Friday, 30 December 2011

Optimizing the Human Resource Supply Chain

Reading the various outlooks for the coming year, I came across the Manpower Employment Outlook for 2012. Interesting statement from the report is that although employers are more inclined to hire new personnel there continues to be uncertainty about the market, so they are reluctant to make the investment in a permanent hire. At the same time employers also have trouble filling vacancies for specialist jobs like technicians, sales people, skilled trade workers and engineers, as indicated by another report from the same agency. This makes balancing the demand and supply of human resources a though job. Overstaffed units will put pressure on the EBIT because of too high cost levels, while understaffed units will suffer from service degradation or loss of revenue. However, when companies link their strategic workforce planning with their business planning the workforce productivity can be optimised which will result in a decrease the total cost of human capital, while maintaining or increasing the overall quality of their workforce and customer service.

Effectively managing demand and supply for human resources requires a structured planning process, starting with a forecast of the demand for human resources as derived from the business planning. Next the supply of resources needs to be forecasted. Since people change jobs, retire, get hired or fired available human capital will change over time, dynamics that need to be taken into account in strategic workforce planning. Matching the forecasted demand and supply for human resources indicates where shortages of overages can be expected. Making a choice on the instruments for adjusting human resource supply a company can than take appropriate action to establish the best possible balance between supply and demand. In this process the HR manager can benefit a lot from the Operations Research models in making these decisions fact based.

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.

In long term capacity planning it is decided how to deploy the expected available capacity, which is not straightforward to accomplish. Human resources differ from ‘normal’ resources since they are not consumed in the make process (they deliver a service) and productivity and efficiency depends upon their workload and utilization (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

The incredible balancing act of Unsold and OutOfStock

Inventory management is one of the key factors determining the performance of a supply chain. A small change of the inventory policy can lead to a dramatic alteration of the supply chain’s efficiency and responsiveness. Traditionally inventory management is challenging because it directly impacts both cost and service. Uncertain demand and uncertain supply make it necessary to hold inventory at certain places in the supply chain to provide adequate service to customers. As a consequence, increasing inventories will increase customer service and revenue, but also increases cost. According to the 22nd annual state of logistics report (pdf), the world is sitting on roughly $8 trillion worth of goods held for sale, and nearly $2 trillion in the U.S. alone. That's a lot of capital tied up in warehouses. Besides being a huge capital absorber, inventory also represents a tremendous amount of environmental footprint. If we could permanently reduce the amount of product sitting idle, we'd save money, energy, and material.

Effective inventory management is very hard. A recent article in the Financial Times illustrates this. Following the earthquake in Japan, many tech companies started to stockpile critical components to avoid shortages later on. However, unexpected low customer demand in US and Europe resulted in tremendous inventory levels. The components for which it was expected that there was a risk of shortage are now having the biggest problems in terms of oversupply. Deciding on the right level of inventory therefore is an incredible balancing act of unsold and out of stock where Operations Research can be your balancing pole supporting you in making the trade-off.

To illustrate, the above figure shows three warehouses and four customers. The supply chain manager has to decide how much stock to keep at each of the warehouses and which customer to serve from which warehouse. Stock keeping cost is given for each warehouse. Transportation cost is presented in the table below.

The supply chain manager is uncertain about customer demand but has to decide immediately on the number of stock keeping units at each warehouse, otherwise the available floor space will be leased to another company. He decides to go for the average demand. Using his MBA skills he optimises for minimal supply chain cost (warehouse and shipping cost) and decides to have 6215 SKU at warehouse 1 and 3000 SKU’s at warehouse 3. Perfect, ….or could he have done a better job? Actual demand will deviate from the average demand (average doesn’t exist, does it?) leaving the supply chain manager with either unsold stock like in the examples above or lost sales (on average 431 of either unsold or lost sales given the demand scenarios). Would minimizing for lost sales be a better option? Total supply chain cost will rise for sure, possible also increasing the level of unsold stock. Here Operations Research can be of assistance to find a balance. Using the available demand scenario’s (min and max in this case) the average total supply chain cost can be determined while varying the level of average lost sales (see graph below).  Note that the supply chain cost of the average demand is not the same as the average supply chain cost of the demand (the flaw of averages!).

This way the supply chain manager can make the trade off between supply chain cost and lost sales leading to better quality decisions. He/She can use this information to share with all players in the supply chain, like marketing and sales, production and procurement, building a shared view and plan. Operations Research will help find the balance between Unsold and OutofStock and keep it when incorporating deviations from forecasted supply and demand, enabling you to practice Sales and Operations Planning fact based.

