Shortly after purchasing this machine on day 189, we

calculated a new reorder quantity of 200 test kits. While doing our

analysis, we determined the average demand to date ____ orders

per day. The standard deviation for the period was 3.64 and the

safety factor was 3.0 (98.86 %). The lead time was 4 days, we

needed 49 kits plus safety stock of 2 * 3.64 * 3 = 22, which gave

us ROP of 71 kits. We determined that EOQ = 2 * 3216 * 1000.1 *

$600 = 327.

We did this without formal calculations at first to ensure we did not suffer anymore stock outs while we did the analyses. Upon further analysis, we determined the average demand to date to have been 12.3 orders per day.   We forecast demand to stay relatively stable throughout the game based on the information provided. The standard deviation for the period was 3.64 and the safety factor we decided to use was 3.0 (98.86% certainty). Based on the consistent lead time of 4 days, we needed ≈49 kits plus safety stock of 2 x 3.64 x 3 ≈22 which gave us our order point of 71 kits.

Immediately afterdetermining this, we moved to the EOQ:EOQ=2* 3216*1000.1* 600

This equation gave us our final order quantity of 327, although based on slight demand fluctuations we had been at 321 prior to that.

Our next move was to determine what machines need to be purchased and how many. Our strategy was to get lead times down below .5 days and offer customers that lead time to maximize revenue. The difference between remaining at $750/order vs. $1250/order could have been as high as 1.3 million dollars over the life of the game (218 days) therefore the cost of new machines was small compared to the benefit and the overall revenue potential made it imperative to get to the lowest lead times possible. Because all stations were at times operating at full, we knew that all would create a bottleneck if left to operate as is. We could also see based on the order intake on a given day as compared to their operating ratio for the various stations, that a single machine added to each may be sufficient.   We immediately decided to purchase machines for all stations believing this may be sufficient to drop lead times to ourtarget. Shortly after purchasing these machines, we changed to contract #2, and after more monitoring we were able to fairly quickly change to contract #3 without any further machine purchases. We monitored lead times and revenues constantly, but at no time felt that the purchase of additional machines was necessary.  

We believe that our speed at getting these decisions made, and the changes put in place, was crucial to our eventual success. We did see large drops in cash when inventory was purchased but believed that we had done the correct calculations and that we were best to stay the course. We did exactly that until shortly before the time we were to lose control of the factory. We looked at several different strategies to ensure stock was available throughout the last 50 days of the game and that we got caught with minimal inventory at the end of the game. The original plan was to order sufficient inventory and safety stock and carry it through, but upon changing our order point, we quickly realized that we had inadvertently order 350 kits immediately. This forced us to change the strategy slightly, we lowered

the order point to almost lead times based on the consistency of the demand and safety stock, and calculated the units we would require, plus enough to ensure that we did not order kits immediately prior to the shutdown. If this plan had worked perfectly, we would have ended up with 51 kits in stock, but that would have required that the demand during the last 50 days be higher than the average. This could have happened based on standard deviation, but as it turns out the daily average demand for the period was exactly 12. We ended up with 182 kits remaining, obviously more than we had hoped, but we did not get caught with an outstanding order, or a huge number of units.

In conclusion we ended the game in first place and therefore would change very little about how we played the game. We would have been able to reduce the inventory on hand at the end of the game, but the fundamental strategy of getting lead times below .5 days and maximizing revenue, and our willingness to trust that the calculations made would lead to maximum revenue despite times when we dropped from first, allowed us to win this game.

-------------------------------------------------Executive SummaryOver 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 & AnalysisChanging 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 adjustedcontract 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 “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. -------------------------------------------------

TheHoneybadgers 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.

Round 1 of Littlefield Technologies was quite different from round 2. We started the game with no real plan in mind unlike round 2 where we formulated multiple strategies throughout the duration of the game. Starting off we could right away see that an additional machine was required at station 2 to handle the dual processing load from station one and three. We purchased a machine for station 2 as soon as we gained control over the factory.

Looking back now I can see that this could have been a risky move had we purchased the machine for the wrong station. Luckily, station 2 seemed to be right choice as the queues at station 2 and 3 began to flush out and the number of jobs completed per day increased.

We then proceeded to watch the game over the next couple of days making no big moves except for switching the priority of station two depending on the load at station 2 and 3.

