OVERVIEW OF HEINEKEN VIETNAM BREWERY CO., LTD
General information about Heineken Vietnam Brewery Co., Ltd
HEINEKEN Vietnam is a member of the HEINEKEN Group, the largest brewer in the world Originating in the Netherlands, HEINEKEN is a family company with a history of more than 150 years, producing and distributing over 300 brands of beer and fermented apple juice in more than 190 countries
Established in 1991, HEINEKEN Vietnam now operates 6 breweries in Hanoi, Da Nang, Quang Nam, Ho Chi Minh City, Vung Tau, and Tien Giang, and 9 commercial offices across Vietnam
From humble beginnings with just 20 employees, HEINEKEN Vietnam is today the second-largest brewer in Vietnam with over 3,500 employees Every year, HEINEKEN Vietnam makes a significant contribution to the Vietnamese economy, accounting for about 0.9% of the national GDP
In Vietnam, HEINEKEN manufactures and distributes beer brands: Heineken, Tiger, Amstel, Larue, BIVINA, Sol, Desperados, Affligem, and Strongbow fermented apple juice
In 2017 and 2018, HEINEKEN Vietnam was honored as the Most Sustainable Manufacturing Enterprise in Vietnam by the Vietnam Chamber of Commerce and Industry (VCCI) according to the Program of Evaluation and Ranking of Sustainable Businesses in Vietnam (CSI).
The process of formation and development
The formation process and some achievements of Vietnam Brewery Co., Ltd
1991: Signing ceremony of Joint Venture Contract Establishing Vietnam Brewery Company Limited (VBL)
1993: Inauguration Ceremony of Ho Chi Minh City Brewery Production of the first batch of Tiger beer
1994: Production of the first batch of Heineken beer
1996: Established HEINEKEN Brewery in Hanoi
1997: Completed the capacity expansion times Producing the first batch of BIVINA beer
2007: Established Quang Nam Brewery Acquired Foster's Vietnam & established
Da Nang Brewery, Acquired Foster's Vietnam & established Tien Giang Brewery
2008: Introduced Tiger Crystal Beer to the market
2016: Acquired Vung Tau Brewery from Carlsberg Celebrating 25 years of establishment and changing its name to HEINEKEN Vietnam Brewery Co., Ltd
Business products of the company
Some main products of Vietnam Brewery Co., Ltd
Loved in more than 192 countries, Heineken is the most international premium beer brand worldwide
The history of Heineken began in 1873 as a family brewery in Amsterdam, the Netherlands Today, with more than 130 breweries in more than 192 countries, Heineken is proud to be the world's leading beer group and the most loved international premium beer brand in Vietnam Heineken always initiates the ultimate experience that no other beer brand can match
Heineken's Credentials campaign aims to bring the message of "Unchanged Perfect Taste Since 1873" to consumers as a milestone marking the love of customers for Heineken over the past 140 years
Each drop of Heineken beer is made with four main ingredients: purified water, barley, hops and Yeast A – an important ingredient in creating the rich flavor and delicate aroma that only Heineken can Together with the unique technology of brewing beer in horizontal tanks for 28 days, it contributes to the perfectly balanced taste of Heineken Tiger
Established in Singapore since 1932, Tiger is the number 1 international premium beer brand in Asia, having won more than 40 quality gold awards since 1991
Corporate governance structure of the company
Heineken Trading is the company in charge of beer distribution for the whole Heineken system in Vietnam The two main companies in charge of Heineken's beer production are Heineken Vietnam Brewery Company Limited (Heineken Vietnam Brewery) and Heineken Hanoi Brewery Company Limited
In addition to the main factory located in District 12, Ho Chi Minh City, Heineken Vietnam also owns 4 subsidiaries in Da Nang, Quang Nam, Tien Giang, and Ba Ria Vung Tau.
Company goals
Our goal is to be the leading, proud and responsible brewer in Vietnam.
Company Mission
Contributing to the development of Vietnam's beverage industry on a global level Promote the culinary culture of the Vietnamese people
Enhancing quality of life through the provision of high-quality, safe, and nutritious beverage products
Bring practical benefits to shareholders, customers, partners, employees, and society
Satisfy and satisfy beverage demand according to international food safety and hygiene standards “Food hygiene and safety and environmental protection”
Fully fulfill obligations to the State on the basis of business transparency
Actively participate in community activities Ensure development towards international integration.
