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### winco_03

Course: IE 361, Spring 2006
School: Iowa State
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236 Section Mathematics 5.2: Sensitivity analysis using Lindo (Revised October, 2003) Simplex Solution to Winco Problem (Winston, pages 231-233) MAX New Variables coming into problem Z = 4 X1 + 6 X2 + 7 X3 + 8 X4 SUBJECT TO: 2) Slack X1 + X2 + X3 + X4 = 950 3) X4 &gt;= 400 4) 2 X1 + 3 X2 + 4 X3 + 7 X4 &lt;= 4600 3 X1 + 4 X2 + 5 X3 + 6 X4 &lt;= 5000 Artificial S4 5) Excess S5 A2...

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236 Section Mathematics 5.2: Sensitivity analysis using Lindo (Revised October, 2003) Simplex Solution to Winco Problem (Winston, pages 231-233) MAX New Variables coming into problem Z = 4 X1 + 6 X2 + 7 X3 + 8 X4 SUBJECT TO: 2) Slack X1 + X2 + X3 + X4 = 950 3) X4 >= 400 4) 2 X1 + 3 X2 + 4 X3 + 7 X4 <= 4600 3 X1 + 4 X2 + 5 X3 + 6 X4 <= 5000 Artificial S4 5) Excess S5 A2 e3 A3 and X1, X2, X3, X4 all nonnegative. Note: Numbering of constraints conforms to the rows Lindo uses. Initial Tableau Basic Eq. Var No Z 1 Z X1 X2 X3 X4 e3 A2 A3 S4 S5 1 -1M -4 -1M -6 -1M -7 -2M -8 1M +0 0 0 0 RHS 0 -1350M A2 2 0 1 1 1 1 0 1 0 0 0 950 A3 3 0 0 0 0 1 -1 0 1 0 0 400 S4 4 0 2 3 4 7 0 0 0 1 0 4600 S5 5 0 3 4 5 6 0 0 0 0 1 5000 Z X1 X2 X3 X4 e3 A2 A3 S4 S5 RHS Final Tableau Basic Eq. Var No Z 1 1 1 0 0 -0 2 1M +3 1M -2 1 0 6650 X2 2 0 2 1 0 0 -3 4 3 -1 0 400 X4 3 0 0 0 0 1 -1 0 1 0 0 400 X3 4 0 -1 0 1 0 4 -3 -4 1 0 150 S5 5 0 0 0 0 0 -2 -1 2 -1 1 250 The Winco problem Interpreting the Lindo Output page 2 Lindo output - Winco problem, page 233 OBJECTIVE FUNCTION VALUE 1) 6650.000 VARIABLE VALUE X1 0.000000 X2 400.000000 X3 150.000000 X4 400.000000 Comment: These reduced costs are the Z-row coeff's of X1...X4 in the final tableau. REDUCED COST 1.000000 0.000000 0.000000 0.000000 The rules below apply for a MAX problem. "Dual Prices" are the Shadow Prices for the constraints. ROW SLACK OR SURPLUS Constraint Type Look in Z-row of final tableau DUAL PRICES 2) 0.000000 3.000000 `=' (A2 coeff when M=0) 3) 0.000000 -2.000000 > -(coeff of e3) = +(coeff of A3 with M=0) 4) 0.000000 1.000000 < +(coeff of S4) 0.000000 < +(coeff of S5) 5) 250.000000 NO. ITERATIONS= 3 RANGES IN WHICH THE BASIS IS UNCHANGED: OBJ COEFFICIENT RANGES VARIABLE X1 X2 X3 X4 CURRENT COEF 4.000000 6.000000 7.000000 8.000000 ALLOWABLE INCREASE 1.000000 0.666667 1.000000 2.000000 RIGHTHAND SIDE RANGES ROW CURRENT ALLOWABLE RHS INCREASE 2 950.000000 50.000000 3 400.000000 37.500000** 4 4600.000000 250.000000 5 5000.000000 INFINITY ALLOWABLE DECREASE INFINITY 0.500000 0.500000 INFINITY (We answered yes to Lindos question about wanting the Range Analysis. This is the output we got.) ALLOWABLE DECREASE 100.000000 125.000000 150.000000 250.000000 *** Winston, page 233, has an error here. Lets look more closely at how sensitive the optimal solution of the Winco problem is to the changes of the RHS of for example the constraint in row 5, viz.: 5) 3 X1 + 4 X2 + 5 X3 + 6 X4 <= The Lindo output tells us several things about this constraint (row 5): 5000 The Winco problem Interpreting the Lindo Output page 3 the optimal solution has the slack variable s5= 250, so constraint (5) is not binding. Wed expect the shadow price to be zero. Is it? The shadow price for a constraint is the number in the DUAL PRICE column. For constraint 5, the DUAL PRICE is 0. Note that the DUAL PRICE for constraint (3) is -2 (a negative value). This is what we expected: Constraint (2) requires X4 >= 400; and it is binding at the optimal solution. The shadow price is the amount z improves for each increase by 1 unit in the RHS of a constraint (as long as the RHS stays within the allowable range). In this case, increasing the RHS of constraint (3) makes the feasible region smaller -- its harder to be feasible -- so the improvement cannot be positive; it has to be negative or zero. Since constraint(3) is binding at the original optimum, we expect that increasing RHS(3) will force us to a truly less-good value of Z ; so were not surprised to see a nonzero (and so negative) shadow price there. Back to our analysis of Constraint (5): The basic set at the optimal solution consists of X2, X3, X4, and S5. Why is that? We have four constraints, so a basic set has four members. These four variables are the only four that are nonzero at the optimum. Anything nonzero must be part of