Methods for drawing up sales forecasts. Collecting match information

In accordance with the forecasts compiled by the CES, the energy needs of the common market countries for the period 1970-1985. will more than double - to approximately 1.8-2.0 billion tons in terms of fuel equivalent, and the average annual increase will be about 5%. According to estimates, the share of industry in total consumption will be 55%, the household sector - 32% and transport - 13%. The structure of the fuel balance of the countries of the common market by 1985 will be as follows (in %) oil - 65 natural gas- 15 solid fuel - 9 nuclear and hydropower - 11.

According to the degree of AIT coverage of management tasks, electronic data processing is distinguished, when using a computer without revising the methodology and organization of management processes, data is processed to solve individual economic problems, and management automation is carried out. In the second case, computing means, including supercomputers and personal computers, are used for comprehensive solution functional tasks, generating regular reporting and working in information and reference mode for preparing management decisions. This group may also include AIT decision support, which provide for the widespread use of models and PPPs for analytical work and the formation of forecasts, drawing up business plans, informed assessments and conclusions on the processes being studied, phenomena of arbitrary economic practice. This group also includes shi-

A. Check that prices are close to the Fifth Wave forecast

Time-aligned bars are much easier to program, but they are not as good for analysis as market-aligned bars. Let's look at bonds as an example. Even though the bond market opens trading at 8:20 a.m., effectively ending its first half hour at 8:50 a.m., the time-aligned bars would begin measuring that market at 8:00 a.m., ending the first bar at 8:30 a.m. In this case, the first 1/ 2 hours (8 00-8 30) will include only 10 minutes of actual data coming from the market. The second half-hour bar will contain information only for 20 minutes of the first half of the hour and 10 minutes of the second half of the trading hour. Another example of time-aligned bars that create "erroneous" highs, lows, and latest data is the hourly S P. In this case, the first hourly S P bar contains information received from 9:00 to 10:00 am, although it does not begin to arrive until 9 30 The second hour starts at 10 00, ending at 11 00 am, instead the right start at 10:30 and ends at 11:30 am. Obviously, if the highs, lows and latest data for these intraday charts are recorded “incorrectly”, all forecasts made from them are also incorrect. Don't let the feeling of satisfaction blind you. Some traders have been using time-aligned bar calculations for years with below average results. Many of these traders have absolutely no understanding of how these forecasts are created. I assure you that the poor performance of indicators may be more likely the result of inappropriate data on the basis of which they are calculated, than the imperfections of the indicators themselves or the trader’s lack of understanding of the rules for their use

This forecast is based on data from the Ministry of Economy of the Russian Federation, forecast

Consequently, the unanimity of the majority of experts is not always a criterion for the reliability of assessments. This implies the need for careful selection of experts. The fact is that when discussing many issues, especially non-standard ones, for example, forecasting the market situation in unstable political and economic conditions, highly qualified experts should participate. Forecasts made by average experts will be based on best case scenario on traditional, habitual assessments, while highly qualified specialists will discover and evaluate hidden factors.

For example, baby food manufacturers may believe that their sales depend (if they do) on the birth rate. Sales of excavators depend on the volume of housing construction. Thus, it is necessary to collect data on sectors/industries related to a particular product and analyze the correlation of this information with its sales data. The disadvantage is that you can use the wrong relationship or several different indicators at the same time. However, this method is useful for explaining some trends and even for testing forecasts made using time series or subjective estimates.

Factors taken into account in a forecast drawn up on the basis of experience must change not only under the influence of trends that arise over time. certain period time, but also as a result of an assessment of general business conditions. This is reflected in revenues, thoughtful changes in sales conditions or credit policy. There are prerequisites for improving this approach, if in practice there are significant differences between geographical regions, consumers or distribution channels.

However, some marketers criticize the reliability of forecasts based on the results of such marketing experiments. The main reason Their negative attitude is that the behavioral characteristics of the buyer and his choice are largely influenced by the environment in which the purchase is made. In this case, it is far from real.

The table shows a complete sales forecast, which many companies use when drawing up budgets. Note that sales for the first quarter are disaggregated to show expected sales for each of the three months. The forecast (sometimes called the sales budget) reveals expected sales by product type and by region. This overall forecast is a consolidated forecast based on sales estimates for each region where products are sold.

Another disadvantage of the method appears in large companies. So, if at each level of the combined forecast (i.e. at the level of sales agents, regional managers) the forecasts are compiled with an excessive number of random indicators (for example, underestimation), then the combined forecast compiled by the top sales manager may be essentially useless. This disadvantage can be overcome by determining the degree of randomness of the forecast results and using an adjustment based on past results, or by encouraging all sales agents to create more realistic forecasts.

In most cases, the forecast developed based on demand determines the upper limit necessary development. The lower limit is set by the forecast calculated on the basis of the descriptive approach, and the middle limit is set by the forecast compiled by developing a program for filling the “gap”.

