Since the beginning the team decided to try an aggressive strategy to win the game, assuming a riskier position with higher potential benefits and costs. For that, it was necessary to identify key parameters of the process and design a dashboard to analyze the information and make decisions in a faster way. The key parameters we started monitoring were demand (jobs accepted), stations utilization and lead times of the entire process. The first goal was to balance the line and satisfying the demand.
Demand analysis and its relation to order kits
In 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 purchase of machines. The model considered the median demand of last 2 weeks projected with the growth rate of those weeks.
Utilization of stations and its relation to purchase machines In order to to satisfy the demand, generate equilibrium in the capacity of the 3 stations, and avoid bottlenecks to get the maximum profit with the contract 3, the purchasing of new machines were made when utilization of any station was steadely over 80% and was justified by the cost-benefit analysis.
Cost-benefit analysis to purchase machines
Considering a demand of 30, 60 & 90, the pay back time will be 29, 15 & 10 days on ideal conditions. Changing the contracts
When the balance was achieved on the process, then we started to intervene contracts since contract 3 provides the best profitability when the Lab is able to accomplish a promised lead time of 0.5 days being careful of change to contract 2 or 1 if the promised lead time would not be accomplished due 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: – Contract 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 waiting for 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.
The table shows the sources and uses of cash including the analysis of main items.
– 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 for non-fulfillment of the contracts.
(Calculations: $ 3.072.000 – 2.770.670)
– 4 stations Nº1 were bought on days 61,115, 141 and 150.
– 2 stations Nº2 were bought on days 116 and 150.
All stations were bought at a certain time which ensures that the investment were payed back before the day 314 considering a pay back period 10-29 days for each station (see cost-benefit analysis). Inventory
– 2.841 kits were bought (including kits ordered by default). – 2.566 kits were ordered on the review period corresponding to day 7. – 2.700 jobs were accepted.
– 134 kits were needed but not ordered (2.700-2.566 kits).
They represent maximum losses of $ 167.500 (134 x $ 1.250) – 141 kits were ordered but not needed (2.841-2.700 kits).
They represent losses of $ 84.600 (141 x $ 600)
The 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.
– 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 have 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.
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