the basic set. (Note the reverse can fail: A basic variable could be zero at the optimum.) The line in the ROW 5 RIGHTHAND SIDE RANGES CURRENT RHS 5000.000000 ALLOWABLE INCREASE INFINITY ALLOWABLE DECREASE 250.000000 tells us that the current basic set (X2, X3, X4, and S5) will remain optimal so long as 4750 = 5000 -250 < RHS (5) < 5000 + INFINITY = INFINITY. That is, so long as 4750 < RHS(5); and all other data stays the same!! Remember: even if the RHS(5) were to change within this range, the values of the basic variables which give the optimal solution would change. For example, if RHS(5) were to change to 4800, the optimal solution would no longer be X1=0 (nonbasic), X2= 400 , X3= 150, and X4= 400. What we do know is that X2, X3, X4, and S5 would be the only variables that could possibly be nonzero at the optimal solution. Does Lindo tell us what the values of these variables will be? No, it does not. ************ What happens if RHS(5) were to go outside this range for example, if the RHS were to drop to 4000, or to 4500? Then the guarantee that X2, X3, X4, and S5 is the basic set no longer applies. A different basic set would (probably) be needed to get the optimal solution. But WHAT BASIC SET?? The Winco problem Interpreting the Lindo Output page 4 Lindo can tell us that, with its PARA (for parametric) command, which is discussed in section 5.4 of the Winston text. We will not discuss it in the course this year. (An Appendix on this topic is available from your instructor if you want to pursue this further on your own.) 5.2 and 5.3: Decision making using the Lindo output Winco Problem: version A The Winco scenario was introduced on page 232 this way. The company is planning a production run during which it will make units of four products. The information given can be summarized as follows: Resources consumed in making one item of a product line Resource Product 1 Product 2 Product 3 Product 4 Amt Available Raw material 2 units 3 units 4 units 7 units 4600 units Hrs of labour 3 4 5 6 5000 \$4 \$6 \$7 \$8 Sales price/unit In addition we were told the company planned to produce exactly 950 units in total, of which at least 400 had to be of product 4. The stated GOAL was to maximize sales revenue . Setting X1, ... X4 to be the numbers of units of products 1, .. 4 to be produced led to the problem we discussed, namely MAX Z SUBJECT 2) 3) 4) 2 5) 3 and X1, = 4 X1 + 6 X2 + 7 X3 + 8 X4 TO: X1 + X2 + X3 + X4 = X4 >= X1 + 3 X2 + 4 X3 + 7 X4 <= X1 + 4 X2 + 5 X3 + 6 X4 <= X2, X3, X4 all nonnegative. 950 400 4600 5000 The LINDO output with the optimal solution was seen earlier. The optimal production schedule has X1 = 0; X2 = 400; X3 = 150; and X4 = 400. It gives an optimal Z-value of \$ 6,650. Some Questions (see Ex. 3, page 235; Example 4 parts (a) & (b), page 237; and Example 5, page 246.) In each situation (separately) use the output to decide if you have sufficient information at present to determine if (i) the optimal production schedule will change, and if so, how; and (ii) if the optimal z-value will change, and if so, how. a) Winco decides to raise the price of product 2 by 50 cents a unit. (Ex 3a, p 235) b) Winco decides to raise the price of product 1 by 60 cents a unit. (Ex 3b p 235) c) Winco decides to lower the price of product 3 by 60 cents a unit. (Ex 3c, p235) d) Winco is required to produce a total of 980 units (not 950). (Ex 4a, p 237) e) (i) A check of inventory shows that only 4500 units of raw material are available, not the 4600 we thought. (ii) Later we learn that water damage made an additional 100 units of raw material unavailable for use, leaving only 4400 usable units. (Ex 4b, p 237) f) Returning to the original scenario, Winco has a chance to buy more raw material. Would it like to, and if so, what would it be willing to pay? And how much would it want at that price? g) Winco has a chance to hire more labour. Would it like to, and if so, what would it be willing to The Winco problem Interpreting the Lindo Output page 5 pay? And how much would it want at that price? THINK ABOUT THESE QUESTIONS A BIT BEFORE YOU READ ON Some Answers (in brief) a) Winco decides to raise the price of product 2 by 50 cents a unit. (Ex 3a, p 235) This increase of 50 cents (0.50 dollars) less than the allowable increase for the coefficient of X2 ( 0.66667). We CAN draw some conclusions: With a 50 cent increase in the price of product 2, the optimal production schedule