Core values of the company
Company Core Values: Respect for People & Planet, Enjoy Life, Quality, Desire for Success.
THEORETICAL BASIS
Forecast overview
Forecasts are a basic input in the decision processes of operations management because they provide information on future demand
The first step in planning is forecasting or estimating future demand for the product or service and the resources needed to produce that product or service When conducting forecasting, we base on collecting and processing data in the past and present to determine the movement trend of future phenomena thanks to a number of mathematical models
Thus, forecasting is a science and art of predicting what will happen in the future, on the basis of scientific analysis of the collected data A forecast can be a subjective prediction or intuition about the future But in order to make the forecast more accurate, one tries to exclude the subjectivity of the forecaster
Here are some examples of uses of forecasts in business organizations:
Accounting: New product/process cost estimates, profit projections, cash management
Finance: Equipment/equipment replacement needs, timing and amount of funding/borrowing needs
Human resources: Hiring activities, including recruitment, interviewing, and training; layoff planning, including outplacement counseling
Marketing: Pricing and promotion, e-business strategies, global competition strategies
MIS: New/revised information systems, Internet services
Operations: Schedules, capacity planning, work assignments and workloads, inventory planning, make-or-buy decisions, outsourcing, project management
Product/service design: Revision of current features, design of new products or services
Today, forecasting is an indispensable need of all economic - social, scientific - technical activities, and is interested in research by all scientific disciplines
Used to forecast future levels of the phenomenon, thereby helping business managers to be proactive in making necessary plans and decisions for the production, business, investment, promotion, production scale, product distribution channels, financial sources and fully prepare physical and technical conditions for development in the coming time (plan to provide elements inputs such as labor, raw materials and labor materials as well as output factors in the form of physical products and services)
In enterprises, if forecasting is done seriously, it also creates conditions to improve competitiveness in the market
Accurate forecasting will reduce the level of risk for businesses in particular and the whole economy in general
Accurate forecasting is the basis for policymakers to develop a socio-economic culture in the entire national economy
Thanks to forecasting economic policies, economic development plans and programs are built with a scientific basis and bring high economic efficiency Thanks to regular and timely forecasts, business managers are able to promptly take measures to adjust the economic activities of their units in order to achieve the highest production and business efficiency
Forecasting is an indispensable part of business operations, in each department such as Sales or Marketing, Production or Human Resources, Accounting and Finance
2.1.4 Features common to all forecasts
Forecasting techniques generally assume that the same underlying causal system that existed in the past will continue to exist in the future
Forecasts are not perfect; actual results usually differ from predicted values; the presence of randomness precludes a perfect forecast Allowances should be made for forecast errors
Forecasts for groups of items tend to be more accurate than forecasts for individual items because forecasting errors among items in a group usually have a canceling effect Opportunities for grouping may arise if parts or raw materials are used for multiple products or if a product or service is demanded by a number of independent sources
Forecast accuracy decreases as the time period covered by the forecast—the time horizon— increases Generally speaking, short-range forecasts must contend with fewer uncertainties than longer-range forecasts, so they tend to be more accurate
A properly prepared forecast should fulfill certain requirements:
The forecast should be timely Usually, a certain amount of time is needed to respond to the information contained in a forecast For example, capacity cannot be expanded overnight, nor can inventory levels be changed immediately Hence, the forecasting horizon must cover the time necessary to implement possible changes
The forecast should be accurate, and the degree of accuracy should be stated This will enable users to plan for possible errors and will provide a basis for comparing alternative forecasts
The forecast should be reliable, it should work consistently A technique that sometimes provides a good forecast and sometimes a poor one will leave users with the uneasy feeling that they may get burned every time a new forecast is issued
The forecast should be expressed in meaningful units Financial planners need to know how many dollars will be needed, production planners need to know how many units will be needed, and schedulers need to know what machines and skills will be required The choice of units depends on user needs
The forecast should be in writing Although this will not guarantee that all concerned are using the same information, it will at least increase the likelihood of it In addition, a written forecast will permit an objective basis for evaluating the forecast once actual results are in
