Revision as of 17:28, 11 February 2023

Forecasting is the process of estimating future outcomes, such as demand, resource requirements, costs, and completion dates, based on past performance and current conditions. 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 analysing past experience and their respective forecasts^[1]. Within the field of project management, forecasting is most often used to forecast cost, activity time, and quality. These are the ground pillars that define successful project management according to the iron triangle. Forecasting is found to be a significant factor, especially within supply chain management, for the effectiveness of strategic execution ^[2].

Throughout this paper, we will focus on the fundamental and state-of-the-art methods used in forecasting and on how organizations can optimize supply chains and management of projects through their use.

The term "forecast" can be associated with different fields, such as business, meteorological conditions, engineering, politics, and project management. In this article, the term forecasting will be approached from an operations management perspective.

Key perspectives: Complexity: Estimating, Uncertainty: Anticipating, Monitoring.

Big Idea

Depending on the discipline (business, economics, politics, industry, society, commerce...), the term "forecast" will have one meaning or another. At the level of operations management, forecasting is defined as an approximation to future circumstances through a subjective and objective assessment being different from past data, but taking them as a starting point. The goal of forecasting is to provide insights and support decision-making. Under no circumstances can forecasts be taken as if they were a "crystal ball" capable of seeing the future, since they will always have a percentage of error, depending on how the forecast has been made, as we will see below. This means that forecasts should always be accompanied by a measure of uncertainty ^[3].

Various methods exist for forecasting, which are usually divided into qualitative and quantitative approaches. The choice of method depends on the characteristics of the data, the nature of the forecasting problem, and the desired level of accuracy. However, there are som key features that are common to all forecasts ^[4]:

1. Forecasts follows the assumption that patterns from the past will, to some extent, remain in the future.

2. Forecasts error is inevitable as forecasts cannot predict the future perfectly. Error should always be incorporated when forecasts are used in decision-making.

3. Forecasting accuracy decreases as the time horizon increases. In this case, uncertainty increases, and short-term forecasts are therefore often more accurate.

Time horizon

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

Forecasts can be classified according to the time horizon covered^[1]:

Short-term forecasts. The main applicability of short-term forecasts lay at the operational level. It can be used in scheduling tasks, assignment of workers or immediate material needs.

Medium-term forecasts. Medium-term forecasts are normally used to establish future resource needs. The goal is to make informed decisions regarding the purchase of raw materials, hiring of personnel, or acquisition of machinery and equipment.

Long-term forecasts. Long-term forecasts are used in strategic planning, as it helps inform decisions by considering market opportunities, external factors, and internal resources.

Forecasting process

The forecasting process can be described in the following 5 steps^[4].

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 collected and cleaned. The more data that can be collected, the more accurate the forecast will be. Outliers in data are identified and resolved.

3. Selecting forecasting technique It is essential to to identify patterns in the collected data such as points of maximum, seasonality, and cycles. In most cases it will be relevant to visualize the data to identify patterns.

4. Conduct the forecast The forecast is conducted with the appropriate forecasting technique.

5. Validation Forecast accuracy should be evaluated and monitored to conclude if the forecast is performing as intended. In the case that it is not, decisions on forecasting technique, validity of data, and assumption can be revised.

Elements of a successful forecast

Moon et al (1998), based on their long involvement and experience in the forecasting field, suggest seven fundamental principles for forecast management, which if correctly applied could increase the efficiency, coordination and performance of the company. ^[5].

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 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.^[6].

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.

Data pattern classification

Time series data patterns can contain a trend, seasonality, and/or a base. These concepts are here explained from the field of demand management.

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: ^[7].

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.

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.

Forecasting problems

There are some circumstances, which at first, would not necessarily affect the forecasts if they are handled properly, but which can sometimes be problematic if the necessary knowledge is not available.

1. One-off period of extraordinary sales. Time windows where the demand for a certain product shoots upwards in a totally unexpected way. This phenomenon of extraordinary demand should not be taken into account when making future forecasts, since it will deviate the forecasts that are real. For example, the peak demand for toilet paper during the first coronavirus crisis. This product suffered a very high demand, but this should not be confused with a seasonal demand or a trend, but rather that due to a specific circumstance the market demanded a lot of paper, a phenomenon which, in principle, will not occur again, or at least cannot be foreseen.

2. Product in phase of disappearance. On many occasions a product finds a substitute or the market launches an improved version of it, so, if it is detected that the product has settled among consumers, even with positive forecasts of the sale of the old product, its disappearance should be planned and the forecasts should be close to zero in the near future.

