Littlefield Lab Essay

Since the beginning the team decided to try an scrappy strategy to win the game, assuming a riskier position with higher authority benefits and costs. For that, it was necessary to identify c tout ensemble parameters of the swear out and design a fascia to analyze the information and make decisions in a faster way. The key parameters we started monitoring were penury (jobs accepted), sites utilization and asterisk snips of the replete(p) process. The first goal was to balance the line and satisfying the essential.Demand compendium and its relation to order kitsIn order to predict the future flows of the demand and match the info with the kit orders we create a model in attempting to avoid stock breaks or overstocks and anticipate the bargain for of railway cars. The model considered the medial demand of last(a) 2 weeks projected with the growth rate of those weeks. enjoyment of stations and its relation to purchase machines In order to to satisfy the demand, go equili brium in the capacity of the 3 stations, and avoid bottlenecks to get the utmost profit with the contract 3, the purchasing of new machines were made when utilization of whatever station was steadely over 80% and was justified by the cost-benefit analysis.Cost-benefit analysis to purchase machinesConsidering a demand of 30, 60 & 90, the pay back time go away be 29, 15 & 10 daylightlights on ideal conditions. changing the contractsWhen the balance was achieved on the process, then we started to intervene contracts since contract 3 provides the outflank profitability when the lab is able to accomplish a promised lead time of 0.5 old age being careful of change to contract 2 or 1 if the promised lead time would non be accomplished overdue to the circumstantial conditions of the process. To optimize the profitability of the jobs received on the first day of every week, we began to modify the contracts according to the following criteria flinch 1 If machine 1 had more than 3 jobs waiting for kits on last day of the previous week. Contract 2 If machine 1 had between 1 to 3 jobs waitingfor kits on last day of the previous week. Contract 3 If machine 1 had 0 jobs waiting for kits on last day of the previous week. Finally, on day 150 we try an all in strategy spending $160.000 in 1 machine for station 1 and 2 to increase the capacity and to process jobs only on conditions of contract 3. This decision was taken based on a demand of 91 jobs and a utilization of station 1 of 0.83 between days 143 and 149.Profits analysisThe table shows the sources and uses of cash including the analysis of main items. descriptionAmount, $CommentsStarting silver+ 1.000.000Revenue+2.770.670 493, 226 & 1981 jobs were accepted under contract 1, 2 & 3 respectively. $ 3.072.000 was the maximum possible revenue.(Calculations 493 x $ 750 + 226 x $ 1.000 + 1.981 x $ 1.250) $ 301.220 were lost(p) for non-fulfillment of the contracts.(Calculations $ 3.072.000 2.770.670)Interest+8 1.993Stationpurchases-560.000 4 stations N1 were bought on days 61,115, 141 and 150. 2 stations N2 were bought on days 116 and 150.All stations were bought at a certain time which ensures that the investment funds were payed back before the day 314 considering a pay back block 10-29 days for each station (see cost-benefit analysis). Inventory-1.704.600 2.841 kits were bought (including kits order by default). 2.566 kits were tenacious on the review period corresponding to day 7. 2.700 jobs were accepted.Inefficiencies 134 kits were needed moreover non ordered (2.700-2.566 kits).They represent maximum losses of $ 167.500 (134 x $ 1.250) 141 kits were ordered but non needed (2.841-2.700 kits).They represent losses of $ 84.600 (141 x $ 600)Cash Balance1.588.064The cash balance shows that investments on machines and kits were payed back but was not possible to get a better profitability because orders were only 80/week instead 91/week as we predicted on day 150.Conclusion The Lab purchased the first 4 machines too late, so the up-grade of the process and the pay back of the investments were got too late, affecting profits. The Lab should not arrive purchased last 2 machines (station 1 &2), since they were not needed to serve 80 orders/week (demand after day 150 was overestimated). It would have saved $ 160.000. The contracts were not changed on time, so because of that there was a maximum lost of $ 301.220. The kits were ordered including the number of jobs waiting for kits at the end of each week, because we do not realize that they were ordered by default. It would saved a maximum of $ 84.600. The Lab should have worked with LIFO instead FIFO system considering that kits queued for station 1 were mostly already late to be ready at the lead time of 0.5 days under contract 3.