The forecasting technique should be simple to understand and use Users often lack confidence in forecasts based on sophisticated techniques; they do not understand either the circumstances in which the techniques are appropriate or the limitations of the techniques Misuse of techniques is an obvious consequence Not surprisingly, fairly simple forecasting techniques enjoy widespread popularity because users are more comfortable working with them
The forecast should be cost-effective: The benefits should outweigh the costs
2.1.6 Steps in the forecasting process
There are six basic steps in the forecasting process:
1 Determine the purpose of the forecast How will it be used and when will it be needed? This step will provide an indication of the level of detail required in the forecast, the amount of resources (personnel, computer time, dollars) that can be justified, and the level of accuracy necessary
2 Establish a time horizon The forecast must indicate a time interval, keeping in mind that accuracy decreases as the time horizon increases
3 Obtain, clean, and analyze appropriate data Obtaining the data can involve significant effort Once obtained, the data may need to be “cleaned” to get rid of outliers and obviously incorrect data before analysis
6 Monitor the forecast errors The forecast errors should be monitored to determine if the forecast is performing in a satisfactory manner If it is not, reexamine the method, assumptions, validity of data, and so on; modify as needed; and prepare a revised forecast
Forecasts can be classified into three categories
Short-term forecast: the forecast period does not exceed 3 months This type of forecast is needed for procurement, work scheduling, task assignment, and other aspects of operational management
Qualitative forecasting methods
In some situations, forecasters rely solely on judgment and opinion to make forecasts If management must have a forecast quickly, there may not be enough time to gather and analyze quantitative data At other times, especially when political and economic conditions are changing, available data may be obsolete and more up-to-date information might not yet be available Similarly, the introduction of new products and the redesign of existing products or packaging suffer from the absence of historical data that would be useful in forecasting In such instances, forecasts are based on executive opinions, consumer surveys, opinions of the sales staff, and opinions of experts
A small group of upper-level managers (e.g., in marketing, operations, and finance) may meet and collectively develop a forecast This approach is often used as a part of long- range planning and new product development It has the advantage of bringing together the considerable knowledge and talents of various managers
However, there is the risk that the view of one person will prevail, and the possibility that diffusing responsibility for the forecast over the entire group may result in less pressure to produce a good forecast
Members of the sales staff or the customer service staff are often good sources of information because of their direct contact with consumers They are often aware of any plans the customers may be considering for the future There are, however, several drawbacks to using salesforce opinions One is that staff members may be unable to distinguish between what customers would like to do and what they actually will do Another is that these people are sometimes overly influenced by recent experiences Thus, after several periods of low sales, their estimates may tend to become pessimistic After several periods of good sales, they may tend to be too optimistic In addition, if forecasts are used to establish sales quotas, there will be a conflict of interest because it is to the salesperson’s advantage to provide low sales estimates
This method collects opinions of experts inside or outside the enterprise according to pre- printed questionnaire forms and is implemented as follows:
Each expert is issued a letter asking to answer some questions for forecasting
The forecaster gathers the responses, sorts them, and summarizes the experts' opinions
Based on this summary, the forecaster continues to raise questions for the experts to answer
Gather new opinions of experts If not satisfied, continue the above process until the forecast requirements are met
The advantage of this method is that it avoids personal contact with each other, there is no collision between experts and they are not influenced by the opinion of someone who is dominant among the respondents
Because it is the consumers who ultimately determine demand, it seems natural to solicit input from them In some instances, every customer or potential customer can be contacted However, usually there are too many customers or there is no way to identify all potential customers Therefore, organizations seeking consumer input usually resort to consumer surveys, which enable them to sample consumer opinions The obvious advantage of consumer surveys is that they can tap information that might not be available elsewhere
On the other hand, a considerable amount of knowledge and skill is required to construct a survey, administer it, and correctly interpret the results for valid information Surveys can be expensive and time-consuming In addition, even under the best conditions, surveys of the general public must contend with the possibility of irrational behavior patterns
For example, much of the consumer’s thoughtful information gathering before purchasing a new car is often undermined by the glitter of a new car showroom or a high-pressure sales pitch Along the same lines, low response rates to a mail survey should—but often don’t— make the results suspect If these and similar pitfalls can be avoided, surveys can produce useful information.
