Merton Truck Company’s Financial Performance and Product Mix

groundworkIn response to your underwrite and request regarding Mertons financial exploit and convergence rumple, I absorb met with your controller, gross revenue manager and crossroadion manager, and have extendd a solution that will improve the party in these dickens celestial orbits. Using a systematic approach, I was able to analyze the present-day(prenominal) motorcar hours, standard cost, and overhead budget. My findings have consent toed me to consider the best periodical convergence commingle that will maximize Mertons centre periodical contribution.Further much, I have addressed the decision regarding forbiddensourcing, and have provided both the maximal rent your company should pay in improver to the maximum visualize of hours that should be rented. When find out the harvest-festival mix, I took c beful consideration of the machine hour constraints that your factory must account for. The following sections will provide further information in reg ards to my analytical proficiency, and how I was able to find out these figures. Current Situation Mertons third and fourth quarters of get year should non be deemed a failure, but rather an bea where the company can improve.It is evident your companys modern product mix is not meeting the financial standards that the company expects. As your sales manager pointed out, object lesson one hundred one trucks flowly cost $40,205 to cook and are selling at a scathe of $39,000, meaning the company is producing this personate at a loss. Some other issues to point out are the current efficiency levels. Although the company is pull ining on apiece Model 102 sold, maxing out mental ability for this model may not be the best solution, as suggested by the controller.An analysis of the provided budget will allow us to tether where the companys mvirtuosoy is being spent, and will suggest reliable areas where possible changes can be made. Evaluating the different scenarios will ans wer our current questions on whether to stop producing Model hundred and ones all together, to bide producing both models but at different amounts, and/or to consider the utilisation of an outside supplier. Data Used in the Analysis To address the chief(prenominal) goal of increasing financial performance, I had to define the objective lens of the current situation.Simply put, the objective is to maximize total contribution from the twain models, which will without delay improve Mertons financial performance. Our focus is contribution rather than profit beca subroutine contribution deals only with changeables be and variable star costs are costs that we can manipulate to better Mertons financial position. By determining exactly how much contribution Merton receives from producing one Model ci and one Model 102, we can attempt to maximize these figures. A products contribution is the amount of money the company receives after subtracting out the variable production costs. ki nd 1 shows the contribution received for producing one truck of Models one hundred one and 102. I was able to calculate this figure using the data provided from Tables B and C in your report. Table B listed the variable costs which take the direct materials and direct labor costs per model. I then added the variable overhead costs per unit that were listed in Table C. Subtracting these variable costs from the total selling price leaves us with Model 101 attributing $3,000 in contribution and Model 102 attributing $5,000. The second goal is to determine an optimal product mix.In order to do so, I had to account for any constraints, or parameters that restrict production and affect total monthly contribution. Table A from your report provided these constraints, which are the production capacities of the four departments, engine assembly, metal stamping, Model 101 assembly and Model 102 assembly. These constraints, which will be discussed in the following sections, are provided in Figure 2. Finding both the contribution per model and the constraints allows us to determine the decision variables.Decision variables ease us do exactly that, brace decisions. Since product mix is the decision we are making, the decision variables represent the modus operandi of 101 and 102 units that Merton should arouse each month. These variables are represented as X101 and X102. Having set our variables I was now able to setup a mathematical compare that will calculate Mertons maximum contribution per month. The equation is as follow Maximum Contribution = $3,000*X101 + $ergocalciferol0*X102 Method of Analysis Linear ProgrammingAfter edition the report and understanding the variables involved, I realized that one-dimensional programing would be a useful tool in this situation. Linear programming (LP) is near because it assists in decision making when resource allocation is involved. Our situation calls for a better approach when allocating labor, machinery, money, du ration and materials, thus making LP the perfect fit. For this situation, analog programming is more(prenominal) than an option. It is a must. Due to our number of constraints, using a linear program will compute exact outputs that will give birth time and eliminate the risk of human error.The program will allow us to input the known variables (101 and 102 contribution), and will calculate the optimal product mix, patch staying within the parameters of our listed constraints (Figure 2). Analyzing the Options with problem solver Optimal Product Mix at one time that you have an understanding of the capabilities of linear programming, I will explain how I was able to use this model when persuading your sales manager, controller and production manager. Although these ternion do not agree on how Merton is currently