Tuesday, 4 October 2011

Deciding on Lean or Green

Imagine getting into your car, entering your destination into the satellite navigation system and getting not only the two obvious options for the shortest or fastest route, but also the most sustainable one. What would you think of that? Calculating the shortest or fastest route is easy from an Operations Research perspective, just use Dijkstra’s shortest path algorithm. How about the most sustainable one? Whether a route is sustainable or not depends on many factors; maximising sustainability therefore is different from minimising travel time or distance. There is however a linear relationship between fuel consumption and CO2 emissions. So when fuel consumption can be taken into account, optimising for the most sustainable route would become possible. That is exactly what two of my colleagues at ORTEC, Goos Kant and Patrick Schittekat, did when researching the net effect of focussing on sustainability in logistics.

Fuel consumption is influenced by factors like engine type and aerodynamics of your car, the type of fuel used, traffic density, variance in driving speed, the weather and not to mention your driving habits. Some of these factors can be modelled easily while others, like the weather, are more challenging. Research as reported by UK’s National Atmospheric Emissions Inventory (NAEI) shows that CO2 emission levels can be expressed as a function of vehicle type, fuel type and engine type and of course speed. So when we know these parameters, we are able to calculate emission levels.

In calculating an optimal route, a digital road network is required. Companies like NavTeq and AND provide these maps. A digital road network consists of points and segments connecting these points, representing the road network in the real world. The segments are of different types, representing different road types each with a different speed. Think op motorways, regional roads, local roads and city areas. Given the length of each segment and speed of a vehicle on each of the segment types, the shortest and fastest route can be calculated. An interesting new development is that more and more information is added to digital networks, like the time/day dependent average speed at which these road segments have been travelled, allowing for a better estimation of actual travel time and therefore speed.

Using the formula for CO2 emission levels, the sustainability cost for each segment in the road network can be calculated, using the travel times reported in the digital network segments. Using Dijkstra’s algorithm, the most sustainable route (lowest emission levels) between each point in the network can be calculated and a comparison can be made with the shortest and the fastest routes. Research of my colleagues Goos and Patrick indicate that the greenest route is about 5% slower than the fastest, while the shortest is 35% slower. Also the greenest route is 2% longer than the shortest, while the fastest route is about 6% longer. Comparing on costs (using social cost per ton CO2 as reported by DEFRA and associated cost for vehicle and driver) the greenest route is about 1% more expensive than the fastest, while the shortest is 17% more expensive. So in short taking the green road home is only slightly slower and costs a little bit more than the fastest (time is money after all).

In logistics making the trade off’s between lean or green isn’t common yet but will change in the near future. A example of this trend is the special programme (Sustainable Logistics) in which Dutch companies have committed themselves to achieve a 20% reduction in CO2 emision levels by the end of 2012. Incorporating emission levels in logistic optimisation models will help create insight and guide companies towards more sustainable choices. By the way, focussing on efficiency (cost reduction) will also lead to more sustainable solutions. Driving less kilometres because routes have been optimised by changing the order in which customers are visited or changing the assignment of customers to routes will directly lead to reduced emission levels. So not Green or Lean but Lean & Green!

Sunday, 25 September 2011

Distinguishing the Good from the Bad

In my work to assist companies in improving their decisions making, adding mathematical rigour and making it fact based, sooner or later my client remarks that now that Operations Research is used the quality of decisions must have improved. It’s tempting to confirm that, but that would be too single minded. While using Operations Research will have a positive influence on decision quality, it is only one of many factors in high quality decision making. In judging the quality of a decision we typically equate decision quality with the attractiveness of the result.  Don’t you feel silly when you’ve carried around your umbrella all day but there wasn’t a drop of rain to shield you from? What does it say about the quality of the decision you made that morning? Is good or bad determined by the result? And what does it tell you about the added value of the decision methodology you used in making the decision?

When we have a good result we are inclined to conclude that we’ve made a good decision.  Likewise, with a bad result, we conclude that we’ve made a bad decision. This is definitely not true. Decisions and results are two different things.  Good results are what we desire, whereas good decisions are what we can do to maximise the likelihood of good results. For decisions that are made at a high frequency (say every day/hour) quality could be measured using statistics, improving consecutive decisions. The conditions under which operational decisions lead to a result can only change slightly, given the short time span between the two. But for decisions on the tactic or strategic level it can take months or even years before achieving a result, for example in developing a new product. Using statistics to measure decision quality in that case is unrealistic. Moreover many of this kind of decisions are of the one-of-a-kind nature. When the time between decision and result increases, uncertainty will have a growing impact on the quality of the result. In the future, events can happen that cannot be controlled or foreseen.  Such events can cause good decisions to have a bad result and vice versa.  Therefore, the quality of the result is not a good indicator of decision quality and the result is irrelevant as a measure of decision quality.

