run 1: I wanted to just jump right in and start pressing buttons, but I resisted this urge. Instead I read the directions to see what it was I was supposed to do/learn. With a 2-week delay between order from wholesaler and delivery, I knew I’d need to order more cases the week that I saw an increase in orders (not wait until another downturn). So, I was able to gradually increase or decrease orders as demand changed. Demand didn’t appear to rapidly increase, so I was able to keep expenditures to just over $200 for the 20-week period (the game said optimum was $250, so I felt pretty good about myself!).
run 2: I did almost as well this time; found myself looking more closely at the exact number of cases ordered by customers, instead of just estimating by watching the bar graph. I was thinking of the model behind this…the causal relationships of: number in stock, number ordered, and 2-week delay.
run 3: I did the worst this time, though still optimum ($249); and I’d been drinking beer. ☺ Looking at the model was interesting, but I didn’t find myself naturally thinking about it as I did this third game. Because I didn’t get any “backordering” on any of the 3 runs, I didn’t have to consider/use that part of the feedback loop. I do find myself wanting to play again to see if I can get it to give me a larger jump in customer orders; then, I could see my reaction and get a backlog of orders. So, for my fourth run, I had the same jump in orders from 4 to 8 during week 4 (?); I got to an expenditure of just $187!
In all three, I don’t feel like I learned anything…rather just played a game. I’d be curious if I could take a test on any underlying principles. Also, it does make me want to try to build my own model of something in Stella. As a student, I think the next step would be to have them change the variables and play again (increase case cost, increase/decrease order lead time, vary randomly the number of orders each week). The next step should then be to have a similar situation, with a different commodity, and have them write out a model that could explain it (in a format similar to the Beer Game model, since they have seen that). Without this last step, I am learning with models, but not learning with modeling. Learning with modeling definitely engages the learners mind in more active thinking and should accomplish more conceptual change than just using this Beer Game (or even seeing the underlying model). Eventually, some “real terminology” should be used, so that I know what to call all these supply/demand principles with which I’ve been working.
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1 comment:
It is interesting to see how Mark play the game.
One of the objectives is keeping the inventory stable at 20 cases per week. I was able to keep the inventory at almost 20 for the whole time, but I could not get before $200. Actually, Mark's performance encourages me to play more to "beat" his score. Of course, in order to beat his score, I need to really understand the math model behind it. I can see that the competition component of the game may encourage students to explore.
This game was not designed very well from the game perspective. First, I did not see the optimal expenditure, and seems like Mark did not see the "optimal" inventory level. So, our goals were not the same. Both of us were able to keep the expenditure below $250 in the first run, and both of us learned very little in the subsequent run. The competition factor has an effect on me (which may be a good game design).
To answer Mark's question (from an "expert" point of view), I think Mark already got the "lead time" issue, and uncertainty demand issue. The game gave him the experience to manage "fixed" lead time, which is usually the a simpler version of inventory problem. As Mark said, if the game put in demand uncertainty, the students should be able to learn the concept better.
My question: is this game effective to teach students inventory management concept?
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