Thus, observations of relative levels of inflation or the causes of inflation (such as differences in the rate of increase in the money supply) can be used to predict changes in exchange rates. However, this approach is not suitable for making forecasts of exchange rate changes for the short term. Rates may differ from forecasts based on parity

From the analysis of Fig. 4.3(6) it follows that, according to the optimistic forecast compiled by our financial manager, the costs of conducting a financial transaction (i.e., potential losses), which are formed at the moment t = 0, should be recouped at the moment vr. By the time vk, the financial transaction should will be completed because, according to this optimistic forecast, the required level of profit will be achieved. Similar points in time for a pessimistic forecast of the course of the profit formation process are indicated in Fig. 4.3(6) via TJ and respectively. We will continue to mark the actual trajectory of the process of changes in profit during a financial transaction in our figure with bold arrows. The articulation points of the arrows in Fig. 4.3(6) indicate the moments of making current management decisions that change the direction of the real process.

Forecasting with the help of a genius. This method is based on the idea of ​​finding a genius and receiving an intuitive forecast from him. This method excludes the use of rational and precise methods. A forecast compiled by a genius cannot be verified, which is fraught with big troubles for an enterprise (company) operating in market conditions.

The main difference is the use of various methods of financial analysis when assessing investment projects and businesses (calculation of cash flow, calculation of present value, risk assessment, etc.), since accounting methods do not provide an adequate description of the processes occurring and expected in the future. However, the use of financial analysis methods is often not possible without the use of accounting documentation and corresponding forecasts compiled as part of the financial planning of the enterprise.

The policy documents of the CPSU have developed a set of measures to further improve management. In this aspect, the development of economic reform based on new principles of planning and economic stimulation of development is especially important. social production strengthening work on drawing up socio-economic forecasts for the long term, on the correct combination of territorial and sectoral planning; improving the organizational structure of management from bottom to top; strengthening state discipline at all levels of the national economy; widespread use of modern computer technology and economic-mathematical methods; increasing involvement of workers in management . On at this stage especially great importance has transformation

Currency exchange ratio different countries. For most currencies in the world, these rates fluctuate constantly, reflecting changes in the situation on the foreign exchange markets. Fluctuations in exchange rates directly affect the entire development of world trade, since the degree of profitability of purchasing or selling goods in the markets changes various countries. Therefore, to decide on the advisability of a conclusion foreign trade contract With a company from a particular country, it is advisable to use information about forecasts of changes in exchange rates by the time the contract is completed and payments are made. Dozens of companies around the world are engaged in the preparation of such forecasts for banks conducting currency transactions on foreign exchange markets, as well as commercial firms.

The methods under consideration are used for long-term and current planning, but they may not be enough when making long-term forecasts, when it is not yet known exactly what the equipment, technology, and forms of organization of production will be. Therefore, when forecasting, several more methods are additionally used.

Relevant specialists have developed several specific methods for compiling and improving the quality of forecasts. In table 8.2. briefly describes the main types of forecasts often used in connection with organizational planning. The forecasting results are included in the organization's goals, determined by management.

In the process of drawing up the French economic development program, forecasts were developed covering the period up to 1985. In Table. 16-V provides a forecast of the energy balance of France until 1985.

However, after the above forecast was compiled, the demand for energy already in 1968 reached the level expected for 1970. This fact necessitated the need to amend the energy forecast. Average annual growth rate of gross national income according to the revised plan in 1969-1975. and in 1976-1985. were planned at 10.6 and 8.5-9.5%, respectively, and industrial production indices compared to the base year 1968 were estimated in 1975 and 1985. 392 and 945-1074, respectively. According to a new estimate, energy demand in terms of oil in 1975 and 1985 respectively, will be 438 million m3 and 933-1029 million m3, i.e. will increase by 2.1 and 4.4-4.8 times compared to 1968 (Table 28-V).

This can be seen from a comparison of reality with forecasts compiled by such well-known organizations, like the RAND Corporation, the 2000 Commission, Resources for the Future, the Hudson Institute, as well as a number of bourgeois scientists.

Statistics that may be useful as industry data over time have already been mentioned in this book. Chapter 6 examines intercompany comparisons using information from Robert Morris Asso iates. If we take into account fluctuations in fund flows over a sufficiently long period of time, including the full economic cycle of the industry, we can calculate the standard deviation of fund flows. It is then easy to calculate for each industry the coefficient of variation, which, when given a specific confidence interval, will allow one to calculate the probability of a shortfall in receipts of funds compared to the forecast compiled by the analyst based on the methods described in Chapters 7 and 8.

Let us now present the forecasts for world gas consumption. Like energy consumption forecasts in general, they also vary widely. For example, the IIASA Report shows a range of forecasts that is several times larger than the minimum forecast, which shows significant uncertainty in long-term forecasts. The discrepancy between the forecast options compiled various organizations, can be quite significant. This is mainly due to different understandings of high and low scenarios, the input data for which is not always specified. The range within which the predicted values ​​differ can reach several hundred percent. This once again demonstrates the need to create a “transparent” forecasting system that allows flexible consideration of various scenarios of global or regional development.

