Greaves Brewery Case Study Analysis Format

Case: Greaves Brewery: Bottle Replenishment Business Logistics and Supply Chain Management January 3, 2013 Submitted to: Prof. Ismael C. Pangilinan Submitted by: Jeniefer Espino Ronald B. Nebres Geinah R. Quiñones Maria Munna S. Gravador I. Statement of the Problem/Key Strategic Issue Greaves Brewery is a manufacturer of beer situated in Trinidad with tourist market as one of marketing niche. Yearly sales peak occur on Carnival, Christmas, Easter and Independence month. Alex Benson, the purchasing manager, have difficulty in determining the number of reusable bottles to order from its long-term German supplier for the year 2004 due to a planned new bottle design on either year 2005 or 2006. This means that remaining all old-designed bottles, new or used, would be scrapped out by the year 2005 or 2006. Added dilemma is the unreliable sales forecast done by both the sales and plant manager of the company for the year 2004. Past company record shows that inadequate available empty bottles for production use resulted in huge revenue loss. Scheduling of new bottle order, timely empty bottle turnaround time and replenishment of bottle in the bottling department must also be taken into consideration. How many reusable bottles must Mr. Benson order to ensure availability of adequate reusable bottles is on-hand to supply the demand of the 2004 sales with minimum inventory in anticipation of the arrival of the new bottle design? Statement of the Objectives  II. To be able to provide an accurate sales forecast for the year 2004 using forecasting techniques and models as basis for decision making  To be able to develop a plan to expedite return of reusable bottles from customers  To be able to determine the ordering process needed for the bottle replacement and replenishment III. Relevant Case Facts/Findings Company: Greaves Brewery Industry: Beer Industry Market Niche: Tourist Location: Carribean Island of Trinidad Founder: John Greaves in 1924 Strengths:  Has established a long-term supplier relationship with a reliable German glass manufacturer Weakness:  Unreliable sales forecast for the year 2004  During peak season, company operates on a tight schedule incurring more labor expense and added production cost which eventually decrease profit margin  Information and data needed for making decisions are not available and unreliable such as bottle turnaround time, length of time of total in-service bottles were replaced with new bottles  Bottle replenishment does not meet the sales demand for the year Threats:  Sudden change in government regulation such as increase in excise tax which directly affect units sales price as the company passes this increase to customer  Local bottle manufacturers were equipped only to produce clear glass bottle Opportunities:  Tourist market niche is growing evidenced by sudden increase in sales during February 2004. Other Relevant Facts: -minimum order quantity is 15,000 cases per year. -Minimum delivery per month is 5,000 cases. -Design of the bottle will be changed by 2005 or 2006. 1. - Order practice is 75% of the annual demand is ordered on March then remaining order on August. IV. Alternatives Alternative Assumptions Pros Cons Assurance that we will Ending inventory total is 793 Order quantity for March The bottle requirement is based on the four biggest meet all the sales thousand to be scrapped at are 154 and 131 for the month demand and demand during peak the beginning of 2006 year 2004 -2005; Order seasons revolve it quantity for August are 131 and 0 for the year Every month there will 2004 -2005 be a 5% base on the sales demand allowance for allowance for additional demand, brokerage, lost, etc. Forecasting tools used is logarithmic 2. 3. The usage rate of new bottles is used to determine the quantity needed. Use the Sales increase as a basis for the increase in volume of bottles. Forecasting tools logarithmic Order quantity for March The bottle requirement is based on the two biggest are 154 and 131 for the month demand and year 2004 -2005; Order quantity for August are 66 revolve it and 0 for the year 2004 Every month there will be 2005 a 5% base on the sales demand allowance for allowance for additional demand, brokerage, lost, etc. Order quantity for March are 100 and 54 for the year 2004 -2005; Order quantity for August are 33 and 0 for the year 2004 2005 Forecasting tool logarithmic Less inventory scrapped at the year 2006 compare to option 1(-14 thousand) Ending