3. New articles. Contrary to the previous point, new products that are launched on the market do not have historical sales records or data to be used to make forecasts of how they will behave or how the market will adopt the inclusion of this new product. Therefore, there are two solutions to be taken by the person in charge. First, use historical records of a similar item or one that is expected to perform similarly. This data could be used as a reference to make initial forecasts. Secondly, there is the option of using standard release curves, i.e. if you assume that the product will have an exponential growth of a specific percentage or a trend increase of x%, this statistic can be used to make the first forecasts. It is important to emphasize that these first forecasts will probably differ more than usual with the real market behavior, but they can help the company to make a first planning.

Benefits and conclusion

The objective of forecasting systems is to provide information about future changes and their impact on market demand. It is a task that today can take up an entire department or even the possibility of having only one person responsible within the organization.

There are multiple approaches to forecasting, from advanced mathematical/statistical methods to simple hunches or intuitions of how the market will behave. The two methods that dominate forecasting in the area of operations management are qualitative and quantitative methods.

Qualitative methods include methods such as the delphi method, consulting market insiders or relying on the experience and opinions of potential customers.Quantitative methods, on the other hand, mathematically analyze what has happened in the past during a specific period of time and, based on the characteristics of the subject, establish forecasts for the future, which means that they are methods that assume that the future is a function of a direct relationship with the past.

Nowadays, the most common global process is to make initial forecasts using advanced quantitative methods by means of specialized software, of which there are dozens on the market. The forecasts obtained are reviewed by related members of the entire organization (sales, project managers, senior management or engineers) who must validate or correct them. In other words, there is no one best method for forecasting, but the use of both together is the best way to obtain results that really give a real vision of the future events to the company.

Forecasts have a great value in terms of advanced project management, since, as mentioned above, they allow the company to obtain a vision of the future in advance, helping the organization to position itself for the future and to be able to make the relevant decisions.

To be more specific, let's take the example of a company that builds cars and decides to make a sales forecast with a time horizon for the next three years. Let's see how this affects the company's organization:

Firstly, the human resources level, as the company has to coordinate to manufacture a specific number of cars per day, it will be able to manage the number of employees it needs at any given time and their work shifts to service the expected demand, avoiding overstaffing or understaffing costs.

Secondly, at supply chain level, the company could reach agreements with its suppliers to reduce costs thanks to the loyalty or recurrence of their orders, as well as plan the expected use of raw materials and thus optimize their use, saving large amounts of money.

At the logistics level, the company will be able to optimize its inventories and warehouses, since by knowing the quantities to be manufactured at any given time, it will be able to have only the necessary parts in stock. In this way, the company would save costs of parts permanence, since keeping meaningless units in the warehouse is a fixed cost that must be taken into account, and on the other hand, in the case of perishable products, the company would avoid the cost of discarding them, since it would only have the necessary products at the necessary time.

Finally, at the economic level, in addition to avoiding costs at the human resources, supply chain and operational levels, the company, by being able to foresee how much it will have to invest, will be able to manage its economic resources efficiently. That is to say, it will know when to keep its capital in cash or when it can afford a large outlay (to increase capacity, expand its facilities, invest in research and development...).

Annotated Bibliography

Advanced Logistic Systems Vol. 6. No. 1. Lucjan Kurzak, 2012.

Operations Management, School of Business Bangladesh Open University. Ziaul Haq Mamun, Ali Ahsan, 2005.

Seven Keys to Better Forecasting. Mark A. Moon, John T. Mentzer, Carlo D. Smith, and Michael S. Garver, 1998.

A Critical Success Factors Model For Enterprise Resource Planning Implementation. Holland, & Light, 1999.

The Quantitative Supply Chain, Vermorel, J., 2018.

Portfolio Management: The standard for portfolio management. Project Management Institution, 2018

References

1. ↑ ^{1.0 1.1} Hyndman, R.J., & Athanasopoulos, G. (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. OTexts.com/fpp2. Accessed on 11-02-2023.
2. ↑ Exploring forecasting and project management characteristics of supply chain management. International Journal of Logistics Systems and Management, 3(2), 174-214. Smith, A. D., & Offodile, O. F. 2007
3. ↑ Operations Management, School of Business Bangladesh Open University. Ziaul Haq Mamun, Ali Ahsan, 2005
4. ↑ ^{4.0 4.1} Stevenson, William J., et al. Operations management. Boston: McMcGraw-Hill Irwin, 2007
5. ↑ Seven Keys to Better Forecasting. Mark A. Moon, John T. Mentzer, Carlo D. Smith, and Michael S. Garver, 1998
6. ↑ Facilities Planning and Design. A. García-Díaz, J. MacGregor Smith, 2014
7. ↑ Introduction to Time Series Analysis and Forecasting. D.Montgomery, C. Jennings, M. Kulahci, 2008