SITUATION ANALYSIS Applichem is a manufacturing company producing Release-ease, a specialty chemical for the molding industry. On mixing with the plastic molding compound, Release-ease prevents any residue when the molded parts are separated from the mold. This helps in keeping the mold clean. They have six plants – one each in Gary, Canada, Frankfurt, Mexico, Venezuela and Sunchem in Japan. Each region has a different specification. This along with capacity constraints leads to export-import amongst the various plants.
Release-ease is manufactured by a four step process. The steps for this process are: •Reaction: This consists of compressing the raw materials under heat/pressure to form Release-ease. This results in formation of a slurry. The size of particles depends on the timing of introducing materials, federates etc. •Cleaning and Filtering: The slurry is then moved on a conveyor belt that is made of mesh. This results in the liquid falling through the belt to the trough below, leaving wet Release-ease particles on the belt. •Drying: Release-ease is dried to give a powder Packing: Release-ease powder is then packaged in bags on an automated filler line. The off-spec material is reworked in some plants, while in other plants it is reclassified as QC-3 and sold at a lower price. Yield is one indicator of plant performance. It is defined as the number of actual pounds of active ingredient in raw material divided by the number of pounds of the active ingredient which would be in Release-ease if all key raw materials were converted to active ingredient. The yield varies across different plants. A well run low volume process has an average yield of 91–92 percent.
Whereas a well run medium volume process has an average yield of 94–95 percent, and a well run high volume process has an average yield of 98–99 percent. The manufacturing process was run 24 hours a day, 7 days a week. A comparison between various plants of Applichem is given below Plant LocationGaryCanadaFrankfurtMexicoVenezuelaJapan Setup Year190519551961196819641957 Release-ease Capacity (Mn. Lbs/yr)18. 53. 747. 022. 04. 55. 0 Capacity Utilization (1982)76%70%81%78%91%80% Packet Sizes80 packet sizes50 Kgs50 Kgs50 Kgs50 Kgs1/2 – 1 Kgs
In order to identify the weak areas of Applichem, we need to evaluate the performance of all its manufacturing plants. This is also necessary to comprehend its operations. Each factory fulfils the needs of a different market. We also see that the plant at Sunchem has an extremely high figure of operating costs. The operating costs of Sunchem are more than twice that of the Gary plant and more than three times the costs of the Mexico plant. One important reason for such high costs for Sunchem is its waste treatment cost which is very high as compared to other plants.
This can be attributed to the fact that it is the only plant with scrubbers for processing gaseous wastes. Sunchem also has high direct labour, salary and fringes cost compared to other facilities. This is due to the severe labour laws and regulations in place in Japan, such as greater employee and license requirements. The utilities costs are also much higher. The Mexico plant has the lowest operating costs. In terms of the raw material costs Frankfurt plant has the lowest requirement for the total raw material used for producing a unit, whereas the Gary plant has the highest.
This is because the Frankfurt plant has efficient processes in place to minimize wastage and optimize the use of raw materials. Analysis of the yields of the individual plants also suggests that the Frankfurt plant has the highest yield. One of the reasons for this can be the high capacity of the Frankfurt plant. Additionally the minimum raw material/unit product cost is also for the Frankfurt plant. This is because of the highly efficient processes. Frankfurt also obtains its raw material at the lowest costs which is one of the reasons for the highest yield.
In terms of energy usage also the Frankfurt plant is the most energy efficient be it in terms of oil usage or electricity usage. The capacity utilization of the Frankfurt plant is also the highest (more than 80%). There is a potential to use the remaining 20% of the capacity and enhance the earnings. Analysis of the costs also suggests that the Frankfurt plant has the lowest cost per unit among all the plants. These costs include the direct and indirect labour costs. PROBLEM ANALYSIS
In order to find out the optimum allocation of production, domestic usage and export to satisfy the demand at a global level, we have maximized the profits using a linear programming algorithm. For finding the solution we have assumed that the custom duty has been applied on the price of Release-Ease Chemical. At the same time, for good consumed in the same country the excise duty has been taken as zero. From the data on variable costs available in the case it is clear that Frankfurt is the most profitable location for production whereas Sunchem is the least profitable.
Hence, while trying to minimize the costs we would look to reduce the production percentage in Sunchem and increase the utilization of the production facility at Frankfurt. METHODOLOGY In order to find out the optimal production schedule to minimize the costs we have formulated the problem as a linear programming problem. The decision variables are the quantities of the chemical produced in location A and exported to location B where A and B are one of Mexico, Canada, Venezuela, Frankfurt, Gary and Sunchem.