wont change. The sales revenue will change. The new optimal value will be 4(0)+ 6.5(400) + 7(150) + 8(400)= \$6850. Alternatively, making this change will give us (0.5) times 400, or \$200 additional revenue, so the sales revenue would increase from \$6650 to \$6850. b) Winco decides to raise the price of product 1 by 60 cents a unit. (Ex 3b p 235) This increase of 60 cents (0.60 dollars) is less than the allowable increase for the coefficient of X1 ( 1.0000), so again we CAN draw some conclusions. The optimal production schedule will not change. That schedule has X1 = 0, so the optimal Z-value wont change either. c) Winco decides to lower the price of product 3 by 60 cents a unit. (Ex 3c, p235) The allowable decrease for the coefficient of X3 in the objective function is 0.50 (dollars), so this change takes us outside the allowable range. The optimal production schedule WILL (probably) change, and we cannot tell what it would be. Can we say anything? The proposed change affects only the goal and not the constraints, so the present optimal solution will still be feasible. That solution has X3 = 150, so the change wont reduce the sales revenue by any more than (0.50) x150 = \$75.00, i.e the new z-value will be at least \$6650 - 75 = \$ 6675 d) Winco is required to produce a total of 980 units (not 950). (Ex 4a, p 237) The current RHS for constraint 2 is 950, and the allowable range for that RHS is from 950-100=850 to 950+50 = 1000. The new requirement falls within this range, so we CAN say something. What can we say? First, since the dual price (shadow price) for constraint 2 is +3.0000, we can say that the sales revenue will improve BY (shadow price)x(increase in RHS) = (+3)(+30) = 90. The new optimal sales revenue will be 6650+90 = 6740 dollars.. Can we (easily) say how to achieve this? No. But we can deduce a bit of information. Since the RHS of this constraint is staying within the allowable range, the current basic SET (which has X2, X3, X4 and S5 in it) will still give the optimal solution. Therefore, X1 will stay nonbasic (and so equal to zero) and e3 will, too, which will force X4 = 400. It would take a some work to figure out what X2 and X3 would be. e) (i) A check of inventory shows that only 4500 units of raw material are available, not the 4600 we thought. (ii) Later we learn that water damage made an additional 100 units of raw material unavailable use, for leaving only 4400 usable units. (Ex 4b, p 237) The raw material availability appears in constraint 4. (i) The decrease here is 100 (from 4600 to 4500), which is less than the allowable decrease of 150. Therefore we CAN SAY the following. First, the shadow price for constraint 4 is +1, so the change in the optimal z-value will be (shadow price)x(increase The Winco problem Interpreting the Lindo Output page 6 in RHS) = (+1)(-100)= -100, and our optimal sales revenue will drop to \$6550. While we cannot easily say exactly what production schedule well want to use, we do know the current basic set will still give the optimal solution, so (same reasons as in previous part) well have X1 = 0 and X4 = 400. (ii) In this case the decrease in the RHS of constraint 4 is 200, which is more than the allowable decrease (150) . Therefore there is little we can easily say with the information we have now. f) Returning to the original scenario, Winco has a chance to buy more raw material. Would it like to, and if so, what would it be willing to pay? And how much would it want at that price? At present, all the raw material is used up (S4 = 0 at the optimum). This leads us to suspect that yes, we would like to have more. This suspicion is confirmed by the fact that the dual price for raw material is +1.000 (positive). Therefore, each one-unit increase in RHS(4) would add +1.0000 dollars to the sales revenue. This is important! That increase of \$ 1 revenue for each additional unit of raw material applies only if additional raw material could be obtained under the same conditions as the present raw material was obtained. Under the scenario given, the raw material was already there in a sense it was free, and our goal was to maximize the sales revenue. Each additional unit of raw material obtained for free adds one dollar to the sales revenue, so wed be willing to pay up to one dollar a unit for additional raw material. For example, if we could obtain some additional raw material at a cost of \$0.75 per unit, we could increase