Diktat is a German word that means "dictation" or "dictatorship." It is often used to refer to the harsh terms imposed on a defeated country by the victors in a war. In the context of Germany, the term diktat is most commonly associated with the Treaty of Versailles, which was signed at the end of World War I in 1919.
The Treaty of Versailles was a peace treaty between the Allied Powers (led by France, the United Kingdom, and the United States) and Germany. It was meant to bring an end to the war and to establish the terms under which the defeated Germany would be forced to pay reparations to the Allied Powers. The treaty also imposed severe limitations on Germany's military and territorial expansion.
Many Germans viewed the Treaty of Versailles as a diktat, or dictate, because they felt that the terms were imposed on them by the victorious Allies without any input from the German government or people. The treaty was seen as extremely harsh and punitive, and many Germans felt that their country had been humiliated and treated unfairly.
The resentment and anger that many Germans felt towards the Treaty of Versailles played a significant role in the rise of Adolf Hitler and the Nazi Party in the 1920s and 1930s. Hitler and the Nazis promised to restore Germany's honor and power, and they used the treaty as a rallying cry to mobilize support for their cause. Hitler came to power in 1933, and he quickly set about tearing up the Treaty of Versailles and rebuilding the German military. This ultimately led to World War II, which ended with the defeat of Germany and the imposition of another set of harsh terms in the form of the Potsdam Agreement.
In conclusion, the term diktat is closely associated with the Treaty of Versailles and its impact on Germany following World War I. Many Germans saw the treaty as a dictate imposed on them by the victorious Allies, and the resentment and anger that it generated played a significant role in the rise of the Nazi Party and the outbreak of World War II.
Littlefield Simulation 2
We attributed the difference to daily compounding interest but were unsure. Final 100 Days Our final inventory purchase occurred shortly after day 447. However, this in fact hurt us because of long setup times at station 1 and 3. The price is the same whether one or two games for a given product are assigned. A huge spike in demand caused a very large queue at station 3 and caused our revenues to drop significantly. We never saw a reason to set the priority to step 2 because we never had more machines at station 3 than at station 1.
In Informs: Transactions on Education, Miyaoka recommends not taking too much stock in reducing lot size because "smaller lot sizes cause queuing problems at stations 1 and 3. This happened because we needed to order raw material with a lower frequency. Please contact rlt-info responsive. Station 2 never required another machine throughout the simulation. . For academic not-for-profit institutions, volume discounts start at 600 students per year in one course. We then set the reorder quantity and reorder point to 0.
While it is important for teams to plan for economic order quantity, cash will be too short in the beginning of the game to implement it, so it is better to wait until the factory is generating money to purchase additional machines. After all of our other purchases, utilization capacity and queuing at station 2 were still very manageable. In other words, the second game is effectively free. Right before demand stopped growing at day 150, we bought machines at station 3 and station 1 again to account for incoming order growth up until that point in time. For most of the time, step 4 was selected as the step to process first. Machine Purchases The first time our revenues dropped at all, we found that the capacity utilization at station 2 was much higher than at any of the other stations. This proved to be the most beneficial contract as long as we made sure that we had the machines necessary to accommodate the increasing demand through day 150.
When this was the case, station 1 would feed station 2 at a faster rate than station 3. We also changed the priority of station 2 from FIFO to step 4. Miyaoka said that after figuring out how to utilize the simulated factory's stations by forecasting demand, students learn when utilization will max out and realize that it is better to lose a little money from not having enough stations running during peak demand rather than spending too much to get the maximum amount of machines possible that sit idle when demand slows. For institutions paying list price, volume discounts start at 11 participants per year. For example, if there are 30 students in a course divided into 10 teams, the price would be the individual student price multiplied by 30 students. So we purchased a machine at station 2 first. However, we wrongly attributed our increased lead times to growing demand.
The queue size in both the Stuffing and Tuning process led us to prioritise expanding the capacity of the Tuning Process first as we aimed to capitalise on the completed boards as soon as we can. In an interview with for the Stanford Graduate School of Business' website, the program's creators, Sunil Kumar and Sam Wood, recommend that student gamers use their knowledge accumulated from concurrent coursework as much as possible while using the 14-day simulation. Thus we adopted a relatively simple method for selecting priority at station 2. The costs of holding inventory at the end were approximately the same as running out of inventory. So we purchased a machine at station 2 first. Initially we set the lot size to 3x20, attempting to take advantage of what we had learned from the goal about reducing the lead-time and WIP. Volume discounts are also available.
The purpose of the game is to be the management team with the most cash at the end of the 14-day simulation run. Pricing is usually on a per-student basis for one course. In our case, we had to decide which process capacity to increase first, as there were budget constraints. Our assumption proved to be true. We also set up financial calculations in a spreadsheet to compare losses on payment sizes due to the interest lost on the payment during the time until the next purchase was required. Machine Purchases The first time our revenues dropped at all, we found that the capacity utilization at station 2 was much higher than at any of the other stations.
As demand began to rise we saw that capacity utilization was now highest at station 1. Assignment options include 2-hour games to be played in class and 7-day games to be played outside class. We nearly bought a machine there, but this would have been a mistake. If priority was set to step 4, station 2 would process the output of station 3 first, and inventory would reach station 3 from station 1 at a slower rate. This proved to be the most beneficial contract as long as we made sure that we had the machines necessary to accommodate the increasing demand through day 150.
Base prices Student prices for institutions in North America and Western Europe are shown below. After viewing the queues and the capacity utilization at each station and finding all measures to be relatively low, we decided that we could easily move to contract 3 immediately. They all agreed that it was a very rewarding educational experience and recommend that it be used for future students. In the capacity management part of the simulation, customer demand is random and student gamers have to use how to forecast orders and build factory capacity around that. Machine Purchases The first time our revenues dropped at all, we found that the capacity utilization at station 2 was much higher than at any of the other stations. Some describe it as addictive.