The key to such a forecast is the indicators of the future dynamics of the company as a whole, and among them the most important are estimates of sales volumes and net profitability standards (a coefficient expressing the ratio of net profit and gross revenue. - Approx. scientific editor). One way to construct a sales forecast is to assume that the company will operate as it has in the past and therefore simply extrapolate from its past trends. For example, if in the past the company’s product sales volumes grew by 10% annually, with this approach to forecasting it can be assumed that they will grow at this same rate in the future. Of course, if there is any economic or industry-wide evidence that the company’s sales volumes will grow faster or, conversely, slower than in the past, the forecast should be adjusted. It is likely that this naive, simplistic approach to forecasting will be as effective as forecasts based on more complex methods. As a rule, the sales volume forecast covers a period of one to three years. An attempt to extend it beyond these limits leads to increased uncertainty and sharply reduces the quality of the forecast.

By chaining managers to himself with “golden chains,” Jenin is able to create tension that drives the company forward. “The key to the system,” explains one of the company’s managers, “is the profit forecast.” Once the forecast is drawn up, reviewed and agreed upon, the manager is obliged to report to Jenin on its implementation. This is how tension arises, on which success depends. Tension permeates the entire company, causing ambition, perhaps even excitement, but always tinged with fear of what will happen if the goal is not achieved 14

Forecasts are useful for planning and implementing changes that an experience-based forecast of business operations is only useful if data on similar products (or development options) provide the basis and limitations contained in the forecast , openly for making a forecast about the fate of your product, but are named. ta How easy or inexpensive it will be There are several ways to do this obtain reliable information about past experience                         Marketing Management (1998) - [

Do sports betting without preliminary analysis And predicting the outcome, perhaps the strangest decision of bookmaker players. In order to get a positive result over a long distance, you must predict the outcome of the meeting, and only then make a decision to bet on it or not. In this article we will share practical tips, tell you how to correctly analyze information and make predictions for matches, and we will also give a clear algorithm of actions.

Predicting sports matches is not an easy task. Making a forecast itself is pure analytics. In addition to the fact that you will need to process a huge flow of information, you must also be able to sort it by importance, as well as “sort it into shelves.” This is comparable to a large library. They brought it to you great amount books, you must sort them by genre, author, significance (after all, uninteresting works can be put away), or according to some other characteristics and parameters, put everything on shelves, and when you are asked to give out the book, in a few seconds you need to figure out where she is lying down. It’s the same with forecasting, first you read and receive information, and then at any moment you will have to pull it out of your head and apply it correctly. Such information can be statistics, injuries, statements by coaches and players, team goals for the season and for a specific match, motivation and many others.

Before we begin, it is worth recalling that the first priority and one of most important tasks the player is right choice bookmaker's office. Bet only in the best bookmakers! You can read about how to find a decent bookmaker. Well, we advise you to pay attention to three offices from our rating: BC "WINLINE", BC "MELBET" and BC "1XBET". These are high-quality and reliable betting operators that do not create problems for players, and this is the most important thing. Plus, in these bookmakers you will find many events for betting, a wide selection and excellent odds.

Selecting a sport and league to predict

First of all, you should choose a sport that you are good at. If you love football and hockey, know the players, teams, various intricacies of these sports (and if you don’t know anything, then it’s generally unclear why you’re betting), then there’s no point in getting into, for example, tennis or basketball. Of course, it is best to specialize in one sport, but predicting two is, in principle, uncritical. In addition, you need to weed out those leagues and championships that you do not know. For example, if you are predicting football matches, then you need to choose the top leagues (EPL, Bundesliga, RFPL, Primera, etc.), and not go into the third division of Zimbabwe or the fourth league of Germany. When predicting hockey matches, choose the KHL and NHL; when predicting basketball, choose the NBA, etc. The most important thing is don’t jump from one sport to another, and don’t be torn between several championships. If you are experiencing difficulties and feel that, frankly, you cannot handle several sports, championships, leagues, then it is better to reduce their number.

Making a forecast includes four phases:

1. Collection of all kinds of information;
2. Processing and sorting information;
3. Analysis and compilation preliminary forecast;
4. Predicting the outcome of a match

Collecting match information

Statistics

At the first stage, you need to collect all the information about the match necessary to make a forecast. First of all, study the statistics. Don’t forget to divide all indicators into away, home and general.

The most important indicators:

• last team meetings;
• last head-to-head meetings between rivals;
• tournament position;
• average value of goals scored per match;
• average value of goals conceded per match;
• average of goals scored over the last five matches;
• average of goals conceded over the last five matches.

Thanks to the statistics of recent meetings, you can determine the current form of the team (players), goals scored/conceded - from the same opera, only you can find out in more detail the indicators at any segments of the season. Based on the history of head-to-head confrontations, one can determine whole line indicators, for example, the performance of teams in a game with each other, how often opponents hit each other’s goals, etc. By the tournament position of the team you can determine general indicator game form(for a certain number of matches or for the season as a whole), as well as learn about the team’s motivation and goals for the next game.

