Costs and Contract Terms Essay

Executive Summary Over the span of 168 simulated days, team Honeybadgers managed the Littlefield Technologies job shop. The team’s objective was to maximize the firm’s cash position relative to the rest of the class. Using 50 days of historical data, the team reviewed re-order points, re-order quantity, capacity, lead times, and therefore contract terms. The team also weighed the cost of new machines against capital for inventory and interest rates, evaluating the return on investment and the impact a new machine had on lead times. Using this consideration set, team Honeybadgers purchased one tuning machine, one stuffing machine, and changed the contract terms on ten occasions. Ultimately, the team placed 5th. Actions & Analysis Changing Contract Terms: A 7 day lead time generated higher revenue than the other contract terms during the first 50 days. However, we observed that there was a stretch of 5-8 days when the lead time was below a 1 day lead time during the first 50 days. Evaluating the first 50 days more closely revealed that approximately every 15-20 days, the lead time dropped substantially. Noticing a pattern, and aware that a different contract time could generate more revenue, we decided to micromanage the contracts to optimize revenue. For the duration of simulation, we adjusted contract according to the trending lead time. In times of high demand, when a lead time was more than 18 hours, we opted not to use contract #3 because of the cost of each order (avg. job cost+ordering cost = $608.33) Micromanaging the contracts according to lead times was a temporary solution. This strategy allowed us to optimize revenue when we did not have the capital to purchase a machine. Purchasing Tuning and Stuffing Machines: We originally wanted to purchase both a tuning and stuffing machine because both stations had long stretches when capacity was maxed out. However, without sufficient capital, we had to ration purchases. The tuning machine was at capacity more often. At one point the machine was at capacity for 18 days in a row. Purchasing the tuning machine eliminated a bottleneck at that station, which allowed us to produce more DSS products. Although the Tuning machine was prioritized, the bottleneck at the Stuffing machine was nearly as problematic as the Tuning station’s. The Stuffing machine was at capacity for 15 days in a row. After purchasing the Stuffing machine, bottleneck shifted again, and we were able to produce more DSS products. We did not purchase a third machine because it was unclear whether the revenue earned would offset the cost of the machine. The lead time was hovering around  ½ a day when we had the capital to make the purchase, and we did not believe the additional machine would improve our lead time enough to justify a purchase. In retrospect both machines should have been purchased earlier. We will evaluate the benefits of this approach in the â€Å"Risks and Evaluations† section. Choosing Not to Borrow: When we became eligible to take out a loan, we decided to forego the option because we did not need to borrow. Our cash standing was relatively high throughout the simulation because micromanaging contract terms proved fairly effective. Another deterrent was the grossly high interest rate. A 20% interest rate mitigated any added benefit gained from taking out a loan. Choosing Not to change re-order point: Re-ordering kits was a sizeable fixed cost, but we did not adjust the re-order point / order quantity because demand variability was fairly high. We were aware there was an opportunity cost associated with holding too much inventory because we could have earned interest revenue from the cash spent on inventory. However, we kept the order amounts Q high because (1)we want to save ordering cost and (2) we were not concerned with having too much inventory on hand when there was no direct cost (such as warehousing) associated with holding inventory. Inventory Strategy Final Hours: During the last 12 simulation days we considered developing a plan to minimize our inventory at the end of the simulation. However, we were not sure how to calculate this, and the costs associated with running of inventory was too high to risk making a mistake. Results The Honeybadgers team finished the Littlefield simulation in fifth place, posting $1,511,424 in cash. The team’s final cash position was $104,192 below the first place team, earning 93.5% of their total revenue. Risks and Evaluations At the beginning of the simulation, we wanted to maintain a high R and Q because we wanted to avoid high ordering costs. While we considered keeping inventory low to save money for a new machine, we were not sure the improved lead time could offset the cost of machines. However, in hindsight we realized that we could have managed R and Q better early in the simulation, so as to minimize the amount of excess raw inventory. We now know that we could have adjusted R according to the variability of demand, holding that the more demand fluctuates; the higher R is and vice versa. We believe that this tactic could have allowed us to accumulate enough cash to purchase machines earlier, possibly as early as day 80 or 90. Purchasing a machine earlier could have improved lead times, allowing us to switch to contract #3 earlier so as to generate more revenue. We should have balanced between ordering costs during the last 100 days and the cost of having excessive or unnecessary inventory after last day. In the last day we still had approximately $80k of inventory, which held no value after demand ceased. Managing inventory better would have given more cash on hand.