The constraints on the supply and demand side are available in the case. The detailed formulation is provided in the Appendix. RESULTS Case 1: Without Custom Duty The output of the solver giving the optimal quantity for export and domestic usage, as per the current production levels is given below. Source/DestinationMexicoCanadaVenezuelaFrankfurtGarySunchem Mexico3000000. 00 0. 00 7700000. 00 0. 00 0. 00 0. 00 Canada0. 00 2600000. 00 0. 00 0. 00 1100000. 00 0. 00 Venezuela0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 Frankfurt0. 00 0. 00 8300000. 0 20000000. 00 6800000. 00 11900000. 00 Gary 0. 00 0. 00 0. 00 0. 00 18500000. 00 0. 00 Sunchem0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 The total production schedule based on the above output is given below: LocationTotal production (in million lbs) Mexico10. 7 Canada3. 7 Venezuela0 Frankfurt47 Gary18. 5 Sunchem0 The total profit in this case is US $ 23. 402 million. From this result it is clear that it is indeed desirable to close down the factory in Sunchem and Venezuela as the profits there are low on account of the high costs of production.
The factories at Canada, Frankfurt and Gary operate at capacity limits. Case 2: With Custom Duty The output of the solver giving the optimal quantity for export and domestic usage, as per the current production levels is given below. Source/DestinationMexicoCanadaVenezuelaFrankfurtGarySunchem Mexico3000000. 00 0. 00 3200000. 00 0. 00 0. 00 0. 00 Canada0. 00 2600000. 00 0. 00 0. 00 1100000. 00 0. 00 Venezuela0. 00 0. 00 4500000. 00 0. 00 0. 00 0. 00 Frankfurt0. 00 0. 00 8300000. 00 20000000. 00 6800000. 00 11900000. 00 Gary 0. 00 0. 00 0. 00 0. 00 18500000. 0 0. 00 Sunchem0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 The total production schedule based on the above output is given below: LocationTotal production (in million lbs) Mexico6. 2 Canada3. 5 Venezuela4. 7 Frankfurt47 Gary18. 5 Sunchem0 The total profit in this case is US $ 16. 238 million. Clearly the profit reduces due to customs duty. Also because of high customs duty in Mexico (60%) the cost of production significantly increases as a result of which the optimal production level goes down. Also it is now profitable to operate the Venezuela factory.
All the plants other than the one at Mexico and Sunchem (which is still unprofitable to operate) operate at capacity. APPENDIX Formulation of the Linear programming algorithm: We define Xij as the quantity of Release Ease (in lbs. ), produced at location ‘i’ and exported to location ‘j’ (where i,j are one of the six locations give in the case). Tij is the transportation cost while shipping the material from location i to j. These locations along with their subscripts are given below: Location 1: Mexico Location 2: Canada Location 3: Venezuela
Location 4: Frankfurt Location 5: Gary, Indiana, USA Location 6: Sunchem Objective function for the problem is: Minimize (? X1j * (0. 84 + T1j) + ? X2j * (0. 76 + T2j) + ? X3j * (0. 97 + T3j) + ? X4j * (0. 59 + T4j) + ? X5j * (0. 68 + T5j) + ? X6j * (1. 14 + T6j)) Subject to the following constraints Supply Constraints: x11 + x12 + x13 + x14 + x15 + x16 = 22 million x21 + x22 + x23 + x24 + x25 + x26 = 3. 7 million x31 + x32 + x33 + x34 + x35 + x36 = 4. 5 million x41 + x42 + x43 + x44 + x45 + x46 = 47 million x51 + x52 + x53 + x54 + x55 + x56 = 18. million x61 + x62 + x63 + x64 + x65 + x66 = 5 million Demand Constraints: x11 + x21 + x31 + x41 + x51 + x61 = 3 million x12 + x22 + x32 + x42 + x52 + x62 = 2. 6 million x13 + x23 + x33 + x43 + x53 + x63 = 16 million x14 + x24 + x34 + x44 + x54 + x64 = 20 million x15 + x25 + x35 + x45 + x55 + x65 = 26. 4 million x16 + x26 + x36 + x46 + x56 + x66 = 11. 9 million Additionally all Xij’s are non negative. In case of customs duty we also add (1. 01 * Cij * Xij) for every i,j. Where Cij? Customs duty for importing in location j.