Compositions

Typically, team lineups become known an hour and a half before the start of the match, but based on information about injuries, you can have a rough idea of ​​the team lineups for the upcoming match. You can also compare the lineups for, say, the team’s last three matches.

Series

Highlight different series: wins in a row, losses in a row, goals scored in a row, goals conceded in a row, clean sheets and games where both teams will score. These are the most important indicators in forecasting.

News

Read the press Special attention should focus on interesting facts rather than statements from players and coaches. An example of an interesting fact: Lokomotiv, visiting CSKA, cannot win ten matches in a row. Here it is immediately obvious interesting fact, and statistics, and a series, in this case without victories. Such information always needs to be noticed, processed and applied.

Motivation

One of the most important factors is motivation. You must accurately find any motivational components. For example, the last round of the group stage of the Champions League is underway. Real Madrid is third in the group, two points behind Borussia Monchengladbach, which is second. Real Madrid's next match is against Borussia, and at the Santiago Bernabeu. It is obvious that the Royal Club sets itself the goal of reaching the Champions League playoffs, and not getting into the Europa League, so the team will enter the match, which will also be a home match, with special emotions.

Revenges

The thirst for revenge is another of the most important factors that can directly affect the result of the match (in the case of opponents of approximately equal levels). There are often situations when, after a major home defeat, a team is eager to rehabilitate itself in front of its fans. Missed advantages and defeats in the last seconds, defeats in overtime, in shootouts, defeat due to a goal that was not counted according to the rules, defeat in the derby - all these situations give rise to the desire to take revenge from the opponent.

Derby

Separately, we need to touch on the derby teams. Derby is a competition between teams from the same city or region. Opponents usually go into such matches overly motivated, because victory in a derby is doubly sweet, and in addition, it very often promises good bonuses.

Processing and sorting information

When you have received a sufficient amount of information about the match, you need to immediately discard everything superfluous, and superfluous is something that will in no way affect the outcome of the match (garbage). Next, you need to structure the information, separating important factors from secondary ones. All factors are divided into direct, indirect and abstract.

Direct factors

Direct factor- this is a factor that can directly affect the outcome of the meeting. For example, an injury to the leading player of Team 1.

Indirect factors

Indirect factor- this is a factor that can only indirectly affect the outcome of the meeting. For example, appointing a referee for a match who does not skimp on red cards. Because of this, there is a high probability of a Team 1 player being sent off in the match, as a result of which Team 1 may miss, fail to score (if an early removal), or lose. Current weather, intra-team intrigues, disagreements, etc. - all these are indirect factors.

Abstract factors

Abstract factors- these are events during the match that cannot be foreseen in any way. For example, a player’s injury during a match, worsening weather (rain, snow), noise from fans, good luck and bad luck. Please note, it is the worsening weather, not the current weather. Worsening weather is an abstract factor, the current weather is indirect, but in combination with others it can become direct. An example of failure: a player goes one on one with the goalkeeper, throws a shot and it hits the post. An example of luck: a player hits the goal, a player appears in the path of the ball, and the ball, after a rebound, ends up in the net. Note that for one team hitting the post is a failure, but for another it will be a success. Abstract factors should simply be neglected. Firstly, it is almost impossible to predict them, and secondly, in the case of successes and failures, we believe that there will be approximately equal numbers of them in the match, and they will compensate for each other.

For us, the most important will be direct factors, and indirect ones only selectively. Why selectively? Yes because indirect factor in combination with other indirect factors and under certain conditions can become direct. If we take into account every little detail and calculate the probability of an event occurring that was generated by another event, we will go crazy. There is also no need to reach the point of idiocy.

How to make a preliminary forecast for a match?

Next, we work only with direct factors (statistics, injuries, etc.). We collect everything that happened and analyze it. At this stage, you must select an event in the match (bet) that, in your opinion, should happen. Forget about calculating the probability of a bet passing or calculating the probability of an event occurring! In this case, you are interested in the following: whether this event will happen or not, 1 or 0, false or true. Naturally, the bet must be supported by something, and not taken at random. This is where you need to use that “library principle”, take out small pieces and start putting together the puzzle. You must be able to not only make a forecast, but also explain your choice in favor of this bet.

How to Calculate Team Strength Rating

When making a preliminary forecast, you can use a very good system for calculating the team strength rating proposed by J. Miller. Ideally it is written for American football games, but we have modified it for football, hockey and basketball. However, this system works better in high-performance sports (volleyball, basketball, handball, American football, etc.).

First of all, you need to remember two main rules:

1. Use this system only after 5-6 rounds of the championship have been played;
2. Do not use results from friendlies or pre-season matches.

You take the last 5 meetings of the team and write down the goals scored, pucks, etc. Cross out the highest and lowest goals scored. Next, add up the remaining three indicators and divide by three. To calculate the defense rating, do the same: cross out the highest and highest small value goals missed, add up what remains and divide by three.

Example

National Hockey League match "Detroit Red Wings" - "Pittsburgh Penguins".