inventory total is 748 thousand bottles to be scrapped at the beginning of 2006 Minimum of 1 thousand empty bottle inventory at the beginning of 2006 A result of faster rate of bottle return from 90% to 94.5% due to the campaign, incentive and prizes given in-line with bottle management concerning endcustomer Prepare and develop a plan or systematic process of collection of empty bottles from retailers or wholesalers, safekeeping, storage and replenishment. Inventory or audit of empty bottles vs items disposed as prizes or incentives is needed V. Conclusion a. Recommendation/s We recommend to implement option three (ACA 3) that in determining the quantity needed the usage rate of new bottles is used. Use the sales increase as a basis for the increase in volume of bottles. The option yielded the least number of inventories, 1,000 empty bottles, by the start of 2006 minimizing scrapped cost. Total sales forecast for year 2004 and 2005 is 4252 and 4405 respectively using the logarithmic forecasting tool. See Appendix for comparison of the different forecasting tools tried such as exponential, linear, polynomial in degree 2 and 3, power, moving average in degree2. Logarithmic forecasting tool yielded the best result with the following forecasting accuracy results: MAD = 96.0472 MSE = 20775.38 MAPE = 2.777 b. Implementation Approach/Timetable 1. Approach on New Product Design and Ordering The design of new bottles must be done early to meet the target year 2005 or 2006. By the second half of 2004, planning for monthly order must be collaborated with the supplier. At this point, the 75% of the year demand has been ordered. First half of 2005, the design for the new bottle must be finalized and turned over to the supplier. Production side must be informed of the design changes to give them enough time to adjust production process if new design includes changes in height, width of bottle etc in time for year 2006. By the third quarter of 2005, the ordering of the new design of bottle must already be done, as well as, recalibration on the production side. Jan 364 376 Feb 542 562 Mar 355 154 367 131 Apr 348 360 May 229 237 Jun 292 303 Jul 326 338 Aug 405 66 420 Sep 337 349 Oct 334 346 Nov 320 332 Dec 400 415 Total 4,252 4,405 Demand Order Demand 2005 Order 2004 2. Bottle Management – this will be implemented on April 2004. Our goal is to develop a plan to expedite return of empty bottles: management of empty bottles like campaign in-line with returning bottles examples follows: a. Retailers (sari-sari stores, mini-marts – establishments that directly communicate with endconsumer ) – proposed incentive: for every 1 case of empty reusable bottles returned there would be free 1 bottle of full goods beer plus refund of deposits .b. End consumer – 1. Raffle stub will be given for a chance to win grocery items, tumblers, coolers, freezers for every 1 bottle empty reusable bottle 2. Outright items in exchange for empty bottles returned such as pens, keychains, tags, glass or mugs etc. APPENDIX BREWERY PROCESS 1. Sugar was boiled with hops, producing a sterilized and concentration. 2. The hops were then removed and been cooled to optimum temperature 10C from the bottom fermentation lasting 7 days. 3. The beer was cooled to -1C and stored for 10 days. 4. Filtered through diatomaceous earth. 5. After 24 hours storage, it would be put to polishing filtration. 6. The beer will undergo artificial carbonation. 7. Bottling and packaging. *** 1 day, pre fermentation process, 7 days fermentation process, 10 days post fermentation process, 1 day polishing, 1 day packaging. *** Total of at least 19 days to prepare the beer. BEER PRICE Retail Price Excise tax (Nov. 1997) Bottle Deposit Additional Tax( July 2001) Additional Tax( July 2003) TOTAL $ $ 0.60 9.90 0.90 0.90 1.20 12.60

Greaves Brewery Case Analysis

1441 WordsMay 4th, 20126 Pages

Greaves Brewery Case Analysis

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…show more content…

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 analysis is captured below. StatTools Report | | | | |

Analysis: | Forecast | | | | |

Performed By: | shart | | | | |

Date: | Friday, November 18, 2011 | | | |

Updating: | Live/Unlinked | | | | |
Forecasting Constant | | | | | |

Span | 3 | | | | |

Moving Averages | | | | | |

Mean Abs Err | 62.10 | | | | |

Root Mean Sq Err | 83.52 | | | | |

Mean Abs Per% Err | 22.27% | | | | |

Forecasting Data | Sales | Forecast |

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