However, despite the addition of the extra machine we seemed to be in a bad spot. A bottleneck started to build up at station 1 and our revenue per day dropped correspondingly. At that point we felt that it might be a little risky to purchase another machine for station 1 in case we could not recover the investment. So we decided to play it safe by continuing to monitor the queues. This did not work well for us. We continued to lag behind the other teams in last place for a couple of days. At this point we were at wits end trying to get out of the rut that we were stuck in. Finally Mark proposed that we go on the offensive and purchase another machine at station 1. The rest of us agreed since we were already in last place and could do no worse.So we crossed our fingers and purchased another machine for station 1. The change was instantaneous. The bottleneck at station 1 was flushed out and the utilization dropped down. We slowly started to

make more money than the other two teams. We continued to keep a very close watch on the factory and even more on the other teams. Mark and I had the...

Managing Customer Responsiveness at Littlefield LabsBackgroundLittlefield Laboratories (LL) has opened another lab. The new lab uses the same process as the lab in the assignment “Capacity Management at Littlefield Labs” — neither the process sequence nor the process time distributions at each machine have changed. On day 0, the lab began operations with three preparers, one tester, and one centrifuge, and an inventory of 160 test kits. This left the lab with $1,000,000 in reserves. Customer demand continues to be random, but the long-run average demand will not change over the product’s 268-day lifetime. At the end of this lifetime, demand will end abruptly and lab operations will be terminated. At this point, all capacity and remaining inventory will be useless, and thus have no value. Management would like to charge the higher prices that customers would pay for dramatically shorter lead times. However, historic lead times often extend into several days, so management has been unwilling to quote the shorter lead times.

Operations Policies at LittlefieldLT uses a Reorder Point / Order Quantity raw material purchase policy. That is, test kits are purchased as soon as the following three criteria are all met: (1) the inventory of test kits is less than or equal to the reorder point, (2) there are no orders for kits currently outstanding, and (3) the lab has sufficient cash to purchase the reorder quantity. No order is placed if any of these three criteria are not met. So, for example, a team could prevent kit orders from being placed at all by setting the order quantity so high that there is insufficient cash to place an order. A reliable supplier delivers exactly the order quantity of kits, four days after the order is placed and paid for. Management considers physical cost of holding kit

inventory negligible compared to the financial costs. Other details concerning the purchasing policy can be found in the “Littlefield Labs — Overview” note. The current...

Here is what we did:

Pre-Game Activities: The team met the Tuesday before class to examine the data and discuss strategies. It was apparent that both Stations 1 and 3 were operating at full capacity, frequently hitting 100% utilization. Station 3 seemed more strained since it had higher queues (Mean=506, STD=498) than Station 1(Mean=187, STD=175).

Since the average job lead time exceeded 2 days during days 43 through 46, inclusive, we thought it would be unprofitable to attempt to move to the $1,000 contracts. We discussed the options of altering the lot sizes, but decided that the extra setup time would only create more bottlenecks downstream.

Stage 1: As a result of our analysis, the team’s initial actions included:1. Leave the contracts at $750.2. Change the reorder point to 3000 (possibly risking running out of stock).3. Change the reorder quantity to 3600 kits.4. Purchase a second machine for Station 3 as soon as our cash balance reached $137,000 ($100K + 37K).

This strategy proved successful and after the second machine for Station 3 was purchased on Day 56 and the queue cleared, we were able to switch to the $1,000 contracts. We occasionally lost a few dollars for being a little late, but we always made more than we would have under the $750 contracts.

Stage 2: The next goal was to save enough cash to purchase a machine for Station 1 so that we could switch to the $1,250 contracts. During the cash building stage, we made the inventory order quantity as high as we could afford, which was 6,900 kits at a purchase price of $70,000. When the 6,900 kits were delivered, we switched the order quantity back to 3,600 so that we could purchase a Station 1 machine as soon as our cash balance reached $127,000 ($90K + 37K). After 21 factory days, we were able to purchase the fourth machine for Station 1 and immediately moved to the $1,250 contracts.

The average lead time declined to under a half a day during factory days 69 through 76. There was a substantial decline in arriving orders during the same time period. The team noticed the drop in lead time and regrets not having moved to the $1,250 contracts sooner. We lost $22,750 of potential revenue for not moving on the information sooner. On the other hand, orders are random and an early move could have backfired on us.

Stage 3: During our preliminary meeting, the team discussed the possibility of purchasing a fifth machine for Station 1. We decided to wait and see if the loss of potential earnings was sufficient to justify a $90 K purchase. We knew that if we were going to buy a fifth machine we should do it as soon as possible to maximize the return on investment. We calculated the loss of potential revenue as ($1,250 – actual average revenues * jobs completed). Our initial estimates showed a potential revenue loss of $266 per day, but within a few factory days the rate of potential loss rose to $419 per day.

