What are the underlying problems that the JITD program is supposed to solve? What causes them? What are the potential program benefits? Our framework will be the following. We will try to list all the problems that are faced by Barilla and the distributors and that the JITD program is supposed to solve, while relating them to their main roots. The main problem in this case is the fluctuating demand whose main causes include: • Promotions: Barilla’s sales strategy relied heavily on promotions, be it through price, transportation and volume discounts. They divided the year into 10 to 12 “canvass” periods, during which different products were offered at discounts with prices ranged from 1.4% to 10%. This obviously made the demand fluctuating as a function of prices but also turning final consumers to strategic ones as dry products have a very long shelf life, allowing consumers to store huge quantities and therefore to buy them at certain periods. Moreover, an intrinsic uncertainty accompanies this fluctuating demand with such consumer behaviour. • Sales Representatives compensation model: The compensation system for the sales representatives was based on sales volume. The main issue with this compensation system is that the sales representatives would push more products during the promotional period to get a bonus, while not being able/willing to push as much during non-promotional periods. This led to wide variations in demand and made forecasting even more difficult. • Large number of SKUs: Barilla’s dry products (which accounted for 75% Barilla’s revenues and are the focus of the JITD proposal) were offered in 800 different packaged Stock Keeping Units (SKUs). These large numbers led to greater complexity and consequently to an aggregated uncertainty since different SKUs were naturally treated separately when managing inventory, while at the end of the day, the different SKUs could somehow cannibalize one another. This large number of SKUs, coupled with promotions schemes that could differ from canvass to canvass, taking into account potential cannibalization, makes forecasting at the individual SKU level an extremely complex task, leading whatsoever to high standard deviations. • Bad forecasting and inventory management by distributors: The distributors not only had no efficient forecasting systems but they also did not have sophisticated analytical tools for determining optimal order quantities based on those forecasts. This could therefore result into excess inventory levels as well as high stock outs. Indeed, exhibit 13 shows average inventory levels much higher than the orders average (exhibit 12) _ an approximate estimate would be an average of 2.5 WOS held in Cortese Northeast DC. The following figure highlights this excess inventory. Moreover, a thorough analysis of stockout levels, shows that the average stockout level is around 6.1%
Ему предложили исчезнуть. - Диагностика, черт меня дери! - бормотал Чатрукьян, направляясь в свою лабораторию. - Что же это за цикличная функция, над которой три миллиона процессоров бьются уже шестнадцать часов. Он постоял в нерешительности, раздумывая, не следует ли поставить в известность начальника лаборатории безопасности. Да будь они прокляты, эти криптографы.