 2011
Fortunately politicians are aware that we are around and know a little of what we can offer. Let me give you an example. In the Netherlands every year on the 3rd Tuesday of September (Prinsjesdag) the Dutch Treasury presents the budget plan for the coming year. Part of the plan is the budget that deals with road construction and maintenance. The height of the budget typically is a political decision (“you need more tarmac to fight traffic jams” is the current political paradigm in the Netherlands. We know they are wrong about that). As with any politically determined budget, it doesn’t cover all that is required to satisfy the ambition levels. So the Ministry of Infrastructure and Environment has to figure out a way to deploy the budget in a way that best satisfies the priorities set in their policy. To support them is this puzzle we at ORTEC constructed a model several years ago that helps them make the trade off. With the model, better insights were available leading to better plans (meeting the budget more closely) with focus on satisfying the ambitions levels from the policy at hand.
Sunday, 23 January 2011
While I’m writing this I’m listening to the latest CD from the Killers. Actually, “Change you’re mind” is playing (changed your mind on math yet?). There is no Math in this, besides the digitized version of the song on the CD is there? No, the CD player uses a Math algorithm from Irving Reed and Gustave Solomon to correct for errors due to scratches on the CD surface. Without it, it wouldn’t sound as good as it does now. There are many other examples of Math in daily life. The money system is one of them. Ever wondered why the split of coins (1, 5, 10, 25, 50 and 1 dollar) doesn’t match the split in notes (1, 2, 5, 10, 20, 50 and 100) ? Your satnav is another one, like using a cell phone and surfing the internet, it requires a shortest path algorithm. Taking the train requires a schedule; making a schedule involves Math and even won the Dutch Railways the Edelman award in 2008. Also when you receive a speeding ticket, Math is used to decipher the speed camera image of your licence plate to identify you as the car owner. Computer chip design and data compression (much used to reduce download times of images while surfing the internet) also use Math massively. Magnetic resonance imaging (MRI) is a imaging technique used in radiology to visualize detailed internal bodily structures. Math is used not only to design a MRI scanner; also using the scanner involves Math. Due to the use of Math the accuracy of MRI scanning is very high, up to 0.3 millimetres! Last but not least Business Analytics, whether being it descriptive, predictive or prescriptive, requires data and Math. As does Operations Research.
The above mentioned examples are only a very small set but give a nice impression on the impact of Math. Math is everywhere and if there is no Math around, things go wrong. If the traders of derivatives would have had more mathematical insights in the products they traded, we might have avoided a financial crisis. Math is a modest discipline, but it should get more credit for what it offers. So let me change your mind and start with a modest celebration and announce 2011, the Year of Math. With 2011 being a prime number, no better choice possible!