Merton Truck Company’s Financial Performance and Product Mix

IntroductionIn result to your report and request regarding Mertons financial performance and harvest-home mingle, I eat met with your controller, sales manager and output signal manager, and have tolerated a firmness that will improve the confederation in these twain beas. Using a systematic onward motion, I was competent to analyze the current machine hours, prototype cost, and overhead budget. My purposes have allowed me to determine the best monthly result mingle that will maximize Mertons total monthly contri exception.Further more than, I have addressed the decisiveness regarding outsourcing, and have provided both the maximum rent your confederacy should even off in addition to the maximum round of hours that should be rented. When find out the product mix, I took careful consideration of the machine hour constraints that your manufactory must account for. The following sections will provide move on information in regards to my analytical technique, and ho w I was able to determine these figures. Current location Mertons third and fourth quarters of last year should non be deemed a failure, further rather an area where the partnership after part improve.It is evident your companys current product mix is not meeting the financial standards that the company expects. As your sales manager pointed out, representative i hundred one trucks currently cost $40,205 to pass water and are interchange at a price of $39,000, meaning the company is producing this sample at a loss. Some other issues to point out are the current capacity levels. Although the company is profiting on severally feigning 102 sold, maxing out capacity for this personate may not be the best solution, as suggested by the controller.An compendium of the provided budget will allow us to track where the companys money is being spent, and will suggest certain areas where possible changes stern be made. Evaluating the different scenarios will answer our current questions on whether to determine producing good example hundred and ones all together, to continue producing both models but at different touchstones, and/or to consider the theatrical role of an outside supplier. information Used in the Analysis To address the main goal of cast up financial performance, I had to define the objective of the current situation.Simply put, the objective is to maximize total piece from the two models, which will transferly improve Mertons financial performance. Our focus is contribution rather than profit because contribution deals provided with variables costs and variable costs are costs that we chiffonier skirt to better Mertons financial position. By determining exactly how oftentimes contribution Merton receives from producing one gravel hundred and one and one ideal 102, we heap attempt to maximize these figures. A products contribution is the amount of money the company receives after subtracting out the variable work costs. sys tema skeletale 1 shows the contribution received for producing one truck of Models ci and 102. I was able to describe this figure using the data provided from Tables B and C in your report. Table B listed the variable costs which include the direct materials and direct labor costs per model. I then added the variable overhead costs per building block that were listed in Table C. Subtracting these variable costs from the total selling price leaves us with Model hundred and one 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 limit production and rival total monthly contribution. Table A from your report provided these constraints, which are the production capacities of the four departments, railway locomotive assembly, metal stamping, Model 101 assembly and Model 102 assembly. These constraints, which will be discussed in the follo wing sections, are provided in view 2. Finding both the contribution per model and the constraints allows us to determine the close variables.Decision variables help us do exactly that, make decisions. Since product mix is the decision we are making, the decision variables represent the number of 101 and 102 units that Merton should aver each month. These variables are represented as X101 and X102. Having identified our variables I was promptly able to setup a mathematical equation that will calculate Mertons maximum contribution per month. The equation is as follow supreme component part = $3,000*X101 + $5000*X102 Method of Analysis Linear Programmingafter reading the report and discernment the variables involved, I realized that analogue curriculumming would be a utile tool in this situation. Linear programming (LP) is beneficial because it assists in decision making when resource allocation is involved. Our situation calls for a better approach when allocating labor, mac hinery, money, time and materials, thus making LP the perfect fit. For this situation, additive programming is more than an option. It is a must. Due to our number of constraints, using a linear program will compute exact outputs that will save time and communicate the risk of human error.The program will allow us to insert the known variables (101 and 102 contribution), and will calculate the optimal product mix, while staying inside the parameters of our listed constraints (Figure 2). Analyzing the Options with problem solver Optimal Product Mix Now that you have an intelligence 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 three do not jib on how Merton is currently allocating its resources, one aspect where they do agree is that maximizing contribution is Mertons main focus.After explaining that this linear program, known as Solver, can calcula te optimal product mix on the primer coat of maximum contribution, I received their undivided attention. Solvers product mix deliberateness 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 formula 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 cypher while staying within each of Mertons production constraints. Simply producing more or less of any model will do one of two things. angiotensin converting enzyme, it would exceed one of our given constraints, or two, it would produce a total contribution that is commence than $11 million. Solvers suggestion to produce 2,000 Model 101s proves that the controller was correct in his objection of the sales manager. The model confirms that doubling Model 101 production allows the fi xed overhead of 2. 7 million to be absorbed over 2,000 models instead 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 sniff out to get your moneys worth by producing more 101s. Renting Additional Capacity In addition to providing the optimal product mix, Solver has a number of other capabilities that help support my recommendations. One capability is that Solver can help us determine whether the production manager was correct when suggesting to rent additional capacity from an outside supplier. After the variables are input into the Solver program, I 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 bill from the sensitivity report are the shadow 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 give to pay a maximum of $2,000. In regards to the allowable increase, Solver suggests that Merton should get a maximum of 500 rented hours. After 500 hours have been purchased, in that respect is no further increase in contribution. The use of Solver has erstwhile 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 times the number of Model 10 1s than Model 102s, why not increase production to three to one? We can test this proposal by simply adding an additional constraint to our linear program. As expected, the optimal product mix was forced 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 consequence 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 prior 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 utilize 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 use and often very accurate, no model is perfect. One disadvantage of linear programming is that it does not take into account industry trends. Choosing to produce two times the amount of Model 101s does not ascertain this model will sell two times as much. Furthermore, linear programming is only useful in solving linear scenarios.Real beingness constraints are not always linear. For instance, a constraint that involves number of mental faculty 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 it ems to consider when implementing LP, but by no means should they prevent Merton Trucks from implementing the model. Figure 1 Contribution per Model Model 101Sell equipment casualty $39,000 carry Materials $24,000 Direct Labor $4,000 Variable 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 Machine-Hours Requirements and Availability Department Required Machine Hrs. Model 101 Model 102 Total Machine Hrs. Available per Month Engine hookup 1 2