How to assure good decisions then? Key in making a good decision is to have a structured decision making process. A structured decision making process starts with three ingredients:
  1. What do I know (Information) about the business opportunity under consideration and the environment in which it resides?
  2. What are the options (Alternatives) open to me?
  3. What are my preferences (Values) in deciding between the alternatives?

Central in a structured decision making process is the logic or mathematical model. It allows you to put Information, Alternatives and Values together in a logically consistent way and make a good decision. 

Because the inputs of the decision are made explicit we can establish that a good decision has been made, before the results of the decision are known. It allows discussion on all the inputs, therefore building a common view and commitment, supporting the implementation of the decision. Notice that the logic follows from all three factors, Information, Alternatives and Values. So Logic alone is not a guarantee to quality decisions.  Putting this process to work starts with framing the decision; making sure that purpose and scope of the decision is discussed and agreed upon. Next is identification of what can change and can’t be changed in making the decision, creating an explicit or implicit set of alternatives. As Michael Trick blogs, accurate data is essential in achieving high quality decisions. Without it, decisions are based on quicksand. It’s the third ingredient in the decision making process. Final step before preparing a mathematical model is deciding on the valuation principles to be used. A decision is made because it will lead to an increase in value within an organisation, like increase in share price, revenue or EBIT. So valuation needs to be explicitly considered in decision making.  With all these ingredients in place and managed right, good decisions will results.

So next time when you return home soaking wet because you left your umbrella at home given the weather forecaster was absolutely sure that it was going to be a sunny day, go back and review your decision making process that morning. Check the information base, the alternatives considered, the values and logic used before you consider yourself a fool. Chances are that it wasn’t a bad decision. The result was bad, but that’s because you can’t trust a weather forecaster. Some things can’t be changed; it’s your decision to prepare for them or not that makes your decision good or bad.

Monday, 15 August 2011

Complexity Defied

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

Modelling Magic

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
  • etc

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

Solving a Mayor’s dilemma; fact based environmental policy development

This week Rotterdam, one of the major cities of the Netherlands, was on the news. The city’s Court of Audit had analysed the city counsel’s policies on improving the environmental conditions of the inner-city and concluded that things weren’t going great. Rotterdam has set itself emission reduction goals, but with the selected policies it wasn’t going to achieve them according to the Court of Audit. The Court even concluded that the situation was getting worse. Compared to 2009 the number of hot spots on NO2-emissions in the city has increased from 1 to 5 making the city a less healthy place to be in. The city council however is still convinced that they will achieve the goals set for 2015. Until now however, several countermeasures have been introduced with little or no effect on the air quality in the inner-city. One very drastic way to accomplish emission reductions would be to close down several parts of the inner-city; this would however impact the local economy heavily. Quite a dilemma for Mayor Aboutaleb.

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

Analytics and Operations Research; a practitioner’s view

The topic whether the O.R. society should embrace (business) analytics is one that will probably go on for a while. It’s THE theme that keeps O.R societies occupied at this moment; all are busy with the question whether they should hook on to analytics since it could boost awareness and interest for O.R. Although the term “Business Analytics” is quite old, it dates back to Frederick Taylor’s time management practice in the late 19th century, it is presented as the latest trend in management and every CEO should start using it. Amazon lists over 1,500 books on the subject, nearly every one of these books has the theme “Start using Analytics in decision making, otherwise you will be doomed to the lower end of the performance ladder and go bust”. In promoting the use of analytics different terms are used which makes it hard to understand what people are really talking about. Terms like business intelligence, business analytics, descriptive analytics, predictive analytics, prescriptive analytics, and so on. From a practitioner’s point of view the discussion on the subject is rather academic, my clients don’t really care whether I use the term Analytics or O.R. in improving their operations or decision making. They just want me to help them solve their problem.

