Tag Archives: Economy


Monday, June 1, 2009 in Washington DC, US Government approves bankruptcy for General Motors Corp. (GM). General Motor is the humbled auto giant that has been part of American life for more than 100 years. Magna International Inc., a company from Germany, has been deal with GM for buying Opel. Germany’s company also dealt to give fund stimulus to Opel.

Before 2008, GM is noted as the first rank global car market shares and Toyota is in the runner up. New phenomenon happens in 2008; GM lost its position as the best global car market shares.  Because of bankruptcy, GM’s market share will be fought over others car manufacturer. In this case, Toyota as the global leader car manufacturer and Ford as the second US car manufacturer have the biggest chance to get it. These data are supported by BBC for 2008 Global Car Sales below.

  • Toyota: 8.97m
  • GM: 8.35m
  • VW: 6.3m
  • Ford: 5.5m
  • Nissan: 3.2m
  • Renault: 2.4m
  • Fiat: 2.15m
  • Chrysler: 1.5m

For illustrated the US market share by Manufacturer, GM is always in the first rank, Toyota in the second rank, and Ford in the third rank. In May 2007 the market share of GM is 23.8%, Toyota with 17.2%, and Ford with 16.5%. But, in May 2008, the market share of GM and Toyota is slightly different. GM’s market share is 19.3%; Toyota with 18.4% keeps away from Ford with 15.4%. In the middle level, Honda can win against Chrysler. If in May 2007 Chrysler with 12.8% is in above Honda with 9.3%, in May 2008 Honda with 12% successes grabbing Chrysler position in the forth rank. More complete, the table is showed below.

U.S. Market Share by Manufacturer

Car Manufacturer

May 2007

May 2008






















BMW (includes Mini)



Volkswagen (includes Audi)



Mercedes (includes Smart)



Source: US News 2008

This phenomenon does not only happen in car market, but also in the world’s economic growth. Data from IMF show that the economic growth of Asia and Africa countries rises significantly from 2007, 2008, and also the projection in 2009 and 2010. Most of Asia and Africa countries’ economic growth is always positive. On the other hand; most of America and European counties are sloping down, even will be projected minus in 2009 and 2010.


IMF Projection 2009Source: IMF, World Economic Outlook, 2009

Europe became the central power of world’s economy in 18th and 19th century. It is signed with Industrial Revolution in England and some development in Germany and France. In 20th century the central power moves from Europe to America, specially the United States. In 21st century, global economic crisis with the epicentre in the US makes the economics of that country and European country unstable. Moreover, Asia and Africa in the fact are able to exist in global economic crisis. But, Asia has more benefit than Africa because many countries in Asia are developed like Japan, Singapore, and Taiwan. Then, the developing countries in Asia like China, India, Indonesia, and Middle East countries are going to growth. Will the centre of world’s economy moving to Asia?



First Published: Thursday, May 7, 2009

One problem when people usually meet in shopping is what people should buy in “clearance sale” with aim to sale again. Usually people confuse to buy between/among two types of products or more. The objective of this problem usually is maximising profit, but this problem will be more difficult because people should decide among different constraints such as sales demand, budget, and capacity. How do we solve this problem?

Solving of this problem will become simple with Linear Programming (LP). LP is a mathematical technique for optimization of linear objective function. In 1939, Leonid Kantorovich, a Russian mathematician, founds this technique. Then, George B. Datzig, John von Neumann, Leonid Khaciyan, and Narendra Karmarkar develop this technique. Clearance sale shirt and t-shirt example below will show that LP can be used to solve this problem.

For example, a department store opens New Year clearance sale for shirt and t-shirt. One shirt prices $25 and $15 for a t-shirt. A shop looks this opportunity to make a $400. In addition, his inventory only can keep 100 clothes, whereas he has kept 53 shirts and 29 t-shirts. Then, he considers that he can sell shirt at least twice than t-shirt in one week. How much shirt and t-shirt should he buy in this clearance sale?