Quantitative forecasting methods
Quantitative forecasting models are based on past data, which are assumed to be relevant to the future and can be found All quantitative forecasting models can be used over time series and these values are observed to measure periods in series
Forecast accuracy refers to the difference between the forecast and the actual data Because the forecast is formed before the actual data occurs, the accuracy of the forecasts can only be assessed after the time has passed The closer the forecast is to the actual data, the more accurate the forecast and the lower the error in the forecast
To serve the goal of the topic is to forecast the consumption volume and demand for raw materials in the short term period 3 November, December 2010 and 1/2011, the author would like to introduce the theoretical basis of forecasting according to Short-term quantitative forecasting methods, including the following methods:
A simple but widely used approach to forecasting is the naive approach A naive forecast uses a single previous value of a time series as the basis of a forecast The naive approach can be used with a stable series (variations around an average), with seasonal variations, or with trend With a stable series, the last data point becomes the forecast for the next period Thus, if demand for a product last week was 20 cases, the forecast for this week is 20 cases With seasonal variations, the forecast for this “season” is equal to the value of the series last “season.”
For example, the forecast for demand for turkeys this Thanksgiving season is equal to demand for turkeys last Thanksgiving; the forecast of the number of checks cashed at a bank on the first day of the month next month is equal to the number of checks cashed on the first day of this month; and the forecast for highway traffic volume this Friday is equal to the highway traffic volume last Friday For data with trend, the forecast is equal to the last value of the series plus or minus the difference between the last two values of the series For example, suppose the last two values were 50 and 53 The next forecast would be 56:
Although at first glance the naive approach may appear too simplistic, it is nonetheless a legitimate forecasting tool Consider the advantages: It has virtually no cost, it is quick and easy to prepare because data analysis is nonexistent, and it is easily understandable The main objection to this method is its inability to provide highly accurate forecasts
However, if resulting accuracy is acceptable, this approach deserves serious consideration Moreover, even if other forecasting techniques offer better accuracy, they will almost always involve a greater cost
Averaging techniques generate forecasts that reflect recent values of a time series (e.g., the average value over the last several periods) These techniques work best when a series tends to vary around an average, although they also can handle step changes or gradual changes in the level of the series Three techniques for averaging are described in this section:
One weakness of the naive method is that the forecast just traces the actual data, with a lag of one period, it does not smooth at all But by expanding the amount of historical data a forecast is based on, this difficulty can be overcome A moving average forecast uses a number of the most recent actual data values in generating a forecast The moving average forecast can be computed using the following equation:
Note that in a moving average, as each new actual value becomes available, the forecast is updated by adding the newest value and dropping the oldest and then recomputing the average Consequently, the forecast “moves” by reflecting only the most recent values
In computing a moving average, including a moving total column—which gives the sum of the n most current values from which the average will be computed—aids computations
To update the moving total: Subtract the oldest value from the newest value and add that amount to the moving total for each update
The advantages of a moving average forecast are that it is easy to compute and easy to understand A possible disadvantage is that all values in the average are weighted equally
A weighted average is similar to a moving average, except that it typically assigns more weight to the most recent values in a time series
For instance, the most recent value might be assigned a weight of 40, the next most recent value a weight of 30, the next after that a weight of 20, and the next after that a weight of 10 Note that the weights must sum to 1.00, and that the heaviest weights are assigned to the most recent values
Note that if four weights are used, only the four most recent demands are used to prepare the forecast
The advantage of a weighted average over a simple moving average is that the weighted average is more reflective of the most recent occurrences However, the choice of weights is somewhat arbitrary and generally involves the use of trial and error to find a suitable weighting scheme
Exponential smoothing is a sophisticated weighted averaging method that is still relatively easy to use and understand Each new forecast is based on the previous forecast plus a percentage of the difference between that forecast and the actual value of the series at that point That is:
Next forecast = Previous forecast + α (Actual – Previous forecast) where (Actual – Previous forecast) represents the forecast error and α is a percentage of the error More concisely,
A linear trend equation has the form
Ft = Forecast for period t a = Value of Ft at t = 0, which is the y intercept b = Slope of the line t = Specified number of time periods from t = 0
The coefficients of the line, a and b, are based on the following two equations: where n = Number of periods y = Value of the time series