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

The Risk of being Just in Time

Most executives don’t realise that optimisation can make companies vulnerable to changes in their environment. They have been taught that efficiency and maximising shareholder value don’t tolerate redundancy. For that reason many manufacturers practice a Just in Time (JIT) stock keeping philosophy for raw materials. Since for most manufacturing operations over 70% of the cost is associated with purchasing of goods and services, keeping stock levels low seems a sensible thing to do. In JIT the focus is on controlling the stock levels of the sourced materials, since finished goods and internal sub-assemblies are within the control of the manufacturer. Unfortunately this is also where the highest risk in the supply chain resides; disruptions in the supply of raw material can cause missed customer shipments or worse shut your customer down.

Buying and shipping of supplies has changed in the past two decades. The locations, at which components are produced, are spread around the world, with technology and low costs as its driving forces. This causes supply chains to become longer and far more complex than in the past, making it hard to asses the risks. An example of such a risk is the bullwhip effect. When supply chains get longer, lead times increase, which in turn amplifies demand fluctuations leading to increased stock levels. Most manufacturing companies have deep knowledge of their primary suppliers; the risks however reside further down the supply chain, at the supplier’s supplier. An example from the electronics industry is the production of silicon wafers, the raw material used to make computer chips which in turn are used in smart phones or tablets. 60% of the world supply in silicon wafers comes from two Japanese companies one of which is the Shin-Etsu Chemical Company. Its main wafer plant in Shirakawa was damaged by the recent earthquake in Japan. Chip makers like Intel, Samsung and Toshiba depend on these wafers for their production of microchips. Typically, they keep stock levels for 4 to 6 weeks of production. So if their supply falls short, Apple and other smart phone/tablet producers might be impacted, just as a tablet war is about to start.

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.

So, a stock keeping strategy should be a result from a thorough analysis of the full supply chain, focussing on the robustness of the complete chain, taking the risk of disruptions in supply into account. Following what’s common (like JIT) can have high impact on the performance of the supply chain, and destroy instead of create shareholder value. Adding OR to SCOR will enable you to asses risk in a standardized and fact based way, leading to informed choices on which risks should be mitigated and how to mitigate them. This allows you to make a fact based decision on which raw materials to keep on a Just-In-Time or Just-in-Case stock level.

Friday, 29 April 2011

Less is Better; A cure for Dutch healthcare

This week a wellness centre of the North Star Alliance visited ORTEC headquarters in Gouda. Walking through the wellness centre and listening to the stories on the care offered by North Star to truck drivers, sex workers and local communities along the traffic corridors of sub-Saharan Africa, I realised that we are very fortunate to live in the Western world. Basic healthcare isn’t a top of mind topic for us; it has been arranged very well. Maybe it’s even a bit overdone? Reading the papers seems to confirm that. In nearly every country in Europe rising costs of healthcare is a main topic for the government. Main reasons for ever rising healthcare costs are increase in life expectancy, welfare increase and an increasing number of illnesses that can be cured due to innovation. The Dutch government also attempts to reduce expenditure in healthcare. But much of the political debate is about believes and less about facts.

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 ). 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

Da’s logisch (That’s logical )

Last Tuesday the Dutch national soccer team played against Hungary. As with any match of the national team, it was on television, encapsulated in a programme in which various self pronounced soccer experts discussed and analysed the match and accomplishments of the team. It is fun to listen to the soccer gibberish used by these experts in their comments on the actions of the coach and team performance. They are always able to tell what went wrong and what would have been a better option (as could any other inhabitant of the Netherlands). A famous expert in soccer gibberish is soccer legend Johan Cruijff. He is famous for quotes like “Every disadvantage has its advantage”, “If they have the ball, you can’t win” or “That’s logical". So, being a coach is tough, especially when it concerns the nation soccer team, because the complete nation is watching you. Since much of the decisions in soccer are made through intuition and common sense it might be a good idea to put some numbers to beliefs/convictions and introduce some rigour in the decision making.

The decisions a coach needs to make are complex. Think of scouting for a new team member to fill in an open position and improve the overall quality of the team. Or deciding on the overall composition of the team selection; which players, how many for each position, etc. A decision a coach needs to make often is the line-up for the next match. A simple calculation shows that there are 308.403.583.488.000 possible combinations for the line-up for a soccer game, given 26 players in the team selection. How will a coach be ever able to identify the best possible line up?

Given that a player performance will differ depending on his assignment to a certain position in the field (a goalie is not much good in a forward position), a certain attractiveness score good be given to such an assignment. Identifying the best possible line-up can than be seen as finding the assignment of players to field positions that maximises the total score. Because every position in the field must be filled in and a player can have at most one position in the field the so called maximum-weight bipartite matching problem comes forward. Introducing a source (supply side 11) and sink (demand side 11) and giving the arcs from the source and towards the sink a maximum capacity of 1, this bipartite matching problem can be solved as a transportation problem. It could be easily implemented as an App on the coach’s iPad. The tricky part in this approach is the scoring of the assignment of players to positions. This is where sports analytics come in.