Consider that X1 is for shirt and X2 is for t-shirt. The objective of this problem is maximising profit from (40-25) X1 + (35-15) X2 = 15 X1 + 20 X2. There are three constraint of this problem. First, sales demand constraint is X1 – 2X2 >= 0. Second, budget constraint is 25X1 +15 X2 <= 400. Third, capacity constraints is X1 + X2 <= (100-53-29) ~ 18. So the mathematical equation is:

Max Profit:         z = 15X1 + 20 X2
Constraint:          X1 – 2X2 >= 0
                                  25X1 +15 X2 <= 400
                                  X1 + X2 <= 18

There are many ways to solve this problem such as LP Graphic and Excel Solver. The graphic and the excel solver output will be showed below.
LP Graph










From graphics, there are three alternatives of optimum solutions. First, in point (18, 0) will make profit $270. Second, in point (13, 5) will make profit $295. Third, in point (12.3, 6.15) will make profit $307.5, but this answer is invisible because shirt and t-shirt cannot be decimal. Hence, the nearest options are (12, 6) with profit $300. Furthermore, He should buy 12 shirts and 6 t-shirts. It is very suitable with the answer from Excel Solver table belSolver Answerow.

The owner of shop also can analyze the changing in the parameters of this LP problem. This analysis is called sensitivity analysis. In this case, Excel Solver is also used to define the sensitivity analysiSensitivity Analysiss.

The table above shows that the final answer will not be change if:

  • The profit of Shirt changes in range from 0 to 20
  • The profit of T-Shirt changes in range from 15 to infinity
  • The constraint of budget change in range from 290 to infinity
  • The constraint of capacity change in range from 0 to 18.461538462
  • The constraint of demand change in range from -36 to 3

Hence, have fun in shopping.


First Published: Friday, March 6, 2009

In the big company, inventory management uses EOQ to calculate the most economic quantity of order. EOQ also can use in the daily activity to calculate of consumer good storage in home. There are three main components to calculate EOQ such as re-ordering cost, demand, and holding cost.

In the daily life, everyone must keep something in home to store vegetables, meats, fruits, milk, book, bath equipments, and other consumer goods. To store everything, people usually don’t consider about the optimal time, size, and quantity when they buy something to stock in home. For example, how much mother should buy milk for her children in the market.

The first component is re-ordering cost (RC). The definition of re-order cost is total expenses involved in repeating supply such as order preparation, communications, transportation, inspection, administration, and other costs. For example, the distance from house to market is 20 km. Assume that the car’s consuming is 10 km per litre and gasoline price is $1 per litre. Therefore, the re-ordering cost is 2 x 20 / 10 x $1 = $4 (the calculation is for 2 ways, go to the market and go home).

The second component is demand (D). Demand in EOQ is define as the estimate of consume or how much the product will be sold in a year.  For example, the children can consume 8 cans of milk in a month. Hence the demand is 8 x 12 = 96 bottles annually.

The second component is holding cost or storage cost (HC). It can be defined as expenses involved in keeping and maintain a stock. It is including rent of space, equipment, materials, labour, insurance, security, interest, and other direct expenses. For example, a $200 locker can store 10 cans of milk. It estimates that have a reliability age 10 years. Therefore, we can calculate the holding cost as $50/10/10 = $2 per can annually.

The calculation of EOQ for this example is EOQ = (2xRCxD/HC)0.5 = Ö(2 x 4 x 96) /2 = 19.6 ~ 20 cans. It means that each order or buy is 20 cans of milk. Moreover, the calculation of total annual inventory cost is VC = (RCxD/EOQ) + HC*EOQ/2 = (4×96/19.6) + (2×19.6/2) = 19.6 + 19.6 = $39.2. In addition, the time between order is 360/5 = 72 days (around 2.4 months).

By this calculation, each family is able to calculate the most optimal storage of daily consumer goods. In this example if the mother buys 15 cans every purchase (below EOQ), the total annual inventory cost will be VC = (RCxD/Q) + HC*Q/2 = (4×96/15) + (2×15/2) = 25.6 + 15 = $40.6. It will be the same if the mother buys 25 cans every purchase (above EOQ), the total annual inventory cost will be VC = (RCxD/Q) + HC*Q/2 = (4×96/25) + (2×25/2) = 15.36 + 25 = $40.36.

For conclusion, each family can save money with EOQ calculation in daily life.