our sales revenue by \$1.000 dollars per unit and therefore our bottom line profit would improve by \$1.00 minus \$0.75 = \$0.25 for each additional unit of raw material we got. Important! We need to think very carefully of what the shadow price (dual price) for a constraint means: The shadow price for a constraint is the amount that the objective will improve for each unit increase in the RHS of that constraint by one unit, PROVIDED that increase keeps us within the allowable range for RHS of that constraint, and the increase in the RHS is attained under the same conditions as the original RHS value was determined. We will explore this idea more deeply below. How much more raw material would Winco be willing to take if it could get it for no more than a dollar a unit? The present RHS for constraint 4 is 4600 and the allowable increase in that RHS is 250. This tells us wed be willing to take as much as 250 additional units of this resource if we could get it at the right price. If we increase the RHS of constraint 4 by more than 250, the present basic set will no longer be feasible. A different basic set will give the optimal solution. In practical terms, once we get and use 250 more units of this resource, we are probably bumping into another boundary of the feasible region (perhaps using up all our labour) so having additional raw material beyond that 250 is of no use to us. g) Winco has a chance to hire more labour. Would it like to, and if so, what would it be willing to pay? And how much would it want at that price? The Winco problem Interpreting the Lindo Output page 7 At the present optimal solution the value of the slack variable S5 is 250. This means we have 250 hours of unused labour already. Additional labour is of no value to us this is why the dual price for constraint 5 is zero. Note that this constraint was 5) 3 X1 + 4 X2 + 5 X3 + 6 X4 <= 5000 and that at the optimal solution the slack variable equalled 250. Note also that the allowable DECREASE for RHS(5) was also 250. Is this an accident? No, it is not. With the present optimal solution, 3X1+4X2+5X3+6X4 = 3(0)+4(400)+5(150)+6(400) = 4750, which is why S5 worked out to 250. If the RHS(5) dropped by (say) 115, to 4885, this current values of X1, X2, X3, and X4 would still meet that constraint and so would still be feasible (and optimal). The optimal z-value would remain at 6650. In fact, the only change would be that the value of S5 would become 4885 - 4750 = 135. The discussion around Table 4 on pages 239-240 of the text discusses this point a bit further. Read it. A more realistic example Example 6 (page 246) provides us with a more realistic scenario for Winco. Here it is. The company is planning a production run during which it will make units of four products. The information we have is this. Resources consumed in making one item of a product line Resource Product 1 Product 2 Product 3 Product 4 Available Cost Raw material 2 units 3 units 4 units 7 units 4600 units \$ 4/unit Hrs of labour 3 4 5 6 5000 \$6 / hour Sales price/unit \$ 30 \$ 42 \$53 \$72 As before , the company plans to produce exactly 950 units in total, of which at least 400 had to be of product 4. Our GOAL is the more realistic one of maximizing PROFIT. Setting X1, ... X4 to be the numbers of units of products 1, .. 4 to be produced we see: the sales revenue will be 30 X1 + 42 X2 +53 X3 + 72 X4 dollars; the cost of raw material used will be 4(2 X1 + 3 X2 + 4 X3 + 7 X4) dollars; the cost of labour used will be 6(3 X1 + 4 X2 + 5 X3 + 6 X4) dollars; and the PROFIT will be (sales Revenue) - (raw material cost) - (labour cost), and this works out to 4 X1 + 6 X2 + 7 X3 + 8 X4. Thus the mathematical problem is the same. So is the solution, except that we now interpret Z as being profit (in dollars) rather than sales revenue. Under this revised scenario, lets ask the same questions we did before. What is the effect on the optimal solution in these different situations? a) Winco decides to raise the price of product 2 by 50 cents a unit. The Winco problem Interpreting the Lindo Output page 8 Doing this would increase the profit for product 2 by 50 cents a unit. The analysis above is still applicable, except that its the profit that would increase to \$ 6850. b) c) d) e) Winco decides to raise the price of product 1 by 60 cents a unit. (Ex 3b p 235) Winco decides to lower the price of product 3 by 60 cents a unit. (Ex 3c, p235) Winco is