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Supply Chain Simulation Case Study

¶ … ordering strategy is fairly simple, and this is why there were seldom backlogs. Basically, what you want to do is understand the baseline demand. In the real world, we would probably already have this data, but in the simulation we needed to figure that out. So most of the order backlogs occurred initially, and during promotion, when nobody was really certain of demand. Once it was established that demand was a fairly consistent 138 per week, it was much easier to align the orders, and keep inventory levels low. The biggest issue with this situation is that the supplier was unable to deliver consistently. The supplier really did not seem to have the ability, even knowing what the demand was, to execute on that. My strategy as the wholesaler was simply to try to be an intermediary, and keep my inventory levels low. That is all I can do in this situation -- if the brewery cannot keep up with demand that is out of my control, but I can at least do my best to match up orders from my customers with orders from the brewery.

Future promotions will be easier to address. In this situation, we have now enough data to establish the mean demand for a week, and from there we are in a position to analyze how a promotion will affect that. With a mean and a standard deviation, we are able to more or less ensure a regular order. If a promotion is known to spike demand, in future we will be better equipped to deal with that. But part of the issue lies with the brewery as well. There was something of a problem getting our orders out to us, and only after the promotion ended was the brewery able to get back to somewhat stable deliveries to us. Yes, this situation cost us money in lost sales, but it cost the retails and the brewery that money as well. One would like to think that the brewery will remedy this by getting some more equipment and upgrading capacity. Such lost sales are a sign of success, even though they cause temporary...

This would allow for greater alignment of brewery deliveries and sales. Outside of the promotion, demand is very stable -- orders don't always come in on the same day but otherwise they are quite consistent. The mean weekly order overall was 136.83, and the standard deviation was 32. The only weeks that went outside of one standard deviation outside of the promotion were weeks 66 and 86, and those required adjustments on the part of EBBD to ensure that there was no an inventory build-up.
Assignment 2. The bullwhip effect is that gross misalignment throughout the supply chain occurs when each side of the supply chain seeks to solve the problem from its own perspective (QuickMBA, 2010). The bullwhip effect costs an intermediary such as EBBD, and the best thing that EBBD can do is to minimize its effects. On the retail side, the retailers order when they have a sufficient backlog. This usually occurs weekly, but the timing is sometimes off by a day or two and in other instances the order to smaller than expected (day 66, 86). The response of EBBD in that situation is to match up orders as best as possible -- it's a kludge but it worked in this case because the mean order is fairly consistent and it is unusual to see an order outside of one standard deviation.

The bullwhip effect on the other side comes from the supplier. The brewery was having all kinds of problems meeting orders. Basically, our good work prevented a serious bullwhip effect from occurring. What happened during the promotion is that the brewery was unable to keep up with demand. Had we simply continued with regular ordering during this period, there would have been a massive backlog of orders outstanding -- and that is…

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QuickMBA. (2010). The bullwhip effect. QuickMBA. Retrieved May 25, 2014 from http://www.quickmba.com/ops/bullwhip-effect/
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