Beer Game Report

We began our analysis by searching for bottlenecks that existed in the current system. It was easily identified that major issues existed in the ordering process. Without calculations, you could tell the reorder point was too low since the historical plots showed inventory levels at zero for two or more days at a time. The number of jobs in customer orders showed correlating spikes at the same time of the inventory outages. We reviewed the utilization and queues of the other stations in the system but were hesitant to make in immediate changes since we were not entirely certain the effects of correcting the inventory policy.

If the operation have not reordered all the goods they need, this can lead to irritated customers and lower revenue for the operation.

The retailers had to estimate their customers’ demands well in advance of the selling season and place bulk orders for each season’s inventory. This involved high risk for the retailers as over-estimation would lead to unsold stock; whereas under-estimation led to stock outs and loss of potential sales.

Changes within the supply chain can disrupt the normal flow of goods and services because each change hasn’t been fully scrutinized. A firm can plan and speculate that a change with have a certain effect on the supply chain, but until those processes have been measured it is impossible to know the true cause and effect of any disruption.

The current demand forecasting method is based on qualitative techniques more than quantitative ones. If the forecast is not accurate, the company would carry both inventory and stock out costs. It might lose customers due to shortage of supply or carry additional holding costs due to excess production. If the actual demand doesn’t match the forecast ones, and the forecast was too high, this will result in high inventories, obsolescence, asset disposals, and increased carrying costs. When a forecast is too low, the customer resorts to a competitive product or retailer. A supplier could lose both sales and shelf space at that retail location forever if their predictions continue to be inaccurate. The tolerance level of the average consumer

For instance (ceteris paribus), if the transportation of the HP products were air shipped, and the lead time passed from four and a half weeks to three days, logically stock levels would decrease. The same would happen if we reduce the time between orders or the level of satisfaction that we want to assurance to our customers.

These spikes and troughs could be the result of price discounts that the company is offering due to which customers are buying in bulk and stocking up on products. This could lead to variability in demand, leading to excess demand or no demand that would lead to non-efficient production by the company. Root cause analysis should be performed, and if it is established that volume discounts are leading to this phenomenon, it is recommended that the company minimize discounting and work with the customers using a partnership model to ensure a steady demand.

Additionally, forward buying activities from distributors due to frequent trade promotions and volume dis-counts enforce the effect of demand fluctuations. During promotions, distributors buy huge amounts of pasta at a lower price. This forward buying leads to lower demand in future periods and contributes to high peaks in or-der quantity and low levels of orders in the following periods. Connected to this problem is the incentive system of the sales force, which relies heavily on trade promotions in order to achieve quarterly targets. Another factor related to these aspects is batch ordering and

Raising maximum distributor order, more than customer demand is one good choice made in the three games. According to the supply chain order, the maximum distributor order totals to 15units compared to a maximum demand of 9units. These figures prove that backordering costs will not occur during business operations. Good inventory management techniques require placing adequate orders to reduce backordering costs. The cost and time of backordering affects operations and hence the need to adequate inventory to match demand changes. An average customer order of 7units compared to average distributor order of 5units points to low backordering costs. A minimum customer order of 4units means that market demand is under control from

As demonstrated in Exhibit 11, our factory processed 15 orders for materials during the course of the simulation. Had we been able to accurately predict our need for materials at the beginning of the simulation, we could of saved $14,000 through frontloading on inventory and taking advantage of the absence of inventory holding costs. Due to the difficulties of accurately predicting order quantities however, we would have decided to batch our material orders into 2 or 3 batches. Batching material orders would have allowed us to save on fixed order fees and also would have enabled us to respond to order fluctuations with increased/decreased order

Company is facing a challenge of potentially higher inventory costs. Rising prices may further result in changes in customer behavior and preferences.

In this final paper for Managerial Finance I will attempt to show how the supply chain inventory management method can be affected depending on the situation of the retailer. Studying the control method for problems in inventory, which would include both, excesses in inventory as well as shortages, and hoping to minimize loss.

The customers, wholesalers and retailers may order in large quantities with the expectation that they will receive a greater allocation of products that are in short supply. The impact on the supply chain is significant as the forecasted demand is greatly, and unrealistically, increased with these inflated orders. Eventually orders disappear and cancellations pour in, making it impossible for the manufacturer to determine the real demand for its products

In the first round of the root beer supply chain game there was a lot of confusion, mystery, and chaos; and the data showed that (Please see separate excel spreadsheet of data with mean, standard deviation, and variation calculated). Analysis of the data shows a classic bullwhip effect; the customer places an order and order fluctuations build up through the supply chain. You can also see that these effects are magnified as you get farther from the customer up the supply chain. This holds true for mean, standard deviation, and variation. Without proper communication between the supply chain, it felt like every group was working blind and three steps behind. This was due to

Even after 2 weeks it could not fulfill V-shopper’s order. This was due to the poor floor layout and assembly table layout. The demand could not be forecasted and resulted in stock outs and expedition. Even though, the demand pattern for seasonal sales was predictable, it was difficult to predict the sales for huge discounts. This could be the Bull-Whip effect.