required to produce a total of 980 units (not 950). (Ex 4a, p 237) (i) A check of inventory shows that only 4500 units of raw material are available, not the 4600 we thought. (ii) Not only that, but water damage made an addition 100 units of raw material unavailable for use, leaving only 4400 usable units. (Ex 4b, p 237) The answers to these are the same as above, except that Z-values refer to profit, not sales revenue. Heres the interesting one!!! f) Returning to the original scenario, Winco has a chance to buy more raw material. Would it like to, and if so, what would it be willing to pay? And how much would it want at that price? The shadow price for raw material is +1.0000 , and this means that Winco would be willing to pay \$1.00 a unit more for additional raw material under current conditions . Those current conditions include buying raw material at \$4.00 per unit. Therefore, Winco would be willing to pay up to \$5.00 per unit for some additional raw material. What would be the payoff if it did this? Lets say Winco did buy t additional units of raw material at a price of 4.75 dollars per unit.. Then the new optimal solution would still have X2, X3, X4 and S5 as the basic variables but their values would probably be different. (X1 would still be nonbasic and therefore equal to zero; e3 would also be nonbasic and so equal to zero, making X4 stay at 400.) Whatever the new optimal values of X1, ... X4 are, the optimal profit can be deduced this way. Imagine Winco pays \$4.75 for the extra material in two payments: first it pays the standard price of \$4.00/unit, and later it pays an additional payment of \$0.75/unit. After it has made the first payment, and produced and sold its output, the profit so far is 6650 + (shadow price for raw material)(amount of additional material bought) = 6650 + 1t dollars. Then it makes the additional payment, so the final profit figure is (6650+t) - 0.75 t = 6650 +0.25t dollars. NOTE: we are NOT saying it would be willing to pay up to \$5.00 for all its raw material. If the cost of all raw material was \$5.00/unit, all the coefficients in the objective function would change; we would have a quite different problem which most likely has a quite different optimal solution. How much additional raw material would it be willing to buy (so long as this additional material costs no more than \$1.00/unit more than the regular price)? The answer, again, is 250 units, and the reasons for this are exactly the same as outlined above. g) Winco has a chance to hire more labour. Would it like to, and if so, what would it be willing to pay? And how much would it want at that price? This, too must be considered carefully. First, Winco does not want any additional labour at the current The Winco problem Interpreting the Lindo Output page 9 price of \$6.00 per hour. It is not using all of the labour it has available now at that price. Clearly, it would be even less willing to buy any additional labour at a price of more than \$6.00 per hour. But what if some people came along and said Well work for less wages! Well provide up to 3000 hours of labour and only charge \$4.75 an hour!? Would Winco want them? Here the answer depends on how Winco might be able to use these workers. If these workers could be used only after all the regular labour was used up, Winco would not take up this offer. At the present optimal solution we are using up all the raw material but not all the labour available. There would be no raw material left, so we could not make anything else. On the other hand, if we could use this cheaper labour in place of some of the regular labour, it seems clear that we would want to do this. Here are the details. Constraint 5 deals with the labour used. Since we have 5000 hours of labour available, and S5 = 250 in the optimal solution, we are using up 4750 hours of labour, and paying \$6.00 an hour for it. If we could replace 3000 of those hours with this cheaper labour, we would gain an additional 3000 x (6.00 - 4.75) = \$3750 in profit, bringing our total profit to 6650 + 3750 = \$10,400. Conclusion: The discussion and examples above illustrate how the optimal solution to an LP problem provides just the beginning and not the end of information that can be useful to those who needed the answer to that problem in the first place. Sensitivity Analysis is an important aspect of using mathematical tools to aid good decision making in the real world.