Note that these two equations are identical to those used for computing a linear regression line, except that t replaces x in the equations Values for the trend equation can be obtained easily by using the Excel template for linear trend
Seasonal variations in time-series data are regularly repeating upward or downward movements in series values that can be tied to recurring events Seasonality may refer to regular annual variations Familiar examples of seasonality are weather variations (e.g., sales of winter and summer sports equipment) and vacations or holidays (e.g., airline travel, greeting card sales, visitors at tourist and resort centers) The term seasonal variation is also applied to daily, weekly, monthly, and other regularly recurring patterns in data For example, rush hour traffic occurs twice a day—incoming in the morning and outgoing in the late afternoon Theaters and restaurants often experience weekly demand patterns, with demand higher later in the week Banks may experience daily seasonal variations (heavier traffic during the noon hour and just before closing), weekly variations (heavier toward the end of the week), and monthly variations (heaviest around the beginning of the month because of Social Security, payroll, and welfare checks being cashed or deposited) Mail volume; sales of toys, beer, automobiles, and turkeys; highway usage; hotel registrations; and gardening also exhibit seasonal variations
Seasonality in a time series is expressed in terms of the amount that actual values deviate from the average value of a series If the series tends to vary around an average value, then seasonality is expressed in terms of that average (or a moving average); if trend is present, seasonality is expressed in terms of the trend value
Forecast Accuracy
Forecast accuracy is a significant factor when deciding among forecasting alternatives Accuracy is based on the historical error performance of a forecast
Three commonly used measures for summarizing historical errors are the mean absolute deviation (MAD), the mean squared error (MSE), and the mean absolute percent error (MAPE) MAD is the average absolute error, MSE is the average of squared errors, and MAPE is the average absolute percent error The formulas used to compute MAD,1 MSE, and MAPE are as follows:
From a computational standpoint, the difference between these measures is that MAD weights all errors evenly, MSE weights errors according to their squared values, and MAPE weights according to relative error
One use for these measures is to compare the accuracy of alternative forecasting methods For instance, a manager could compare the results to determine one which yields the lowest MAD, MSE, or MAPE for a given set of data Another use is to track error performance over time to decide if attention is needed Is error performance getting better or worse, or is it staying about the same?
Overall, the operations manager must settle on the relative importance of historical performance versus responsiveness and whether to use MAD, MSE, or MAPE to measure historical performance MAD is the easiest to compute, but weights errors linearly MSE squares errors, thereby giving more weight to larger errors, which typically cause more problems MAPE should be used when there is a need to put errors in perspective For example, an error of 10 in a forecast of 15 is huge Conversely, an error of 10 in a forecast of 10,000 is insignificant Hence, to put large errors in perspective, MAPE would be used.
FORECASTING METHOD FOR HEINEKEN BEER COMPANY
Situation Analysis
Vietnam's beer industry has a history and tradition of over 100 years with two French breweries built in the North and the South since 1890 Up to now, the beer industry has developed into a strong economic sector of the country actively contribute to the state budget, create jobs for a large number of workers
In 2019 the total beer production output reached more than 5 billion liters (up 22.9% over the same period in 2018); consumption reached more than 4 billion liters (up 29.1% over the same period last year) Beer market revenue reached more than 65 billion VND (up 0.5% over the same period last year) In terms of consumption categories, canned beer consumption accounted for 66.8% of total beer consumption in Vietnam, followed by bottled beer at 29.9%; draft beer 3.1% and a modest market share is draft beer 0.1%
Regarding imports, imported beer output reached more than 37 million liters (up 8.9% over the same period in 2018), the three main beer suppliers of Vietnam are the Netherlands (25%), Mexico (17%) and Belgium (16%) Compared to beer consumption in Vietnam, beer imports into Vietnam account for a relatively small proportion Domestic and FDI enterprises dominate the domestic beer market, with the advantage of cheap beer prices, suitable for the taste of the majority of customers
Source: VIRAC, GSO Figure 3 1 Situation of production and consumption of the beer industry 2010-2019
Regarding exports, export beer output increased over the previous year to more than 46 million liters, worth 45.87 million USD Export volume decreased by about 7% over the same period, mainly because the quality of Vietnam's beer has not been appreciated and has not yet created a brand in the international market Equatorial Guinea (accounting for about 20%) is the largest market for Vietnamese beer consumption While Mexico and the Netherlands are the two largest beer suppliers to Vietnam
However, the Vietnam Beer, Wine and Beverage Association (VBA) once forecast that the average beer consumption in Vietnam will increase by 65% from 2011 to 2021 But entering early 2020, when Decree 100 As a result of the government's official announcement, Bloomberg News estimates beer consumption is down by at least 25%, while Heineken announced a 4% drop in beer sales But not yet, soon after, the Covid-19 epidemic spread, forcing restaurants and pubs to close to comply with social distancing
As a result, beer from one of the hottest growth products in the entire FMCG industry became the biggest.
