LL Bean Essay

1. How significant (quantitatively) of a problem is the mismatch between supply and demand for LL hit? As per the historical series and its associated statistical exposition (see graph below), we can observe that there is a significant spread between the A/F ratios sine the standard deviation equals 1/3 of the mean. Besides in cases, there is mismatch beyond 50% between the forecast and the actual demand. Besides the mean value shows that there is a 9% bias meaning that on average the actual is always 9% above the forecast. It should be noticed as well that there distribution is skew to the left with higher values meaning that there is a 100% underestimation for certain items.2. Use the provided Excel file that contains demand and forecast selective information for a collection of items. Suppose those are the entropy LL bonce will use to plan their next season. Consider an item that retails for $45 dollars and costs LL Bean $25. The liquidation price for this item will be $15. Th e sales forecast for this item is 12,000. What order quantity would LL Bean choose for this item?Based on the Cu/(Co+Cu) ratio that equals 20/(10+20) =0,667 and the A/F distribution, we end-up with a probability of 0,676 given the round up rule. Hence LL Bean should order 12 000 * 1,179975 = 14160 items to maximise its profit. (We used the distribution derived from the data rather than the normal distribution with the same mean and standard deviation. Indeed despite the important gaps between the different percentiles of the accepted distribution, we reject the hypothesis that the distribution is normal at a 5% level as per the Anderson Darling test result with p-value= 3%).3. Assuming LL Bean manages to derive the correct forecast, what do you theorise about their ordering process? (You may wish to begin with Mark Fasolds concerns at the end of the case. Also, think about Rol Fessendens concern about estimating contribution margin and liquidation costs). If the contribution marg in and liquidations costs are wrongly assessed this has a direct publication on the commitment order size as per the newsvendor model methods (cf. the Cu/(Co+Cu) ratio). There is a grey area in the case to make love how LL Bean really assesses the number of actual for products generating a demand higher than the forecast. An overestimation of lost sales can create a bias curve since it will impact the next year order commitment by generating mechanically higher commitment orders. As per the mean (8% above 1) and the distribution that is reorient to the left, it could be inferred that there is a systematic overestimation of lost sales which may explain that there are not different cat valium configuration across items and buyers. We cant suggest any bias due to outlier since they mention that there have not found any specific pattern. The stock split between new and never out for the historical errors makes sense since both nature of articles share a common property. We rec ommend making use of the environ calls and orders through all interchange channel to constitute more robust analytical data and smother the potential bias of data used to build the A/F distribution.4. What do you think about LL Beans forecasting process? Is that the best that they can do?Problems It seems unreliable and not data driven as per the use of rules of thumb and use of consensus that may reduce the weight of the expert. Forecast reconciliation issue with the bottom up (items by items) and the confidential information down ( compile) border on forecast approach. A lot of the forecast relies on the inaccurate slash at the end of the process. Aggregation of demand for item common to different catalogs seems unclear and prone to error, there may be an overestimation of the demand forecast by double counting the expected sales (cf. catalog arriving to same customer that are considered the best i.e. buying the most). Issues with the impact of new products and cannibal ization Differences observed between the aggregationSuggestions More frequent interactions between bottom up and top down approach to repeal or at least reduce the slash of the end. Such interactions could be achieved through the so-called W approach that implies meeting points at different levels over the process. For items common to several catalogs, consider a customer approach instead of a catalog approach to avoid counting several times the expected purchase of one customer receiving several catalogs. We recommend making use of the phone calls and orders through all selling channel to build more robust analytical data in order to improve the forecasting process. Try to find alternate sources of supply to reduce the current lead time of 9 months and allow finalizing the forecasting process closer to the sales time.