By keeping track of the player performance during matches, the effectiveness of the player can be measured. This score could than be used in the optimisation of the next match line-up. By measuring the number of intercepts, the number of safes, the number of passes, etc all kinds of statistics can be calculated. Using these statistics an overall efficiency score can be calculated, see for example the Match index of SoccerLab. Not only after the match, but in real time as well. The score gives an indication of the players’ ability and effectiveness of the assignment to a position in the field. Companies like ORTEC TSS provide the kind of statistics to calculate this kind of efficiency scores. So instead of listening to the soccer gibberish of the experts, the facts can be used and the actual performance evaluated. The coach could use the statistics together with the line-up model, during the match, to decide on which player the exchange (a much discussed subject in the Netherlands; Bosvelt for Robben EC 2004) and decide on the line up for the next match. With an analytical approach and structural analysis of player performance, deciding on the line-up becomes more fact based leading to more sustainable team results. It doesn’t however offer a guarantee on winning championships, but that’s logical.

Saturday, 26 March 2011

Fuel for thought

With the struggle for a more democratic regime in Libya and other North African countries and the debate on nuclear power given the trouble in Japan, oil prices have risen in the last couple of months to the highest values since august 2008. To illustrate; a barrel of North Sea Brent has gone up from about $80 per barrel in March 2010 to about $110 per barrel today. That’s an increase of nearly 40% in just one year. How does this rapidly increasing oil price effect supply chains and their operations? Should companies just endure this increase in transportation costs, or are there alternatives?

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

Does Prime Minister Rutte require an OR/MS counsellor?

Would the world look a little better if politicians made more use of Operations Research? I think it would. Apart from improving the voting system a little, democracy is mathematically impossible after all, Operations Research will also add value to political decisions. Many of the complex problems a politician has to deal with are the ones we as Operations Researchers like for breakfast!

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.

But Operations Research can offer more than just a capital budgeting model to help manage the public expenditure. Think of major investments like flood protection systems or aircraft like the Joint Strike Fighter. These decisions are very complex and have a high degree of uncertainty with respect to the costs and benefits involved. For example, the estimated cost per flight for the JSF has risen with 90% since 2002. Ever tried to defend a decision based on that kind of uncertainty? Politicians have to. Use of Operations Research can help here, to show the impact of uncertainty and structure the decision process.

It’s not only investment decisions that are complex; the same applies to the organisation of healthcare, deciding on the location of hospitals and the kind of care to offer there. The growing (and sometimes locally shrinking) population and aging are difficult trends to deal with. How to assure public safety with a possible shortage of policemen? How would you make the society more sustainable, organise country and social security, health insurance and pensions? This list of questions could go on forever. Each of them will benefit from the use Operations Research. With Operations Research these questions are dealt with using facts, not feelings or (political) beliefs. Insight will be gained on the major factors that influence the decisions, improving the overall quality, avoiding big mistakes (like CCS under a densely populated residential area) making the world a little better. So dear Mark, let’s get in touch.

Sunday, 23 January 2011

2011, the Year of Math!

This week Google announced that it will commit itself to support the International Mathematical Olympiad (IMO) for the next 5 years. Google donated one million Euros to help IMO! That’s a large amount of money. With this donation, the IMO will have a jumpstart in organising this global event in the coming years, enabling high school kids from countries around the world to pursue their passion for Mathematics. This year the IMO will be organised in the Netherlands, in Amsterdam to be precise (See ). A good reason for making 2011 not the year of the rabbit or the bat, but the Year of Math, at least in the Netherlands.

When trying to promote Math my experience is that, as with Operations Research, people are not very keen to support it. In trying to find sponsors to finance the IMO 2011 event, many times I was turned down. Math suffers from the same syndrome as Operations Research, it has great and a positive influence on our daily lives but nobody knows it. Math is, contrary to management and politics, honest, undisputed, straightforward, consistent and creative. It can offer elegant and uncompromising solutions to tough problems. But many people see it otherwise. They think that Math is like Chess, no more than brain training and of no practical use. Maybe they see Math as an art, given the beauty of fractals. When looking back at the Math courses at high school and University, I can understand that (that is something that should improve!) But look around you, there is no escape. Math is everywhere and in a positive way.

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!