1. Describe the historical data on Expresso & Pastel de Nata Bars sales, including a discussion of the general direction of sales and any seasonal tendencies that might be occurring.

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Expresso & Pastel de Nata Bars

 

Pastel de nata, the little custard tarts in puff pastry cases which are not just Portugal’s most famous sweet creation, but one of its greatest dishes of any kind. Invented in a Lisbon monastery in (probably) the 17th century to use up surplus egg yolks, they have now become a standard sweet and a favourite companion for bica, the Portuguese equivalent of espresso. 

They are common in other countries including Brazil, Goa, Malacca, Macau China, Canada, Australia, Luxembourg, the United States and France among others.

Pastéis de nata were created before the 17th century by Catholic monks at the Jerónimos Monastery (PortugueseMosteiro dos Jerónimos) in the civil parish of Santa Maria de Belém, in Lisbon. These monks were originally based in France and loved these pastries which could be found in local French bakeries. At the time, convents and monasteries used large quantities of egg-whites for starching of clothes, such as nuns` habits. It was quite common for monasteries and convents to use the leftover egg yolks to make cakes and pastries, resulting in the proliferation of sweet pastry recipes throughout the country.

Following the extinction of the religious orders and in the face of the impending closing of many of the convents and monasteries in the aftermath of the Liberal Revolution of 1820, the monks started selling pastéis de nata at a nearby sugar refinery to secure some revenue. In 1834 the monastery was closed and the recipe was sold to the sugar refinery, whose owners in 1837 opened the Fábrica de Pastéis de Belém. The descendents own the business to this day.

Since 1837, locals and visitors to Lisbon have visited the bakery to purchase fresh from the oven pastéis, sprinkled with cinnamon and powdered sugar. Their popularity normally results in long lines at the take-away counters, in addition to waiting lines for sit-down service.

Now, they can be purchased in many places all over the world, in bakeries, and sold in many hotels etc.

Very simple things are often difficult to make perfectly. The company behind Expresso & Pastel de Nata Bars (EPNB) has figured it out. Their natas are assembled elsewhere and baked daily in there little café bars throughout London. The puff pastry is light, flaky, and profoundly buttery. The filling is runny (much better than the common stiffly-set version) and a rich yellow. If you don’t lick it off your fingers, you’re a model of self-restraint.

After the foundation of EPNB in the mid-2000s, EPNBs continued its upward climb of record sales around the world. Suppose the figures shown in Table 1 are EPNB’s monthly sales figures from January 2007 through December 2015 (in £000s).

 

Assume you are the Sales Director at Expresso & Pastel de Nata Bars. You are required to prepare a report for the Board of Directors on the following:

  1. Describe the historical data on Expresso & Pastel de Nata Bars sales, including a discussion of the general direction of sales and any seasonal tendencies that might be occurring.
  2. Choose, giving your justification, which time series forecasting technique is likely to be most appropriate for producing forecasts with this data set.

 

  1. Use your chosen forecasting model to generate forecasts for each of the months in year 2016.

 

  1. Discuss how these forecasts might be integrated into the planning operations and policy makings in EPNB. 

 

Software

You can use either MiniTab, or any other statistical packages. 

Word limit: 1500 words maximum (for main report, excluding all graphs, tables, appendices and references)

 

Table 1. Expresso & Pastel de Nata Bar’s sales (in £000s)

Month

2007

2008

2009

2010

2011

2012

2013

2014

2015

January

142.7

168.1

180.8

231.6

269.7

434.8

384.0

434.8

498.3

February

117.3

180.8

206.2

257.0

320.5

460.2

409.4

447.5

536.4

March

104.6

180.8

231.6

269.7

371.3

460.2

434.8

498.3

638.0

April

155.4

206.2

282.4

345.9

434.8

485.6

460.2

536.4

676.1

May

218.9

244.3

320.5

358.6

460.2

536.4

498.3

561.8

752.3

June

231.6

282.4

333.2

409.4

574.5

625.3

587.2

650.7

815.8

July

218.9

295.1

371.3

447.5

549.1

663.4

612.6

676.1

803.1

August

193.5

320.5

358.6

434.8

485.6

523.7

561.8

663.4

739.6

September

180.8

206.2

244.3

333.2

434.8

511.0

511.0

612.6

688.8

October

142.7

180.8

218.9

333.2

409.4

485.6

498.3

587.2

638.0

November

142.7

168.1

218.9

307.8

396.7

460.2

447.5

523.7

625.3

December

155.4

180.8

206.2

295.1

409.4

434.8

422.1

485.6

625.3

 

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