FORECASTING DEMAND AT ANADOLU EFES

FORECASTING DEMAND AT ANADOLU EFES

Efes Beverage Group is the beverage division of Anadolu Holding, one of Turkey’s leading conglomerates. Anadolu Holding was founded in 1969 and has principal interests in the beverage, automotive, finance, restaurant chain management, and office supplies sectors. Efes Beverage Group produces and markets beer, malt, and soft drinks across a geography that consists of Turkey, Russia, the CIS countries, Southeast Europe, and the Middle East. Anadolu Efes (or Efes in short) is the beer division under the Efes Beverage Group (see http://www.anadoluefes.com).

As of 2006, Efes was one of the leaders of the beer industry in the region with its 16 breweries, 6 malt plants, and 1 hops processing facility in six countries. Efes has a brewing capacity of 3.3 billion liters and a malt production capacity of 236,500 tons annually. Efes has a share of about 78% in the Turkish beer market.

As a part of their yearly planning efforts, Efes forecasts the monthly demand for the coming year during the fall of the current year. Historically, high-level sales managers, based on input from their sales personnel, have done this forecasting mostly subjectively. Because they have been in the sector for a long time, their estimates have been pretty accurate in the past. There have been cases where the estimates were off, but these were rare instances. In January 2014, they are discussing how they could come up with a more formal way of forecasting. The Managing Director, Serdar Bölükbaşi, suggested to Levent Tomaç, the Vice President of Marketing, that Efes could perform a study analyzing different inputs to its forecasting process. He explained that such a study would be useful to test their intuitions on the factors they thought had a significant effect on beer demand. Levent agreed immediately. He explained that he had been thinking about this subject as well, and he felt that they could still use their judgments after the formal analysis if they thought some aspects had been overlooked. They remembered the young employee in the Logistics department, Selin Baydar, who recently did a great job studying the distribution of beer and the location of new breweries.

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Selin was excited about her new assignment, because it would enable her to show off her business analytics skills. She wondered how she could go about building a better forecasting model. She had not been much of a beer drinker, but having worked for Efes for four years, she had some ideas about what affects beer demand.

The vast majority of the population of Turkey practices the Muslim faith. Thus, Selin started by gathering some information about the holy month of Ramadan — see Table 1 below. (Ramadan is considered the most blessed month of the Islamic year and is linked to the lunar calendar. Many Muslims fast during the month of Ramadan. Those who fast refrain from eating, drinking, and smoking during the day. At the end of Ramadan, a three-day feast starts when friends and relatives visit each other, and candies, sweet pastries, and sweet liquors are served.)

Next, Selin gathered information about beer consumption in different countries — see Table 2 below.

Use these tables, outside sources, and your own experiences and judgment to help Selin move toward a better forecasting method.

QUESTIONS
1.    What are some factors that might influence beer consumption in general? What about consumption in Turkey in particular? What about in the other countries served by Efes?
2.    Should Efes approach different countries/markets differently than it approaches the market in Turkey? Explain.
3.    What type of forecasting approach does Efes use now? Do you see anything wrong with their approach? How might the approach be enhanced?
4.    What additional data should Efes collect? Why?

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Rather than provide brief answers to all of the above questions, pick two (2) and digging deeper. Also respond to the post below, agree or disagree (discussion board).

Post1
Beer consumption is influenced by not only super fun social events like holidays and sporting events, but emotional tragedy as well.  Increased marketing around holidays and during whatever would equate a play-off season in this area of the world would most likely help to bump up beer drinking during these times.  However, in the interest of sales, it would be beneficial to boost advertising during times of emotional hardships.  Over the last week alone there have been three mine collapses in Turkey.  I don’t know about anyone else but when I face the sudden and unexpected death of a loved one I usually grab some alcohol and swap stories with others.  If the company could shot gun advertisement and promotional sales when unexpected bad stuff happens, as well as during anniversaries of bad stuff that has previously happened that effected large groups of people sales would definitely see temporary boost at these times.

Table 1: Beginning and Ending Dates of Ramadan

Year
2010    First day
August 11    Last day
September 9
2011    August 1    August 29
2012    July 20    August 18
2013    July 9    August 7
2014    June 29    July 27
2015    June 18    July 16
2016    June 7    July 5
2017    May 27    June 25
2018    May 16    June 14
2019    May 5    June 3
2020     April 24     May 23

Table 2: Beer Consumption per Capita by Country (2012)

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Rank
Country    Consumption per capita (litres)    2011–2012
change (633– ml bottles)    Total national consumption (106 L)
11    Australia    83.1    –5.1    1830
12    Panama    82.3    6.2    290
13    Slovenia    80.1    –6.6    160
14    United States    77.1    0.8    24186
15    Bulgaria    76.8    7.7    540
16    Netherlands    75.7    0.8    1270
17    Russia    74.1    –1.1    10560
18    Belgium    74    –6.3    823
19    Lithuania    72.5    –2.4    260
20    Hungary    71.3    1.7    710
21    Slovakia    70.3    0.6    390
22    United Kingdom    68.5    –4.9    4319
23    Spain    68.4    –2.8    3220
24    Brazil    68.3    2.5    12800
25    Canada    66.9    –0.5    2300
26    Latvia    66.2    –5.2    150
27    New Zealand    64.7    –8.2    280
28    Angola    64.2    10.4    1160
29    Denmark    62.1    –6.3    340
30    Ukraine    61.6    –1.4    2760
31    South Africa    61.1    1.9    2980
32    Mexico    59.9    0.8    6890
33    Switzerland    58.3    –0.9    460
34    Bosnia and Herzegovina    56.1    2.5    220
35    Sweden    52.7    1.3    480
36    Argentina    49        1980
37    Iceland    45        14
38    Japan    43.5    –0.6    5547
39    Namibia    40
40    China    32        44683
41    Italy    29        1772
42    Vietnam    19
43    Israel    14
44    Turkey    13
45    Kenya    12
46    Uzbekistan    11
47    Tanzania    8
48    Uganda    6
49    Sri Lanka    2        50
50    India    2        1400

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