Forecasting
Forecasting material demand at Vietnam Brewery is based on the sales report of the sales department The planning staff will rely on that to forecast product demand, raw material consumption demand, and production planning Forecasting and planning activities are carried out with the support of Future Master software, after the planning staff enters the information, the software will calculate the number of products to be produced, the number of materials needed Then, staff can make plans based on the actual situation and adjust accordingly
Every year, the company sets sales volume targets for the next month, quarter, and year Based on the factors of the annual growth rate of the factory, the growth rate of the market according to the assessment of the Ministry of Industry and Trade, and the growing expectation of the factory to adjust each other and set the target output of pepper consumed
Forecast of consumption in the past time
According to the General Statistics of Vietnam, in the first 6 months of 2020, beer production reached nearly 1.96 billion liters, down 17.4% over the same period in 2019 Although the beer market dropped sharply, consumption, SSI predicts that in 2020, consumers will also drink about 4.4 billion liters of beer
According to the statistics of the three-quarters of 2020 of the market research company Nielsen Vietnam, beer production decreased sharply In which, the second quarter of 2020 saw the biggest decrease of more than 22% due to social distancing nationwide, beverage service establishments classified as non-essential services had to close for a longer time than other industries other business from mid-March to early June 2020 For Saigon Beer
- Alcohol - Beverage Corporation (Sabeco, HOSE: SAB), the first half of 2020 is a challenging period for the Company company In particular, the first quarter of 2020 was assessed by SSI Research as the worst quarter for the Company's profit In this quarter, Sabeco's profit after tax was only nearly 717 billion dongs, down more than 44.4% over the same period in 2019
Figure 3 2 Graph of beer consumption in Vietnam from 2010-2018
Figure 3 3 Graph of beer consumption by month from 2016-2020
Comment: The chart shows that beer consumption through the months of 2020 has quite a big difference due to many factors It can be seen that the consumption output after February is the period after Tet, the output decreases slightly and due to the influence of the epidemic, the output decreases after that, until about June, it increases again.
Heineken’s sales forecasting activity
Forecasting is an important input activity, which is the basis for managers to predict the future and plan production in line with Heineken's consumption needs There are many managers who have researched and analyzed the market to forecast Heineken's beer consumption over the months For many years, Heineken has applied the moving average forecasting method (n=3) to forecast beer consumption in the following months
Table 3 1 Heineken beer consumption statistics from 2018 to October 2020
Apply three-period moving average method to forecast beer demand in the market in November, December 2020 and January 2021
Through forecasting, managers know that the demand for Heineken beer in November
2020 is 454 million liters, 448 million liters in December 2020 and 467 million liters in
January 2021 Based on that, managers will make production plans suitable to market needs and the company's ability to meet orders on time and in full.
Evaluation of the moving average forecasting method of Heineken company
Each forecasting method has its own strengths and weaknesses However, many companies chase after sales, make deliveries, manufacture goods, and maintain the company's revenue but forget how to improve so that the company's revenue can be maintained and consumption is more accurate and efficient The author analyzes the strengths and weaknesses of Heineken's forecasting activities using the moving average method to propose the most optimal solutions to improve the company's forecasting activities effectively and efficiently
Beer is a type of product used by consumers with fixed demand, it can be said that the amount of beer consumed and sold is regular, regular, without much fluctuation or seasonality Therefore, applying the Moving average method is completely suitable and effective for the Heineken company because the Moving average is a simple and easy-to- understand prediction method that can use multiple moving averages at the same time without messing up Developed as a statistical tool for use with data sets spanning a specific time period, moving averages have proven to be suitable for price charts and other indicators A notable feature of this method is that it is the most popular in the market, so it responds very closely to customer sentiment at support or resistance levels With this forecasting method, managers of Heineken company value can observe the market overview, filter out price fluctuations randomly, predict whether the future market trend will decrease or increase further, thereby providing solutions the best in terms of sales volume of Heineken company
When using the moving average forecasting method, it needs to be applied on multiple timeframes at the same time to be able to make an accurate prediction because this method may not accurately reflect the most recent trend, it often misses out through fluctuations and complex market influences In addition, this method is less responsive to subjective market fluctuations (e.g cyclical or seasonal factors) or less responsive to rapid price changes making the Heineken company unable to grasp the situation of the product consumption market.
