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The LittleField Simulation Strategy

August 12th, 2009

I have been told that this post voilates the use of Littlefield simulation software. So hereby I am removing the contents of this post. Apologies if I am causing inconvenience to anyone by deleting this post. Good luck with your Littlefield Simulation experience, Enjoy!

Updated on 8/22/2011


Economy, Markets and Finance, MBA Articles , , , ,

  1. Cz
    September 25th, 2009 at 01:47 | #1

    I have a question. What made you consider to buy the 3rd machine at station 3 later on in the simulation?

  2. Cz
    September 25th, 2009 at 01:49 | #2

    I have a question. What made you consider to buy the 3rd machine at station3 later on in the simulation?

  3. Cz
    September 25th, 2009 at 01:54 | #3

    I have a question.
    What made you consider to buy the 3rd machine at station3 later on in the simulation?

  4. September 25th, 2009 at 09:51 | #4

    Well, our throughput for the third machine had become a bottleneck and we were unable to complete orders on time. As a result we were losing money. So we decided to go ahead and buy the machine anyway, considering we still have a good 70 days to go. We bought this machine about 36 hours late and lost lot of money as we couldnt fulfill the orders.

  5. Cz
    September 28th, 2009 at 15:13 | #5

    Sorry about the duplicates. I am just wondering your station 3 can process 14*2=28 orders/day. How did that become your bottleneck compared to station2(which is 10*2=20orders/day)?

  6. kd
    October 26th, 2009 at 09:44 | #6

    Have you done LittleField Simulation 2? I’d be interested in seeing your strategy for that.

  7. October 26th, 2009 at 22:13 | #7

    Nope, Havent done Simulation 2.

  8. October 26th, 2009 at 22:18 | #8

    Cz, The output from Machine 2 doesnt necessarily come out perfect after round 1. Tuning machine (Station 3) was running at full force and we could see the Station 3 maxing out. The moment you see the utilization graph hitting 100% more than once, You probably want to consider next step carefully.

    I am not saying buy a machine….but it depends on lot of things, for example do you know what would be the rate of incoming orders? Is the simulation coming to an end where the orders will slowly die out?

    The point I am trying to make is, if you see a station hitting 100% make some decisions, sitting and waiting for a miracle to happen may hurt your prospects.

  9. lab
    November 1st, 2009 at 19:38 | #9

    Alright i have a scenario… i’m doing the second simulation which allows you to pick a contract and go for it… should we stick with the normal jobs or go for the jobs where we can get $1000 per order. There are obviously shorter lead times. If you want i can show you the instruction sheet.

  10. November 2nd, 2009 at 12:10 | #10

    Other variant is possible also

  11. November 2nd, 2009 at 19:12 | #11

    I would like to look at the sheet, but if you are competing for the top spot (if any) then yeah why not go with premium jobs. Its risky….you have to do your queueing analysis to estimate the number of machines you want to buy. If you fail to fulfill orders you will have penalties and also lot of work to get through before your stations can proceed with new orders

  12. lab
    November 3rd, 2009 at 09:56 | #12

    Well the way the game is right now, i believe demand is steady around 12. However it can go from 20 to 4. When it is 20, my machine’s utilization is through the roof, however, when it is 4, obviously my utilization is quite low. Right now, i’m taking about $1000 per job with a lead time of around .4. The normal lead time to get all $1000 is 1 day but it decreases proportionally till 3 days. For the $1250 job, the normal required lead time is .5 which means i could possibly do the jobs right now and earn that extra $250, but if demand were to spike like it does during the 20 sessions, i would be in trouble because i might end up losing out. Should i buy more machines to account for fluctuations in future demand? Or should i just stick with the safe $1000?

