Greaves Brewery Case Analysis

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Greaves Brewery: Bottle Replenishment Case Analysis
Case Synopsis
The following is an analysis of the case, Greaves Brewery: Bottle Replenishment. It details the growing beer operation of Greaves Brewery located in the Caribbean island of Trinidad. The purchasing manager for the company, Alex Benson, is uncertain about how many bottles to order from the company’s German glass supplier. His decision is complicated by the possibility of a new bottle design being introduced that would compromise his existing inventory of bottles. Additionally, he is faced with storage limitations and erratic sales, all of which are impacting his decision. He is also concerned about over ordering to avoid issues from an off year, impact from
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Purchasing manager, Benson had difficulty in determining how many bottles he needed to order to support sales. He was concerned about over ordering to avoid issues with overstocking bottles that would not be used and incur a cost or become a loss when all of the bottles were changed with the new design. The challenge for him was to determine an accurate forecast for Greave’s 2004 sales. When a company plans its ordering or production schedule for a product it sells to the public, it must forecast the customer demand for this product so that it can stock appropriate quantities – neither too much nor too little (Albright, Winston & Zappe, 2010). Data Analysis to Support Decision
According to the case, prior to the implementation of the Trinidadian’s government excise taxes, Greaves Brewery was showing signs of growth (Erskine, Leenders & Piper, 2004). To show growth trends, a Time Series graph was run from 1999 to 2004. However, the purpose of the case is to determine the quantity of bottles that Greaves needs to purchase based on a sales forecast for 2004.
Greaves provided five years and two months of annual sales data. Using Stat Tools, the following analysis were run: Moving Average, Exponential Smoothing Simple, Exponential Smoothing Holt’s, and Exponential Smoothing Winter’s. Following a comparison on the average on all models, the Exponential Smoothing Winter’s was found to be the most suitable model for the case. A graph
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