Base Stock Model Essay

L.L. Beans approach to a one shot order increases the risk of lost sales due to insufficient inventory as well as increases the risks of higher costs due to excessive inventory. The case indicates that lead times are significantly long from their suppliers (in the order of 8 to 12 weeks). We are not clear on the size of these orders but assuming these are mostly a one shot batch, it is feasible to estimate that these lead times would be reduced for a smaller quantity per order and more frequent orders. Although this approach would increase the transactional costs with suppliers (because a higher volume of individual orders would be placed at the supplier) it might generate significantly more impactful benefits such as:

E[Rp ] = (1 − wTb )RF,b + wTb E[RTb ] = (1 − 1.1735) × 0.02 + 1.1735 × 0.04386 = 4.80%.

3. List the probability value for each possibility in the binomial experiment that was calculated in MINITAB with the probability of a success being ½. (Complete sentence not necessary)

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

During the game, I realized that wide gaps in orders of every role in the supply chain such as factory, distributor and retailer create inventory management challenges. For example, distributor records 0units between week1-week 4 compared to retailer within the same period. The retailer records 3units, 5units, 2units and 2units between weeks 1- week 4. The same applies to factory with 0units from weeks 2-4. Addressing inventory management problems requires developing an average unit level to avoid disappointing customers when demand

Since we solved it by plugging in random numbers for c and R, we should now do the general form of the problem without plugging in number.

E(rIndex ) = rf + β [E(rm ) − rf ] = 1.6% + 1.0 × 4.83% = 6.43% ,

b.P( X = 25 ) = ( 27 25 ) * ( 0.92^25) * ( 1 - 0.92 )^2

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

Chabot needs to increase the flexibility in its supply chain. Chabot is an innovative product manufacturer trying to utilize an efficient supply chain. In order to be able to meet demand, the company needs to deploy excess buffer capacity. Lead time is not up to par so Chabot needs to increase its inventory if it plans to meet unpredictable demand. This will also help increase Chabot’s level of

3. The expected return for each firm was calculated: Expected Return = Alpha + (Beta x BSE 500 actual return).

Since the expected demand is 2000, thus, the mean µ is 2000. Through Excel, we get the z value given a 95% probability is 1.96. Thus, we have: z= (x-µ)/ σ=(30000-20000)/

Expected value of IRR = IRR1 * probability 1 + IRR2 * probability 2 + IRR3 * probability 3

However, the discount associated with advance payment makes the replenishment cycle larger. Zhou et al. [14] developed EPQ (economic production quantity) models to evaluate the optimal make-or-buy decisions when a buyer faces a one-time-only discount offered by a vendor. They showed that their decision depends on the discounted price as well as its timing. Shu et al. [15] assumed that transportation lead time in a vendor buyer supply chain is exponentially distributed and the transportation cost is a function of lot size. They showed that these assumptions lead to noticeable cost reductions when compared with the traditional coordination models.