Applying other methods to demand forecast for Heineken
Table 3 2 Forecast results according to Naive forecast method
Period Actual demand Nạve Forecast
Forecasted output in November 2020 (equal to consumption in October 2020): 498.7 million liters
With the aggregated data table, the forecast according to the simple moving average method is found by averaging the consumption in the previous months The selection of the number (n) how many months ago to calculate the average is done by trial and error with the aim of finding the value n at which the MAD forecast error is smallest
After calculating (Appendix 3.1), we get the following results: n 2 3 4 5
Thus, according to the moving average method, with the value n = 2, the MAD prediction error is the smallest
Expected sales results are as follows:
The forecasted consumption volume in November 2020 (calculated by the average of the consumption volume of September and October 2010) is 445.5 million liters
The forecasted consumption volume in December 2020 (calculated by the average of October consumption volume and forecasted consumption volume in November 2010) is 472.1 million liters
The forecasted consumption volume in January 2021 (calculated by the average of the forecasted consumption volume in November and the forecasted consumption volume in December 2010) is 458.8 million liters
The mean absolute error is MAD = 12.54
Table 3 3 Forecast results by moving average method
Month Consumption Consumption forecast Forecast
This method is similar to the method above, but the averaged data are assigned different weights Choosing the number of months (n) to average and assigning weights (k) to those values greatly affects the forecast results The weights in the months closer to the forecast time will account for a large value and the sum of the weights is 1, below is the weight table for the selected N periods
We use the trial method to find the value of N with the smallest MAD prediction error After calculating (Appendix 3.2), we get the following results:
MAD 11.79 12.46 12.96 12.7 Result of forecasted sales volume (table 3.3)
The forecasted output in November 2020 (calculated by the actual production in October 2020 multiplied by the weight of 2/3 plus the actual output in September
2020 multiplied by the weight of 1/3) is 463.2 million liters
Since there is no actual output in November and December, the forecasted output for these months is as follows:
The forecasted output in December 2010 (calculated by the forecasted output in November 2020 multiplied by the weight of 2/3 and then added the actual output in October 2020 multiplied by the weight of 1/3) is 475 million liters
The forecasted output in January 2011 (calculated by the forecasted output in December 2010 multiplied by 2/3 and then added the forecasted output in November
2010 multiplied by the weight of one third) is 471.1 million liters
The mean absolute error is MAD = 11.79
Table 3 4 Forecast results by weighted moving average method
Month Consumption Consumption forecast Forecast
Let the coefficient α run from 0.1 to 0.9 (Appendix 3.3)
We get the following result: α α=0.1 α=0.2 α=0.3 α=0.4 α=0.5 α=0.6 α=0.7 α=0.8 α=0.9 MAD 11.51 11.06 10.84 10.8 10.61 10.51 10.37 10.18 9.91
Table 3 5 Forecast results according to Exponential smoothing method
Month Consumption Consumption forecast Forecast error Forecast Absolute error MAD α=0.9
Forecasted consumption volume in November 2020 = forecasted consumption volume in October 2020 + α (consumption volume in October 2020 - forecasted consumption volume in October 2020)
The forecasted consumption output according to table 4.4 is
Suggest the best method
After calculating by the above methods, we have the following results:
Table 3 6 Table comparing mean absolute deviation between methods
Weighted moving average 11.79 Exponential smoothing 9.91
Thus, Exponential smoothing method gives the smallest mean absolute deviation (MAD) Therefore, this method is the most optimal method to perform forecasting.