Detroit's last five meetings:

Detroit 1-2 Anaheim
Detroit 3-4 Vancouver
Detroit 5-1 San Jose
Detroit 3-1 Florida
Detroit 3-2 Edmonton

Pittsburgh's last five meetings:

Pittsburgh 1-3 Buffalo
Pittsburgh 3-2 Arizona
Pittsburgh 6-1 Toronto
Pittsburgh 2-3 Montreal
Pittsburgh 3-1 New Jersey

Detroit goals scored: 1, 3, 5, 3, 3.
Detroit's missed goals: 2, 4, 1, 1, 2.

Pittsburgh goals scored: 1, 3, 6, 2, 3.
Pittsburgh's missed goals: 3, 2, 1, 3, 1.

We cross out 1 and 5 goals (the smallest and largest performance indicator), and add the remaining three indicators: 3+3+3=9. Now we divide this value by three: 9\3=3.

We cross out 1 and 4 goals (the smallest and largest indicator of goals conceded), and add up the remaining three indicators: 1+2+2=5. Now we divide this value by three: 5\3=1.66.

We cross out 1 and 6 goals (the smallest and largest performance indicator), and add the remaining three indicators: 3+2+3=8. Now we divide this value by three: 8\3=2.66.

We cross out 1 and 3 goals (the smallest and largest indicator of goals conceded), and add up the remaining three indicators: 3+2+1=6. Now we divide this value by three: 6\3=2.

How to make a forecast for the exact score of a match?

The Power Ranking can be used to determine the preliminary score of a meeting between two teams. Let's try to estimate how many points Detroit can score and how many points Pittsburgh can score. To do this, add Detroit's offensive rating to Pittsburgh's defensive rating, and then subtract 3 (the average team points in the NHL).

Average totals for leagues and sports:

• NHL = 3
• KHL = 2
• NFL = 20
• NBA = 100
• Football = 1

Calculation:

"Detroit": 3+2-3 = 2
"Pittsburgh": 2.66+1.66-3 = 1.32

For home teams, you should almost always round up, or add 0.5 or 1, but you should also look at the opponent's average goals conceded in away games. If the visiting team concedes quite a lot, feel free to round up. You can also calculate the average value of goals scored/conceded for a season, or for a period of at least 10 matches. In our example, we will round both values ​​upward. Let’s imagine that “Detroit” at home has a goals/conceded record of 2.66/2.53, and “Pittsburgh” on the road has a record of 2.71/2.55. “Pittsburgh” concedes more than 2.5 goals on the road per match, so we increase the value of Detroit’s goals scored. In the same way, “Detroit” concedes more than 2.5 at home, while “Pittsburgh” has a high conversion rate on the road - 2.71 on average per match.

Thus, we calculated that the match “Detroit” - “Pittsburgh” could end with a score 2-2 or 3-2 (Borderline value). But then you need to build on additional information and indirect factors that could affect the outcome of the meeting.

How to make a final match prediction?

First we need to check everything again. See if you interpreted the information you received correctly, did you correctly separate direct and indirect factors?, have you missed anything important? Are you correct calculated the team strength rating. Further check if new information has appeared, and if it appears, then you need to take it into account when making a forecast. Now analyze whether a combination of indirect factors will affect the successful passage of the bet. For example, if there are a lot of them, look to see if they have anything in common. Wouldn't it turn out that one indirect factor in combination with another will give rise to a direct one? In any case, all this needs to be taken into account, but there is no need to confuse yourself. Just analyze, ask yourself questions and answer them. You are just thinking, and confusion will not lead to anything good. It will all end with you simply getting confused in your own thoughts and doubts.

This is roughly how forecasts for matches are made. Please remember that every match and every situation is unique, so your situation may differ from the above. The most important thing, as has already been said, is to collect information, discard the unnecessary, analyze it and make a forecast, and then check yourself. Your main tool is statistics and news. Think and analyze, but don't get carried away. You need to analyze deeply, but quickly, otherwise you will drown in your own thoughts. Happy forecasting!

Basic forecasting methods

Introduction

1. Forecasting and types of forecasts

2. Forecasting methods

3. Statistical forecasting

4. Forecasting based on seasonal fluctuations

5. Expert forecasting

6. Sales forecasting

7. Information obtained from competitors' stores

8. Suppliers and purchasing centers

Conclusion

Bibliography

Introduction

The relevance of the topic is due to the fact that for most Russian enterprises, marketing management is becoming one of the conditions for survival and successful operation. At the same time, ensuring the effectiveness of such management requires the ability to foresee the likely future state of the enterprise and the environment in which it exists, to prevent possible failures and disruptions in time. This is achieved through forecasting both planned and practical work enterprises in all areas of their activities, and in particular, in the field of forecasting sales of products (goods, works, services).

The variety of problems that arise in ensuring the life of an enterprise and are the subject of forecasting leads to the emergence of large quantity various forecasts developed based on certain methods forecasting. Since modern economic science has a large number of different forecasting methods, every manager and planner must master the skills of applied forecasting, and the manager responsible for making strategic decisions, must also be able to make the right choice of forecasting method.