There is another consideration in the decision to purchase a fifth machine for Station 1. The title of the Littlefield Technologies game 2 is Customer Responsiveness. The title implies that we should be concerned about the consistency with which we deliver on our service level agreements (SLAs). The potential loss of $419 per day barely covers the $90,000 machine purchase; however we were missing our SLAs 13 out of 15 days and the percent of potential revenues lost due to missing SLAs was 3%. We decided to purchase the fifth machine on Day 94 primarily to improve our customer responsiveness.

This strategy did not perform as well as we had hoped. While our potential revenues lost declined to 1%, we were still missing our SLAs six out of seven days.

Stage 4. During Stage 4, we explored job splitting as a solution to the SLA problem. First, we split jobs into two batch of 30 kits each. This strategy worked so well that we wondered why we hadn’t explored job splitting during Stage 2 or 3. We met our SLAs 12 out of 16 days and our percent of potential revenues lost declined to 0.4%. We calculated the setup times as a proportion of a machine to be 0.007, 0.003, and 0.002 for S1, S2, and S3, respectively.

2S1 + P = 0.194458 =>   S1 + P = 0.187256  =>   S1 = 0.007202 2S2 + P = 0.082479 =>   S2 + P = 0.079424  =>   S2 = 0.003055 2S3 + P = 0.064835 =>   S3 + P = 0.062434  =>   S3 = 0.002401Where the right hand side is calculated as Sum(%Utilization *

#Machines)/#Jobs Completed

We thought that if setup time was so insignificant, maybe the other job splits would be equally good or better. Accordingly, we tried the 3-way job split for eight days, but we were not impressed with the results. On one of the days, our average revenues dropped below $1,200, which we hadn’t seen since purchasing the fifth machine for Station 1.

We thought that maybe it was because of the mismatch between machines and splits. So we tried the 5-way split thinking each job would be split equally among the five machines. This turned out to be a HUGE mistake! After only one factory day it was apparent the 5-way split was a bad thing and we switched back to the 2-way split. Even so, it took an additional four days for the system to recover from the backlogs and we lost $46,693 in potential revenues. (Morale of the story – 2 way splits are great as soon as the queue clears with the purchase of machines. Forget the other splits.)

A one-way ANOVA demonstrated that the differences between the job splits were statistically significant at the alpha=.01 level. Group 1 was no splits. Group 2 was a two-way split. Group 3 was a three-way split. Group 5 was a five-way split. Data for all groups were collected after all machine purchases explaining the small number of observations for Group 1.

We chose to stay with the 2-way split not only because it had the highest average revenues, but also because the 2-way split had the lowest variance. With the 2-way split we were meeting our service level agreements more consistently resulting in higher customer satisfaction and higher profits per job.

Stage 5. With our factory humming, our attention turned to inventory purchases. We calculated the reorder quantity using the equation:

Q* = SQRT(2DS/H) = SQRT(2 * 12 * 365 * 1,000 / 66.31) = 363 batchesWhere D = annual demand = 12 * 365S = fixed cost per order = $1,000, andH = the handling costs = $60 x (1 + .10/365)365 = 66.31

The calculated reorder quantity was surprisingly close to the value obtained from running our numbers through the Inventory example from Chapter 7 of our text (363 vs. 382).

The text also mentioned that small variations in reorder quantity do not matter much and so people usually round to a convenient number. Thus, we set our re-order quantity to 400. Stage 6. Previously we had been stockpiling inventory by purchasing more as soon as money was available to purchase, but we realized that we may be missing out on nontrivial interest payments. So we re-set the reorder point to 3600, which provides a four day inventory plus a safety net.

Stage 7. The Exit Strategy – We do not have control of the factory during the last 100 days of its life. We know from the instructions for the game that the demand is expected to stay consistent although orders are random. We do not feel it is wise to leave a large reorder quantity while the factory is out of our control because we might have a sudden increase in jobs during the last few days that sparks a $241,000 inventory purchase, most of which will go to waste. So before we lose control, we will buy (100 * 11.8 * 60) kits and then set the reorder quantity to 60 (or 3,600 kits). We hope this exit strategy works.

The exit strategy did work although if we had purchased another 1,200 kits in Stage 7, we could have set the reorder quantity to 0 and reorder point to 0. This would have saved use another $24,000.

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Littlefield Technologies Simulation Game

2 strateg...