    Also i do not know how to attach the sheets so if you could show me how to do that i would appreciate it

  13. November 4th, 2009 at 20:38 | #13

    How many days remaining in the simulation? How many days elapsed? If you look at the current trend and look at the utilization ( since you already saw the demand going through the roof!) you should be able to calculate the throughput of each station – How many stations do you have ? I havent looked at LFS2 so need more information. If you think you have a long way to go in the competition and the demand is going to increase, then I would suggest you buy the machine – ofcourse look at your throughput and see the need before you make any decision.

    I have updated the comments box with added ability to attach files, you can attach a file here.

    Good Luck!

  14. Juan
    November 17th, 2009 at 02:45 | #14

    I am on day 133 for simulation 2. I am on contract 2. My inventory is going through the roof. Do you have any suggestions on how to decrease my inventory? Earlier today the inventory was at 836,000 and now that I checked it, it is at 907000. Do i have to adjust the reorder point? Any suggestions would help.



  15. November 18th, 2009 at 20:07 | #15


    I have no idea how/what you would do with that inventory unless you get more orders. When I played this, the focus was entirely on utilization of the machines and queueing theory application so I didnt have to deal with inventory. May be someone else can comment on your question,….

    Good Luck!


  16. Ben
    December 3rd, 2009 at 09:34 | #16

    How do you get Mu? In other words, how do you calculate the capacity at each station? For instance, I have 6 machines at station 1, there are 9 jobs arriving daily (avg) and jobs are taking 12 hours on avergae to complete in the system?
    I am not sure how to use the utilization of each machine.

  17. varun
    April 19th, 2010 at 04:00 | #17

    Hi Kumar,
    Can you explain how you calculate Mu of each machine?
    I am bit lost on how to relate the utilization with the average input rate for each day. Appreciate your help.

    • April 19th, 2010 at 23:54 | #18

      I dont have access to the littlefield simulation data now, but here is the concept. We are given a plot of utilization for each day. Since we have the order arrival rate for each day we have been given an instantaneous λ and can easily solve for an approximate µ, using Utilization = λ/ µ.

  18. karen
    September 24th, 2010 at 13:12 | #19

    I am really lost to calcuate capacity and utilization to find out how many machines I need, I did calcuate regression with intercept = 1.401632653, slope = 0.043073229 and next period = 3.598367347 Day 51. Can you help?

  19. dre
    November 3rd, 2010 at 22:16 | #20

    is there any way to solving this with out obtaining the standard deviation and the mean value, pehaps by the use of a regression analysis?

  20. rvox
    April 7th, 2011 at 06:59 | #21


    Make the following changes. You will win. Guaranteed!

    Day 50 Order Quantity 24000 kits, Reorder point 4680 kits, Station 1 machine count 5, station 2 machine count 2, station 3 machine count 2
    Day 60 Contract number 3
    Day 215 Order quantity 60000 kits, Reorder point 0 kits

    Its that simple.

    • April 8th, 2011 at 01:59 | #22

      Thanks Rvox, I hope that Helps everyone! but I guess it also depends on the orders coming in…anyways Good Luck!

  21. jtgosox
    April 21st, 2011 at 22:45 | #23

    rvox, did your simulation game include a holding cost? I haven’t crunched any numbers yet, but it looks like you’d be holding a lot of inventory.

  22. May 25th, 2011 at 20:48 | #24


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  23. Mark
    July 15th, 2011 at 09:30 | #25

    Anyone know a strategy for simulation 2?

  24. HB
    July 17th, 2011 at 20:23 | #26

    Just went through this last semester. We ended up in first place even though we made a few mistakes. 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.

    Station 1 Station 2 Station 3
    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 = 0.002401
    Where 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. 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 batches

    Where D = annual demand = 12 * 365
    S = fixed cost per order = $1,000, and
    H = 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.

  25. Sam
    July 18th, 2011 at 22:47 | #27

    @HB, how much money did you gain on Simulation 2 for the customer responsive?

  26. S.Kumar
    February 28th, 2012 at 22:38 | #28

    Closing the comments section for this post.

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