Purpose of the work: to consider sales forecasting. Based on the goal, this work formulates tasks, including:

the essence of basic concepts in the field of forecasting;

classification features, types of forecasts and their brief characteristics;

forecasting methods (considered, if possible, using specific examples);

1. Forecasting and types of forecasts

Forecasting (Greek Prognosis - knowledge in advance) is a type of foresight (prediction), since it deals with obtaining information about the future. Prediction “involves a description of possible or desirable aspects, states, solutions, problems of the future. In addition to the formal, based on scientific methods forecasting, prediction includes premonition and prediction. A premonition is a description of the future based on erudition, the work of the subconscious. Prediction uses everyday experience and knowledge of circumstances.” In a broad sense, both scientific forecasting and premonition and prediction are included in the concept of “forecasting the activities of an enterprise.”

A forecast is the result of the forecasting process, expressed in verbal, mathematical, graphic or other form of judgment about the possible state of an object (in particular, an enterprise) and its environment in a future period of time.

Various criteria for classifying forecasts are identified. We will use the approach developed at the Financial Academy under the Government of the Russian Federation and, based on it, we will draw up the following classification table.

Table 1

Types of forecasts

For specific forecasts, other criteria for classifying forecasts may be used. For example, to forecast market conditions, it is important to highlight such a feature as the coverage of research objects - depending on it, the forecast can be global, regional, local (systemic). In other words, it can cover the entire market of a country or be limited to the market of a certain region; it can also cover the local market of an individual enterprise. It may consider the market situation as a whole or its subject will be the market for a particular product.

Below is a description of each of the types of forecasts listed in Table 1.

Depending on the forecasting horizon, the forecast can be developed for a very short period of time - up to a month (for example, weekly and monthly forecasts of sales volumes, cash flows), for a year, as well as for 2-3 years (medium-term forecast), 5 or more years ( long-term forecast).

Long-term forecasts are also called long-term forecasts. Often five-year forecasts are classified as medium-term.

By type of forecasting, forecasts are classified into search, normative, and forecasts based on creative vision.

Search forecasting is a method of scientific forecasting from the present to the future: forecasting starts from today, builds on existing information and gradually penetrates into the future.

There are two types of search forecasting:

extrapolative (traditional),

alternative (innovative).

The extrapolative approach assumes that economic and other developments occur smoothly and continuously, so the forecast can be a simple projection (extrapolation) of the past into the future. To draw up such a forecast, it is necessary to first assess the past performance of the enterprise and their development trends (trends), then transfer these trends into the future.

The extrapolative approach is very widely used in forecasting and is reflected in one way or another in most forecasting methods.

An alternative approach is based on the fact that external and internal environment business is subject to constant changes, as a result of which: the development of the enterprise occurs not only smoothly and continuously, but also spasmodically and intermittently; There are a certain number of options for the future development of the enterprise.

Based on this, as part of an alternative approach:

firstly, alternative forecasting can combine in a single logic two methods of enterprise development - smooth and abrupt, creating a synthetic picture of the future;

second, forecasts are created that include a combination various options development of selected indicators and phenomena. Moreover, each of the development options underlies a special future scenario.

The alternative approach is relatively young (became widely used in the 80s) and is now rapidly spreading in the practice of intra-company planning.

Both types of search forecasting rely on both quantitative and qualitative forecasting methods.

Normative (normative-target) forecasting involves:

firstly, determining the general goals and strategic guidelines of the enterprise for the future period;

secondly, an assessment of the development of the enterprise based on these goals.

Regulatory forecasting is most often used when an enterprise does not have the necessary historical data. Because of this, it relies on qualitative research methods and, like extrapolation, is a largely traditional approach to predicting the future environment of an enterprise.

Forecasting, based on a creative vision of the future, uses the subjective knowledge of the forecaster, his intuition.

Predictions of this kind often take the form of “utopias” or “dystopias” - literary descriptions fictional future. Despite the apparent distance from the world of economics, such works are a good addition to a dry quantitative forecast.

This type of forecasting can be used to directly predict the future performance of an enterprise.

Depending on the degree of probability of future events, forecasts are divided into variant and invariant.

An invariant forecast assumes only one option for the development of future events. It is possible under conditions high degree certainty of the future environment. As a rule, such a forecast is based on an extrapolative approach (a simple continuation of the current trend in the future).

A variant forecast is based on the assumption of significant uncertainty in the future environment and, therefore, the presence of several probable development options.

Each of the development options takes into account the specific state of the future environment of the enterprise and, based on this, determines the main parameters of this business. This kind of version of the future state of the enterprise is called a scenario.

Based on the way the results are presented, forecasts are divided into point and interval.

A point forecast assumes that this development option includes a single value of the forecast indicator, for example, the average daily trade turnover next month will increase by 5%.

An interval forecast is a prediction of the future in which a certain interval, a range of values ​​of the predicted indicator is assumed, for example: the average daily trade turnover next month will increase by 5-8%.