Request
The forecast of demand for consumption at the company is focused and done quite well, but due to the influence of the actual situation and market factors, it is always necessary to adjust the results to match the actual situation economic However, at present, our team finds that the application of the Moving average forecasting method to Heineken company is not the most effective and optimal Therefore, to improve this, Heineken company should pay more attention to the following issues:
Heineken should increase personnel for forecasting, historical data statistics combined with surveys, monitoring and grasping the situation outside the market, and customer needs to provide more accurate forecast results to serve the production plan to reduce the adjustment
Continuously researching and regularly updating and innovating and creating methods of forecasting demand for consumption
Heineken should put the Exponential smoothing method into immediate application in demand forecasting because the calculation results show that this method has the lowest mean absolute error (MAD) has the ability to react quickly and show the most recent price movements Thereby helping to make Heineken's forecasting activities most accurate and effective
It is very necessary to use the Exponential smoothing method in Heineken's forecasting because it is the method most used by many companies out of all forecasting methods, which helps the company to be flexible the analysis and forecasting activities in the time series apply to both trend and seasonal data, making it an easy-to-use method that requires little historical data and is effective for short- term forecasting Applying this method, the forecasting activities of Heineken company will take place quickly, smoothly, and effectively
Forecasts are an important input to the design and operation of production systems because they help managers predict the future Forecasting techniques can be classified as qualitative or quantitative Furthermore, all forecasts include a certain degree of inaccuracy and should be provisioned for Techniques often assume that the same underlying causal system that existed in the past will continue to exist in the future
Through the research, it can be said that forecasting product demand is the starting point of production management Because, during the operation process, enterprises need to prepare resources such as raw materials, fuel, labor, machinery and equipment, etc needed, thanks to forecasting production needs, businesses can make accurate decisions at the right time If the forecast is not accurate, it will greatly affect this activity The topic has applied a number of forecasting models according to the theoretical basis and compared the mean deviation absolute average (MAD) of the models to determine the optimal model with the smallest MAD
Through this topic, the authors presented an overview of forecasting methods that can be used for Heineken Beer Company On that basis, the team analyzed the reality and applied the theory of the forecast to complete the company's output demand forecast
In order to contribute to the improvement of the forecast of the company, the group has strongly made some small comments, especially proposing a more optimal forecasting method to the company so that Heineken's forecasting can take place smoothly and effectively However, due to limited qualifications, knowledge and experience, certain errors and defects cannot be avoided Therefore, the team is looking forward to receiving help from the instructor to make our discussion more practical
1 HEINEKEN (2019) ANNUAL REPORT HỒ CHÍ MINH.
2 HEINEKEN (2020) ANNUAL REPORT HỒ CHÍ MINH
3 HEINEKEN (n.d.) HEINEKEN Retrieved from ABOUT HEINEKEN VIETNAM: https://heineken- vietnam.com.vn/en/about-us/
4 OFFICE, G S (n.d.) GENERAL STATISTICS OFFICE Retrieved from GENERAL STATISTICS OFFICE: https://www.gso.gov.vn/en/homepage/
5 SSI (n.d.) Retrieved from https://webtrading.ssi.com.vn/Logon
6 Stevenson, W J (n.d.) Operations Management thirteenth Edition
7 VIRAC (n.d.) Retrieved from https://viracresearch.com/trang-chu
(HEINEKEN, 2019) (HEINEKEN, ANNUAL REPORT, 2020) (HEINEKEN,
HEINEKEN, n.d.) (OFFICE) (SSI, n.d.) (VIRAC, n.d.) (Stevenson)
APPENDIX Appendix 1: Forecast results according to the moving average method when changing n
Month Actual Forecast Absolute forecast error Forecast Absolute forecast error n= 2 n=3
Month Actual Forecast Absolute forecast error Forecast n=4 n=5
Appendix 2: forecast results according to the weighted moving average method when changing n
Month Actual Forecast Absolute forecast error Forecast Absolute forecast error n= 2 n=3
Month Forecast Absolute forecast error Forecast Absolute forecast error n=4 n=5
Appendix 3: Forecast results by exponential smoothing method when changing smoothing coefficient α
Month Actual Forecast Forecast error
Absolute forecast error Forecast Forecast error
Absolute forecast error Forecast Forecast error
Absolute forecast error Forecast Forecast error