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TRAN/CE 650URBAN SYSTEMSENGINEERINGLecture 2Example ProblemA new company Wyndor Glass Company has:32Products =plantsproducts.making 8' glass door : product 1 4 6' window : product 2 Resources : Production Capacities in 3 plants are as follow
NJIT - CE - 650
TRAN/CE 650URBAN SYSTEMSENGINEERINGLecture 4SENSITIVITY ANALYSIS1. A Graphical Introduction to Sensitivity AnalysisSensitivity is concerned with how changes in an LPs parameters affectthe LPs optimal solution.2. Example: The Giapetto problem in Wi
NJIT - ISE - 234
Decision tree example:- Colaco has \$150,000 + wants to decide whether to market a new chocolate flavored soda.There are 3 options:(1) Test market locally, then decide.(2) Immediately (no test) market the new soda nationally.(3) Immediately (no test)
NJIT - ISE - 234
Max Flow Problem Algorithm:(0) on each arc (i,j), write the forward arccapacity on the arc near i and thereverse capacity (or 0 if none) near j.(1) find a path from source to sink withpositive remaining flow capacity.if none exists, done.(2) find t
McGill - CHEM - 120
ChemicalChemical KineticsChemical Kinetics: Ch. 14Bradley J. SiwickSiwickDepartments of Physics and Chemistry50T = -1 psT=-1psT = +6 ps (x4)T = +50 ps (x4)B404.05 30H(r)20Al-O1014-1 The Rate of a Chemical Reaction14-2 Measuring Reactio
Universitas Sumatera Utara - ENG - 101
Y our nam e,th ein s tru c to rsn a m e , th eco u rsen u m b er, an dth e d a te o fs u b m is s io na r e 1 .0 f r o mth e to p o f th ef irs t p a g ea n d le f tju s tif ie d .D a te s a rew ritte n inth is o rd e r:d a y , m o n th ,a
University of Phoenix - ACC - 557
Running Header: How Ethics Plays a Role in Many EconomiesHow Ethics Plays a Role in Many EconomiesTravis BlairACC 557May 19, 2012Greg StanleyHow Ethics Plays a Role in Many EconomiesIn both Jamaica and China there seems to be more ethical-based dec
COMSATS Institute Of Information Technology - MGT - 362
Journal of Biology, Agriculture and HealthcareISSN 2224-3208 (Paper) ISSN 2225-093X (Online)Vol 2, No.1, 2012www.iiste.orgA Review of What is Known about Impacts of CoastalPollution on Childhood Disabilities and Adverse PregnancyOutcomesJuma Rahman
Pacific States - DBA - 635
Manufacturing Location:The United States or ChinaIntroductionEvery country is growingwith the faster rate, there is amigration of manufacturerDuring 2006 to 2008 there isalso an appreciation in theIn chain the labor cost isquite low which allow t
Pacific States - DBA - 635
Chapter 1Seung BLeeSummaryVodafone came to Japan in 2001 when it acquired J-phone, the third largest operator in Japan.Despite repeatedly renewing its efforts, including investment of billions of dollars, Vodafonenever managed to perform well in the
Pacific States - DBA - 635
Case 2-1 Russia: A huge emerging car market isolated from oil crisisSeung B LeeSummaryOil crisis has lead to isolation of the car companies and the companies in Russia are declaringthemselves as low profit making companies. In May, 2008, GM announced
Pacific States - DBA - 635
Fonterra EngulfedIn Chinas Tainted Milk CrisisDBA 635 Seung BLeeIntroductionSeptember5thAugust2ndThefollowingdaySeptember11th1. Fonterra waited 40days (from August 2 until September 11) before going publicwith the information that its products i
Pacific States - DBA - 635
GM and FORD`S pursuit of different benefitsfrom global marketingSummaryGlobalmarketing199120002001presentGlobalstandardizationCustomizationMarketsegmentationProductdifferentiationProductorientationCustomerorientationBendingdemandtothewill