2. Forecasting methods

To understand the essence of this issue, it is necessary to first define some concepts, in particular, such as: method, technique, methodology.

IN in a broad sense words - method (gr. methodos) - this is: 1) a way of cognition, research of natural phenomena and social life; 2) a technique or system of techniques in any activity.

Applied to economic science and practice - a method is: 1) a system of rules and techniques for approaching the study of phenomena and patterns of nature, society and thinking; 2) path, way of achieving certain results in knowledge and practice; 3) a method of theoretical research or practical implementation of something, based on knowledge of the laws of development of objective reality and the object, phenomenon, or process being studied.

When developing a sales forecast, it is important A complex approach, the use of several methods of forecasting and comparison of the results obtained simultaneously. Among these methods, the most common are the following:

• 1. Survey of a group of managers of various services and departments of the company. These managers must first obtain relevant information regarding market analysis. In this case, the sales forecast is something “average” of the views and outlines of the surveyed group of managers. This forecasting method is most suitable for new businesses that do not have enough experience in using other methods. This method is also applicable when there are no detailed calculations about the state of the market, there are no complete statistics on sales trends for certain types of products.
• 2. Generalization of assessments of individual sales agents of the company and heads of its sales divisions. In this case, market analysis is supplemented by the opinion of those who directly experience the reaction of consumers and most acutely feel the slightest fluctuations in consumer preferences. The regional aspect is also taken into account here: individual employees or sales managers can provide Additional information about the specifics of selling certain products in different regions of the country. Accordingly, the accuracy of estimates with this method is higher than with the first. But organizing such work is associated with large overhead costs (primarily additional costs for remuneration of specialists and analysts, data processing, etc.). And although companies that value their brand (especially leading industrial companies with world-class production or striving to become such) never skimp on them, it often requires the development of special procedures for controlling and budgeting these expenses. Otherwise, the accuracy of the forecast may negatively affect the financial position of the enterprise.
• 3. Forecasting based on past turnover. In this case, sales data for the past year are taken as the basis for predicting likely sales in the future. It is assumed that the turnover next year will exceed or be lower than the current year’s turnover by a certain amount (usually a percentage increase to the data for the previous year is taken according to the so-called “achieved” principle):

This forecasting method is suitable for industries and markets with stable economic conditions, a weakly changing range of goods and services, with sluggish scientific and technical progress, where significant fluctuations in trade turnover occur extremely rarely. The most typical example Such an industry is public utilities. Applying this method, it is impossible to take into account rapid changes in character commercial activities, in the structure of consumer demand, etc. As for competition, its degree is not taken into account here at all.

• 4. Analysis of trends and cycles, factors causing changes in sales volume. The sales forecast is based on identifying probabilistic trends and statistically significant factors underlying them using market analysis. Typically, the following main factors are taken into account: long-term growth trends of the company, cyclical fluctuations business activity, seasonal changes company sales, possible impacts of strikes, technical changes, the emergence of new competitors in the market. This method is most preferable when making long-term forecasts. Statistical patterns, identified trends and dependencies over the years neutralize the effect of random and minor factors. At the same time, using this method it is difficult to predict for a period of less than 3-5 years, the sample, the array of processed statistical information, as well as the period of manifestation of cyclical fluctuations are too small. This method is most suitable in capital-intensive industries.
• 5. Correlation analysis, i.e. identification of statistically significant factors influencing the sales of the company's products. It logically complements the previous method, but is based on more complex scientific tools statistical analysis market. Usually, within the framework of special surveys, the closeness of the correlation between the level of sales of the enterprise and various parties is determined economic activity, the impact on sales of which can be logically proven or justified. Thus, the most significant factors are identified and ranked (by degree of influence), depending on which the sales volume may change in the future. It should be noted that this forecasting method necessarily requires serious special and complex, and therefore quite expensive, not always economically justified market research. The most accurate results, however, can be obtained using this method in the most stable industries in terms of economic conditions.
• 6. Forecasting based on “market share” of a firm’s sales, in which turnover is forecast as a certain percentage of the firm’s market share in a given industry, i.e. First, sales are forecast for the entire industry, and then a calculation is made of the enterprise’s share in the total sales volume of the entire industry. When using this method, it is important, firstly, to be confident in the accuracy of the forecast for the entire industry, and secondly, not to take into account non-price competition in it (at the level of new products and services).
• 7. End use analysis. The forecast here is based on the expected volumes of orders from the main customers of the enterprise (turnover usually exceeds this indicator by a certain predetermined percentage). The application of this method requires special research on the main industries consuming products of this enterprise, collection and processing of significant statistical and factual material. This method is preferred in the raw materials and energy complex, as well as at enterprises producing components and components.
• 8. Analysis of the product range, in which sales forecasts for individual types of products are brought together and form the company’s planned turnover. This method is most suitable for highly diversified enterprises, but the accuracy of the overall forecast depends entirely on a detailed survey of the market for each type of product. And this, in turn, requires considerable costs.

