Wednesday, January 16, 2008

Industrial Visit - BMW, Munich

BMW (Bayerische Motoren Werke AG) is one of the leading German manufacturers of automobiles and motorcycles. BMW also owns the Mini and Rolls-Royce car brands. It has its beginnings in 1913 and was also involved with building airplane engines during the World Wars.
BMW is one of the best known car brands in the world known for their high quality and safety standards. Hence, they are able to afford a delivery time of 4-8 weeks in this competitive world and even use it as a marketing tool to showcase their brand value.
The Munich plant is one of BMW’s oldest and is right across the road from its corporate headquarters. As part of our industrial visit, we were taken many shops including welding, painting and assembly. All the shops except the final assembly were heavily automated. The level of automation reached by the BMW plant is mind-boggling. BMW customers are allowed to customize their products over the internet or through dealers. This means that every car in the BMW assembly line could be different from the next one. BMW is able use techniques of mass production with the help of RFID tags. These tags communicate with the machines in all the shops and inform them how exactly it has to be processed.
It all starts with rolls of steel sheets being brought in from the suppliers. These sheets are cut and bent into various parts of the car’s body. These parts are placed together and welded almost completely by robots with workers needed mainly to overlook the process.
The body frame is then passed through the painting shop. Each car goes through four rounds of painting. The frame is first immersed into a tank of paint which acts as an anti-oxidant. The second coating consists of a base color over which the actual color is sprayed. The last coating of paint is the one which gives the cars their glossy shine. The body has to spend quite a while in the paint shop as it takes time to dry.
Parallely, the engine is machined and assembled in the engine shop. It is then tested at no-load and full-load and boarded onto the respective chassis. In the final assembly shop, the chassis is then matched to its body frame using RFID tags and fastened together.
After the assembly, the cars are pretty much read for the road. But the vehicle is driven for an equivalent of 3000 km in order to test it thoroughly and also to ensure smoother driving for the customer.
An important aspect that was visible in Europe in general and BMW in particular was the importance given to human capital management. BMW makes all possible effort to reduce any physical or mental stress on the employees. The employees are rotated from job to another in order to remove monotony. All automations are designed by keeping in mind the comfort of the employee. For example, the entire vehicle is upturned during some sections of the final assembly so that the employee can work in a more ergonomic position. All such measures do tend to keep job satisfaction high. And besides that, it also ensures that the product is of higher quality and there are lesser chances of failure or rejections. This, according to the BMW management, makes all such measures worthwhile in the long run.
One of BMW’s competitive advantages is its very strong research and design focus. Besides designing the cars, they also come up with important innovations in production techniques (some of which have come up in collaboration with Japanese companies) which have enabled them to maintain their premium quality

Based on an industrial visit to BMW, Munich by Charan Nallapa Reddy, PGP22, IIM Lucknow, as part of his Exchange programme to the University of St. Gallen, Switzerland


  1. The Premier Padmini car design resembled design of a car launched by Fiat in India. Which car are we talking about?
  2. Founded in 1918 by the founder at age 23 years, the first product of the firm was an attachment plug. This electronics giant imports and distributes’ Famous Grouse’ Scotch whisky. Identify the company
  3. Connect the Following: Sabeer Bhatia, Anil Ambani, Kavita Iyer

    Kindly mail your answers to

Tuesday, January 8, 2008

Dealing with uncertainty through Robust Optimization

“The Goal is to make Money”, as stated by Goldratt ; and that can only be done through optimal use of the resources like material, money, man, machine or anything which goes into the system as input. Business is all about managing these resources efficiently and making best use out of it to make profits and provide better services. For that matter Business Manager is exposed to several problem situations where he has to take decisions based on his knowledge and available information using suitable supporting tools and methodologies. Most of the resource planning problems can be modeled as optimization problems and can be handled using methods like Mathematical Programming. The knowledge of the Decision-maker helps in understanding the problem situation, identifying the decision variables and modeling the problem as a Mathematical Program. The information is retrieved pertaining to values of several parameters with respect to the problem in focus, which is used for solving the optimization model.

One may say that Information is also a type of input which goes into the system to facilitate the process of managing business and thus making profits. In fact, in the current business milieu it has become one of the very crucial inputs; as right amount of information available at the right time can result into right decisions. But often this information is not available in the precise form. Decisions are required to be taken before actual values of the parameters are known. This imprecision in information may cause serious disruptions in the planning and decision-making process. Sometimes the decision becomes totally useless as the solution happens to be infeasible.

Most of the real-life optimization problems are characterized with this imprecision in the parameter values (for example uncertainty of demand, sales, transportation costs, share prices etc). There are ways of addressing this problem of imprecision or fluctuation in the values of the parameters, like sensitivity analysis or using point estimates like averages or expectations but solving a deterministic optimization model using a point-estimate of the parameters may not be a wise proposition. These approaches are reactive methods and not the proactive ones. Using reactive methods one can only find out the impact of fluctuations in the values but what is needed, is to take care of this imprecision into the modeling aspects.

Imprecision is inherent into the system. This imprecision in the parameter values may occur because of several reasons like forecasting or estimation errors, measurement errors and implementation errors etc. Two situations arise with respect to imprecision in the data – first, when a probability distribution of the values is known; second when there is no information regarding the probabilities also. First situation is called the risk and second is called true uncertainty. In the case of uncertainty what we just know is the range of the values, so it is also called interval uncertainty.

