Revision as of 20:47, 19 February 2021

Abstract

It is very common in any project or program that deviations occur on different scales between what is expected and reality. On many occasions, these deviations could have been reduced by statistical analysis of past examples and their respective forecasts. Throughout this article, we will focus on studying the main statistical methods used when making forecasts and how, by using them, organizations could optimize their supply chain or the management of their projects by forecasting the quantity of units or resources that the market or project in question will demand, based on historical data from previous seasons. As an output, the advanced management of the project will be optimized, resources will be assigned to the tasks or processes that really require them, unnecessary costs will be eliminated, and the time used to complete the project will be reduced.

From the beginning of the 20th century to the present day, the importance of forecasts has grown exponentially, since it gives the company a competitive advantage, as it will be able to plan its actions and take measures based on expectations. The term "forecast" can be associated with different fields, such as business, engineering, politics, project management... In this article we will approach the term forecast from an operations management perspective, defining the term as an accurate prediction of the future, based on past events and whose objective is to give the company valuable time to face future events, to be able to respond to the market demand and allowing the company to adapt capacity of production to fluctuations in demand: raw material consumption planning, supply chain management, quantities to order from suppliers, work shifts...

Forecasting approaches

The term Forecast can have different meanings in different disciplines (e.g., business, economics, and political communities). In operations management, we rather adopt a specific definition of forecast, distinguishing it from the broader concept of prediction. Therefore a forecast is an inference of what is likely to happen in the future. It is estimated by systematically combining and casting forward data about the past in a predetermined way. It is objective in nature but not an absolutely certain prophesy. Even very carefully prepared forecasts can be wrong. In fact, it is extremely rare for a forecast to be exactly right. A prediction, on the other hand, is an estimate of a future event achieved through subjective considerations other than just past data. In case of prediction subjective consideration need not occur in any predetermined way. Therefore the implications of forecasting and/or prediction are: ^[1].

1. Forecasting is both objective and subjective. Forecasting is the art and science of predicting future events, which may involve taking historical data and projecting them into the future with some sort of mathematical model. It may be subjective or intuitive prediction of the future or it may be of both.

2. Forecasting prepares an organization for a common future objective through coordination. Forecasting helps develop and implement a continuous improvement program specially tailored for any particular business and organization culture and environment.

3. Forecasting aids in both short-term and long-term planning. Forecasts are important inputs for long-range planning and strategic decisions. At the same time they are an important basis for shorter-range decisions in day-to-day operations. Operations managers try to forecast a wide range of future events that potentially affect success.

Demand classification

Demand is a pattern that attempts to express the consumption or market need for a product. Depending on the sales patterns of the product, demand can be classified as follows:

In the first place, stable demand is characterized by following a common sales or production needs pattern during the time analyzed. That is, even with maximums and minimums, it can be assumed that during the set period the level of sales (and therefore production) will not fluctuate from the expected standards, that is, it will have a constant average.

Secondly, a trending demand pattern is defined as a constant or systematic increase or decrease in demand as time progresses. An example of trending demand could be the sale of electric scooters, whose demand has increased day by day in recent years.

Thirdly, seasonal demand is one that during specific periods of time the market is stronger and experiences an exceptional peak demand. An example of seasonal demand could be the sale of ice cream during summer periods or the increase in demand for bouquets of flowers on Valentine's Day.

Forecasting classification (short, middle and long term)

The forecast o estimation process can be structured in different levels, which are defined according to the objectives and the time horizon of visualization (short, medium and long term). These must be carried out under a continuous improvement model, executed periodically and their performance should de measured, in order to keep improving the quality of forecasts.

Forecasts can be classified according to the time horizon covered:

Short-term forecasts. Generally made with a vision of three months and with a maximum of one year, whose main applicability at the operational level is the planning of tasks, assignment of workers or immediate material needs.

Medium-term forecasts. Intended to anticipate events six months ahead, and up to a maximum of three years. Their main operational functionality is to make a sales forecast, from which a bill of materials (BOM) and a production planning will be made.

Long-term forecasts. Are made with a time horizon longer than three years and their main purpose is the development of new products or the planning of the use of facilities.

Forecasting methods

It can be projected into the future in two ways: through qualitative methods, in which we rely on past actions or on the implicit knowledge of the subject to intuit future actions. Or through quantitative methods, in which through the use of statistics or mathematical models, historical data are projected into the future.

Qualitative methods

Qualitative methods allow describing or forecasting events when there is no historical data or when such data is not really relevant for forecasting. They are based on intuition and past experience, such as the amount of resources used to manufacture a product or the number of items sold during a given period, for example.

There is a wide variety of intuitive methods, as will be seen below, all of which are highly complex and their results will not always coincide, as it depends on the group of participants who have been involved, whether they are employees who know the industry at first hand or people who are able to give sound advice on future demand.