allocating its resources, one face where they do agree is that maximizing contribution is Mertons main focus.After explaining that this linear program, known as Solver, can calculate optimal product mix on the basis of maximum contribution, I received their undivided attention. Solvers product mix calculation stated that Merton Truck Co. should produce 2,000 Model 101 trucks and 1,000 Model 102 trucks each month. Using this product mix will provide a maximum contribution of $11,000,000 per month. The objective commandment that was presented above shows this calculation $3,000*(2,000101)+5000*(1,000102)= $11,000,000 total contribution per month.Remember, this formula is calculated while staying within each of Mertons production constraints. Simply producing more or less of either model will do one of two things. unitary, it would exceed one of our given constraints, or two, it would produce a total contribution that is lower than $11 million. Solvers suggestion to produce 2,000 Model 101s proves that the controller was correct in his dissent of the sales manager. The model confirms that doubling Model 101 production allows the bushel overhead o f 2. 7 million to be absorbed over 2,000 models alternatively of 1,000 as the company is currently doing.Since Merton pays fixed overhead of 2. 7M. for 101s and only 1. 5M for 102s, it makes sense to get your moneys worth by producing more 101s. Renting Additional Capacity In rundown to providing the optimal product mix, Solver has a number of other capabilities that help support my testimonys. One capability is that Solver can help us determine whether the production manager was correct when suggesting to rent additive capacity from an outside supplier. After the variables are input into the Solver program, I trial run the calculation.Once the program has calculated the data, it provides us with a sensitivity report that focuses on our available resources (constraints) and tests a number of what-if scenarios. For this situation, it will help us determine the amount to pay per rented hour and exactly how many additional hours to rent. two relevant categories to note from the s ensitivity report are the tone price and the allowable increase. The program provides a shadow price which states that for each additional unit produced, Merton will receive X dollars in contribution. The shadow price for engine assembly was $2,000.Therefore, for each additional unit of capacity (rented hours), Merton can afford to pay a maximum of $2,000. In regards to the allowable increase, Solver suggests that Merton should purchase a maximum of 500 rented hours. After 500 hours have been purchased, there is no further increase in contribution. The use of Solver has once again proven beneficial. Although the production managers suggestion was correct, Solver has strengthened his argument by providing objective data that tells us a max price to pay in addition to the maximum number of hours to rent.Additional Constraint Producing at a 31? After finding out from the optimal product mix that it is more beneficial to produce two propagation the number of Model 101s than Model 10 2s, why not increase production to troika to one? We can test this proposal by simply adding an additional constraint to our linear program. As expected, the optimal product mix was squeeze to change to a 31 ratio. Adhering to this constraint provided a product mix of 2,250 Model 101s and 750 Model 102s. However, the unwanted force is noticed in total monthly contribution.Plugging this product mix into our objective equation shows that contribution actually decreases. $3,000*(2,250101)+$5000*(750102) = $10,500,000. Seeing this drop in monthly contribution further proves that our previous optimal product mix of a 21 ratio should remain in place. Closing As mentioned in the previous sections, linear programming is a useful technique that should be applied to help improve Mertons financial performance. My recommendation is that the company immediately implements a product mix of 2,000 Model 101 trucks and 1,000 Model 102s.Secondly, the company should rent additional capacity from an outside supplier. However, your company must not pay more than $2,000 per hour, and not rent more than 500 hours because this would no longer increase total contribution. Although linear programming is widely used and often very accurate, no model is perfect. One disadvantage of linear programming is that it does not take into account industriousness trends. Choosing to produce two times the amount of Model 101s does not guarantee this model will sell two times as much. Furthermore, linear programming is only useful in answer linear scenarios.Real world constraints are not always linear. For instance, a constraint that involves number of staff members required per model would be impossible to calculate when the other constraints are based on hours. Additionally, linear programming does not account for risk. What if the supplier cannot provide materials for one months time? What if Model 101 is using defective parts and the line becomes halted? These are items to consider when impl ementing LP, but by no means should they prevent Merton Trucks from implementing the model. Figure 1 Contribution per Model Model 101Sell legal injury $39,000 Direct Materials $24,000 Direct Labor $4,000 Variable command overhead * $8,000 Contribution $3,000 Model 102 Sell Price $38,000 Direct Materials $20,000 Direct Labor $4,500 Variable Overhead * $8,500 Contribution $5,000 Figure 2 Constraints tool-Hours Requirements and Availability Department Required Machine Hrs. Model 101 Model 102 Total Machine Hrs. Available per calendar month Engine Assembly 1 2 <= 4,000 Metal Stamping 2 2 <= 6,000 Model 101 Assembly 2 - <= 5,000 Model 102 Assembly - 3 <= 4,500