There are some proactive methods like Stochastic Programming, Chance constrained programming and a few other probability based models to deal with modeling under risk. But practically, the reliability of probability estimates is also questionable. These estimates are often biased and prone to error, which leads us to the second situation of interval uncertainty. To deal with such problem situations new methods called as Robust Optimization (RO) methods are coming up. RO methods are proactive and take care of this uncertainty by searching for a robust solution which is feasible and close to optimal for all realizations of the uncertain parameters. RO methods are immune to uncertainty and do not depend on the probability distributions. These methods assume that parameter can realize any value from the given interval. There is not a single method as such, different researchers have explored and exploited different modeling aspects using different methodologies but the purpose is same, looking for a feasible and close-to-optimal solution for all scenarios.

To make it clear let us consider a case of an FMCG company which has to transport its goods to different cities from its one or more manufacturing plants and they have to decide the number and location of warehouses or distribution centers (DC) for different regions of the country. These DCs will further supply to the retailers (the demand points) so another decision is which DC will supply to which retailers. Though, company has methods to estimate the demand at retailers end and the cost of transportation per unit demand is also known, but it is very obvious that demand and the cost of transportation practically fluctuates. Moreover the forecasted demand will not be same for every month, whereas we have to take the location decision for a long-term. Location Decisions are strategic level decisions and involve huge expenditures, thus cannot be changed frequently. Also, the DCs have certain capacity beyond which these cannot hold inventory.

If this location-allocation problem is solved using the average demand, it might happen many times that the solution becomes infeasible due to the capacity constraints. Fluctuations in demand may result in low capacity utilization at one place and capacity being exhausted at another place, at the same time. Lower than expected demand at one DC will result in overstocking and therefore extra inventory carrying costs. If the product is of perishable nature, the total value of the item is lost. If it comes from the product segment characterized by rapidly changing technology, then it becomes obsolete and, either loses its whole value or is sold at a lesser value. On the other hand, if the demand is more than expected, then understocking of items will result in either lost sales, backlogging or, if possible than, demand fulfillment at a higher cost. This also impacts upon the goodwill of the firm in the market and results in losing to the benefit of the competitors.

In many cases relying upon the probability distributions of the parameters based on past data might not be a good idea. It is just like asking someone, “What is the probability that this probability distribution is correct and what is the probability that probability of probability distribution being correct is correct and so on….” In such cases RO may be helpful in taking robust decisions by finding a solution which remains feasible and thus implementable in most of the scenarios.

The literature related to RO is new and sparse. Though researchers had started talking about uncertainty and robustness long time back in the past but most of the work in this domain has been done in last 5-7 years. Seeing to the current scenario, where the uncertainty is becoming more prevalent and unpredictable, researchers are trying possibilities of applying these methods into different areas of management and engineering. There lies a wide and deep scope for further research into this domain, in terms of developing new methods and applying the methods into different disciplines.

RO can be a very helpful tool for new breed of Mangers who have to regularly work and take decisions under conditions of uncertain future. RO can help them into different areas of business. It has found application into domains like Finance, Marketing, Economics and Operations etc, for taking strategic and operational level decisions involving activities like Portfolio optimization, Credit Line Optimization, SCM and Logistics, Inventory Management, Location Decisions, Capacity Planning, Production Planning and Scheduling etc.

Business Managers need to be equipped with advanced tools and methods to be prepared for the uncertain future. As told by someone, “The trouble with the future is that there are so many of them”. This reflects the philosophy of RO which makes them prepare for many and not the just one predicted future. Neils Bohr once said, “Prediction is very difficult, especially if it's about the future”. Business Mangers should realize that with spiraling economy and increasing competition, ignoring the uncertainty and relying just upon prediction might be very dangerous for the business and might result in losing money. After all, it’s all about money honey.
Written by Jitendra Kachhawa, FPM Student (Operations), IIM Lucknow

Monday, January 7, 2008

Molecular Logistics - Teleportation and Molecular Nanotechnology

Most all of us remember the Transporter on Star Trek and the "beam me up Scotty" phrase that typified use of this particle beam transportation technology. And with the millennium only five years old, we see an opportunity to introduce technologies we believe will be to the 21st Century what the truck, tractor trailer, intermodalism and forklift were to this century.
The basic concept of Teleportation is that a physical object would get broken down to its' component molecular parts at one location and then transmitted to another location where they would be reassembled back to the original form completing the process of transport without any of the current costs, equipment or time delays. Molecular Nano-Technology is a slightly different although similar approach whereby the required molecules for the creation of a specific item are precisely assembled to "build" the item at or near the location of consumption.

This takes the concept of Teleportation back even one step further, to actually eliminate current production processes. In fact in most cases production today is based on our taking matter and cutting/forming it into the product required. Molecular Nano-Technology uses the reverse of this concept where the intent is to microscopically place through exact placement precisely only the molecules required to create the given item.

Welcome to the future of Logistics!


Ops News: McCarran Airport RFID System Takes Off

Two years after announcing its plan to replace bar-coding with RFID as a means of sorting and tracking baggage, McCarran International Airport in Las Vegas has completed the first phase of its RFID deployment, according to Swanson Rink, the consulting engineering firm that designed the system.

On Labor Day weekend, Alaska, AirTran and Champion airlines started placing RFID tags on checked baggage. RFID interrogators (readers), mounted on conveyors that bring the luggage through an explosive-detection system, read the tags, identifying each bag before it is checked for explosives. The tag then routes each piece of luggage to the appropriate plane or, if the explosives detector finds suspect contents, to another security-screening station.

RFID is a favorable alternative to bar-coding for luggage identification. Due to the unpredictable orientation of the label to the optical scanner, 15 to 30 percent of the bar-coded labels being used to identify the luggage at McCarran are not properly read as the bags move through the airport luggage handling equipment. Each piece of luggage for which the bar code is not successfully scanned is diverted and manually read. Because RFID tags do not require line-of-sight with the interrogator, they are much more easily read.


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