Barilla Spa Case Study Solution Pdf Viewer

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%

 

Problem Description

We will attempt to first list all the problems that are faced by Barilla and the distributorsso that we can better understand them in order to make recommendations on how Barillashould use JITD program. The main problem in this case is the fluctuating demand andsome of the causes of this fluctuating demand are:

Promotions: Barilla’s sales strategy relied heavily on the use of promotions, in theform of price, transportation and volume discounts. They divided the year into 10to 12 promotional periods, during which different products were offered atdiscounts with prices ranged from 1.4% to 10%.

Sales Representatives: The compensation system for the sales representatives wasthat they were rewarded based on the amount of the products that they sold to thedistributors. This was causing problems as the sales reps would try and push more products during the promotional period to get a bonus and were not able to sell asmuch during non-promotional periods. This led to wide variation in demand andmade forecasting very difficult.

Large number of SKU’s: Barilla’s dry products (the focus of the JITD proposal)were offered in 800 different packaged stock keeping units (SKUs). These largenumbers led to greater complexity.

Bad forecasting by Distributors: The distributors did not have forecasting systemsor sophisticated analytical tools for determining order quantities and this resultedin bad forecasts.

Long Lead Times: Barilla supplied its distributors between 8 and 14 days after itreceived their orders, the average lead-time being 10 days. This was slightly longand a lot could change in the supply chain during this period, causing rise invariability.

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