1. Market research. These are consumer surveys to determine the interest of customers or users of a product or service.

2. Group consensus. The opinion of market experts, company insiders or industry executives is sought. Thanks to the experience of these people, who have a relatively accurate perception of market trends and the impact of possible changes, the company in question will be able to obtain fairly accurate data for its planning.

3. Delphi method. This method consists of obtaining the opinion of a group of anonymous experts through a list of questions about the industry and its market.

The method is as follows:

1. A series of questions about the desired industry and market is sent out.

2. Once the experts have responded, a report is compiled compiling the answers and their respective arguments.

3. The same report is sent to the respondents so that they can jointly adjust or modify their opinion based on the views of the others.

4. The process is repeated until the group of experts reaches a common agreement.

4. Salespersons' composite. Each salesperson makes an approximation of expected sales over a specific period of time in the region in which he/she is engaged, which will then be reviewed for validation.

The main advantage of qualitative methods is that they take into account intangible factors that are not considered in quantitative methods. In addition, it is very useful when historical data is not available, such as when a company enters a new industry or launches a new product in the market.

Quantitative methods

Quantitative methods assume that the past defines the future, neglecting changes in the environment that could affect future demand. However, once systems are in place that allow for the collection and analysis of demand data (data that most for-profit companies already possess), these methods are less expensive, quicker to use, and appear to be more "objective" than qualitative methods.

These methods are based on forecasting models built from time series. Therefore, statistical and mathematical methods, algorithms and optimization models, such as neural networks, are used to obtain future forecasts.

As mentioned above, their use depends directly on the availability of historical data.

Other factors that can affect forecasts

Demand tends to vary for different reasons that must be taken into account when forecasting. These factors are implicitly taken into account when qualitative methods are used in order to have accurate and realistic forecasts. While when quantitative methods are used, a readjustment of the results obtained must be made or, if a software is used to obtain them, they must be taken into account as external variables.

1. Seasonality pattern. Many goods and services are affected by seasonal variations in demand, suffering during a specific and predictable time, demand increases, and then falling back to standard levels.

2. Market conditions. All PESTEL elements (political, economic, social, technological, ecological and legal) are capable of influencing the market upwards or downwards, changing unexpectedly the market behavior. An example of this could be the increase in demand for electric cars as batteries improve their autonomy and charging points increase.

3. Commercial actions of the company. There are certain actions, such as marketing campaigns, sales periods or new store openings, where demand is expected to increase considerably.

Global forecasting process

1. Define the objective. Once the objective of a forecast has been defined, a planning horizon and degree of accuracy must be set (long, medium or short term). For example, to plan the operations of a company dedicated to the manufacture of scooters, it will be necessary to forecast the company's global sales for the coming year, in order to be able to make a detailed plan for the supply of raw materials, production processes, necessary resources or management of logistics for delivery to the customer.

2. Collect data. The main source of data collection is sales history (last weeks, last months, last years). The longer the sales history, the more accurate the forecasts (estimation techniques) will be, as history plays a key role in detecting patterns, trends or seasonal periods. To these historical data will be added data provided by sales records/customer surveys, such as demographic references, most influential products or reports of incoming and outgoing stock.

3. Validation and exploration analysis of the data collected. It is essential to graphically represent the historical demand obtained, in order to identify points of maximum or minimum demand, seasonal patterns or repetitive cycles of market demand. On the other hand, if data validation is to be performed by different areas of the company, the production department should confirm that enough units were manufactured to support the collected sales, or the marketing department should analyze whether the peak sales points coincide with the times when intensive marketing campaigns were carried out.

4. Choose the appropriate forecasting method and launch forecasts. Depending on the degree of accuracy desired, the resources and data available, qualitative or quantitative forecasts will be launched, with a given forecast horizon. Once these preliminary steps have been carried out, the desired forecasts will be launched.

5. Validate the forecasts obtained. Once the results have been obtained, all departments involved should verify that the data obtained are valid. Once this process has been completed, planning will be carried out based on the new forecasts. In other words, plan how to achieve the desired results. This planning, as mentioned above, will vary depending on the market in which the company is located and the type of industry in which it operates.

Moon et al. (1998) suggest seven key principles for forecasting management that, if applied correctly, could increase a company’s efficiency and accuracy in forecasting. These principles are based on the author’s long involvement and experience in the field of forecasting. Their empirical evidence is proofed via application in numerous national and multinational companies. ^[2].

These seven key principles are the following:

1. Understand what forecasting is and is not.

2. Forecast demand, plan supply.

3. Communicate, Cooperate, and Collaborate (CCC).

4. Eliminate islands of analysis.

5. Use tools wisely.

6. Make it important.

7. Measure, Measure, and Measure.

Forecasting problems

Benefits and conclusion

Annotated Bibliography

References

1. ↑ Operations Management, School of Business Bangladesh Open University. Ziaul Haq Mamun, Ali Ahsan, 2005
2. ↑ Seven Keys to Better Forecasting. Mark A. Moon, John T. Mentzer, Carlo D. Smith, and Michael S. Garver, 1998