The effectiveness of using a particular method depends entirely on the specific conditions and specifics of the enterprise’s economic activity and can only be determined in a system of general market research activities. In a marketing-oriented company, as a rule, several options for sales forecasts are compiled using various methods (usually 3-4 methods are selected.). The resulting estimates are then compared to identify any differences in estimates that may arise. It is usually considered that the forecast is made correctly if the difference between the estimated and actual sales does not exceed 5%. If these discrepancies are significant (dispersion of sales forecast indicators across various methods exceeds 10%), then most likely errors were made when drawing up the sales forecast using some method.

In some cases, when drawing up sales forecasts, so-called test marketing can be used. If the company does not have a well-established market research service and experience in working with information sources, this method may be the most accurate when drawing up sales forecasts. The essence of this method is as follows: an enterprise or firm begins selling a product in a very small market (for example, within one city, district). Even one retail outlet can be taken as the object of analysis if market research is carried out competently and its most typical location is selected (in terms of the target market segment, consumer profile and sales channels). Thus, in a small part of the market, an attempt is made to model everything that is then supposed to be implemented on the scale of the entire sales region. Here the main components of product promotion on the market can be checked (forms of advertising, sales promotion methods, price policy, distribution channel, packaging, etc.). They are sort of tested on a small group of consumers. After processing the information received on the volumes and growth rates of sales of a new product, the corresponding Sales Forecast Outlines are distributed to the entire region. However, this method is one of the most expensive, and its use requires good preparation of all marketing services in the company.

One of important elements drawing up a sales forecast involves developing several forecast options. Typically, three options for sales forecasts are made: the most likely, optimistic and pessimistic. As a basis for drawing up optimistic and pessimistic versions of the sales forecast, an analysis of influencing factors is used. The enterprise, firstly, must identify which factors in the coming period can most seriously affect the level and dynamics of product sales; secondly, assess the degree of their influence (by what percentage each of the identified factors can contribute to an increase or decrease in sales volumes compared to the most likely values). For example, the completion of a major investment project in the region can increase the number of potential consumers by 30%. In this case, the optimistic version of the sales forecast will be 30% higher than the most probable one

When developing a sales forecast, an integrated approach, the simultaneous use of several forecasting methods and comparison of the results obtained are important. Among these methods, the most common are the following:

1) Method of expert assessments (including the opinion of a group of managers and a combination of opinions of sales employees). This forecasting method is most suitable for new businesses that do not have enough experience in using other methods. This method is also applicable when there are no detailed calculations about the state of the market, there are no complete statistics on sales trends for certain types of products.

2) Extrapolation of trends and cycle. When using this method, errors are inevitable, but it is invariably used in sales forecasting; the low percentage of predicting the consequences of socio-economic phenomena does not contribute to high accuracy of the forecast. The use of this method is possible if the analyst has at his disposal massive amounts of information on various areas of the company’s activities over the past 10 years.

The use of this method is based on the following techniques:

A) Determination of moving averages.

The product sales diagram most often has an abrupt character. Averaging the observation results will allow us to construct a sales curve over time. A suitable number of observation results are averaged. It can use quarters, which means adding the first three results and dividing the sum by three. Then the results of the second, third and fourth observations are added and divided by three, etc. The result is a quarterly moving average. The constructed graph determines the prospective sales values.

B) Smoothing models.

Over time, more and more observations are made and the size of the forecast errors is determined. At the same time, it seems rational to take past mistakes into account when predicting the future. One way is to add a fixed percentage of last month's error to last month's actual sales and use the result to forecast the next month. Using this method, you can get quite good short-term forecasts. Such forecasts are useful for production planning and inventory management, but are practically inapplicable for financial planning.

3) Forecasting based on a portfolio of orders, that is, based on existing or expected orders from potential buyers of products, which is preferable for generating sales volume in high-tech industries. The application of this method requires conducting special research on the main industries consuming the products of a given enterprise, collecting and processing significant statistical and factual material. This method is preferable in the raw materials and energy sectors, as well as in enterprises that produce components and components.

4) Correlation analysis, that is, the identification of statistically significant factors influencing the sales of the company’s products. Using the correlation relationship, the closeness of the connection between the level of sales and various results of the enterprise’s economic activity is determined, the impact on sales of which can be logically proven and justified. Thus, the most significant factors, depending on which the sales volume may change in the future, are identified and ranked (according to the degree of their influence). This method requires special and expensive research. The most accurate results can be obtained in the most stable industries in terms of economic conditions.

The effectiveness of using a particular method depends entirely on competitive conditions and the specifics of the enterprise’s economic activity and can only be determined in a system of general market research activities. In marketing-oriented companies, several forecast options are compiled using various methods (3-4 methods). The resulting estimates are then compared to identify any differences in estimates that may arise. It is usually considered that the forecast is made correctly if the difference between the estimated and actual sales does not exceed 5%. If these discrepancies are significant (the spread of sales forecast indicators using various methods exceeds 10%), then most likely errors were made when drawing up the sales forecast using some method.