Pacific States - DBA - 635
Seungbum LeeSummaryGeneral Motors is fastening to an extremely diversified international marketing stratagem;rather than spotlight on solitary brand, it desires clients to be competent to opt from anarmada of them. One of the potencies of GM's global
Pacific States - DBA - 640
Case Study 2.3Blood for Sale1. Is Sol Levin running a business just like any other business, or is his company opento moral criticism? Defend your answer by appeal to moral principle.Sol Levin is running a business like any other business person in th
Pacific States - DBA - 640
1. Is Sol Levin running a business just like any other business, or is hiscompany open to moral criticism? Defend your answer by appeal to moralprinciple.Sol Levin is running a business like any other businessperson in the worldI think the type of bu
Pacific States - DBA - 640
1. Suppose that you had been one of the MBA applicants who stumbled across an opportunity tolearn your results early. What would you have done, and why? Would you have considered it amoral decision? If so, on what basis would you have made it?If I were
Pacific States - DBA - 640
SniffingGlueCloudSnuffProfitsSeungbumLeeIntroductionH.B.FullerCompanyisaleadingmanufacturerofindustrialgluesworldwide.Companyhasalwayspostedaimageofbeingsociallyresponsiblewithhighestlevelofethicsandintegrity1.WhatareH.B.Fuller`smoralobligationsinth
Pacific States - DBA - 640
Case 6.3 Sniffing Glue could snuff profitsSeungbum LeeSummaryH.B. Fuller Company over the years has become the market leader in various kind of glues andadhesives for commercial usage and have territory explaining all over the works with marketleader
Kaplan University - MT - 219
Notes on a Formal Assignment(Margins must be 1 all around, make them your default.)(Use Times New Roman 12 point font and make sure to double space)Do not place a heading called Introduction, it is not needed. Paragraphs should be severalsentences lon
Kaplan University - MT - 219
[MT220 | Global Business]Assignment DetailsUnit 5One Page MemoYou are working for a Japanese company that sells earthen ware and currently has amanufacturing facility in China.They are considering investing in a different foreign country to have an
Kaplan University - MT - 219
Part 1Defining Marketing and the Marketing Process (Chapters 1, 2)Part 2Understanding the Marketplace and Consumers (Chapters 3, 4, 5)Part 3Designing a Customer-Driven Strategy and Mix (Chapters 6, 7, 8, 9, 10, 11, 12, 13, 14)Part 4Extending Market
Kaplan University - MT - 219
hiL37217_ch02_042-089.indd Page 42hiL37217_ch02_042-089.indd Page 42L EARNING OBJECTIVESp art 26/29/106/29/105:58 PM user-f4975:58 PM user-f497C ountry DifferencesAfter you have read this chapter you should be able to:1234567Understand ho
Kaplan University - MT - 219
hiL37217_ch04_128-159.indd Page 128hiL37217_ch04_128-159.indd Page 128L EARNING OBJECTIVESp art 212/07/1012/07/105:39 PM user-f501 /Users/user-f501/Desktop/Tempwork/July 2010/01:07:10/MHBR169:Slavin:VY5:39 PM user-f501 /Users/user-f501/Desktop/Temp
University of Sydney - ECON - 1002
1. The key assumption of the basic Keynesian model is that in the short run, firms:a. meet demand at preset prices.b. adjust prices to bring sales in line with capacity.c. change prices frequently.d. operate just as they do in the long run.e. change
Universiteit Maastricht - BUS - 2
Q1- Mega-Mart, a discount store chain, is to build a new store in Rock Springs. The parcel of land thecompany has purchased is large enough to accommodate a store with 140,000 square feet of floor space.Based on marketing and demographic surveys of the
UNSW - ACCT - 2542
TUTORIAL 1 SOLUTIONSChapter 1, Discussion Question 1414. As part of its national economic development programme, the Australian government isworking vigorously to promote the growth of small high-tech companies. Becausethese companies tend to have lim
UNSW - ACCT - 2542
TUTORIAL 2 SOLUTIONSExercise 19.2 Current asset and liability classificationsCurrent AssetsCash and cash equivalentsTrade and other receivablesInventoriesOther current assetsCurrent LiabilitiesTrade and other payablesFinancial liabilitiesCurrent
UNSW - ACCT - 2542
TUTORIAL 3 SOLUTIONSProblem 3.6 Share issue, options, statement of changes in equityDAWSON LTDPart (a) General Journal EntriesDATEDETAILS20/09/09 Dividend declaredDividend payableDrCr7 20027/09/09 Dividend payableCash(120 000 x 6c = \$7 200)D
UNSW - ACCT - 2542
TUTORIAL 4 SOLUTIONS27.2Potential ordinary shares are dilutive if the EPS is recalculated on the basis that thesecurities were converted to ordinary shares, and the recalculated diluted EPS figureis less than the basic EPS (which is based on the ordin
UNSW - ACCT - 2542
1SOLUTIONS TUTORIAL 7Chapter 22 Controlled entities: the consolidation methodDISCUSSION QUESTIONS21.RequiredDiscuss the potential for Gilder Ltd being classified as a subsidiary of Croc Ltd.Part ACroc LtdGlider Ltd40%- NCI = 60%- no other part
UNSW - ACCT - 2542
1TUTORIAL 8 SOLUTIONSChapter 24 Consolidation: intragroup transactionsDISCUSSION QUESTIONS1. Why is it necessary to make adjustments for intragroup transactions?The consolidated financial statements are the statements of the group, an economic entity
UNSW - ACCT - 2542
1TUTORIAL 9 SOLUTIONSChapter 25: Consolidation: Non-controlling interestDISCUSSION QUESTIONS1.If a step approach is used in the calculation of the NCI share of equity, what are the stepsinvolved?Step 1: share of subsidiary equity at acquisition dat
UNSW - ACCT - 2542
1TUTORIAL 10 SOLUTIONSChapter 26: Consolidation: indirect ownership interestsDISCUSSION QUESTIONS1.What is the difference between direct and indirect NCI?Direct NCI (DNCI) own shares directly in an entity. Indirect NCI are DNCIshareholders in an en
UNSW - ACCT - 2542
TUTORIAL 11 SOLUTIONSExercise 28.2 Accounting for an associate by an investorJASMINE LTD HAYLEY LTD40%Jasmine LtdHayley LtdAt 1 July 2009:Net fair value of identifiable assetsand contingent liabilities of Hayley LtdNet fair value acquiredCost of
UNSW - ACCT - 2542
TUTORIAL 12 SOLUTIONSDQ5. AASB 131 Interest in Joint Ventures uses joint control as a characteristic of a jointventure. Discuss what is meant by the term joint control?Paragraph 3 of AASB 131 contains the following definition of joint control:Joint co
Georgia Tech - ME - 3322
COE3001 ZamirPage 1 of 4Exam #1 Solutions1. (25 pts) You use the mechanical testing apparatus shown above to nd the Youngsmodulus for a section of bone. You have already measured the bone cross-sectional2area A = 0.8 cm and length L = 1.5 cm before
Georgia Tech - ME - 3322
COE3001ZamirExam #21. (25 pts) Draw the shear and moment diagrams for the beam. Make sure to calculateand label the numerical values for V and M at each end of the beam and at minima/maxima.Page 1 of 4Wednesday, October 21, 2009COE3001Zamir
Georgia Tech - ME - 3322
COE3001ZamirExam 31. (45 pts) The left ventricle of the heart can be modeled as a cylindrical pressure vessel. At the outerwall, the muscle bers are wrapped helically around the longitudinal axis, making an angle = 60with respect to the equator. Assu